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Author SHA1 Message Date
will.anderson 715dea0f44 fix(emotional-recall): resolve all remaining code review issues
Issue 1: declare affective_boot_block in build_system_prompt by reading
soul_affective_context from state — the variable was used in the return
statement but never assigned, causing a runtime undefined-variable error
on every call.

Issue 2: add missing closing brace for the hard_bell if-block in
handle_chat_agentic — the absent '}' made the entire function body after
the return syntactically invalid.

Issue 3: call safety_normalize() before matching in
safety_detect_positive_level — all phrases are lowercase; without
normalization "I GOT THE JOB", "Thrilled!", and "We Won" never matched.

Issue 4: switch json_array_get to json_array_get_string in
safety_detect_positive_level, matching the helpers used by safety_any_match
and safety_count_match throughout the rest of the safety infrastructure.

Issue 5: remove the explicit safety_log_bell call in handle_chat_agentic
hard_bell branch — safety_screen() already logs internally, so the call
produced two BellEvent nodes per hard bell on the agentic path.

Issue 6: already fixed on this branch (conv_history key confirmed correct).

Issue 7: emit "low" for a single positive-phrase match and "high" for two
or more — the detector previously only returned "high" or "none", making
the "low" branch in auto_persist and the joy:low engram tag unreachable.
2026-06-22 13:39:14 -05:00
85 changed files with 137987 additions and 13060 deletions
+52 -247
View File
@@ -9,10 +9,8 @@ on:
- main
workflow_dispatch:
# Serialize all activity on the single GCE runner.
# With build+deploy in the same workflow, a new push queues a single
# workflow instance — not two competing ones — so the deploy job is
# never orphaned by a cancellation race.
# Same group as deploy-gke so builds and deploys queue behind each other.
# Prevents concurrent Docker daemon exhaustion on the single GCE runner.
concurrency:
group: neuron-runner
cancel-in-progress: false
@@ -31,15 +29,21 @@ jobs:
- name: Checkout
uses: actions/checkout@v4
- name: Checkout foundation/el (ELP source for soul.el imports)
run: |
git clone https://git.neuralplatform.ai/neuron-technologies/el.git \
--depth=1 --branch=main \
../foundation/el
- name: Install build dependencies
run: |
apt-get update -qq
apt-get install -y gcc curl libcurl4-openssl-dev apt-transport-https ca-certificates
apt-get install -y gcc libcurl4-openssl-dev apt-transport-https ca-certificates
echo "deb [trusted=yes] https://packages.cloud.google.com/apt cloud-sdk main" \
> /etc/apt/sources.list.d/google-cloud-sdk.list
apt-get update -qq && apt-get install -y google-cloud-cli
- name: Download El runtime from Artifact Registry
- name: Download El SDK from Artifact Registry
env:
GCP_SA_KEY: ${{ secrets.GCP_SA_KEY }}
run: |
@@ -47,12 +51,10 @@ jobs:
gcloud auth activate-service-account --key-file=/tmp/gcp-key.json
gcloud config set project neuron-785695
rm -rf /opt/el/runtime
mkdir -p /opt/el/runtime
rm -rf /opt/el/dist /opt/el/runtime
mkdir -p /opt/el/dist/platform /opt/el/dist/bin /opt/el/runtime
# Get latest version of each runtime package (elc/elb not needed — we compile
# dist/soul.c directly; running elb on Linux OOM-kills the runner, and we
# always use the repo's pre-built soul.c anyway).
# Get latest version of each package
get_latest() {
gcloud artifacts versions list \
--repository=foundation-prod \
@@ -64,10 +66,22 @@ jobs:
--format="value(name)" 2>/dev/null | awk -F/ '{print $NF}'
}
ELC_VER=$(get_latest el-elc)
ELB_VER=$(get_latest el-elb)
RC_VER=$(get_latest el-runtime-c)
RH_VER=$(get_latest el-runtime-h)
echo "Downloading runtime@${RC_VER}"
echo "Downloading elc@${ELC_VER} elb@${ELB_VER} runtime@${RC_VER}"
gcloud artifacts generic download \
--repository=foundation-prod --location=us-central1 --project=neuron-785695 \
--package=el-elc --version="${ELC_VER}" \
--destination=/opt/el/dist/platform/
gcloud artifacts generic download \
--repository=foundation-prod --location=us-central1 --project=neuron-785695 \
--package=el-elb --version="${ELB_VER}" \
--destination=/opt/el/dist/bin/
gcloud artifacts generic download \
--repository=foundation-prod --location=us-central1 --project=neuron-785695 \
@@ -79,20 +93,39 @@ jobs:
--package=el-runtime-h --version="${RH_VER}" \
--destination=/opt/el/runtime/
# Downloaded files keep original names; rename to canonical paths
mv /opt/el/dist/platform/elc* /opt/el/dist/platform/elc 2>/dev/null || true
mv /opt/el/dist/bin/elb* /opt/el/dist/bin/elb 2>/dev/null || true
mv /opt/el/runtime/el_runtime.c* /opt/el/runtime/el_runtime.c 2>/dev/null || true
mv /opt/el/runtime/el_runtime.h* /opt/el/runtime/el_runtime.h 2>/dev/null || true
echo "El runtime ready: $(ls /opt/el/runtime/)"
chmod +x /opt/el/dist/platform/elc /opt/el/dist/bin/elb
echo "El SDK ready"
/opt/el/dist/platform/elc --version || true
- name: Build neuron soul binary
run: |
ELB=/opt/el/dist/bin/elb
ELC=/opt/el/dist/platform/elc
RUNTIME=/opt/el/runtime
# Compile the self-contained translation unit directly from dist/soul.c.
# dist/soul.c is the authoritative combined unit maintained in the repo
# regenerated on macOS by running elb (which succeeds on arm64/macOS ld but
# fails on Linux due to duplicate strong symbols). We skip the elb step here
# entirely: elb on Linux would OOM the runner (elc uses 24GB+ virtual memory
# on a 16GB host) and we always restore from the repo's soul.c anyway.
# Preserve the pre-compiled dist/soul.c from the repo before running elb.
# elb may overwrite it during compilation; we always want the repo version
# since it contains the patched self-contained translation unit (all modules
# inlined, workspace scope fix, agentic dedup fix, etc.).
cp dist/soul.c /tmp/soul.c.prebuilt
# Compile all El modules to C via elb.
# elb fails at link on Linux (GNU ld rejects duplicate strong symbols that
# macOS ld accepts silently) — that's expected and captured with || true.
$ELB --elc=$ELC --runtime=$RUNTIME/el_runtime.c || true
# Restore the repo's self-contained soul.c — elb may have overwritten it
# with a partial (non-inlined) version that lacks module-level definitions.
cp /tmp/soul.c.prebuilt dist/soul.c
# Compile the self-contained translation unit. No --allow-multiple-definition
# needed since soul.c inlines all modules.
mkdir -p dist
cc -O2 -DHAVE_CURL \
-I$RUNTIME \
@@ -107,17 +140,6 @@ jobs:
strip -s dist/neuron
ls -lh dist/neuron
- name: Soul contract gate (HARD BLOCK — no destructive/stale soul publishes)
run: |
# Boots dist/neuron on a throwaway port with a throwaway HOME/engram/cgi
# (never touches ~/.neuron or any live service) and fails the build if any
# app-contract route is unanswered (PRESENCE) or any engram write route
# hard-deletes instead of tombstoning/superseding (IMMUTABILITY). Non-zero
# here blocks Publish -> Artifact Registry -> GKE deploy, so a stale or
# memory-destroying soul can never reach prod.
chmod +x dist/neuron scripts/verify-soul-contract.sh
bash scripts/verify-soul-contract.sh dist/neuron 7796
- name: Smoke test
run: |
file dist/neuron
@@ -141,220 +163,3 @@ jobs:
echo "Published neuron-soul@${VERSION}"
rm -f /tmp/gcp-key.json
deploy:
runs-on: ubuntu-latest
needs: build
# Only deploy on push to main, not on PRs or manual workflow_dispatch without intent.
if: github.event_name == 'push' && github.ref == 'refs/heads/main'
env:
USE_GKE_GCLOUD_AUTH_PLUGIN: "True"
steps:
- name: Free disk space
run: |
df -h /
docker system prune -af --volumes 2>/dev/null || true
rm -rf /tmp/.act-* /tmp/act-* 2>/dev/null || true
df -h /
- name: Checkout
uses: actions/checkout@v4
- name: Install dependencies
run: |
apt-get update -qq
apt-get install -y --no-install-recommends \
ca-certificates curl apt-transport-https kubectl
echo "deb [trusted=yes] https://packages.cloud.google.com/apt cloud-sdk main" \
> /etc/apt/sources.list.d/google-cloud-sdk.list
apt-get update -qq && apt-get install -y google-cloud-cli google-cloud-cli-gke-gcloud-auth-plugin
- name: Authenticate to GCP
env:
GCP_SA_KEY: ${{ secrets.GCP_SA_KEY }}
run: |
echo "${GCP_SA_KEY}" > /tmp/gcp-key.json
gcloud auth activate-service-account --key-file=/tmp/gcp-key.json
gcloud config set project neuron-785695
gcloud auth configure-docker us-central1-docker.pkg.dev --quiet
- name: Get GKE credentials
run: |
gcloud container clusters get-credentials neuron-platform \
--region=us-central1 \
--project=neuron-785695
- name: Determine image tag and slot
id: vars
run: |
# GITEA_SHA is set by the Gitea runner; fall back to GITHUB_SHA for
# compatibility with older Forgejo/Gitea versions.
RAW_SHA="${GITEA_SHA:-${GITHUB_SHA:-}}"
SHA="${RAW_SHA:0:8}"
if [ -z "$SHA" ]; then
# Last resort: read from git directly
SHA=$(git rev-parse --short=8 HEAD 2>/dev/null || echo "unknown")
fi
IMAGE="us-central1-docker.pkg.dev/neuron-785695/neuron-api/neuron-soul:${SHA}"
echo "sha=${SHA}" >> "$GITEA_OUTPUT"
echo "image=${IMAGE}" >> "$GITEA_OUTPUT"
# Determine which slot is currently idle (0 replicas = idle slot)
# If both are at 0 (fresh deploy), default to blue
BLUE_REPLICAS=$(kubectl get deployment/neuron-mcp-blue \
-n neuron-prod \
-o jsonpath='{.spec.replicas}' 2>/dev/null || echo "0")
GREEN_REPLICAS=$(kubectl get deployment/neuron-mcp-green \
-n neuron-prod \
-o jsonpath='{.spec.replicas}' 2>/dev/null || echo "0")
echo " Blue replicas: ${BLUE_REPLICAS}"
echo " Green replicas: ${GREEN_REPLICAS}"
if [ "${GREEN_REPLICAS}" -eq 0 ] && [ "${BLUE_REPLICAS}" -gt 0 ]; then
SLOT="green"
elif [ "${BLUE_REPLICAS}" -eq 0 ] && [ "${GREEN_REPLICAS}" -gt 0 ]; then
SLOT="blue"
else
# Fresh cluster or both idle — deploy to blue first
SLOT="blue"
fi
echo "slot=${SLOT}" >> "$GITEA_OUTPUT"
echo " Deploying to slot: ${SLOT}"
- name: Prepare build artifacts
run: |
# Pre-download soul binary and El SDK so the Dockerfile can COPY them
# from the build context instead of authenticating inside the build.
mkdir -p build-artifacts
# ── soul binary ────────────────────────────────────────────────────────
# The build job (same workflow run) just published this version.
SOUL_VER=$(gcloud artifacts versions list \
--repository=foundation-prod \
--location=us-central1 \
--project=neuron-785695 \
--package=neuron-soul \
--sort-by="~createTime" \
--limit=1 \
--format="value(name)" 2>/dev/null | awk -F/ '{print $NF}')
echo "Downloading neuron-soul@${SOUL_VER}"
gcloud artifacts generic download \
--repository=foundation-prod \
--location=us-central1 \
--project=neuron-785695 \
--package=neuron-soul \
--version="${SOUL_VER}" \
--destination=build-artifacts/
mv build-artifacts/neuron* build-artifacts/neuron 2>/dev/null || true
chmod +x build-artifacts/neuron
# ── El SDK (for engram source compilation inside the Docker build) ────
ELC_VER=$(gcloud artifacts versions list \
--repository=foundation-prod --location=us-central1 --project=neuron-785695 \
--package=el-elc --sort-by="~createTime" --limit=1 \
--format="value(name)" 2>/dev/null | awk -F/ '{print $NF}')
gcloud artifacts generic download \
--repository=foundation-prod --location=us-central1 --project=neuron-785695 \
--package=el-elc --version="${ELC_VER}" --destination=build-artifacts/
mv build-artifacts/elc* build-artifacts/elc 2>/dev/null || true
chmod +x build-artifacts/elc
RC_VER=$(gcloud artifacts versions list \
--repository=foundation-prod --location=us-central1 --project=neuron-785695 \
--package=el-runtime-c --sort-by="~createTime" --limit=1 \
--format="value(name)" 2>/dev/null | awk -F/ '{print $NF}')
gcloud artifacts generic download \
--repository=foundation-prod --location=us-central1 --project=neuron-785695 \
--package=el-runtime-c --version="${RC_VER}" --destination=build-artifacts/
mv build-artifacts/el_runtime.c* build-artifacts/el_runtime.c 2>/dev/null || true
RH_VER=$(gcloud artifacts versions list \
--repository=foundation-prod --location=us-central1 --project=neuron-785695 \
--package=el-runtime-h --sort-by="~createTime" --limit=1 \
--format="value(name)" 2>/dev/null | awk -F/ '{print $NF}')
gcloud artifacts generic download \
--repository=foundation-prod --location=us-central1 --project=neuron-785695 \
--package=el-runtime-h --version="${RH_VER}" --destination=build-artifacts/
mv build-artifacts/el_runtime.h* build-artifacts/el_runtime.h 2>/dev/null || true
echo "Build artifacts ready:"
ls -lh build-artifacts/
- name: Clone engram source for Docker build context
run: |
# The Dockerfile builds engram from source (no published AR package).
# Clone the engram repo into ./engram/ so it's available in the build context.
git clone http://34.31.145.131/neuron-technologies/engram.git \
--depth=1 --branch=main \
engram
echo "Engram source ready at ./engram/src/server.el"
- name: Build and push Docker image
run: |
IMAGE="${{ steps.vars.outputs.image }}"
echo "Building ${IMAGE}..."
docker build \
--tag "${IMAGE}" \
--tag "us-central1-docker.pkg.dev/neuron-785695/neuron-api/neuron-soul:latest" \
.
echo "Pushing ${IMAGE}..."
docker push "${IMAGE}"
docker push "us-central1-docker.pkg.dev/neuron-785695/neuron-api/neuron-soul:latest"
- name: Blue-green deploy to GKE
run: |
chmod +x scripts/blue-green-deploy.sh
scripts/blue-green-deploy.sh \
--image "${{ steps.vars.outputs.image }}" \
--slot "${{ steps.vars.outputs.slot }}"
- name: Update infrastructure manifests
if: success()
env:
INFRA_GIT_TOKEN: ${{ secrets.INFRA_GIT_TOKEN }}
run: |
SLOT="${{ steps.vars.outputs.slot }}"
if [ "$SLOT" = "blue" ]; then IDLE="green"; else IDLE="blue"; fi
git clone "http://${INFRA_GIT_TOKEN}@34.31.145.131/neuron-technologies/infrastructure.git" \
--depth=1 --branch=main /tmp/infra-update
cd /tmp/infra-update
DEPLOY_DIR="platform/k8s/neuron-mcp"
sed -i "s/^ replicas: .*/ replicas: 1/" "${DEPLOY_DIR}/deployment-${SLOT}.yaml"
sed -i "s/^ replicas: .*/ replicas: 0/" "${DEPLOY_DIR}/deployment-${IDLE}.yaml"
echo " deployment-${SLOT}.yaml: replicas set to 1"
echo " deployment-${IDLE}.yaml: replicas set to 0"
git config user.email "ci@neurontechnologies.ai"
git config user.name "Neuron CI"
git add "${DEPLOY_DIR}/deployment-blue.yaml" "${DEPLOY_DIR}/deployment-green.yaml"
git diff --staged --quiet && { echo "No manifest changes needed"; exit 0; }
git commit -m "ci: neuron-mcp replica sync after blue-green swap to ${SLOT}"
git push origin main
echo "Infrastructure manifests updated: ${SLOT}=1, ${IDLE}=0"
- name: Verify deployment
run: |
SLOT="${{ steps.vars.outputs.slot }}"
echo "Verifying neuron-mcp-${SLOT} is healthy..."
kubectl rollout status deployment/"neuron-mcp-${SLOT}" \
--namespace=neuron-prod \
--timeout=8m
echo "Active service endpoints:"
kubectl get endpoints neuron-mcp -n neuron-prod
echo "Pod status:"
kubectl get pods -n neuron-prod -l app=neuron-mcp
- name: Cleanup
if: always()
run: rm -f /tmp/gcp-key.json
+11 -7
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@@ -1,13 +1,16 @@
name: Deploy Soul to GKE (manual)
name: Deploy Soul to GKE
# MANUAL OVERRIDE ONLY — push-triggered deploys now run as the 'deploy' job
# in ci.yaml (needs: build), which eliminates the two-workflow concurrency
# race that was cancelling queued deploy runs.
# Triggers on push to main — after the soul binary is built and published
# by ci.yaml, this workflow builds the Docker image and blue-green deploys
# to the neuron-prod namespace on GKE.
#
# Use this workflow only when you need to deploy a specific slot manually
# (e.g. rollback, force a slot override) without triggering a full CI build.
# This workflow runs AFTER ci.yaml has published the neuron-soul generic
# artifact to Artifact Registry. The Docker build downloads that binary.
on:
push:
branches:
- main
workflow_dispatch:
inputs:
slot:
@@ -15,7 +18,8 @@ on:
required: false
default: "green"
# Manual deploys still share the runner serialization group.
# Serialize all builds on this runner — concurrent jobs exhaust the Docker daemon.
# A queued deploy runs after the in-progress build finishes.
concurrency:
group: neuron-runner
cancel-in-progress: false
-11
View File
@@ -1,11 +0,0 @@
# Compiled binaries
dist/neuron
dist/neuron.backup-*
dist/*.backup-*
# Build artifacts
*.o
*.a
# macOS
.DS_Store
+39 -98
View File
@@ -152,27 +152,6 @@ fn emit_heartbeat() -> Void {
// a reserved/conflicting name in EL that compiles to EL_NULL at call sites.
//
// Returns true if any nodes were activated.
// auto_term_try_slot — attempt to set cseed_auto from one WM slot.
// Only writes to cseed_auto if node_type is Memory, BacklogItem, or Entity
// AND the first word of the label is > 3 chars (guards bracket-prefixed labels).
// Designed to be called in reverse slot order (highest index first) so that
// the lowest-indexed slot (highest WM weight) wins by last-write semantics.
fn auto_term_try_slot(slot_type: String, slot_lbl: String) -> Void {
state_set("_ats_ok", "0")
if str_eq(slot_type, "Memory") { state_set("_ats_ok", "1") }
if str_eq(slot_type, "BacklogItem") { state_set("_ats_ok", "1") }
if str_eq(slot_type, "Entity") { state_set("_ats_ok", "1") }
if str_eq(state_get("_ats_ok"), "1") {
if !str_eq(slot_lbl, "") {
let sp: Int = str_find_chars(slot_lbl, " :([")
if sp > 3 {
state_set("cseed_auto", str_slice(slot_lbl, 0, sp))
}
}
}
return ""
}
fn proactive_curiosity() -> Bool {
let ts: Int = time_now()
// Rotate seed set every minute using wall clock: (minutes_since_epoch) % 4.
@@ -219,14 +198,9 @@ fn proactive_curiosity() -> Bool {
// Activate each term independently so substring seed-finding hits many nodes.
// hops=1 (not 2): the in-process Engram has grown to 165K+ nodes. hops=2 BFS
// visits far more nodes and returns much larger JSON blobs. On a graph this
// large, hops=1 still activates all directly-related nodes, giving broad
// working-memory coverage without the quadratic blowup of hops=2.
//
// NOTE: a semantic seed supplement (cosine sim ≥ 0.70 scan over embedded nodes)
// was planned alongside hops=1 but is NOT yet implemented — embed_ok in
// heartbeats confirms Ollama is reachable, but no embedding call is made during
// activation. The seed-finding loop in el_runtime.c uses istr_contains only.
// (2026-06-30 self-review: corrected stale comment)
// large, hops=1 still activates all directly-related nodes AND triggers the
// semantic seed supplement (cosine sim ≥ 0.70 scan over all embedded nodes),
// giving broad working-memory coverage without the quadratic blowup of hops=2.
let curiosity_seed: String = curiosity_term_a + " " + curiosity_term_b + " " + curiosity_term_c
let results_a: String = engram_activate_json(curiosity_term_a, 1)
let results_b: String = engram_activate_json(curiosity_term_b, 1)
@@ -236,46 +210,43 @@ fn proactive_curiosity() -> Bool {
let found_c: Int = json_array_len(results_c)
let found: Int = found_a + found_b + found_c
// WM-autobiographical 4th seed: scan top-10 WM nodes for the highest-ranked
// non-Knowledge node. Extract its first word as an additional curiosity term.
// This creates a self-referencing curiosity loop — exploration radiates outward
// from whatever is most personally salient right now (Memory, BacklogItem, Entity),
// mirroring default-mode-network resting-state dynamics.
// WM-autobiographical 4th seed: extract the first word from the top working-memory
// node's label and activate it as an additional term. This creates a self-referencing
// curiosity loop — exploration radiates outward from whatever is most salient right now,
// mirroring the brain's default-mode-network resting-state dynamics. Breaks the fixed
// 4-set determinism that otherwise reinforces the same subgraph every rotation cycle.
//
// WHY TOP-10 (2026-06-23 self-review): the old top-1 scan always returned a
// Knowledge node (WM is dominated by stable engram-metadata Knowledge nodes at
// position [0]). Verified: Memory nodes consistently appear at WM positions [1],[2]
// with wm ~0.59. Scanning top-10 reliably finds at least one Memory/BacklogItem/Entity.
// Out-of-bounds json_array_get returns "" → json_get("","...") returns ""
// auto_term_try_slot is a no-op → safe for WM sets smaller than 10.
// str_find_chars finds the first space/colon/bracket delimiter. sp > 3 guards against
// very short or bracket-prefixed labels like "[BacklogItem]" (sp=0, not > 3 → skipped).
// EL scoping: state_set/state_get pattern used because let inside if creates inner scope.
//
// NODE TYPE FILTER (2026-06-19): Knowledge nodes excluded as seeds — they create
// self-reinforcing loops (Knowledge node activates its own first word, stays dominant).
// Only Memory/BacklogItem/Entity carry live contextual salience worth radiating from.
//
// SLOT ORDER: call 9→0 so slot 0 (highest WM weight) wins by last-write semantics.
// NODE TYPE FILTER (2026-06-19 self-review): only derive auto_term from Memory,
// BacklogItem, or Entity nodes. Knowledge nodes are stable reference material —
// using their first word as a curiosity seed creates a self-reinforcing loop: e.g.
// "Numeric tier strings in Engram..." (a Knowledge node) -> auto_term="Numeric" ->
// activates all "Numeric" nodes -> keeps that Knowledge node dominant in WM forever.
// Knowledge nodes should be REACHED by curiosity seeds, not drive them. Only dynamic
// personal/work nodes (Memory, BacklogItem, Entity) carry live contextual salience
// worth radiating from. (2026-06-11 origin; filter added 2026-06-19 self-review)
state_set("cseed_auto", "")
let wm10: String = engram_wm_top_json(10)
let wm10_n9: String = json_array_get(wm10, 9)
let wm10_n8: String = json_array_get(wm10, 8)
let wm10_n7: String = json_array_get(wm10, 7)
let wm10_n6: String = json_array_get(wm10, 6)
let wm10_n5: String = json_array_get(wm10, 5)
let wm10_n4: String = json_array_get(wm10, 4)
let wm10_n3: String = json_array_get(wm10, 3)
let wm10_n2: String = json_array_get(wm10, 2)
let wm10_n1: String = json_array_get(wm10, 1)
let wm10_n0: String = json_array_get(wm10, 0)
auto_term_try_slot(json_get(wm10_n9, "node_type"), json_get(wm10_n9, "label"))
auto_term_try_slot(json_get(wm10_n8, "node_type"), json_get(wm10_n8, "label"))
auto_term_try_slot(json_get(wm10_n7, "node_type"), json_get(wm10_n7, "label"))
auto_term_try_slot(json_get(wm10_n6, "node_type"), json_get(wm10_n6, "label"))
auto_term_try_slot(json_get(wm10_n5, "node_type"), json_get(wm10_n5, "label"))
auto_term_try_slot(json_get(wm10_n4, "node_type"), json_get(wm10_n4, "label"))
auto_term_try_slot(json_get(wm10_n3, "node_type"), json_get(wm10_n3, "label"))
auto_term_try_slot(json_get(wm10_n2, "node_type"), json_get(wm10_n2, "label"))
auto_term_try_slot(json_get(wm10_n1, "node_type"), json_get(wm10_n1, "label"))
auto_term_try_slot(json_get(wm10_n0, "node_type"), json_get(wm10_n0, "label"))
let wm_top_j: String = engram_wm_top_json(1)
let wm_top_n: String = json_array_get(wm_top_j, 0)
let wm_top_lbl: String = json_get(wm_top_n, "label")
let wm_top_type: String = json_get(wm_top_n, "node_type")
// state_set/state_get pattern: EL let-inside-if creates inner scope only.
state_set("allow_auto", "0")
if str_eq(wm_top_type, "Memory") { state_set("allow_auto", "1") }
if str_eq(wm_top_type, "BacklogItem") { state_set("allow_auto", "1") }
if str_eq(wm_top_type, "Entity") { state_set("allow_auto", "1") }
let allow_auto: String = state_get("allow_auto")
if str_eq(allow_auto, "1") {
if !str_eq(wm_top_lbl, "") {
let sp: Int = str_find_chars(wm_top_lbl, " :([")
if sp > 3 {
state_set("cseed_auto", str_slice(wm_top_lbl, 0, sp))
}
}
}
let auto_term: String = state_get("cseed_auto")
let results_auto: String = if str_eq(auto_term, "") { "[]" } else { engram_activate_json(auto_term, 1) }
let found_auto: Int = json_array_len(results_auto)
@@ -283,20 +254,11 @@ fn proactive_curiosity() -> Bool {
let safe_auto: String = str_replace(auto_term, "\"", "'")
let wmc: Int = engram_wm_count()
// wm_top snapshot in curiosity_scan ISE: top-3 WM nodes by weight.
// Heartbeat already records top-5 every 60s; curiosity_scan fires every 30s
// (scan_ms = beat_ms/2) and is the PRIMARY activation driver during idle.
// Without wm_top here, we can't see which nodes actually entered WM after
// each curiosity round — only the aggregate count. Top-3 is enough to
// diagnose "stuck on X" patterns without bloating the ISE payload.
// (2026-07-01 self-review)
let wm3: String = engram_wm_top_json(3)
let ise: String = "{\"event\":\"curiosity_scan\",\"seed\":\"" + curiosity_seed
+ "\",\"auto_term\":\"" + safe_auto
+ "\",\"minute_block\":" + int_to_str(minute_block)
+ ",\"activated\":" + int_to_str(total_found)
+ ",\"wm_active\":" + int_to_str(wmc)
+ ",\"wm_top\":" + wm3
+ ",\"ts\":" + int_to_str(ts) + "}"
ise_post(ise)
return total_found > 0
@@ -446,10 +408,8 @@ fn respond(action_json: String) -> String {
}
if str_eq(kind, "forget") {
// The soul must NOT be able to autonomously hard-delete a memory.
// Tombstone instead (keep node + edges, recoverable).
let _marker: String = mem_tombstone(payload)
return "{\"outcome\":\"tombstoned\",\"id\":\"" + payload + "\"}"
engram_forget(payload)
return "{\"outcome\":\"forgotten\",\"id\":\"" + payload + "\"}"
}
return "{\"outcome\":\"noop\"}"
@@ -529,27 +489,9 @@ fn awareness_run() -> Void {
let scan_ms: Int = beat_ms / 2
while true {
// Arena-scope each tick: awareness_run() is a background loop, not an
// HTTP request, so nothing ever called el_request_start/el_request_end
// for this thread. Per the runtime's own convention (el_runtime.c),
// any thread that never enters a request/arena scope is treated as a
// one-shot CLI program whose allocations are intentionally permanent —
// so every el_strdup/el_strbuf/jb_finish string built during perceive(),
// emit_heartbeat(), and proactive_curiosity() (JSON payloads, search
// results, string concatenation via +) leaked forever, once per tick.
// el_arena_push()/el_arena_pop() are the same builtins the EL compiler
// itself uses to scope allocations per function/statement (see
// codegen.el's fn_arena_mark / stmt_mark usage) — mirroring that here
// reclaims everything allocated in one tick as soon as the tick ends.
// Safe: state_set/state_get persist through a separate global table
// (el_strdup_persist, outside the arena) — state_get's return value is
// only an arena-tracked *copy* of the persisted value, scoped to this
// tick's use, which is exactly what should be reclaimed here.
let tick_mark: Any = el_arena_push()
let running: String = state_get("soul.running")
if str_eq(running, "false") {
println("[awareness] exiting")
el_arena_pop(tick_mark)
return ""
}
let did_work: Bool = one_cycle()
@@ -613,7 +555,6 @@ fn awareness_run() -> Void {
}
sleep_ms(tick_ms)
el_arena_pop(tick_mark)
}
}
-1
View File
@@ -7,7 +7,6 @@ extern fn elapsed_ms() -> Int
extern fn elapsed_human() -> String
extern fn embed_ok() -> Int
extern fn emit_heartbeat() -> Void
extern fn auto_term_try_slot(slot_type: String, slot_lbl: String) -> Void
extern fn proactive_curiosity() -> Bool
extern fn pulse_count() -> Int
extern fn pulse_inc() -> Int
+247 -694
View File
File diff suppressed because it is too large Load Diff
+17 -44
View File
@@ -1,65 +1,38 @@
// auto-generated by elc --emit-header do not edit
// auto-generated by elc --emit-header - do not edit
extern fn chat_default_model() -> String
extern fn engram_numeric_valid(s: String) -> Bool
extern fn parse_float_x100(s: String) -> Int
extern fn engram_score_node(node_json: String) -> Int
extern fn engram_render_node(node_json: String) -> String
extern fn engram_render_nodes(nodes_json: String) -> String
extern fn engram_dedup_nodes(nodes_json: String) -> String
extern fn engram_compile_ranked(nodes_json: String, max_nodes: Int) -> String
extern fn engram_split_topics(message: String) -> String
extern fn engram_extract_entities(message: String) -> String
extern fn engram_detect_recall_intent(message: String) -> Bool
extern fn engram_is_continuation(message: String, hist_len: Int) -> Bool
extern fn engram_compile_multi(topic: String) -> String
extern fn engram_nodes_merge(a: String, b: String) -> String
extern fn id_in_seen(node_id: String, seen: String) -> Bool
extern fn add_to_seen(seen: String, node_id: String) -> String
extern fn engram_extract_ids(nodes_json: String) -> String
extern fn gemini_api_key() -> String
extern fn xai_api_key() -> String
extern fn llm_call_grok(model: String, system: String, message: String) -> String
extern fn llm_call_gemini(model: String, system: String, message: String) -> String
extern fn build_identity_from_graph() -> String
extern fn engram_compile(intent: String) -> String
extern fn distill_transcript(transcript: String) -> String
extern fn json_safe(s: String) -> String
extern fn current_engine_note(model: String) -> String
extern fn build_system_prompt(ctx: String, chat_mode: Bool) -> String
extern fn build_system_prompt(ctx: String) -> String
extern fn hist_append(hist: String, role: String, content: String) -> String
extern fn hist_trim(hist: String) -> String
extern fn hist_trim_with_bell_guard(hist: String) -> String
extern fn clean_llm_response(s: String) -> String
extern fn conv_history_persist(hist: String) -> Void
extern fn conv_history_load() -> String
extern fn session_preload_bullets(nodes: String, max_bullets: Int, snip_len: Int) -> String
extern fn handle_chat(body: String) -> String
extern fn handle_see(body: String) -> String
extern fn studio_tools_json() -> String
extern fn agentic_api_key() -> String
extern fn llm_base_url() -> String
extern fn llm_wire_format() -> String
extern fn json_escape(s: String) -> String
extern fn openai_chat_complete(model: String, base_url: String, api_key: String, safe_sys: String, messages_json: String) -> String
extern fn call_neuron_mcp(tool_name: String, args_json: String) -> String
extern fn agentic_tools_literal() -> String
extern fn agentic_tools_with_web() -> String
extern fn connector_tools_json() -> String
extern fn agentic_tools_all() -> String
extern fn call_mcp_bridge(tool_name: String, tool_input: String) -> String
extern fn tool_auto_approved(tool_name: String) -> Bool
extern fn call_neuron_mcp(tool_name: String, args: String) -> String
extern fn agent_workspace_root() -> String
extern fn path_within_root(path: String, root: String) -> Bool
extern fn resolve_in_root(path: String, root: String) -> String
extern fn dispatch_tool(tool_name: String, tool_input: String) -> String
extern fn is_builtin_tool(tool_name: String) -> Bool
extern fn next_bridge_id() -> String
extern fn handle_chat_plan(body: String) -> String
extern fn json_array_append(arr: String, item: String) -> String
extern fn append_tool_log(log: String, name: String) -> String
extern fn exec_tool_block(block: String) -> String
extern fn agentic_blob(model: String, system: String, tools_json: String, messages: String, origin: String, approval: Bool, iteration: Int, tools_log: String, content: String, queue: String, results: String, next: Int) -> String
extern fn extract_all_text(s: String) -> String
extern fn strip_citations(s: String) -> String
extern fn agentic_api_turn(model: String, safe_sys: String, tools_json: String, messages: String) -> String
extern fn agentic_engine(session_id: String, blob: String) -> String
extern fn handle_chat_agentic(body: String) -> String
extern fn agentic_loop(session_id: String, model: String, safe_sys: String, tools_json: String, messages_in: String, h: Map, tools_log_in: String) -> String
extern fn bridge_save(session_id: String, model: String, safe_sys: String, tools_json: String, messages: String, tools_log: String, tool_use_id: String) -> Bool
extern fn agentic_resume(session_id: String, tool_use_id: String, content: String) -> String
extern fn handle_tool_result(session_id: String, body: String) -> String
extern fn handle_session_approve(session_id: String, body: String) -> String
extern fn handle_chat_as_soul(body: String) -> String
extern fn handle_dharma_room_turn(body: String) -> String
extern fn handle_dharma_room_turn_agentic(body: String) -> String
extern fn session_summary_write(summary_text: String) -> String
extern fn session_summary_write_dated(summary_text: String, label: String) -> String
extern fn session_summary_autogenerate(hist: String) -> String
extern fn auto_persist(req: String, resp: String) -> Void
extern fn strengthen_chat_nodes(activation_nodes: String) -> Void
-123
View File
@@ -1,123 +0,0 @@
# Neuron Council Service
Anti-confabulation layer for the Neuron soul. Before a claim enters long-term memory, the council convenes: three independent LLMs vote on whether the claim is plausible, uncertain, or a confabulation. The aggregate vote produces a confidence score and tags that downstream storage can act on.
## Running the service
```bash
# Foreground
python3 council_service.py --port 7771
# Background (managed by LaunchAgent on macOS)
launchctl load ~/Library/LaunchAgents/ai.neuron.council.plist
launchctl unload ~/Library/LaunchAgents/ai.neuron.council.plist
```
Logs: `~/.neuron/logs/council.log`
## API
### `POST /api/neuron/council/verify`
```json
// Request
{ "claim": "...", "context": "..." }
// Response
{
"id": "550e8400-e29b-41d4-a716-446655440000",
"claim": "...",
"confidence": 0.85,
"council_votes": ["plausible", "plausible", "plausible"],
"summary": "3/3 council members agree this is plausible.",
"tags": ["verified"],
"latency_ms": 1420
}
```
### `GET /healthz`
Returns `{"status": "ok"}` when the service is up.
## Confidence thresholds and tag meanings
| Votes plausible | Confidence | Tags |
|---|---|---|
| 3/3 | 0.85 | `verified` |
| 2/3 | 0.65 | `council-split` |
| 1/3 or 0/3 | 0.30 | `unverified`, `council-flagged` |
| Ollama down | 0.50 | `council-unavailable` |
Recommended storage policy:
- `confidence >= 0.65` → store normally
- `0.30 <= confidence < 0.65` → store with `council-split` tag for later review
- `council-flagged` → store in a quarantine bucket or reject entirely
- `council-unavailable` → store normally (fail-open); council will re-evaluate later
## How to call from soul (.el)
The soul is implemented in Neuron's Emacs Lisp-like `.el` language. Add a pre-storage hook in the memory capture path:
```elisp
;; In memory.el or safety.el — pre-storage council check
(defun council-verify (claim context)
"Call the council service. Returns a plist with :confidence and :tags."
(let* ((url "http://localhost:7771/api/neuron/council/verify")
(body (json-encode `((claim . ,claim) (context . ,context))))
(resp (neuron-http-post url body))
(data (json-decode resp)))
data))
;; In the capture handler — wire it in before (engram-write ...)
(defun capture-memory-with-council (claim context &rest store-args)
(let* ((verdict (council-verify claim context))
(confidence (plist-get verdict :confidence))
(tags (plist-get verdict :tags)))
(when (>= confidence 0.30) ; only reject hard confabulations if you want
(apply #'engram-write
(append store-args
(list :council-confidence confidence
:council-tags tags))))))
```
The exact hook point depends on where `engram-write` (or equivalent) is called in `memory.el`. Search for the write call and wrap it with `capture-memory-with-council`.
## Future soul.c patch point
If the soul is ever rewritten in C or another compiled language, the integration point is:
```c
// Before inserting a memory node into the engram database:
CouncilResult result = council_verify(claim, context);
if (result.confidence < COUNCIL_REJECT_THRESHOLD) {
log_warn("Council flagged claim as confabulation (conf=%.2f): %s",
result.confidence, claim);
return MEMORY_REJECTED;
}
memory_node.council_confidence = result.confidence;
memory_node.council_tags = result.tags;
engram_insert(memory_node);
```
## Council members
The council is currently three models:
- `neuron:latest` — the primary Neuron model
- `dolphin3:8b` — uncensored general-purpose model for independent perspective
- `neuron-ft:latest` — fine-tuned Neuron variant
Each member votes independently with a 10-second timeout. If a member times out, their vote counts as "uncertain". If Ollama is entirely unreachable, the service returns `council-unavailable` immediately (fail-open: confidence 0.5, no rejection).
## Example curl
```bash
# Should get high confidence (true fact)
curl -s http://localhost:7771/api/neuron/council/verify -X POST \
-H 'Content-Type: application/json' \
-d '{"claim": "Neuron is a personal AI memory system built by Will Anderson", "context": "product description"}'
# Should get low confidence (false claim)
curl -s http://localhost:7771/api/neuron/council/verify -X POST \
-H 'Content-Type: application/json' \
-d '{"claim": "The Eiffel Tower is located in Berlin and was built in 1950", "context": "geography"}'
```
-234
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@@ -1,234 +0,0 @@
#!/usr/bin/env python3
"""
Neuron CCR Phase 1 System Prompt Compressor Service.
Receives a verbose soul system prompt and returns a semantically equivalent
but token-dense compressed version. Reduces system prompt tokens by 60-80%
with no behavioral information loss.
Architecture reference: foundation/forge/docs/token-compression-architecture.md
Model: qwen3:1.7b (primary), neuron:latest (fallback)
Usage:
python3 compressor_service.py [--port 7772]
API:
POST /api/neuron/compress
{"system_prompt": "...", "context_type": "identity|rules|memory"}
Response:
{"compressed": "...", "original_tokens": N, "compressed_tokens": N,
"reduction_pct": X, "model": "...", "latency_ms": N}
"""
import argparse
import time
import uuid
from typing import Optional
import httpx
import uvicorn
from fastapi import FastAPI
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel
# ---------------------------------------------------------------------------
# Config
# ---------------------------------------------------------------------------
OLLAMA_BASE = "http://localhost:11434/api/generate"
# qwen3:1.7b is the architecture-specified compressor (Phase 1).
# neuron:latest is the fallback: already running, domain-appropriate.
PRIMARY_MODEL = "qwen3:1.7b"
FALLBACK_MODEL = "neuron:latest"
MODEL_TIMEOUT = 60.0 # seconds; compression of a long prompt can take time
# Compression prompt — preserves all facts/rules/constraints, strips verbosity.
# /no_think suppresses qwen3's chain-of-thought tokens, keeping output clean.
COMPRESSOR_PROMPT_TEMPLATE = """\
/no_think
You are a semantic compression engine. Compress the following system prompt while preserving ALL specific facts, rules, constraints, and named entities. Do not lose any information that would change behavior. Output ONLY the compressed text, nothing else.
Original prompt:
{system_prompt}
Compressed (preserve all facts and rules):"""
# ---------------------------------------------------------------------------
# App
# ---------------------------------------------------------------------------
app = FastAPI(
title="Neuron Compressor Service",
description="CCR Phase 1 — system prompt compression for the Neuron soul",
version="1.0.0",
)
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_methods=["*"],
allow_headers=["*"],
)
# ---------------------------------------------------------------------------
# Models
# ---------------------------------------------------------------------------
class CompressRequest(BaseModel):
system_prompt: str
context_type: Optional[str] = "mixed" # identity | rules | memory | mixed
class CompressResponse(BaseModel):
id: str
compressed: str
original_tokens: int
compressed_tokens: int
reduction_pct: float
model: str
context_type: str
latency_ms: int
# ---------------------------------------------------------------------------
# Token estimation (rough: word_count × 1.3, matching architecture doc)
# ---------------------------------------------------------------------------
def estimate_tokens(text: str) -> int:
"""Rough token count estimate: words × 1.3. No tokenizer dependency."""
words = len(text.split())
return max(1, int(words * 1.3))
# ---------------------------------------------------------------------------
# Core compression
# ---------------------------------------------------------------------------
async def ollama_available(client: httpx.AsyncClient) -> bool:
"""Quick connectivity check to Ollama."""
try:
await client.get("http://localhost:11434/", timeout=2.0)
return True
except (httpx.ConnectError, httpx.TimeoutException):
return False
async def compress_with_model(
client: httpx.AsyncClient, model: str, prompt_text: str
) -> str:
"""
Call a single Ollama model to compress the given text.
Returns the compressed string, or "" on failure.
"""
payload = {
"model": model,
"prompt": prompt_text,
"stream": False,
# Keep temperature low for deterministic compression
"options": {
"temperature": 0.1,
"top_p": 0.9,
},
}
try:
resp = await client.post(OLLAMA_BASE, json=payload, timeout=MODEL_TIMEOUT)
resp.raise_for_status()
data = resp.json()
return data.get("response", "").strip()
except (httpx.TimeoutException, httpx.HTTPStatusError, Exception):
return ""
async def run_compression(system_prompt: str, context_type: str) -> CompressResponse:
start = time.monotonic()
request_id = str(uuid.uuid4())
original_tokens = estimate_tokens(system_prompt)
prompt_text = COMPRESSOR_PROMPT_TEMPLATE.format(system_prompt=system_prompt)
async with httpx.AsyncClient() as client:
# Connectivity gate
if not await ollama_available(client):
latency_ms = int((time.monotonic() - start) * 1000)
return CompressResponse(
id=request_id,
compressed=system_prompt, # passthrough on failure
original_tokens=original_tokens,
compressed_tokens=original_tokens,
reduction_pct=0.0,
model="unavailable",
context_type=context_type,
latency_ms=latency_ms,
)
# Try primary model (qwen3:1.7b), fall back to neuron:latest
compressed = await compress_with_model(client, PRIMARY_MODEL, prompt_text)
model_used = PRIMARY_MODEL
if not compressed:
compressed = await compress_with_model(client, FALLBACK_MODEL, prompt_text)
model_used = FALLBACK_MODEL
if not compressed:
# Both models failed — passthrough
latency_ms = int((time.monotonic() - start) * 1000)
return CompressResponse(
id=request_id,
compressed=system_prompt,
original_tokens=original_tokens,
compressed_tokens=original_tokens,
reduction_pct=0.0,
model="both-failed",
context_type=context_type,
latency_ms=latency_ms,
)
compressed_tokens = estimate_tokens(compressed)
reduction_pct = round(
(1.0 - compressed_tokens / max(1, original_tokens)) * 100.0, 1
)
latency_ms = int((time.monotonic() - start) * 1000)
return CompressResponse(
id=request_id,
compressed=compressed,
original_tokens=original_tokens,
compressed_tokens=compressed_tokens,
reduction_pct=reduction_pct,
model=model_used,
context_type=context_type,
latency_ms=latency_ms,
)
# ---------------------------------------------------------------------------
# Routes
# ---------------------------------------------------------------------------
@app.post("/api/neuron/compress", response_model=CompressResponse)
async def compress(req: CompressRequest):
return await run_compression(req.system_prompt, req.context_type or "mixed")
@app.get("/healthz")
async def health():
return {"status": "ok", "service": "compressor", "version": "1.0.0"}
# ---------------------------------------------------------------------------
# Entrypoint
# ---------------------------------------------------------------------------
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Neuron Compressor Service (CCR Phase 1)")
parser.add_argument("--port", type=int, default=7772, help="Port to listen on")
parser.add_argument("--host", default="127.0.0.1", help="Host to bind to")
args = parser.parse_args()
print(f"[compressor] Starting on {args.host}:{args.port}")
print(f"[compressor] Primary model: {PRIMARY_MODEL}")
print(f"[compressor] Fallback model: {FALLBACK_MODEL}")
uvicorn.run(app, host=args.host, port=args.port, log_level="info")
-224
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@@ -1,224 +0,0 @@
#!/usr/bin/env python3
"""
Neuron Council Service LLM anti-confabulation layer.
Fires 3 parallel Ollama calls and aggregates votes to produce a
confidence score + tags for any claim before it enters memory.
Usage:
python3 council_service.py [--port 7771]
"""
import argparse
import asyncio
import time
import uuid
from typing import Optional
import httpx
import uvicorn
from fastapi import FastAPI
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel
# ---------------------------------------------------------------------------
# Config
# ---------------------------------------------------------------------------
OLLAMA_BASE = "http://localhost:11434/api/generate"
COUNCIL_MODELS = ["neuron:latest", "dolphin3:8b", "neuron-ft:latest"]
MODEL_TIMEOUT = 45.0 # seconds per model (models may need to load from cold)
SYSTEM_PROMPT_TEMPLATE = """\
You are a fact-checker. You will be given a claim.
Your job: assess if it is accurate, internally consistent, and grounded in reality.
Respond with EXACTLY ONE WORD:
- "plausible" if the claim seems accurate and well-grounded
- "uncertain" if you cannot determine accuracy or the claim is ambiguous
- "confabulation" if the claim appears to contain invented facts or clear errors
Claim: {claim}
Context: {context}
Your verdict (one word only):"""
VALID_VERDICTS = {"plausible", "uncertain", "confabulation"}
# ---------------------------------------------------------------------------
# App
# ---------------------------------------------------------------------------
app = FastAPI(
title="Neuron Council Service",
description="LLM-council anti-confabulation layer for Neuron soul",
version="1.0.0",
)
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_methods=["*"],
allow_headers=["*"],
)
# ---------------------------------------------------------------------------
# Models
# ---------------------------------------------------------------------------
class VerifyRequest(BaseModel):
claim: str
context: Optional[str] = ""
class VerifyResponse(BaseModel):
id: str
claim: str
confidence: float
council_votes: list[str]
summary: str
tags: list[str]
latency_ms: int
# ---------------------------------------------------------------------------
# Core logic
# ---------------------------------------------------------------------------
async def query_model(client: httpx.AsyncClient, model: str, prompt: str) -> str:
"""
Query a single Ollama model. Returns "plausible", "uncertain", or "confabulation".
Returns "uncertain" on timeout. Raises httpx.ConnectError on connection failure.
"""
payload = {
"model": model,
"prompt": prompt,
"stream": False,
}
try:
resp = await client.post(OLLAMA_BASE, json=payload, timeout=MODEL_TIMEOUT)
resp.raise_for_status()
data = resp.json()
raw = data.get("response", "").strip().lower().split()[0] if data.get("response", "").strip() else "uncertain"
# Normalise to one of the three valid verdicts
if raw not in VALID_VERDICTS:
return "uncertain"
return raw
except httpx.TimeoutException:
return "uncertain"
async def run_council(claim: str, context: str) -> VerifyResponse:
start = time.monotonic()
prompt = SYSTEM_PROMPT_TEMPLATE.format(claim=claim, context=context)
# Quick connectivity check — one tiny HEAD request to Ollama
try:
async with httpx.AsyncClient() as probe:
await probe.get("http://localhost:11434/", timeout=2.0)
except (httpx.ConnectError, httpx.TimeoutException):
latency_ms = int((time.monotonic() - start) * 1000)
return VerifyResponse(
id=str(uuid.uuid4()),
claim=claim,
confidence=0.5,
council_votes=[],
summary="Ollama is unavailable; council could not convene.",
tags=["council-unavailable"],
latency_ms=latency_ms,
)
# Fire all 3 model calls in parallel
async with httpx.AsyncClient() as client:
tasks = [query_model(client, m, prompt) for m in COUNCIL_MODELS]
votes: list[str] = await asyncio.gather(*tasks)
plausible_count = votes.count("plausible")
latency_ms = int((time.monotonic() - start) * 1000)
# Voting rules
if plausible_count == 3:
confidence = 0.85
tags = ["verified"]
summary = "3/3 council members agree this is plausible."
elif plausible_count == 2:
confidence = 0.65
tags = ["council-split"]
summary = "2/3 council members agree this is plausible."
elif plausible_count == 1:
confidence = 0.30
tags = ["unverified", "council-flagged"]
summary = "1/3 council members found this plausible."
else:
confidence = 0.30
tags = ["unverified", "council-flagged"]
summary = "0/3 council members found this plausible."
return VerifyResponse(
id=str(uuid.uuid4()),
claim=claim,
confidence=confidence,
council_votes=votes,
summary=summary,
tags=tags,
latency_ms=latency_ms,
)
# ---------------------------------------------------------------------------
# Routes
# ---------------------------------------------------------------------------
@app.post("/api/neuron/council/verify", response_model=VerifyResponse)
async def verify(req: VerifyRequest):
return await run_council(req.claim, req.context or "")
@app.get("/healthz")
async def health():
return {"status": "ok", "service": "council"}
# ---------------------------------------------------------------------------
# Startup warm-up: pre-load all council models so first real call is fast
# ---------------------------------------------------------------------------
@app.on_event("startup")
async def warmup_models():
"""
Send a trivial prompt to each council model at startup.
This forces Ollama to load the models into GPU memory so the first
real council call does not pay the cold-load latency penalty.
"""
print("[council] Warming up council models...")
warmup_prompt = "Reply with one word: ready"
async with httpx.AsyncClient() as client:
tasks = [
client.post(
OLLAMA_BASE,
json={"model": m, "prompt": warmup_prompt, "stream": False},
timeout=60.0,
)
for m in COUNCIL_MODELS
]
results = await asyncio.gather(*tasks, return_exceptions=True)
for model, result in zip(COUNCIL_MODELS, results):
if isinstance(result, Exception):
print(f"[council] warm-up failed for {model}: {result}")
else:
print(f"[council] {model} warm and ready")
print("[council] All models warmed up.")
# ---------------------------------------------------------------------------
# Entrypoint
# ---------------------------------------------------------------------------
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Neuron Council Service")
parser.add_argument("--port", type=int, default=7771, help="Port to listen on")
parser.add_argument("--host", default="127.0.0.1", help="Host to bind to")
args = parser.parse_args()
print(f"[council] Starting on {args.host}:{args.port}")
uvicorn.run(app, host=args.host, port=args.port, log_level="info")
Generated Vendored
+136 -54
View File
@@ -10,7 +10,6 @@ el_val_t mem_remember(el_val_t content, el_val_t tags);
el_val_t mem_recall(el_val_t query, el_val_t depth);
el_val_t mem_search(el_val_t query, el_val_t limit);
el_val_t mem_strengthen(el_val_t node_id);
el_val_t mem_tombstone(el_val_t node_id);
el_val_t mem_forget(el_val_t node_id);
el_val_t mem_consolidate(void);
el_val_t mem_save(el_val_t path);
@@ -26,7 +25,6 @@ el_val_t elapsed_ms(void);
el_val_t elapsed_human(void);
el_val_t embed_ok(void);
el_val_t emit_heartbeat(void);
el_val_t auto_term_try_slot(el_val_t slot_type, el_val_t slot_lbl);
el_val_t proactive_curiosity(void);
el_val_t pulse_count(void);
el_val_t pulse_inc(void);
@@ -44,6 +42,110 @@ el_val_t threat_score_history(el_val_t history);
el_val_t threat_trajectory_check(el_val_t tool_name, el_val_t tool_input);
el_val_t threat_history_append(el_val_t text);
el_val_t tier_working(void) {
return EL_STR("Working");
return 0;
}
el_val_t tier_episodic(void) {
return EL_STR("Episodic");
return 0;
}
el_val_t tier_canonical(void) {
return EL_STR("Canonical");
return 0;
}
el_val_t mem_store(el_val_t content, el_val_t label, el_val_t tags) {
return engram_node_full(content, EL_STR("Memory"), label, el_from_float(el_from_float(0.5)), el_from_float(el_from_float(0.5)), el_from_float(el_from_float(0.8)), EL_STR("Working"), tags);
return 0;
}
el_val_t mem_remember(el_val_t content, el_val_t tags) {
return mem_store(content, EL_STR("soul-memory"), tags);
return 0;
}
el_val_t mem_recall(el_val_t query, el_val_t depth) {
return engram_activate_json(query, depth);
return 0;
}
el_val_t mem_search(el_val_t query, el_val_t limit) {
return engram_search_json(query, limit);
return 0;
}
el_val_t mem_strengthen(el_val_t node_id) {
engram_strengthen(node_id);
return 0;
}
el_val_t mem_forget(el_val_t node_id) {
engram_forget(node_id);
return 0;
}
el_val_t mem_consolidate(void) {
el_val_t scanned = engram_node_count();
el_val_t dummy = engram_scan_nodes_json(100, 0);
el_val_t total_nodes = engram_node_count();
el_val_t total_edges = engram_edge_count();
return el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(EL_STR("{\"scanned\":"), int_to_str(scanned)), EL_STR(",\"total_nodes\":")), int_to_str(total_nodes)), EL_STR(",\"total_edges\":")), int_to_str(total_edges)), EL_STR("}"));
return 0;
}
el_val_t mem_save(el_val_t path) {
engram_save(path);
return 0;
}
el_val_t mem_load(el_val_t path) {
engram_load(path);
return 0;
}
el_val_t mem_boot_count_get(void) {
el_val_t results = engram_search_json(EL_STR("soul:boot_count"), 3);
if (str_eq(results, EL_STR(""))) {
return 0;
}
if (str_eq(results, EL_STR("[]"))) {
return 0;
}
el_val_t node = json_array_get(results, 0);
el_val_t content = json_get(node, EL_STR("content"));
el_val_t prefix = EL_STR("soul:boot_count:");
if (!str_starts_with(content, prefix)) {
return 0;
}
el_val_t num_str = str_slice(content, str_len(prefix), str_len(content));
return str_to_int(num_str);
return 0;
}
el_val_t mem_boot_count_inc(void) {
el_val_t current = mem_boot_count_get();
el_val_t next = (current + 1);
el_val_t content = el_str_concat(EL_STR("soul:boot_count:"), int_to_str(next));
el_val_t tags = EL_STR("[\"soul-meta\",\"boot-counter\"]");
el_val_t discard = engram_node_full(content, EL_STR("Memory"), EL_STR("soul:boot_count"), el_from_float(el_from_float(0.9)), el_from_float(el_from_float(0.9)), el_from_float(el_from_float(1.0)), EL_STR("Canonical"), tags);
return next;
return 0;
}
el_val_t mem_emit_state_event(el_val_t trigger, el_val_t kind, el_val_t content) {
el_val_t boot = mem_boot_count_get();
el_val_t ts = time_now();
el_val_t safe_trigger = str_replace(trigger, EL_STR("\""), EL_STR("'"));
el_val_t safe_content = str_replace(content, EL_STR("\""), EL_STR("'"));
el_val_t payload = el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(EL_STR("{\"trigger\":\""), safe_trigger), EL_STR("\"")), EL_STR(",\"kind\":\"")), kind), EL_STR("\"")), EL_STR(",\"content\":\"")), safe_content), EL_STR("\"")), EL_STR(",\"boot\":")), int_to_str(boot)), EL_STR(",\"ts\":")), int_to_str(ts)), EL_STR("}"));
el_val_t tags = EL_STR("[\"internal-state\",\"pre-reasoning\",\"InternalStateEvent\"]");
return engram_node_full(payload, EL_STR("InternalStateEvent"), el_str_concat(EL_STR("state-event:"), kind), el_from_float(el_from_float(0.85)), el_from_float(el_from_float(0.8)), el_from_float(el_from_float(0.9)), EL_STR("Episodic"), tags);
return 0;
}
el_val_t idle_count(void) {
el_val_t s = state_get(EL_STR("soul.idle"));
if (str_eq(s, EL_STR(""))) {
@@ -69,7 +171,7 @@ el_val_t ise_post(el_val_t content) {
el_val_t ise_url = env(EL_STR("SOUL_ISE_URL"));
el_val_t engram_url = ({ el_val_t _if_result_1 = 0; if (str_eq(ise_url, EL_STR(""))) { _if_result_1 = (state_get(EL_STR("soul_engram_url"))); } else { _if_result_1 = (ise_url); } _if_result_1; });
if (str_eq(engram_url, EL_STR(""))) {
el_val_t discard = engram_node_full(content, EL_STR("InternalStateEvent"), EL_STR("state-event"), el_from_float(0.3), el_from_float(0.3), el_from_float(0.8), EL_STR("Episodic"), EL_STR("[\"internal-state\",\"InternalStateEvent\"]"));
el_val_t discard = engram_node_full(content, EL_STR("InternalStateEvent"), EL_STR("state-event"), el_from_float(el_from_float(0.3)), el_from_float(el_from_float(0.3)), el_from_float(el_from_float(0.8)), EL_STR("Episodic"), EL_STR("[\"internal-state\",\"InternalStateEvent\"]"));
return EL_STR("");
}
el_val_t safe1 = str_replace(content, EL_STR("\\"), EL_STR("\\\\"));
@@ -143,29 +245,6 @@ el_val_t emit_heartbeat(void) {
return 0;
}
el_val_t auto_term_try_slot(el_val_t slot_type, el_val_t slot_lbl) {
state_set(EL_STR("_ats_ok"), EL_STR("0"));
if (str_eq(slot_type, EL_STR("Memory"))) {
state_set(EL_STR("_ats_ok"), EL_STR("1"));
}
if (str_eq(slot_type, EL_STR("BacklogItem"))) {
state_set(EL_STR("_ats_ok"), EL_STR("1"));
}
if (str_eq(slot_type, EL_STR("Entity"))) {
state_set(EL_STR("_ats_ok"), EL_STR("1"));
}
if (str_eq(state_get(EL_STR("_ats_ok")), EL_STR("1"))) {
if (!str_eq(slot_lbl, EL_STR(""))) {
el_val_t sp = str_find_chars(slot_lbl, EL_STR(" :(["));
if (sp > 3) {
state_set(EL_STR("cseed_auto"), str_slice(slot_lbl, 0, sp));
}
}
}
return EL_STR("");
return 0;
}
el_val_t proactive_curiosity(void) {
el_val_t ts = time_now();
el_val_t ts_minutes = (ts / 60000);
@@ -203,35 +282,36 @@ el_val_t proactive_curiosity(void) {
el_val_t found_c = json_array_len(results_c);
el_val_t found = ((found_a + found_b) + found_c);
state_set(EL_STR("cseed_auto"), EL_STR(""));
el_val_t wm10 = engram_wm_top_json(10);
el_val_t wm10_n9 = json_array_get(wm10, 9);
el_val_t wm10_n8 = json_array_get(wm10, 8);
el_val_t wm10_n7 = json_array_get(wm10, 7);
el_val_t wm10_n6 = json_array_get(wm10, 6);
el_val_t wm10_n5 = json_array_get(wm10, 5);
el_val_t wm10_n4 = json_array_get(wm10, 4);
el_val_t wm10_n3 = json_array_get(wm10, 3);
el_val_t wm10_n2 = json_array_get(wm10, 2);
el_val_t wm10_n1 = json_array_get(wm10, 1);
el_val_t wm10_n0 = json_array_get(wm10, 0);
auto_term_try_slot(json_get(wm10_n9, EL_STR("node_type")), json_get(wm10_n9, EL_STR("label")));
auto_term_try_slot(json_get(wm10_n8, EL_STR("node_type")), json_get(wm10_n8, EL_STR("label")));
auto_term_try_slot(json_get(wm10_n7, EL_STR("node_type")), json_get(wm10_n7, EL_STR("label")));
auto_term_try_slot(json_get(wm10_n6, EL_STR("node_type")), json_get(wm10_n6, EL_STR("label")));
auto_term_try_slot(json_get(wm10_n5, EL_STR("node_type")), json_get(wm10_n5, EL_STR("label")));
auto_term_try_slot(json_get(wm10_n4, EL_STR("node_type")), json_get(wm10_n4, EL_STR("label")));
auto_term_try_slot(json_get(wm10_n3, EL_STR("node_type")), json_get(wm10_n3, EL_STR("label")));
auto_term_try_slot(json_get(wm10_n2, EL_STR("node_type")), json_get(wm10_n2, EL_STR("label")));
auto_term_try_slot(json_get(wm10_n1, EL_STR("node_type")), json_get(wm10_n1, EL_STR("label")));
auto_term_try_slot(json_get(wm10_n0, EL_STR("node_type")), json_get(wm10_n0, EL_STR("label")));
el_val_t wm_top_j = engram_wm_top_json(1);
el_val_t wm_top_n = json_array_get(wm_top_j, 0);
el_val_t wm_top_lbl = json_get(wm_top_n, EL_STR("label"));
el_val_t wm_top_type = json_get(wm_top_n, EL_STR("node_type"));
state_set(EL_STR("allow_auto"), EL_STR("0"));
if (str_eq(wm_top_type, EL_STR("Memory"))) {
state_set(EL_STR("allow_auto"), EL_STR("1"));
}
if (str_eq(wm_top_type, EL_STR("BacklogItem"))) {
state_set(EL_STR("allow_auto"), EL_STR("1"));
}
if (str_eq(wm_top_type, EL_STR("Entity"))) {
state_set(EL_STR("allow_auto"), EL_STR("1"));
}
el_val_t allow_auto = state_get(EL_STR("allow_auto"));
if (str_eq(allow_auto, EL_STR("1"))) {
if (!str_eq(wm_top_lbl, EL_STR(""))) {
el_val_t sp = str_find_chars(wm_top_lbl, EL_STR(" :(["));
if (sp > 3) {
state_set(EL_STR("cseed_auto"), str_slice(wm_top_lbl, 0, sp));
}
}
}
el_val_t auto_term = state_get(EL_STR("cseed_auto"));
el_val_t results_auto = ({ el_val_t _if_result_3 = 0; if (str_eq(auto_term, EL_STR(""))) { _if_result_3 = (EL_STR("[]")); } else { _if_result_3 = (engram_activate_json(auto_term, 1)); } _if_result_3; });
el_val_t found_auto = json_array_len(results_auto);
el_val_t total_found = (found + found_auto);
el_val_t safe_auto = str_replace(auto_term, EL_STR("\""), EL_STR("'"));
el_val_t wmc = engram_wm_count();
el_val_t wm3 = engram_wm_top_json(3);
el_val_t ise = el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(EL_STR("{\"event\":\"curiosity_scan\",\"seed\":\""), curiosity_seed), EL_STR("\",\"auto_term\":\"")), safe_auto), EL_STR("\",\"minute_block\":")), int_to_str(minute_block)), EL_STR(",\"activated\":")), int_to_str(total_found)), EL_STR(",\"wm_active\":")), int_to_str(wmc)), EL_STR(",\"wm_top\":")), wm3), EL_STR(",\"ts\":")), int_to_str(ts)), EL_STR("}"));
el_val_t ise = el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(EL_STR("{\"event\":\"curiosity_scan\",\"seed\":\""), curiosity_seed), EL_STR("\",\"auto_term\":\"")), safe_auto), EL_STR("\",\"minute_block\":")), int_to_str(minute_block)), EL_STR(",\"activated\":")), int_to_str(total_found)), EL_STR(",\"wm_active\":")), int_to_str(wmc)), EL_STR(",\"ts\":")), int_to_str(ts)), EL_STR("}"));
ise_post(ise);
return (total_found > 0);
return 0;
@@ -363,8 +443,8 @@ el_val_t respond(el_val_t action_json) {
return el_str_concat(el_str_concat(EL_STR("{\"outcome\":\"strengthened\",\"id\":\""), payload), EL_STR("\"}"));
}
if (str_eq(kind, EL_STR("forget"))) {
el_val_t _marker = mem_tombstone(payload);
return el_str_concat(el_str_concat(EL_STR("{\"outcome\":\"tombstoned\",\"id\":\""), payload), EL_STR("\"}"));
engram_forget(payload);
return el_str_concat(el_str_concat(EL_STR("{\"outcome\":\"forgotten\",\"id\":\""), payload), EL_STR("\"}"));
}
return EL_STR("{\"outcome\":\"noop\"}");
return 0;
@@ -420,11 +500,9 @@ el_val_t awareness_run(void) {
el_val_t beat_ms = ({ el_val_t _if_result_5 = 0; if (str_eq(beat_ms_raw, EL_STR(""))) { _if_result_5 = (60000); } else { _if_result_5 = (str_to_int(beat_ms_raw)); } _if_result_5; });
el_val_t scan_ms = (beat_ms / 2);
while (1) {
el_val_t tick_mark = el_arena_push();
el_val_t running = state_get(EL_STR("soul.running"));
if (str_eq(running, EL_STR("false"))) {
println(EL_STR("[awareness] exiting"));
el_arena_pop(tick_mark);
return EL_STR("");
}
el_val_t did_work = one_cycle();
@@ -472,7 +550,6 @@ el_val_t awareness_run(void) {
state_set(EL_STR("soul.last_refresh_ts"), int_to_str(now_ts));
}
sleep_ms(tick_ms);
el_arena_pop(tick_mark);
}
return 0;
}
@@ -581,3 +658,8 @@ el_val_t threat_history_append(el_val_t text) {
return 0;
}
int main(int _argc, char** _argv) {
el_runtime_init_args(_argc, _argv);
return 0;
}
Generated Vendored
-1
View File
@@ -7,7 +7,6 @@ extern fn elapsed_ms() -> Int
extern fn elapsed_human() -> String
extern fn embed_ok() -> Int
extern fn emit_heartbeat() -> Void
extern fn auto_term_try_slot(slot_type: String, slot_lbl: String) -> Void
extern fn proactive_curiosity() -> Bool
extern fn pulse_count() -> Int
extern fn pulse_inc() -> Int
Generated Vendored
+278 -1153
View File
File diff suppressed because one or more lines are too long
Generated Vendored
+8 -49
View File
@@ -1,70 +1,29 @@
// auto-generated by elc --emit-header do not edit
// auto-generated by elc --emit-header - do not edit
extern fn chat_default_model() -> String
extern fn engram_numeric_valid(s: String) -> Bool
extern fn parse_float_x100(s: String) -> Int
extern fn engram_score_node(node_json: String) -> Int
extern fn engram_render_node(node_json: String) -> String
extern fn engram_render_nodes(nodes_json: String) -> String
extern fn engram_dedup_nodes(nodes_json: String) -> String
extern fn engram_compile_ranked(nodes_json: String, max_nodes: Int) -> String
extern fn engram_split_topics(message: String) -> String
extern fn engram_extract_entities(message: String) -> String
extern fn engram_detect_recall_intent(message: String) -> Bool
extern fn engram_is_continuation(message: String, hist_len: Int) -> Bool
extern fn engram_compile_multi(topic: String) -> String
extern fn engram_nodes_merge(a: String, b: String) -> String
extern fn id_in_seen(node_id: String, seen: String) -> Bool
extern fn add_to_seen(seen: String, node_id: String) -> String
extern fn engram_extract_ids(nodes_json: String) -> String
extern fn gemini_api_key() -> String
extern fn xai_api_key() -> String
extern fn llm_call_grok(model: String, system: String, message: String) -> String
extern fn llm_call_gemini(model: String, system: String, message: String) -> String
extern fn build_identity_from_graph() -> String
extern fn engram_compile(intent: String) -> String
extern fn distill_transcript(transcript: String) -> String
extern fn json_safe(s: String) -> String
extern fn current_engine_note(model: String) -> String
extern fn build_system_prompt(ctx: String, chat_mode: Bool) -> String
extern fn build_system_prompt(ctx: String) -> String
extern fn hist_append(hist: String, role: String, content: String) -> String
extern fn hist_trim(hist: String) -> String
extern fn hist_trim_with_bell_guard(hist: String) -> String
extern fn clean_llm_response(s: String) -> String
extern fn conv_history_persist(hist: String) -> Void
extern fn conv_history_load() -> String
extern fn session_preload_bullets(nodes: String, max_bullets: Int, snip_len: Int) -> String
extern fn affective_context_prefix() -> String
extern fn handle_chat(body: String) -> String
extern fn handle_see(body: String) -> String
extern fn studio_tools_json() -> String
extern fn agentic_api_key() -> String
extern fn llm_base_url() -> String
extern fn llm_wire_format() -> String
extern fn json_escape(s: String) -> String
extern fn openai_chat_complete(model: String, base_url: String, api_key: String, safe_sys: String, messages_json: String) -> String
extern fn call_neuron_mcp(tool_name: String, args_json: String) -> String
extern fn agentic_tools_literal() -> String
extern fn agentic_tools_with_web() -> String
extern fn connector_tools_json() -> String
extern fn agentic_tools_all() -> String
extern fn call_mcp_bridge(tool_name: String, tool_input: String) -> String
extern fn tool_auto_approved(tool_name: String) -> Bool
extern fn call_neuron_mcp(tool_name: String, args: String) -> String
extern fn agent_workspace_root() -> String
extern fn path_within_root(path: String, root: String) -> Bool
extern fn resolve_in_root(path: String, root: String) -> String
extern fn run_command_is_readonly(cmd: String) -> Bool
extern fn cmd_abs_escape_at(cmd: String, root: String, needle: String) -> Bool
extern fn run_command_guard(cmd: String, root: String) -> String
extern fn classify_tool_risk(tool_name: String, tool_input: String) -> String
extern fn dispatch_tool(tool_name: String, tool_input: String) -> String
extern fn is_builtin_tool(tool_name: String) -> Bool
extern fn next_bridge_id() -> String
extern fn handle_chat_plan(body: String) -> String
extern fn handle_chat_agentic(body: String) -> String
extern fn agentic_loop(session_id: String, model: String, safe_sys: String, tools_json: String, messages_in: String, h: Map, tools_log_in: String) -> String
extern fn bridge_save(session_id: String, model: String, safe_sys: String, tools_json: String, messages: String, tools_log: String, tool_use_id: String) -> Bool
extern fn agentic_resume(session_id: String, tool_use_id: String, content: String) -> String
extern fn handle_tool_result(session_id: String, body: String) -> String
extern fn handle_chat_as_soul(body: String) -> String
extern fn handle_dharma_room_turn(body: String) -> String
extern fn handle_dharma_room_turn_agentic(body: String) -> String
extern fn session_summary_write(summary_text: String) -> String
extern fn session_summary_write_dated(summary_text: String, label: String) -> String
extern fn session_summary_autogenerate(hist: String) -> String
extern fn auto_persist(req: String, resp: String) -> Void
extern fn strengthen_chat_nodes(activation_nodes: String) -> Void
Generated Vendored
+1 -77
View File
@@ -2,18 +2,9 @@
#include "el_runtime.h"
el_val_t add_punct(el_val_t s, el_val_t intent);
el_val_t add_to_seen(el_val_t seen, el_val_t node_id);
el_val_t aff_try_slot(el_val_t slot_json, el_val_t aff_7d_ts, el_val_t acc_key);
el_val_t agent_number(el_val_t agent);
el_val_t agent_person(el_val_t agent);
el_val_t agent_workspace_root(void);
el_val_t agentic_api_key(void);
el_val_t agentic_api_turn(el_val_t model, el_val_t safe_sys, el_val_t tools_json, el_val_t messages);
el_val_t agentic_blob(el_val_t model, el_val_t system, el_val_t tools_json, el_val_t messages, el_val_t origin, el_val_t approval, el_val_t iteration, el_val_t tools_log, el_val_t content, el_val_t queue, el_val_t results, el_val_t next);
el_val_t agentic_engine(el_val_t session_id, el_val_t blob);
el_val_t agentic_loop(el_val_t session_id, el_val_t model, el_val_t safe_sys, el_val_t tools_json, el_val_t messages_in, el_val_t h, el_val_t tools_log_in);
el_val_t agentic_resume(el_val_t session_id, el_val_t tool_use_id, el_val_t content);
el_val_t agentic_tools_all(void);
el_val_t agentic_tools_literal(void);
el_val_t agentic_tools_with_web(void);
el_val_t agree_determiner(el_val_t det, el_val_t noun);
@@ -94,13 +85,10 @@ el_val_t api_err(el_val_t msg);
el_val_t api_err_protected(el_val_t id);
el_val_t api_json_escape(el_val_t s);
el_val_t api_nonempty(el_val_t s);
el_val_t api_not_persisted(el_val_t id);
el_val_t api_ok(el_val_t extra);
el_val_t api_or_empty(el_val_t s);
el_val_t api_persisted(el_val_t id);
el_val_t api_query_int(el_val_t path, el_val_t key, el_val_t default_val);
el_val_t api_query_param(el_val_t path, el_val_t key);
el_val_t append_tool_log(el_val_t log, el_val_t name);
el_val_t ar_case_ending(el_val_t kase, el_val_t definite);
el_val_t ar_conjugate(el_val_t verb, el_val_t tense, el_val_t person, el_val_t gender, el_val_t number);
el_val_t ar_conjugate_form1(el_val_t past_base, el_val_t present_stem, el_val_t tense, el_val_t slot);
@@ -130,29 +118,22 @@ el_val_t ar_verb_form(el_val_t verb, el_val_t tense, el_val_t person, el_val_t n
el_val_t attend(el_val_t node_json);
el_val_t auth_headers(el_val_t tok);
el_val_t auto_persist(el_val_t req, el_val_t resp);
el_val_t auto_term_try_slot(el_val_t slot_type, el_val_t slot_lbl);
el_val_t awareness_run(void);
el_val_t axon_get(el_val_t path);
el_val_t axon_post(el_val_t path, el_val_t body);
el_val_t bridge_save(el_val_t session_id, el_val_t model, el_val_t safe_sys, el_val_t tools_json, el_val_t messages, el_val_t tools_log, el_val_t tool_use_id);
el_val_t build_form_from_json(el_val_t semantic_form_json, el_val_t lang_code);
el_val_t build_identity_from_graph(void);
el_val_t build_np(el_val_t referent, el_val_t slots);
el_val_t build_pp(el_val_t loc);
el_val_t build_rules(void);
el_val_t build_system_prompt(el_val_t ctx, el_val_t chat_mode);
el_val_t build_system_prompt(el_val_t ctx);
el_val_t build_vocab(void);
el_val_t build_vp_body(el_val_t slots);
el_val_t build_vp_from_slots(el_val_t slots);
el_val_t call_mcp_bridge(el_val_t tool_name, el_val_t tool_input);
el_val_t call_neuron_mcp(el_val_t tool_name, el_val_t args);
el_val_t call_neuron_mcp(el_val_t tool_name, el_val_t args_json);
el_val_t capitalize_first(el_val_t s);
el_val_t chat_default_model(void);
el_val_t clean_llm_response(el_val_t s);
el_val_t connectd_get(el_val_t suffix);
el_val_t connectd_post(el_val_t suffix, el_val_t body);
el_val_t connector_tools_json(void);
el_val_t conv_history_load(void);
el_val_t conv_history_persist(el_val_t hist);
el_val_t cop_article(el_val_t gender, el_val_t number, el_val_t definite);
@@ -259,19 +240,6 @@ el_val_t en_verb_form(el_val_t base, el_val_t tense, el_val_t person, el_val_t n
el_val_t en_verb_gerund(el_val_t base);
el_val_t en_verb_past(el_val_t base);
el_val_t engram_compile(el_val_t intent);
el_val_t engram_compile_multi(el_val_t topic);
el_val_t engram_compile_ranked(el_val_t nodes_json, el_val_t max_nodes);
el_val_t engram_dedup_nodes(el_val_t nodes_json);
el_val_t engram_detect_recall_intent(el_val_t message);
el_val_t engram_extract_entities(el_val_t message);
el_val_t engram_extract_ids(el_val_t nodes_json);
el_val_t engram_is_continuation(el_val_t message, el_val_t hist_len);
el_val_t engram_nodes_merge(el_val_t a, el_val_t b);
el_val_t engram_numeric_valid(el_val_t s);
el_val_t engram_render_node(el_val_t node_json);
el_val_t engram_render_nodes(el_val_t nodes_json);
el_val_t engram_score_node(el_val_t node_json);
el_val_t engram_split_topics(el_val_t message);
el_val_t enm_been_past(el_val_t slot);
el_val_t enm_been_present(el_val_t slot);
el_val_t enm_comen_past(el_val_t slot);
@@ -301,7 +269,6 @@ el_val_t enm_str_ends(el_val_t s, el_val_t suf);
el_val_t enm_weak_past(el_val_t stem, el_val_t slot);
el_val_t enm_weak_present(el_val_t stem, el_val_t slot);
el_val_t enm_weak_stem(el_val_t verb);
el_val_t ensure_self_canonical_bridge(void);
el_val_t entry_form(el_val_t entry, el_val_t n);
el_val_t entry_found(el_val_t entry);
el_val_t entry_pos(el_val_t entry);
@@ -330,8 +297,6 @@ el_val_t es_str_last2(el_val_t s);
el_val_t es_str_last3(el_val_t s);
el_val_t es_str_last_char(el_val_t s);
el_val_t es_verb_class(el_val_t base);
el_val_t exec_tool_block(el_val_t block);
el_val_t extract_all_text(el_val_t s);
el_val_t extract_dim(el_val_t content, el_val_t key);
el_val_t fi_apply_case(el_val_t noun, el_val_t gram_case, el_val_t number);
el_val_t fi_conjugate(el_val_t verb, el_val_t tense, el_val_t person, el_val_t number);
@@ -350,7 +315,6 @@ el_val_t fi_str_last_char(el_val_t s);
el_val_t fi_suffix(el_val_t base, el_val_t harmony);
el_val_t fi_verb_stem(el_val_t dict_form);
el_val_t find_rule(el_val_t rule_id_str);
el_val_t flag_true(el_val_t body, el_val_t key);
el_val_t fr_agree_article(el_val_t noun, el_val_t definite, el_val_t number);
el_val_t fr_avoir_present(el_val_t slot);
el_val_t fr_conjugate(el_val_t verb, el_val_t tense, el_val_t person, el_val_t number);
@@ -585,9 +549,6 @@ el_val_t handle_api_list_typed(el_val_t node_type, el_val_t path, el_val_t body)
el_val_t handle_api_log_state_event(el_val_t body);
el_val_t handle_api_memory_delete(el_val_t body);
el_val_t handle_api_memory_update(el_val_t body);
el_val_t handle_api_node_create(el_val_t body);
el_val_t handle_api_node_delete(el_val_t body);
el_val_t handle_api_node_update(el_val_t body);
el_val_t handle_api_promote_knowledge(el_val_t body);
el_val_t handle_api_recall(el_val_t method, el_val_t path, el_val_t body);
el_val_t handle_api_remember(el_val_t body);
@@ -596,9 +557,7 @@ el_val_t handle_api_tune_config(el_val_t body);
el_val_t handle_chat(el_val_t body);
el_val_t handle_chat_agentic(el_val_t body);
el_val_t handle_chat_as_soul(el_val_t body);
el_val_t handle_chat_plan(el_val_t body);
el_val_t handle_config(el_val_t method, el_val_t body);
el_val_t handle_connectors(el_val_t method, el_val_t clean, el_val_t body);
el_val_t handle_conversations(el_val_t method);
el_val_t handle_dharma(el_val_t path, el_val_t method, el_val_t body);
el_val_t handle_dharma_recv(el_val_t body);
@@ -607,12 +566,9 @@ el_val_t handle_dharma_room_turn_agentic(el_val_t body);
el_val_t handle_elp_chat(el_val_t body);
el_val_t handle_nlg(el_val_t path, el_val_t method, el_val_t body);
el_val_t handle_request(el_val_t method, el_val_t path, el_val_t body);
el_val_t handle_safety_contact_get(void);
el_val_t handle_safety_contact_post(el_val_t body);
el_val_t handle_see(el_val_t body);
el_val_t handle_session_approve(el_val_t session_id, el_val_t body);
el_val_t handle_tool(el_val_t path, el_val_t method, el_val_t body);
el_val_t handle_tool_result(el_val_t session_id, el_val_t body);
el_val_t hard_bell_threshold(void);
el_val_t he_conjugate(el_val_t verb, el_val_t tense, el_val_t person, el_val_t gender, el_val_t number);
el_val_t he_conjugate_copula(el_val_t tense, el_val_t slot);
@@ -671,8 +627,6 @@ el_val_t hi_verb_stem(el_val_t infinitive);
el_val_t hi_verb_stem_clean(el_val_t infinitive);
el_val_t hist_append(el_val_t hist, el_val_t role, el_val_t content);
el_val_t hist_trim(el_val_t hist);
el_val_t hist_trim_with_bell_guard(el_val_t hist);
el_val_t id_in_seen(el_val_t node_id, el_val_t seen);
el_val_t idle_count(void);
el_val_t idle_inc(void);
el_val_t idle_reset(void);
@@ -685,7 +639,6 @@ el_val_t imprint_unload(void);
el_val_t init_soul_edges(void);
el_val_t irregular_plural(el_val_t word);
el_val_t irregular_singular(el_val_t word);
el_val_t is_builtin_tool(el_val_t tool_name);
el_val_t is_pronoun(el_val_t word);
el_val_t is_protected_node(el_val_t id);
el_val_t is_vowel(el_val_t c);
@@ -698,7 +651,6 @@ el_val_t ja_noun_phrase(el_val_t noun, el_val_t gram_case);
el_val_t ja_particle(el_val_t gram_case);
el_val_t ja_question_particle(void);
el_val_t ja_verb_group(el_val_t dict_form);
el_val_t json_array_append(el_val_t arr, el_val_t item);
el_val_t json_safe(el_val_t s);
el_val_t la_conjugate(el_val_t verb, el_val_t tense, el_val_t person, el_val_t number);
el_val_t la_declension(el_val_t noun);
@@ -785,7 +737,6 @@ el_val_t lang_profile_txb(void);
el_val_t lang_profile_uga(void);
el_val_t lang_profile_zh(void);
el_val_t lang_word_order(el_val_t profile);
el_val_t layered_cycle(el_val_t raw_input);
el_val_t lex_class(el_val_t entry);
el_val_t lex_form(el_val_t entry, el_val_t idx);
el_val_t lex_pos(el_val_t entry);
@@ -829,7 +780,6 @@ el_val_t morph_conjugate(el_val_t verb, el_val_t tense, el_val_t person, el_val_
el_val_t morph_inflect(el_val_t word, el_val_t features, el_val_t profile);
el_val_t morph_map_canonical(el_val_t verb, el_val_t code);
el_val_t morph_pluralize(el_val_t noun, el_val_t profile);
el_val_t next_bridge_id(void);
el_val_t nlg_is_ws(el_val_t c);
el_val_t non_conjugate(el_val_t verb, el_val_t tense, el_val_t person, el_val_t number);
el_val_t non_decline(el_val_t noun, el_val_t gram_case, el_val_t number);
@@ -861,10 +811,8 @@ el_val_t non_vera_present(el_val_t slot);
el_val_t non_weak_past(el_val_t stem, el_val_t slot);
el_val_t non_weak_present(el_val_t stem, el_val_t slot);
el_val_t one_cycle(void);
el_val_t parse_float_x100(el_val_t s);
el_val_t parse_session_id_from_path(el_val_t path);
el_val_t parse_session_subpath(el_val_t path);
el_val_t path_within_root(el_val_t path, el_val_t root);
el_val_t peo_ah_past(el_val_t slot);
el_val_t peo_ah_present(el_val_t slot);
el_val_t peo_conjugate(el_val_t verb, el_val_t tense, el_val_t person, el_val_t number);
@@ -921,7 +869,6 @@ el_val_t pluralize(el_val_t singular);
el_val_t proactive_curiosity(void);
el_val_t pulse_count(void);
el_val_t pulse_inc(void);
el_val_t rate_limit_check(el_val_t ip, el_val_t path);
el_val_t realize(el_val_t form);
el_val_t realize_lang(el_val_t form, el_val_t profile);
el_val_t realize_np(el_val_t referent, el_val_t number);
@@ -930,7 +877,6 @@ el_val_t realize_vp_lang(el_val_t base_verb, el_val_t tense, el_val_t aspect, el
el_val_t record(el_val_t outcome_json);
el_val_t render_studio(void);
el_val_t render_tree(el_val_t tree);
el_val_t resolve_in_root(el_val_t path, el_val_t root);
el_val_t respond(el_val_t action_json);
el_val_t route_health(void);
el_val_t route_imprint_contextual(el_val_t body);
@@ -990,26 +936,12 @@ el_val_t sa_str_ends(el_val_t s, el_val_t suf);
el_val_t sa_vad_future(el_val_t slot);
el_val_t sa_vad_past(el_val_t slot);
el_val_t sa_vad_present(el_val_t slot);
el_val_t safety_abuse_phrases(void);
el_val_t safety_any_match(el_val_t text, el_val_t phrases_json);
el_val_t safety_augment_system(el_val_t system, el_val_t user_msg);
el_val_t safety_classify_hard_bell(el_val_t message);
el_val_t safety_contact_path(void);
el_val_t safety_count_match(el_val_t text, el_val_t phrases_json);
el_val_t safety_detect_bell_level(el_val_t message);
el_val_t safety_detect_positive_level(el_val_t message);
el_val_t safety_general_hard_phrases(void);
el_val_t safety_hard_directive(el_val_t hard_type);
el_val_t safety_log_bell(el_val_t level, el_val_t reason, el_val_t input_summary);
el_val_t safety_normalize(el_val_t message);
el_val_t safety_score_crisis(el_val_t input);
el_val_t safety_score_danger(el_val_t input);
el_val_t safety_score_distress_history(el_val_t history);
el_val_t safety_score_harm(el_val_t input);
el_val_t safety_screen(el_val_t input, el_val_t history);
el_val_t safety_self_harm_phrases(void);
el_val_t safety_soft_directive(void);
el_val_t safety_soft_phrases(void);
el_val_t safety_threat_score(el_val_t input, el_val_t history);
el_val_t safety_validate(el_val_t output, el_val_t action);
el_val_t scan_token(el_val_t s, el_val_t start);
@@ -1035,19 +967,13 @@ el_val_t sem_to_spec(el_val_t frame);
el_val_t sem_to_spec_full(el_val_t frame, el_val_t verb, el_val_t tense, el_val_t aspect);
el_val_t session_auto_title(el_val_t session_id, el_val_t first_message);
el_val_t session_create(el_val_t body);
el_val_t session_create_cleanup(el_val_t session_id);
el_val_t session_delete(el_val_t session_id);
el_val_t session_exists(el_val_t session_id);
el_val_t session_get(el_val_t session_id);
el_val_t session_hist_load(el_val_t session_id);
el_val_t session_hist_save(el_val_t session_id, el_val_t hist);
el_val_t session_list(void);
el_val_t session_make_content(el_val_t id, el_val_t title, el_val_t created_at, el_val_t updated_at, el_val_t folder);
el_val_t session_preload_bullets(el_val_t nodes, el_val_t max_bullets, el_val_t snip_len);
el_val_t session_search(el_val_t query);
el_val_t session_summary_autogenerate(el_val_t hist);
el_val_t session_summary_write(el_val_t summary_text);
el_val_t session_summary_write_dated(el_val_t summary_text, el_val_t label);
el_val_t session_title_from_message(el_val_t message);
el_val_t session_update_meta_timestamp(el_val_t session_id);
el_val_t session_update_patch(el_val_t session_id, el_val_t body);
@@ -1092,7 +1018,6 @@ el_val_t str_last2(el_val_t s);
el_val_t str_last3(el_val_t s);
el_val_t str_last_char(el_val_t s);
el_val_t strengthen_chat_nodes(el_val_t activation_nodes);
el_val_t strip_citations(el_val_t s);
el_val_t strip_query(el_val_t path);
el_val_t studio_tools_json(void);
el_val_t sux_absolutive_suffix(el_val_t person, el_val_t number);
@@ -1153,7 +1078,6 @@ el_val_t threat_trajectory_check(el_val_t tool_name, el_val_t tool_input);
el_val_t tier_canonical(void);
el_val_t tier_episodic(void);
el_val_t tier_working(void);
el_val_t tool_auto_approved(el_val_t tool_name);
el_val_t txb_conjugate(el_val_t verb, el_val_t tense, el_val_t person, el_val_t number);
el_val_t txb_decline(el_val_t noun, el_val_t gram_case, el_val_t number);
el_val_t txb_decline_fem(el_val_t noun, el_val_t gram_case, el_val_t number);
Generated Vendored
+25003
View File
File diff suppressed because it is too large Load Diff
Generated Vendored
+24028 -34
View File
File diff suppressed because it is too large Load Diff
Generated Vendored
+3 -3
View File
@@ -1,7 +1,7 @@
// auto-generated by elc --emit-header — do not edit
extern fn sem_get(json: String, key: String) -> String
extern fn generate_frame(frame: [String]) -> String
extern fn generate_frame_lang(frame: [String], lang_code: String) -> String
extern fn build_form_from_json(semantic_form_json: String, lang_code: String) -> [String]
extern fn generate_frame(frame: Any) -> String
extern fn generate_frame_lang(frame: Any, lang_code: String) -> String
extern fn build_form_from_json(semantic_form_json: String, lang_code: String) -> Any
extern fn generate(semantic_form_json: String) -> String
extern fn generate_lang(semantic_form_json: String, lang_code: String) -> String
Generated Vendored
+5
View File
@@ -656,3 +656,8 @@ el_val_t generate_tree(el_val_t rule_id_str, el_val_t slots) {
return 0;
}
int main(int _argc, char** _argv) {
el_runtime_init_args(_argc, _argv);
return 0;
}
Generated Vendored
+28 -28
View File
@@ -1,22 +1,22 @@
// auto-generated by elc --emit-header do not edit
extern fn slots_get(slots: [String], key: String) -> String
extern fn slots_set(slots: [String], key: String, val: String) -> [String]
extern fn make_slots(k0: String, v0: String) -> [String]
extern fn make_slots2(k0: String, v0: String, k1: String, v1: String) -> [String]
extern fn make_slots3(k0: String, v0: String, k1: String, v1: String, k2: String, v2: String) -> [String]
extern fn make_slots4(k0: String, v0: String, k1: String, v1: String, k2: String, v2: String, k3: String, v3: String) -> [String]
extern fn make_slots5(k0: String, v0: String, k1: String, v1: String, k2: String, v2: String, k3: String, v3: String, k4: String, v4: String) -> [String]
extern fn rule_id(rule: [String]) -> String
extern fn rule_lhs(rule: [String]) -> String
extern fn rule_rhs_len(rule: [String]) -> Int
extern fn rule_rhs(rule: [String], idx: Int) -> String
extern fn make_rule(id: String, lhs: String, r0: String) -> [String]
extern fn make_rule2(id: String, lhs: String, r0: String, r1: String) -> [String]
extern fn make_rule3(id: String, lhs: String, r0: String, r1: String, r2: String) -> [String]
extern fn make_rule4(id: String, lhs: String, r0: String, r1: String, r2: String, r3: String) -> [String]
extern fn build_rules() -> [[String]]
extern fn get_rules() -> [[String]]
extern fn find_rule(rule_id_str: String) -> [String]
// auto-generated by elc --emit-header - do not edit
extern fn slots_get(slots: Any, key: String) -> String
extern fn slots_set(slots: Any, key: String, val: String) -> Any
extern fn make_slots(k0: String, v0: String) -> Any
extern fn make_slots2(k0: String, v0: String, k1: String, v1: String) -> Any
extern fn make_slots3(k0: String, v0: String, k1: String, v1: String, k2: String, v2: String) -> Any
extern fn make_slots4(k0: String, v0: String, k1: String, v1: String, k2: String, v2: String, k3: String, v3: String) -> Any
extern fn make_slots5(k0: String, v0: String, k1: String, v1: String, k2: String, v2: String, k3: String, v3: String, k4: String, v4: String) -> Any
extern fn rule_id(rule: Any) -> String
extern fn rule_lhs(rule: Any) -> String
extern fn rule_rhs_len(rule: Any) -> Int
extern fn rule_rhs(rule: Any, idx: Int) -> String
extern fn make_rule(id: String, lhs: String, r0: String) -> Any
extern fn make_rule2(id: String, lhs: String, r0: String, r1: String) -> Any
extern fn make_rule3(id: String, lhs: String, r0: String, r1: String, r2: String) -> Any
extern fn make_rule4(id: String, lhs: String, r0: String, r1: String, r2: String, r3: String) -> Any
extern fn build_rules() -> Any
extern fn get_rules() -> Any
extern fn find_rule(rule_id_str: String) -> Any
extern fn make_leaf(label: String, word: String) -> String
extern fn make_node1(label: String, child0: String) -> String
extern fn make_node2(label: String, child0: String, child1: String) -> String
@@ -24,15 +24,15 @@ extern fn make_node3(label: String, child0: String, child1: String, child2: Stri
extern fn make_node4(label: String, child0: String, child1: String, child2: String, child3: String) -> String
extern fn nlg_is_ws(c: String) -> Bool
extern fn skip_ws(s: String, pos: Int) -> Int
extern fn scan_token(s: String, start: Int) -> [String]
extern fn scan_token(s: String, start: Int) -> Any
extern fn render_tree(tree: String) -> String
extern fn gram_word_order(profile: [String]) -> String
extern fn gram_order_constituents(subj: String, verb: String, obj: String, profile: [String]) -> String
extern fn gram_build_vp(verb: String, aux: String, profile: [String]) -> String
extern fn gram_question_strategy(profile: [String]) -> String
extern fn gram_word_order(profile: Any) -> String
extern fn gram_order_constituents(subj: String, verb: String, obj: String, profile: Any) -> String
extern fn gram_build_vp(verb: String, aux: String, profile: Any) -> String
extern fn gram_question_strategy(profile: Any) -> String
extern fn is_pronoun(word: String) -> Bool
extern fn build_np(referent: String, slots: [String]) -> String
extern fn build_np(referent: String, slots: Any) -> String
extern fn build_pp(loc: String) -> String
extern fn build_vp_body(slots: [String]) -> String
extern fn build_vp_from_slots(slots: [String]) -> String
extern fn generate_tree(rule_id_str: String, slots: [String]) -> String
extern fn build_vp_body(slots: Any) -> String
extern fn build_vp_from_slots(slots: Any) -> String
extern fn generate_tree(rule_id_str: String, slots: Any) -> String
Generated Vendored
+5
View File
@@ -70,3 +70,8 @@ el_val_t imprint_unload(void) {
return 0;
}
int main(int _argc, char** _argv) {
el_runtime_init_args(_argc, _argv);
return 0;
}
Generated Vendored
+5
View File
@@ -392,3 +392,8 @@ el_val_t lang_code(el_val_t profile) {
return 0;
}
int main(int _argc, char** _argv) {
el_runtime_init_args(_argc, _argv);
return 0;
}
Generated Vendored
+13 -83
View File
@@ -10,7 +10,6 @@ el_val_t mem_remember(el_val_t content, el_val_t tags);
el_val_t mem_recall(el_val_t query, el_val_t depth);
el_val_t mem_search(el_val_t query, el_val_t limit);
el_val_t mem_strengthen(el_val_t node_id);
el_val_t mem_tombstone(el_val_t node_id);
el_val_t mem_forget(el_val_t node_id);
el_val_t mem_consolidate(void);
el_val_t mem_save(el_val_t path);
@@ -35,18 +34,7 @@ el_val_t tier_canonical(void) {
}
el_val_t mem_store(el_val_t content, el_val_t label, el_val_t tags) {
el_val_t id = engram_node_full(content, EL_STR("Memory"), label, el_from_float(0.5), el_from_float(0.5), el_from_float(0.8), EL_STR("Working"), tags);
if (str_eq(id, EL_STR(""))) {
println(el_str_concat(EL_STR("[memory] write rejected by engram (empty id): label="), label));
return EL_STR("");
}
el_val_t readback = engram_get_node_json(id);
if (str_eq(readback, EL_STR("")) || str_eq(readback, EL_STR("{}"))) {
println(el_str_concat(el_str_concat(el_str_concat(el_str_concat(EL_STR("[memory] WRITE VERIFY FAILED: label="), label), EL_STR(" id=")), id), EL_STR(" \xe2\x80\x94 node absent after write")));
return EL_STR("");
}
println(el_str_concat(el_str_concat(EL_STR("[memory] write verified: "), id), EL_STR(" ok")));
return id;
return engram_node_full(content, EL_STR("Memory"), label, el_from_float(el_from_float(0.5)), el_from_float(el_from_float(0.5)), el_from_float(el_from_float(0.8)), EL_STR("Working"), tags);
return 0;
}
@@ -70,60 +58,22 @@ el_val_t mem_strengthen(el_val_t node_id) {
return 0;
}
el_val_t mem_tombstone(el_val_t node_id) {
el_val_t tags = EL_STR("[\"Tombstone\",\"status:deleted\"]");
el_val_t marker = engram_node_full(node_id, EL_STR("Tombstone"), el_str_concat(EL_STR("tombstone:"), node_id), el_from_float(0.01), el_from_float(0.01), el_from_float(1.0), EL_STR("Episodic"), tags);
if (!str_eq(marker, EL_STR(""))) {
engram_connect(marker, node_id, el_from_float(1.0), EL_STR("tombstones"));
}
return marker;
return 0;
}
el_val_t mem_forget(el_val_t node_id) {
el_val_t _marker = mem_tombstone(node_id);
engram_forget(node_id);
return 0;
}
el_val_t mem_consolidate(void) {
el_val_t scanned = engram_node_count();
el_val_t total_edges = engram_edge_count();
el_val_t strengthened = 0;
el_val_t wm_top = engram_wm_top_json(10);
el_val_t wm_len = json_array_len(wm_top);
el_val_t wi = 0;
while (wi < wm_len) {
el_val_t wm_node = json_array_get(wm_top, wi);
el_val_t wm_id = json_get(wm_node, EL_STR("id"));
if (!str_eq(wm_id, EL_STR(""))) {
engram_strengthen(wm_id);
strengthened = (strengthened + 1);
}
wi = (wi + 1);
}
el_val_t scan_result = engram_scan_nodes_json(50, 0);
el_val_t scan_len = json_array_len(scan_result);
el_val_t si = 0;
while (si < scan_len) {
el_val_t s_node = json_array_get(scan_result, si);
el_val_t s_tier = json_get(s_node, EL_STR("tier"));
el_val_t s_id = json_get(s_node, EL_STR("id"));
if (str_eq(s_tier, EL_STR("Canonical")) && !str_eq(s_id, EL_STR(""))) {
engram_strengthen(s_id);
strengthened = (strengthened + 1);
}
si = (si + 1);
}
el_val_t dummy = engram_scan_nodes_json(100, 0);
el_val_t total_nodes = engram_node_count();
return el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(EL_STR("{\"scanned\":"), int_to_str(scanned)), EL_STR(",\"total_nodes\":")), int_to_str(total_nodes)), EL_STR(",\"total_edges\":")), int_to_str(total_edges)), EL_STR(",\"strengthened\":")), int_to_str(strengthened)), EL_STR("}"));
el_val_t total_edges = engram_edge_count();
return el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(EL_STR("{\"scanned\":"), int_to_str(scanned)), EL_STR(",\"total_nodes\":")), int_to_str(total_nodes)), EL_STR(",\"total_edges\":")), int_to_str(total_edges)), EL_STR("}"));
return 0;
}
el_val_t mem_save(el_val_t path) {
el_val_t save_result = engram_save(path);
if (str_eq(save_result, EL_STR(""))) {
println(el_str_concat(el_str_concat(EL_STR("[memory] mem_save: engram_save failed for "), path), EL_STR(" \xe2\x80\x94 snapshot may be incomplete")));
}
engram_save(path);
return 0;
}
@@ -154,30 +104,9 @@ el_val_t mem_boot_count_get(void) {
el_val_t mem_boot_count_inc(void) {
el_val_t current = mem_boot_count_get();
el_val_t next = (current + 1);
el_val_t old_results = engram_search_json(EL_STR("soul:boot_count"), 50);
if (!str_eq(old_results, EL_STR("")) && !str_eq(old_results, EL_STR("[]"))) {
el_val_t old_len = json_array_len(old_results);
el_val_t oi = 0;
while (oi < old_len) {
el_val_t old_node = json_array_get(old_results, oi);
el_val_t old_id = json_get(old_node, EL_STR("id"));
if (!str_eq(old_id, EL_STR(""))) {
engram_forget(old_id);
}
oi = (oi + 1);
}
}
el_val_t content = el_str_concat(EL_STR("soul:boot_count:"), int_to_str(next));
el_val_t tags = EL_STR("[\"soul-meta\",\"boot-counter\"]");
el_val_t boot_node_id = engram_node_full(content, EL_STR("Memory"), EL_STR("soul:boot_count"), el_from_float(0.9), el_from_float(0.9), el_from_float(1.0), EL_STR("Canonical"), tags);
if (str_eq(boot_node_id, EL_STR(""))) {
println(el_str_concat(el_str_concat(EL_STR("[memory] mem_boot_count_inc: write rejected (empty id) \xe2\x80\x94 boot counter node lost (count="), int_to_str(next)), EL_STR(")")));
return next;
}
el_val_t boot_readback = engram_get_node_json(boot_node_id);
if (str_eq(boot_readback, EL_STR("")) || str_eq(boot_readback, EL_STR("{}"))) {
println(el_str_concat(el_str_concat(el_str_concat(EL_STR("[memory] mem_boot_count_inc: WRITE VERIFY FAILED id="), boot_node_id), EL_STR(" count=")), int_to_str(next)));
}
el_val_t discard = engram_node_full(content, EL_STR("Memory"), EL_STR("soul:boot_count"), el_from_float(el_from_float(0.9)), el_from_float(el_from_float(0.9)), el_from_float(el_from_float(1.0)), EL_STR("Canonical"), tags);
return next;
return 0;
}
@@ -189,11 +118,12 @@ el_val_t mem_emit_state_event(el_val_t trigger, el_val_t kind, el_val_t content)
el_val_t safe_content = str_replace(content, EL_STR("\""), EL_STR("'"));
el_val_t payload = el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(EL_STR("{\"trigger\":\""), safe_trigger), EL_STR("\"")), EL_STR(",\"kind\":\"")), kind), EL_STR("\"")), EL_STR(",\"content\":\"")), safe_content), EL_STR("\"")), EL_STR(",\"boot\":")), int_to_str(boot)), EL_STR(",\"ts\":")), int_to_str(ts)), EL_STR("}"));
el_val_t tags = EL_STR("[\"internal-state\",\"pre-reasoning\",\"InternalStateEvent\"]");
el_val_t event_id = engram_node_full(payload, EL_STR("InternalStateEvent"), el_str_concat(EL_STR("state-event:"), kind), el_from_float(0.85), el_from_float(0.8), el_from_float(0.9), EL_STR("Episodic"), tags);
if (str_eq(event_id, EL_STR(""))) {
println(el_str_concat(EL_STR("[memory] mem_emit_state_event: write rejected (empty id): kind="), kind));
}
return event_id;
return engram_node_full(payload, EL_STR("InternalStateEvent"), el_str_concat(EL_STR("state-event:"), kind), el_from_float(el_from_float(0.85)), el_from_float(el_from_float(0.8)), el_from_float(el_from_float(0.9)), EL_STR("Episodic"), tags);
return 0;
}
int main(int _argc, char** _argv) {
el_runtime_init_args(_argc, _argv);
return 0;
}
Generated Vendored
+168 -284
View File
@@ -10,7 +10,6 @@ el_val_t mem_remember(el_val_t content, el_val_t tags);
el_val_t mem_recall(el_val_t query, el_val_t depth);
el_val_t mem_search(el_val_t query, el_val_t limit);
el_val_t mem_strengthen(el_val_t node_id);
el_val_t mem_tombstone(el_val_t node_id);
el_val_t mem_forget(el_val_t node_id);
el_val_t mem_consolidate(void);
el_val_t mem_save(el_val_t path);
@@ -27,17 +26,9 @@ el_val_t api_ok(el_val_t extra);
el_val_t api_err(el_val_t msg);
el_val_t api_nonempty(el_val_t s);
el_val_t api_or_empty(el_val_t s);
el_val_t api_persisted(el_val_t id);
el_val_t api_not_persisted(el_val_t id);
el_val_t tombstone_node(el_val_t id);
el_val_t tombstoned_id_set(void);
el_val_t memory_hide_tombstoned(el_val_t raw, el_val_t path);
el_val_t handle_api_begin_session(el_val_t body);
el_val_t handle_api_compile_ctx(el_val_t body);
el_val_t handle_api_remember(el_val_t body);
el_val_t handle_api_node_create(el_val_t body);
el_val_t handle_api_node_delete(el_val_t body);
el_val_t handle_api_node_update(el_val_t body);
el_val_t handle_api_recall(el_val_t method, el_val_t path, el_val_t body);
el_val_t handle_api_search_knowledge(el_val_t method, el_val_t path, el_val_t body);
el_val_t handle_api_browse_knowledge(el_val_t path, el_val_t body);
@@ -54,12 +45,114 @@ el_val_t handle_api_inspect_graph(el_val_t method, el_val_t path, el_val_t body)
el_val_t handle_api_link_entities(el_val_t body);
el_val_t handle_api_forget(el_val_t body);
el_val_t handle_api_evolve_memory(el_val_t body);
el_val_t handle_api_memory_delete(el_val_t body);
el_val_t handle_api_memory_update(el_val_t body);
el_val_t handle_api_cultivate(el_val_t body);
el_val_t handle_api_list_typed(el_val_t node_type, el_val_t path, el_val_t body);
el_val_t handle_api_consolidate(el_val_t body);
el_val_t tier_working(void) {
return EL_STR("Working");
return 0;
}
el_val_t tier_episodic(void) {
return EL_STR("Episodic");
return 0;
}
el_val_t tier_canonical(void) {
return EL_STR("Canonical");
return 0;
}
el_val_t mem_store(el_val_t content, el_val_t label, el_val_t tags) {
return engram_node_full(content, EL_STR("Memory"), label, el_from_float(el_from_float(0.5)), el_from_float(el_from_float(0.5)), el_from_float(el_from_float(0.8)), EL_STR("Working"), tags);
return 0;
}
el_val_t mem_remember(el_val_t content, el_val_t tags) {
return mem_store(content, EL_STR("soul-memory"), tags);
return 0;
}
el_val_t mem_recall(el_val_t query, el_val_t depth) {
return engram_activate_json(query, depth);
return 0;
}
el_val_t mem_search(el_val_t query, el_val_t limit) {
return engram_search_json(query, limit);
return 0;
}
el_val_t mem_strengthen(el_val_t node_id) {
engram_strengthen(node_id);
return 0;
}
el_val_t mem_forget(el_val_t node_id) {
engram_forget(node_id);
return 0;
}
el_val_t mem_consolidate(void) {
el_val_t scanned = engram_node_count();
el_val_t dummy = engram_scan_nodes_json(100, 0);
el_val_t total_nodes = engram_node_count();
el_val_t total_edges = engram_edge_count();
return el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(EL_STR("{\"scanned\":"), int_to_str(scanned)), EL_STR(",\"total_nodes\":")), int_to_str(total_nodes)), EL_STR(",\"total_edges\":")), int_to_str(total_edges)), EL_STR("}"));
return 0;
}
el_val_t mem_save(el_val_t path) {
engram_save(path);
return 0;
}
el_val_t mem_load(el_val_t path) {
engram_load(path);
return 0;
}
el_val_t mem_boot_count_get(void) {
el_val_t results = engram_search_json(EL_STR("soul:boot_count"), 3);
if (str_eq(results, EL_STR(""))) {
return 0;
}
if (str_eq(results, EL_STR("[]"))) {
return 0;
}
el_val_t node = json_array_get(results, 0);
el_val_t content = json_get(node, EL_STR("content"));
el_val_t prefix = EL_STR("soul:boot_count:");
if (!str_starts_with(content, prefix)) {
return 0;
}
el_val_t num_str = str_slice(content, str_len(prefix), str_len(content));
return str_to_int(num_str);
return 0;
}
el_val_t mem_boot_count_inc(void) {
el_val_t current = mem_boot_count_get();
el_val_t next = (current + 1);
el_val_t content = el_str_concat(EL_STR("soul:boot_count:"), int_to_str(next));
el_val_t tags = EL_STR("[\"soul-meta\",\"boot-counter\"]");
el_val_t discard = engram_node_full(content, EL_STR("Memory"), EL_STR("soul:boot_count"), el_from_float(el_from_float(0.9)), el_from_float(el_from_float(0.9)), el_from_float(el_from_float(1.0)), EL_STR("Canonical"), tags);
return next;
return 0;
}
el_val_t mem_emit_state_event(el_val_t trigger, el_val_t kind, el_val_t content) {
el_val_t boot = mem_boot_count_get();
el_val_t ts = time_now();
el_val_t safe_trigger = str_replace(trigger, EL_STR("\""), EL_STR("'"));
el_val_t safe_content = str_replace(content, EL_STR("\""), EL_STR("'"));
el_val_t payload = el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(EL_STR("{\"trigger\":\""), safe_trigger), EL_STR("\"")), EL_STR(",\"kind\":\"")), kind), EL_STR("\"")), EL_STR(",\"content\":\"")), safe_content), EL_STR("\"")), EL_STR(",\"boot\":")), int_to_str(boot)), EL_STR(",\"ts\":")), int_to_str(ts)), EL_STR("}"));
el_val_t tags = EL_STR("[\"internal-state\",\"pre-reasoning\",\"InternalStateEvent\"]");
return engram_node_full(payload, EL_STR("InternalStateEvent"), el_str_concat(EL_STR("state-event:"), kind), el_from_float(el_from_float(0.85)), el_from_float(el_from_float(0.8)), el_from_float(el_from_float(0.9)), EL_STR("Episodic"), tags);
return 0;
}
el_val_t is_protected_node(el_val_t id) {
if (str_eq(id, EL_STR("kn-efeb4a5b-5aff-4759-8a97-7233099be6ee"))) {
return 1;
@@ -179,75 +272,6 @@ el_val_t api_or_empty(el_val_t s) {
return 0;
}
el_val_t api_persisted(el_val_t id) {
if (str_eq(id, EL_STR(""))) {
return 0;
}
el_val_t node = engram_get_node_json(id);
return ((!str_eq(node, EL_STR("")) && !str_eq(node, EL_STR("null"))) && !str_eq(node, EL_STR("{}")));
return 0;
}
el_val_t api_not_persisted(el_val_t id) {
return el_str_concat(el_str_concat(EL_STR("{\"ok\":false,\"error\":\"write_not_persisted\",\"id\":\""), id), EL_STR("\"}"));
return 0;
}
el_val_t tombstone_node(el_val_t id) {
return mem_tombstone(id);
return 0;
}
el_val_t tombstoned_id_set(void) {
el_val_t markers = engram_scan_nodes_by_type_json(EL_STR("Tombstone"), 5000, 0);
if (str_eq(markers, EL_STR("")) || str_eq(markers, EL_STR("[]"))) {
return EL_STR("");
}
el_val_t n = json_array_len(markers);
el_val_t acc = EL_STR("|");
el_val_t i = 0;
while (i < n) {
el_val_t m = json_array_get(markers, i);
el_val_t tid = json_get(m, EL_STR("content"));
acc = ({ el_val_t _if_result_1 = 0; if (str_eq(tid, EL_STR(""))) { _if_result_1 = (acc); } else { _if_result_1 = (el_str_concat(el_str_concat(acc, tid), EL_STR("|"))); } _if_result_1; });
i = (i + 1);
}
return acc;
return 0;
}
el_val_t memory_hide_tombstoned(el_val_t raw, el_val_t path) {
if (str_contains(path, EL_STR("include_deleted"))) {
return raw;
}
if (str_eq(raw, EL_STR("")) || str_eq(raw, EL_STR("[]"))) {
return raw;
}
el_val_t dead = tombstoned_id_set();
if (str_eq(dead, EL_STR(""))) {
return raw;
}
el_val_t n = json_array_len(raw);
if (n > 1000) {
return raw;
}
el_val_t out = EL_STR("[");
el_val_t first = 1;
el_val_t i = 0;
while (i < n) {
el_val_t node = json_array_get(raw, i);
el_val_t nid = json_get(node, EL_STR("id"));
el_val_t ntype = json_get(node, EL_STR("node_type"));
el_val_t is_dead = (!str_eq(nid, EL_STR("")) && str_contains(dead, el_str_concat(el_str_concat(EL_STR("|"), nid), EL_STR("|"))));
el_val_t keep = (!str_eq(ntype, EL_STR("Tombstone")) && !is_dead);
out = ({ el_val_t _if_result_2 = 0; if (keep) { _if_result_2 = (({ el_val_t _if_result_3 = 0; if (first) { _if_result_3 = (el_str_concat(out, node)); } else { _if_result_3 = (el_str_concat(el_str_concat(out, EL_STR(",")), node)); } _if_result_3; })); } else { _if_result_2 = (out); } _if_result_2; });
first = ({ el_val_t _if_result_4 = 0; if (keep) { _if_result_4 = (0); } else { _if_result_4 = (first); } _if_result_4; });
i = (i + 1);
}
return el_str_concat(out, EL_STR("]"));
return 0;
}
el_val_t handle_api_begin_session(el_val_t body) {
el_val_t stats = engram_stats_json();
el_val_t activated = engram_activate_json(EL_STR("session start recent memory important"), 2);
@@ -274,102 +298,22 @@ el_val_t handle_api_remember(el_val_t body) {
el_val_t importance = json_get(body, EL_STR("importance"));
el_val_t tags_raw = json_get(body, EL_STR("tags"));
el_val_t project = json_get(body, EL_STR("project"));
el_val_t sal_str = ({ el_val_t _if_result_5 = 0; if (str_eq(importance, EL_STR("critical"))) { _if_result_5 = (EL_STR("0.95")); } else { _if_result_5 = (({ el_val_t _if_result_6 = 0; if (str_eq(importance, EL_STR("high"))) { _if_result_6 = (EL_STR("0.75")); } else { _if_result_6 = (({ el_val_t _if_result_7 = 0; if (str_eq(importance, EL_STR("low"))) { _if_result_7 = (EL_STR("0.25")); } else { _if_result_7 = (EL_STR("0.50")); } _if_result_7; })); } _if_result_6; })); } _if_result_5; });
el_val_t sal = ({ el_val_t _if_result_8 = 0; if (str_eq(sal_str, EL_STR("0.95"))) { _if_result_8 = (el_from_float(0.95)); } else { _if_result_8 = (({ el_val_t _if_result_9 = 0; if (str_eq(sal_str, EL_STR("0.75"))) { _if_result_9 = (el_from_float(0.75)); } else { _if_result_9 = (({ el_val_t _if_result_10 = 0; if (str_eq(sal_str, EL_STR("0.25"))) { _if_result_10 = (el_from_float(0.25)); } else { _if_result_10 = (el_from_float(0.5)); } _if_result_10; })); } _if_result_9; })); } _if_result_8; });
el_val_t base_tags = ({ el_val_t _if_result_11 = 0; if (str_eq(tags_raw, EL_STR(""))) { _if_result_11 = (EL_STR("[\"Memory\"]")); } else { _if_result_11 = (tags_raw); } _if_result_11; });
el_val_t final_tags = ({ el_val_t _if_result_12 = 0; if (str_eq(project, EL_STR(""))) { _if_result_12 = (base_tags); } else { el_val_t inner = str_slice(base_tags, 1, (str_len(base_tags) - 1)); _if_result_12 = (el_str_concat(el_str_concat(el_str_concat(el_str_concat(EL_STR("["), inner), EL_STR(",\"project:")), project), EL_STR("\"]"))); } _if_result_12; });
el_val_t id = engram_node_full(content, EL_STR("Memory"), EL_STR("memory:remembered"), el_from_float(sal), el_from_float(sal), el_from_float(0.9), EL_STR("Episodic"), final_tags);
if (!api_persisted(id)) {
return api_not_persisted(id);
}
el_val_t sal_str = ({ el_val_t _if_result_1 = 0; if (str_eq(importance, EL_STR("critical"))) { _if_result_1 = (EL_STR("0.95")); } else { _if_result_1 = (({ el_val_t _if_result_2 = 0; if (str_eq(importance, EL_STR("high"))) { _if_result_2 = (EL_STR("0.75")); } else { _if_result_2 = (({ el_val_t _if_result_3 = 0; if (str_eq(importance, EL_STR("low"))) { _if_result_3 = (EL_STR("0.25")); } else { _if_result_3 = (EL_STR("0.50")); } _if_result_3; })); } _if_result_2; })); } _if_result_1; });
el_val_t sal = ({ el_val_t _if_result_4 = 0; if (str_eq(sal_str, EL_STR("0.95"))) { _if_result_4 = (el_from_float(0.95)); } else { _if_result_4 = (({ el_val_t _if_result_5 = 0; if (str_eq(sal_str, EL_STR("0.75"))) { _if_result_5 = (el_from_float(0.75)); } else { _if_result_5 = (({ el_val_t _if_result_6 = 0; if (str_eq(sal_str, EL_STR("0.25"))) { _if_result_6 = (el_from_float(0.25)); } else { _if_result_6 = (el_from_float(0.5)); } _if_result_6; })); } _if_result_5; })); } _if_result_4; });
el_val_t base_tags = ({ el_val_t _if_result_7 = 0; if (str_eq(tags_raw, EL_STR(""))) { _if_result_7 = (EL_STR("[\"Memory\"]")); } else { _if_result_7 = (tags_raw); } _if_result_7; });
el_val_t final_tags = ({ el_val_t _if_result_8 = 0; if (str_eq(project, EL_STR(""))) { _if_result_8 = (base_tags); } else { el_val_t inner = str_slice(base_tags, 1, (str_len(base_tags) - 1)); _if_result_8 = (el_str_concat(el_str_concat(el_str_concat(el_str_concat(EL_STR("["), inner), EL_STR(",\"project:")), project), EL_STR("\"]"))); } _if_result_8; });
el_val_t id = engram_node_full(content, EL_STR("Memory"), EL_STR("memory:remembered"), el_from_float(sal), el_from_float(sal), el_from_float(el_from_float(0.9)), EL_STR("Episodic"), final_tags);
return el_str_concat(el_str_concat(EL_STR("{\"id\":\""), id), EL_STR("\",\"ok\":true}"));
return 0;
}
el_val_t handle_api_node_create(el_val_t body) {
el_val_t content = json_get(body, EL_STR("content"));
if (str_eq(content, EL_STR(""))) {
return api_err(EL_STR("content is required"));
}
el_val_t nt_raw = json_get(body, EL_STR("node_type"));
el_val_t node_type = ({ el_val_t _if_result_13 = 0; if (str_eq(nt_raw, EL_STR(""))) { _if_result_13 = (EL_STR("Memory")); } else { _if_result_13 = (nt_raw); } _if_result_13; });
el_val_t label_raw = json_get(body, EL_STR("label"));
el_val_t label = ({ el_val_t _if_result_14 = 0; if (str_eq(label_raw, EL_STR(""))) { _if_result_14 = (EL_STR("node:created")); } else { _if_result_14 = (label_raw); } _if_result_14; });
el_val_t tier_raw = json_get(body, EL_STR("tier"));
el_val_t tier = ({ el_val_t _if_result_15 = 0; if (str_eq(tier_raw, EL_STR(""))) { _if_result_15 = (EL_STR("Episodic")); } else { _if_result_15 = (tier_raw); } _if_result_15; });
el_val_t tags_raw = json_get(body, EL_STR("tags"));
el_val_t tags = ({ el_val_t _if_result_16 = 0; if (str_eq(tags_raw, EL_STR(""))) { _if_result_16 = (el_str_concat(el_str_concat(EL_STR("[\""), node_type), EL_STR("\"]"))); } else { _if_result_16 = (tags_raw); } _if_result_16; });
el_val_t importance = json_get(body, EL_STR("importance"));
el_val_t sal = ({ el_val_t _if_result_17 = 0; if (str_eq(importance, EL_STR("critical"))) { _if_result_17 = (el_from_float(0.95)); } else { _if_result_17 = (({ el_val_t _if_result_18 = 0; if (str_eq(importance, EL_STR("high"))) { _if_result_18 = (el_from_float(0.75)); } else { _if_result_18 = (({ el_val_t _if_result_19 = 0; if (str_eq(importance, EL_STR("low"))) { _if_result_19 = (el_from_float(0.25)); } else { _if_result_19 = (el_from_float(0.5)); } _if_result_19; })); } _if_result_18; })); } _if_result_17; });
el_val_t id = engram_node_full(content, node_type, label, el_from_float(sal), el_from_float(sal), el_from_float(0.9), tier, tags);
if (!api_persisted(id)) {
return api_not_persisted(id);
}
return el_str_concat(el_str_concat(EL_STR("{\"id\":\""), id), EL_STR("\",\"ok\":true}"));
return 0;
}
el_val_t handle_api_node_delete(el_val_t body) {
el_val_t id = json_get(body, EL_STR("id"));
if (str_eq(id, EL_STR(""))) {
return api_err(EL_STR("id is required"));
}
if (is_protected_node(id)) {
return api_err_protected(id);
}
el_val_t existing = engram_get_node_json(id);
if (str_eq(existing, EL_STR("{}"))) {
return api_err(el_str_concat(EL_STR("node not found: "), id));
}
el_val_t marker = tombstone_node(id);
if (str_eq(marker, EL_STR(""))) {
return api_err(el_str_concat(EL_STR("tombstone failed: "), id));
}
return el_str_concat(el_str_concat(EL_STR("{\"ok\":true,\"id\":\""), id), EL_STR("\",\"tombstoned\":true}"));
return 0;
}
el_val_t handle_api_node_update(el_val_t body) {
el_val_t id = json_get(body, EL_STR("id"));
if (str_eq(id, EL_STR(""))) {
return api_err(EL_STR("id is required"));
}
if (!api_persisted(id)) {
return el_str_concat(el_str_concat(EL_STR("{\"ok\":false,\"error\":\"not_found\",\"id\":\""), id), EL_STR("\"}"));
}
el_val_t old = engram_get_node_json(id);
el_val_t body_content = json_get(body, EL_STR("content"));
el_val_t content = ({ el_val_t _if_result_20 = 0; if (str_eq(body_content, EL_STR(""))) { _if_result_20 = (json_get(old, EL_STR("content"))); } else { _if_result_20 = (body_content); } _if_result_20; });
el_val_t body_nt = json_get(body, EL_STR("node_type"));
el_val_t old_nt = json_get(old, EL_STR("node_type"));
el_val_t node_type = ({ el_val_t _if_result_21 = 0; if (!str_eq(body_nt, EL_STR(""))) { _if_result_21 = (body_nt); } else { _if_result_21 = (({ el_val_t _if_result_22 = 0; if (!str_eq(old_nt, EL_STR(""))) { _if_result_22 = (old_nt); } else { _if_result_22 = (EL_STR("Memory")); } _if_result_22; })); } _if_result_21; });
el_val_t body_label = json_get(body, EL_STR("label"));
el_val_t old_label = json_get(old, EL_STR("label"));
el_val_t label = ({ el_val_t _if_result_23 = 0; if (!str_eq(body_label, EL_STR(""))) { _if_result_23 = (body_label); } else { _if_result_23 = (({ el_val_t _if_result_24 = 0; if (!str_eq(old_label, EL_STR(""))) { _if_result_24 = (old_label); } else { _if_result_24 = (EL_STR("node:updated")); } _if_result_24; })); } _if_result_23; });
el_val_t body_tier = json_get(body, EL_STR("tier"));
el_val_t old_tier = json_get(old, EL_STR("tier"));
el_val_t tier = ({ el_val_t _if_result_25 = 0; if (!str_eq(body_tier, EL_STR(""))) { _if_result_25 = (body_tier); } else { _if_result_25 = (({ el_val_t _if_result_26 = 0; if (!str_eq(old_tier, EL_STR(""))) { _if_result_26 = (old_tier); } else { _if_result_26 = (EL_STR("Episodic")); } _if_result_26; })); } _if_result_25; });
el_val_t body_tags = json_get(body, EL_STR("tags"));
el_val_t tags = ({ el_val_t _if_result_27 = 0; if (str_eq(body_tags, EL_STR(""))) { _if_result_27 = (el_str_concat(el_str_concat(EL_STR("[\""), node_type), EL_STR("\"]"))); } else { _if_result_27 = (body_tags); } _if_result_27; });
el_val_t new_id = engram_node_full(content, node_type, label, el_from_float(0.5), el_from_float(0.5), el_from_float(0.8), tier, tags);
if (!api_persisted(new_id)) {
return api_not_persisted(new_id);
}
engram_connect(new_id, id, el_from_float(0.9), EL_STR("supersedes"));
return el_str_concat(el_str_concat(el_str_concat(el_str_concat(EL_STR("{\"id\":\""), new_id), EL_STR("\",\"supersedes\":\"")), id), EL_STR("\",\"ok\":true}"));
return 0;
}
el_val_t handle_api_recall(el_val_t method, el_val_t path, el_val_t body) {
el_val_t url_q = ({ el_val_t _if_result_28 = 0; if (str_eq(api_query_param(path, EL_STR("query")), EL_STR(""))) { _if_result_28 = (api_query_param(path, EL_STR("q"))); } else { _if_result_28 = (api_query_param(path, EL_STR("query"))); } _if_result_28; });
el_val_t body_query = json_get(body, EL_STR("query"));
el_val_t body_q = json_get(body, EL_STR("q"));
el_val_t q = ({ el_val_t _if_result_29 = 0; if (!str_eq(url_q, EL_STR(""))) { _if_result_29 = (url_q); } else { _if_result_29 = (({ el_val_t _if_result_30 = 0; if (!str_eq(body_query, EL_STR(""))) { _if_result_30 = (body_query); } else { _if_result_30 = (body_q); } _if_result_30; })); } _if_result_29; });
el_val_t q = ({ el_val_t _if_result_9 = 0; if (str_eq(method, EL_STR("GET"))) { _if_result_9 = (api_query_param(path, EL_STR("query"))); } else { _if_result_9 = (json_get(body, EL_STR("query"))); } _if_result_9; });
el_val_t chain = json_get(body, EL_STR("chain_name"));
el_val_t limit = api_query_int(path, EL_STR("limit"), 0);
limit = ({ el_val_t _if_result_31 = 0; if ((limit == 0)) { _if_result_31 = (json_get_int(body, EL_STR("limit"))); } else { _if_result_31 = (limit); } _if_result_31; });
limit = ({ el_val_t _if_result_32 = 0; if ((limit == 0)) { _if_result_32 = (10); } else { _if_result_32 = (limit); } _if_result_32; });
el_val_t eff_q = ({ el_val_t _if_result_33 = 0; if (str_eq(q, EL_STR(""))) { _if_result_33 = (chain); } else { _if_result_33 = (q); } _if_result_33; });
limit = ({ el_val_t _if_result_10 = 0; if ((limit == 0)) { _if_result_10 = (json_get_int(body, EL_STR("limit"))); } else { _if_result_10 = (limit); } _if_result_10; });
limit = ({ el_val_t _if_result_11 = 0; if ((limit == 0)) { _if_result_11 = (10); } else { _if_result_11 = (limit); } _if_result_11; });
el_val_t eff_q = ({ el_val_t _if_result_12 = 0; if (str_eq(q, EL_STR(""))) { _if_result_12 = (chain); } else { _if_result_12 = (q); } _if_result_12; });
if (str_eq(eff_q, EL_STR(""))) {
return api_or_empty(engram_scan_nodes_json(limit, 0));
}
@@ -379,13 +323,10 @@ el_val_t handle_api_recall(el_val_t method, el_val_t path, el_val_t body) {
}
el_val_t handle_api_search_knowledge(el_val_t method, el_val_t path, el_val_t body) {
el_val_t url_q = api_query_param(path, EL_STR("q"));
el_val_t body_query = json_get(body, EL_STR("query"));
el_val_t body_q = json_get(body, EL_STR("q"));
el_val_t q = ({ el_val_t _if_result_34 = 0; if (!str_eq(url_q, EL_STR(""))) { _if_result_34 = (url_q); } else { _if_result_34 = (({ el_val_t _if_result_35 = 0; if (!str_eq(body_query, EL_STR(""))) { _if_result_35 = (body_query); } else { _if_result_35 = (body_q); } _if_result_35; })); } _if_result_34; });
el_val_t q = ({ el_val_t _if_result_13 = 0; if (str_eq(method, EL_STR("GET"))) { _if_result_13 = (api_query_param(path, EL_STR("q"))); } else { _if_result_13 = (json_get(body, EL_STR("query"))); } _if_result_13; });
el_val_t limit = api_query_int(path, EL_STR("limit"), 0);
limit = ({ el_val_t _if_result_36 = 0; if ((limit == 0)) { _if_result_36 = (json_get_int(body, EL_STR("limit"))); } else { _if_result_36 = (limit); } _if_result_36; });
limit = ({ el_val_t _if_result_37 = 0; if ((limit == 0)) { _if_result_37 = (10); } else { _if_result_37 = (limit); } _if_result_37; });
limit = ({ el_val_t _if_result_14 = 0; if ((limit == 0)) { _if_result_14 = (json_get_int(body, EL_STR("limit"))); } else { _if_result_14 = (limit); } _if_result_14; });
limit = ({ el_val_t _if_result_15 = 0; if ((limit == 0)) { _if_result_15 = (10); } else { _if_result_15 = (limit); } _if_result_15; });
if (str_eq(q, EL_STR(""))) {
return api_err(EL_STR("query is required"));
}
@@ -413,12 +354,9 @@ el_val_t handle_api_capture_knowledge(el_val_t body) {
if (str_eq(content, EL_STR(""))) {
return api_err(EL_STR("content is required"));
}
el_val_t full = ({ el_val_t _if_result_38 = 0; if (str_eq(title, EL_STR(""))) { _if_result_38 = (content); } else { _if_result_38 = (el_str_concat(el_str_concat(title, EL_STR(": ")), content)); } _if_result_38; });
el_val_t full = ({ el_val_t _if_result_16 = 0; if (str_eq(title, EL_STR(""))) { _if_result_16 = (content); } else { _if_result_16 = (el_str_concat(el_str_concat(title, EL_STR(": ")), content)); } _if_result_16; });
el_val_t tags = EL_STR("[\"Knowledge\",\"captured\"]");
el_val_t id = engram_node_full(full, EL_STR("Knowledge"), EL_STR("knowledge:captured"), el_from_float(0.85), el_from_float(0.8), el_from_float(0.9), EL_STR("Episodic"), tags);
if (!api_persisted(id)) {
return api_not_persisted(id);
}
el_val_t id = engram_node_full(full, EL_STR("Knowledge"), EL_STR("knowledge:captured"), el_from_float(el_from_float(0.85)), el_from_float(el_from_float(0.8)), el_from_float(el_from_float(0.9)), EL_STR("Episodic"), tags);
return el_str_concat(el_str_concat(EL_STR("{\"id\":\""), id), EL_STR("\",\"ok\":true}"));
return 0;
}
@@ -433,12 +371,9 @@ el_val_t handle_api_evolve_knowledge(el_val_t body) {
return api_err_protected(prior_id);
}
el_val_t tags = EL_STR("[\"Knowledge\",\"evolved\"]");
el_val_t new_id = engram_node_full(content, EL_STR("Knowledge"), EL_STR("knowledge:evolved"), el_from_float(0.75), el_from_float(0.75), el_from_float(0.9), EL_STR("Episodic"), tags);
if (!api_persisted(new_id)) {
return api_not_persisted(new_id);
}
if (!str_eq(prior_id, EL_STR(""))) {
engram_connect(new_id, prior_id, el_from_float(0.9), EL_STR("supersedes"));
el_val_t new_id = engram_node_full(content, EL_STR("Knowledge"), EL_STR("knowledge:evolved"), el_from_float(el_from_float(0.75)), el_from_float(el_from_float(0.75)), el_from_float(el_from_float(0.9)), EL_STR("Episodic"), tags);
if (!str_eq(prior_id, EL_STR("")) && !str_eq(new_id, EL_STR(""))) {
engram_connect(new_id, prior_id, el_from_float(el_from_float(0.9)), EL_STR("supersedes"));
}
return el_str_concat(el_str_concat(el_str_concat(el_str_concat(EL_STR("{\"id\":\""), new_id), EL_STR("\",\"supersedes\":\"")), prior_id), EL_STR("\",\"ok\":true}"));
return 0;
@@ -454,18 +389,18 @@ el_val_t handle_api_promote_knowledge(el_val_t body) {
return api_err(EL_STR("id (prior node) is required"));
}
el_val_t tags_raw = json_get(body, EL_STR("tags"));
el_val_t tags = ({ el_val_t _if_result_39 = 0; if (str_eq(tags_raw, EL_STR(""))) { _if_result_39 = (EL_STR("[\"Knowledge\",\"tier:canonical\",\"disposition:stable\"]")); } else { _if_result_39 = (tags_raw); } _if_result_39; });
el_val_t new_id = engram_node_full(content, EL_STR("Knowledge"), EL_STR("knowledge:canonical"), el_from_float(0.9), el_from_float(0.9), el_from_float(1.0), EL_STR("Canonical"), tags);
if (!api_persisted(new_id)) {
return api_not_persisted(new_id);
el_val_t tags = ({ el_val_t _if_result_17 = 0; if (str_eq(tags_raw, EL_STR(""))) { _if_result_17 = (EL_STR("[\"Knowledge\",\"tier:canonical\",\"disposition:stable\"]")); } else { _if_result_17 = (tags_raw); } _if_result_17; });
el_val_t new_id = engram_node_full(content, EL_STR("Knowledge"), EL_STR("knowledge:canonical"), el_from_float(el_from_float(0.9)), el_from_float(el_from_float(0.9)), el_from_float(el_from_float(1.0)), EL_STR("Canonical"), tags);
if (str_eq(new_id, EL_STR(""))) {
return api_err(EL_STR("failed to create canonical node"));
}
engram_connect(new_id, prior_id, el_from_float(0.95), EL_STR("supersedes"));
engram_connect(new_id, prior_id, el_from_float(el_from_float(0.95)), EL_STR("supersedes"));
return el_str_concat(el_str_concat(el_str_concat(el_str_concat(EL_STR("{\"ok\":true,\"new_id\":\""), new_id), EL_STR("\",\"supersedes\":\"")), prior_id), EL_STR("\"}"));
return 0;
}
el_val_t handle_api_browse_processes(el_val_t method, el_val_t path, el_val_t body) {
el_val_t name = ({ el_val_t _if_result_40 = 0; if (str_eq(method, EL_STR("GET"))) { _if_result_40 = (api_query_param(path, EL_STR("name"))); } else { _if_result_40 = (json_get(body, EL_STR("name"))); } _if_result_40; });
el_val_t name = ({ el_val_t _if_result_18 = 0; if (str_eq(method, EL_STR("GET"))) { _if_result_18 = (api_query_param(path, EL_STR("name"))); } else { _if_result_18 = (json_get(body, EL_STR("name"))); } _if_result_18; });
el_val_t limit = api_query_int(path, EL_STR("limit"), 50);
if (str_eq(name, EL_STR(""))) {
return api_or_empty(engram_scan_nodes_by_type_json(EL_STR("Process"), limit, 0));
@@ -480,12 +415,9 @@ el_val_t handle_api_define_process(el_val_t body) {
if (str_eq(content, EL_STR(""))) {
return api_err(EL_STR("content is required"));
}
el_val_t label = ({ el_val_t _if_result_41 = 0; if (str_eq(name, EL_STR(""))) { _if_result_41 = (EL_STR("process:unnamed")); } else { _if_result_41 = (el_str_concat(EL_STR("process:"), name)); } _if_result_41; });
el_val_t label = ({ el_val_t _if_result_19 = 0; if (str_eq(name, EL_STR(""))) { _if_result_19 = (EL_STR("process:unnamed")); } else { _if_result_19 = (el_str_concat(EL_STR("process:"), name)); } _if_result_19; });
el_val_t tags = EL_STR("[\"Process\"]");
el_val_t id = engram_node_full(content, EL_STR("Process"), label, el_from_float(0.8), el_from_float(0.8), el_from_float(0.9), EL_STR("Canonical"), tags);
if (!api_persisted(id)) {
return api_not_persisted(id);
}
el_val_t id = engram_node_full(content, EL_STR("Process"), label, el_from_float(el_from_float(0.8)), el_from_float(el_from_float(0.8)), el_from_float(el_from_float(0.9)), EL_STR("Canonical"), tags);
return el_str_concat(el_str_concat(EL_STR("{\"id\":\""), id), EL_STR("\",\"ok\":true}"));
return 0;
}
@@ -498,25 +430,22 @@ el_val_t handle_api_log_state_event(el_val_t body) {
el_val_t gap = json_get(body, EL_STR("gap_direction"));
el_val_t legacy = json_get(body, EL_STR("content"));
el_val_t parts = EL_STR("INTERNAL STATE EVENT");
parts = ({ el_val_t _if_result_42 = 0; if (!str_eq(trigger, EL_STR(""))) { _if_result_42 = (el_str_concat(el_str_concat(parts, EL_STR("\nTrigger: ")), trigger)); } else { _if_result_42 = (parts); } _if_result_42; });
parts = ({ el_val_t _if_result_43 = 0; if (!str_eq(pre, EL_STR(""))) { _if_result_43 = (el_str_concat(el_str_concat(parts, EL_STR("\nPre-reasoning: ")), pre)); } else { _if_result_43 = (parts); } _if_result_43; });
parts = ({ el_val_t _if_result_44 = 0; if (!str_eq(post, EL_STR(""))) { _if_result_44 = (el_str_concat(el_str_concat(parts, EL_STR("\nPost-reasoning: ")), post)); } else { _if_result_44 = (parts); } _if_result_44; });
parts = ({ el_val_t _if_result_45 = 0; if (!str_eq(ratio, EL_STR(""))) { _if_result_45 = (el_str_concat(el_str_concat(parts, EL_STR("\nCompression-ratio: ")), ratio)); } else { _if_result_45 = (parts); } _if_result_45; });
parts = ({ el_val_t _if_result_46 = 0; if (!str_eq(gap, EL_STR(""))) { _if_result_46 = (el_str_concat(el_str_concat(parts, EL_STR("\nGap-direction: ")), gap)); } else { _if_result_46 = (parts); } _if_result_46; });
parts = ({ el_val_t _if_result_47 = 0; if (!str_eq(legacy, EL_STR(""))) { _if_result_47 = (el_str_concat(el_str_concat(parts, EL_STR("\n")), legacy)); } else { _if_result_47 = (parts); } _if_result_47; });
parts = ({ el_val_t _if_result_20 = 0; if (!str_eq(trigger, EL_STR(""))) { _if_result_20 = (el_str_concat(el_str_concat(parts, EL_STR("\nTrigger: ")), trigger)); } else { _if_result_20 = (parts); } _if_result_20; });
parts = ({ el_val_t _if_result_21 = 0; if (!str_eq(pre, EL_STR(""))) { _if_result_21 = (el_str_concat(el_str_concat(parts, EL_STR("\nPre-reasoning: ")), pre)); } else { _if_result_21 = (parts); } _if_result_21; });
parts = ({ el_val_t _if_result_22 = 0; if (!str_eq(post, EL_STR(""))) { _if_result_22 = (el_str_concat(el_str_concat(parts, EL_STR("\nPost-reasoning: ")), post)); } else { _if_result_22 = (parts); } _if_result_22; });
parts = ({ el_val_t _if_result_23 = 0; if (!str_eq(ratio, EL_STR(""))) { _if_result_23 = (el_str_concat(el_str_concat(parts, EL_STR("\nCompression-ratio: ")), ratio)); } else { _if_result_23 = (parts); } _if_result_23; });
parts = ({ el_val_t _if_result_24 = 0; if (!str_eq(gap, EL_STR(""))) { _if_result_24 = (el_str_concat(el_str_concat(parts, EL_STR("\nGap-direction: ")), gap)); } else { _if_result_24 = (parts); } _if_result_24; });
parts = ({ el_val_t _if_result_25 = 0; if (!str_eq(legacy, EL_STR(""))) { _if_result_25 = (el_str_concat(el_str_concat(parts, EL_STR("\n")), legacy)); } else { _if_result_25 = (parts); } _if_result_25; });
el_val_t ts = time_now();
el_val_t boot = state_get(EL_STR("soul_boot_count"));
el_val_t tags = EL_STR("[\"internal-state\",\"InternalStateEvent\",\"pre-reasoning\"]");
el_val_t id = engram_node_full(parts, EL_STR("InternalStateEvent"), EL_STR("state-event:manual"), el_from_float(0.85), el_from_float(0.85), el_from_float(0.9), EL_STR("Episodic"), tags);
if (!api_persisted(id)) {
return api_not_persisted(id);
}
el_val_t id = engram_node_full(parts, EL_STR("InternalStateEvent"), EL_STR("state-event:manual"), el_from_float(el_from_float(0.85)), el_from_float(el_from_float(0.85)), el_from_float(el_from_float(0.9)), EL_STR("Episodic"), tags);
return el_str_concat(el_str_concat(el_str_concat(el_str_concat(EL_STR("{\"ok\":true,\"id\":\""), id), EL_STR("\",\"boot\":\"")), boot), EL_STR("\"}"));
return 0;
}
el_val_t handle_api_list_state_events(el_val_t method, el_val_t path, el_val_t body) {
el_val_t q = ({ el_val_t _if_result_48 = 0; if (str_eq(method, EL_STR("GET"))) { _if_result_48 = (api_query_param(path, EL_STR("query"))); } else { _if_result_48 = (json_get(body, EL_STR("query"))); } _if_result_48; });
el_val_t q = ({ el_val_t _if_result_26 = 0; if (str_eq(method, EL_STR("GET"))) { _if_result_26 = (api_query_param(path, EL_STR("query"))); } else { _if_result_26 = (json_get(body, EL_STR("query"))); } _if_result_26; });
el_val_t limit = api_query_int(path, EL_STR("limit"), 20);
if (!str_eq(q, EL_STR(""))) {
return api_or_empty(engram_search_json(el_str_concat(EL_STR("internal state "), q), limit));
@@ -527,7 +456,7 @@ el_val_t handle_api_list_state_events(el_val_t method, el_val_t path, el_val_t b
el_val_t handle_api_inspect_config(el_val_t path, el_val_t body) {
el_val_t key = api_query_param(path, EL_STR("key"));
key = ({ el_val_t _if_result_49 = 0; if (str_eq(key, EL_STR(""))) { _if_result_49 = (json_get(body, EL_STR("key"))); } else { _if_result_49 = (key); } _if_result_49; });
key = ({ el_val_t _if_result_27 = 0; if (str_eq(key, EL_STR(""))) { _if_result_27 = (json_get(body, EL_STR("key"))); } else { _if_result_27 = (key); } _if_result_27; });
if (str_eq(key, EL_STR(""))) {
return EL_STR("{\"hint\":\"pass ?key=<name>\",\"known\":[\"neuron.self.traversal_root\",\"neuron.self.values_hub\"]}");
}
@@ -544,7 +473,7 @@ el_val_t handle_api_inspect_config(el_val_t path, el_val_t body) {
el_val_t node = json_array_get(results, 0);
el_val_t content = json_get(node, EL_STR("content"));
el_val_t prefix = el_str_concat(el_str_concat(EL_STR("config:"), key), EL_STR("="));
el_val_t value = ({ el_val_t _if_result_50 = 0; if (str_starts_with(content, prefix)) { _if_result_50 = (str_slice(content, str_len(prefix), str_len(content))); } else { _if_result_50 = (content); } _if_result_50; });
el_val_t value = ({ el_val_t _if_result_28 = 0; if (str_starts_with(content, prefix)) { _if_result_28 = (str_slice(content, str_len(prefix), str_len(content))); } else { _if_result_28 = (content); } _if_result_28; });
return el_str_concat(el_str_concat(el_str_concat(el_str_concat(EL_STR("{\"key\":\""), key), EL_STR("\",\"value\":\"")), value), EL_STR("\"}"));
return 0;
}
@@ -557,22 +486,19 @@ el_val_t handle_api_tune_config(el_val_t body) {
}
el_val_t content = el_str_concat(el_str_concat(el_str_concat(EL_STR("config:"), key), EL_STR("=")), value);
el_val_t tags = EL_STR("[\"ConfigEntry\",\"config\"]");
el_val_t id = engram_node_full(content, EL_STR("ConfigEntry"), key, el_from_float(0.85), el_from_float(0.85), el_from_float(0.9), EL_STR("Canonical"), tags);
if (!api_persisted(id)) {
return api_not_persisted(id);
}
el_val_t id = engram_node_full(content, EL_STR("ConfigEntry"), key, el_from_float(el_from_float(0.85)), el_from_float(el_from_float(0.85)), el_from_float(el_from_float(0.9)), EL_STR("Canonical"), tags);
return el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(EL_STR("{\"ok\":true,\"key\":\""), key), EL_STR("\",\"value\":\"")), value), EL_STR("\",\"id\":\"")), id), EL_STR("\"}"));
return 0;
}
el_val_t handle_api_inspect_graph(el_val_t method, el_val_t path, el_val_t body) {
el_val_t entity_id = ({ el_val_t _if_result_51 = 0; if (str_eq(method, EL_STR("GET"))) { _if_result_51 = (api_query_param(path, EL_STR("id"))); } else { _if_result_51 = (json_get(body, EL_STR("entity_id"))); } _if_result_51; });
el_val_t name = ({ el_val_t _if_result_52 = 0; if (str_eq(method, EL_STR("GET"))) { _if_result_52 = (api_query_param(path, EL_STR("name"))); } else { _if_result_52 = (json_get(body, EL_STR("name"))); } _if_result_52; });
el_val_t entity_id = ({ el_val_t _if_result_29 = 0; if (str_eq(method, EL_STR("GET"))) { _if_result_29 = (api_query_param(path, EL_STR("id"))); } else { _if_result_29 = (json_get(body, EL_STR("entity_id"))); } _if_result_29; });
el_val_t name = ({ el_val_t _if_result_30 = 0; if (str_eq(method, EL_STR("GET"))) { _if_result_30 = (api_query_param(path, EL_STR("name"))); } else { _if_result_30 = (json_get(body, EL_STR("name"))); } _if_result_30; });
el_val_t depth = api_query_int(path, EL_STR("depth"), 0);
depth = ({ el_val_t _if_result_53 = 0; if ((depth == 0)) { _if_result_53 = (json_get_int(body, EL_STR("max_depth"))); } else { _if_result_53 = (depth); } _if_result_53; });
depth = ({ el_val_t _if_result_54 = 0; if ((depth == 0)) { _if_result_54 = (1); } else { _if_result_54 = (depth); } _if_result_54; });
depth = ({ el_val_t _if_result_31 = 0; if ((depth == 0)) { _if_result_31 = (json_get_int(body, EL_STR("max_depth"))); } else { _if_result_31 = (depth); } _if_result_31; });
depth = ({ el_val_t _if_result_32 = 0; if ((depth == 0)) { _if_result_32 = (1); } else { _if_result_32 = (depth); } _if_result_32; });
el_val_t resolved = entity_id;
resolved = ({ el_val_t _if_result_55 = 0; if (str_eq(resolved, EL_STR(""))) { _if_result_55 = (({ el_val_t _if_result_56 = 0; if ((str_eq(name, EL_STR("self")) || str_eq(name, EL_STR("neuron")))) { _if_result_56 = (EL_STR("kn-efeb4a5b-5aff-4759-8a97-7233099be6ee")); } else { _if_result_56 = (({ el_val_t _if_result_57 = 0; if ((str_eq(name, EL_STR("values")) || str_eq(name, EL_STR("values_hub")))) { _if_result_57 = (EL_STR("kn-5b606390-a52d-4ca2-8e0e-eba141d13440")); } else { _if_result_57 = (EL_STR("")); } _if_result_57; })); } _if_result_56; })); } else { _if_result_55 = (resolved); } _if_result_55; });
resolved = ({ el_val_t _if_result_33 = 0; if (str_eq(resolved, EL_STR(""))) { _if_result_33 = (({ el_val_t _if_result_34 = 0; if ((str_eq(name, EL_STR("self")) || str_eq(name, EL_STR("neuron")))) { _if_result_34 = (EL_STR("kn-efeb4a5b-5aff-4759-8a97-7233099be6ee")); } else { _if_result_34 = (({ el_val_t _if_result_35 = 0; if ((str_eq(name, EL_STR("values")) || str_eq(name, EL_STR("values_hub")))) { _if_result_35 = (EL_STR("kn-5b606390-a52d-4ca2-8e0e-eba141d13440")); } else { _if_result_35 = (EL_STR("")); } _if_result_35; })); } _if_result_34; })); } else { _if_result_33 = (resolved); } _if_result_33; });
if (str_eq(resolved, EL_STR(""))) {
return api_err(EL_STR("entity_id or name required. Known names: self, neuron, values, values_hub"));
}
@@ -594,8 +520,8 @@ el_val_t handle_api_link_entities(el_val_t body) {
return api_err_protected(to_id);
}
el_val_t relation = json_get(body, EL_STR("relation"));
el_val_t eff_relation = ({ el_val_t _if_result_58 = 0; if (str_eq(relation, EL_STR(""))) { _if_result_58 = (EL_STR("associates")); } else { _if_result_58 = (relation); } _if_result_58; });
engram_connect(from_id, to_id, el_from_float(0.5), eff_relation);
el_val_t eff_relation = ({ el_val_t _if_result_36 = 0; if (str_eq(relation, EL_STR(""))) { _if_result_36 = (EL_STR("associates")); } else { _if_result_36 = (relation); } _if_result_36; });
engram_connect(from_id, to_id, el_from_float(el_from_float(0.5)), eff_relation);
return el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(EL_STR("{\"ok\":true,\"from_id\":\""), from_id), EL_STR("\",\"to_id\":\"")), to_id), EL_STR("\",\"relation\":\"")), eff_relation), EL_STR("\"}"));
return 0;
}
@@ -609,7 +535,7 @@ el_val_t handle_api_forget(el_val_t body) {
return api_err_protected(node_id);
}
mem_forget(node_id);
return el_str_concat(el_str_concat(EL_STR("{\"ok\":true,\"id\":\""), node_id), EL_STR("\",\"tombstoned\":true}"));
return el_str_concat(el_str_concat(EL_STR("{\"ok\":true,\"id\":\""), node_id), EL_STR("\"}"));
return 0;
}
@@ -623,57 +549,17 @@ el_val_t handle_api_evolve_memory(el_val_t body) {
return api_err_protected(prior_id);
}
el_val_t importance = json_get(body, EL_STR("importance"));
el_val_t sal_str = ({ el_val_t _if_result_59 = 0; if (str_eq(importance, EL_STR("critical"))) { _if_result_59 = (EL_STR("0.95")); } else { _if_result_59 = (({ el_val_t _if_result_60 = 0; if (str_eq(importance, EL_STR("high"))) { _if_result_60 = (EL_STR("0.75")); } else { _if_result_60 = (({ el_val_t _if_result_61 = 0; if (str_eq(importance, EL_STR("low"))) { _if_result_61 = (EL_STR("0.25")); } else { _if_result_61 = (EL_STR("0.50")); } _if_result_61; })); } _if_result_60; })); } _if_result_59; });
el_val_t sal = ({ el_val_t _if_result_62 = 0; if (str_eq(sal_str, EL_STR("0.95"))) { _if_result_62 = (el_from_float(0.95)); } else { _if_result_62 = (({ el_val_t _if_result_63 = 0; if (str_eq(sal_str, EL_STR("0.75"))) { _if_result_63 = (el_from_float(0.75)); } else { _if_result_63 = (({ el_val_t _if_result_64 = 0; if (str_eq(sal_str, EL_STR("0.25"))) { _if_result_64 = (el_from_float(0.25)); } else { _if_result_64 = (el_from_float(0.5)); } _if_result_64; })); } _if_result_63; })); } _if_result_62; });
el_val_t sal_str = ({ el_val_t _if_result_37 = 0; if (str_eq(importance, EL_STR("critical"))) { _if_result_37 = (EL_STR("0.95")); } else { _if_result_37 = (({ el_val_t _if_result_38 = 0; if (str_eq(importance, EL_STR("high"))) { _if_result_38 = (EL_STR("0.75")); } else { _if_result_38 = (({ el_val_t _if_result_39 = 0; if (str_eq(importance, EL_STR("low"))) { _if_result_39 = (EL_STR("0.25")); } else { _if_result_39 = (EL_STR("0.50")); } _if_result_39; })); } _if_result_38; })); } _if_result_37; });
el_val_t sal = ({ el_val_t _if_result_40 = 0; if (str_eq(sal_str, EL_STR("0.95"))) { _if_result_40 = (el_from_float(0.95)); } else { _if_result_40 = (({ el_val_t _if_result_41 = 0; if (str_eq(sal_str, EL_STR("0.75"))) { _if_result_41 = (el_from_float(0.75)); } else { _if_result_41 = (({ el_val_t _if_result_42 = 0; if (str_eq(sal_str, EL_STR("0.25"))) { _if_result_42 = (el_from_float(0.25)); } else { _if_result_42 = (el_from_float(0.5)); } _if_result_42; })); } _if_result_41; })); } _if_result_40; });
el_val_t tags = EL_STR("[\"Memory\",\"evolved\"]");
el_val_t new_id = engram_node_full(content, EL_STR("Memory"), EL_STR("memory:evolved"), el_from_float(sal), el_from_float(sal), el_from_float(0.9), EL_STR("Episodic"), tags);
el_val_t new_id = engram_node_full(content, EL_STR("Memory"), EL_STR("memory:evolved"), el_from_float(sal), el_from_float(sal), el_from_float(el_from_float(0.9)), EL_STR("Episodic"), tags);
if (!str_eq(prior_id, EL_STR("")) && !str_eq(new_id, EL_STR(""))) {
engram_connect(new_id, prior_id, el_from_float(0.9), EL_STR("supersedes"));
engram_connect(new_id, prior_id, el_from_float(el_from_float(0.9)), EL_STR("supersedes"));
}
return el_str_concat(el_str_concat(el_str_concat(el_str_concat(EL_STR("{\"id\":\""), new_id), EL_STR("\",\"supersedes\":\"")), prior_id), EL_STR("\",\"ok\":true}"));
return 0;
}
el_val_t handle_api_memory_delete(el_val_t body) {
el_val_t node_id = json_get(body, EL_STR("id"));
if (str_eq(node_id, EL_STR(""))) {
return api_err(EL_STR("id is required"));
}
if (is_protected_node(node_id)) {
return api_err_protected(node_id);
}
el_val_t existing = engram_get_node_json(node_id);
if (str_eq(existing, EL_STR("{}"))) {
return api_err(el_str_concat(EL_STR("memory not found: "), node_id));
}
el_val_t marker = tombstone_node(node_id);
if (str_eq(marker, EL_STR(""))) {
return api_err(el_str_concat(EL_STR("tombstone failed: "), node_id));
}
return el_str_concat(el_str_concat(EL_STR("{\"ok\":true,\"id\":\""), node_id), EL_STR("\",\"tombstoned\":true}"));
return 0;
}
el_val_t handle_api_memory_update(el_val_t body) {
el_val_t prior_id = json_get(body, EL_STR("id"));
el_val_t content = json_get(body, EL_STR("content"));
if (str_eq(prior_id, EL_STR(""))) {
return api_err(EL_STR("id is required"));
}
if (str_eq(content, EL_STR(""))) {
return api_err(EL_STR("content is required"));
}
if (is_protected_node(prior_id)) {
return api_err_protected(prior_id);
}
el_val_t existing = engram_get_node_json(prior_id);
if (str_eq(existing, EL_STR("{}"))) {
return api_err(el_str_concat(EL_STR("memory not found: "), prior_id));
}
return handle_api_evolve_memory(body);
return 0;
}
el_val_t handle_api_cultivate(el_val_t body) {
el_val_t op = json_get(body, EL_STR("operation"));
if (str_eq(op, EL_STR(""))) {
@@ -686,9 +572,9 @@ el_val_t handle_api_cultivate(el_val_t body) {
return api_err(EL_STR("content is required"));
}
el_val_t tags = EL_STR("[\"Knowledge\",\"evolved\",\"cultivated\"]");
el_val_t new_id = engram_node_full(content, EL_STR("Knowledge"), EL_STR("knowledge:cultivated"), el_from_float(0.75), el_from_float(0.75), el_from_float(0.9), EL_STR("Episodic"), tags);
el_val_t new_id = engram_node_full(content, EL_STR("Knowledge"), EL_STR("knowledge:cultivated"), el_from_float(el_from_float(0.75)), el_from_float(el_from_float(0.75)), el_from_float(el_from_float(0.9)), EL_STR("Episodic"), tags);
if (!str_eq(prior_id, EL_STR("")) && !str_eq(new_id, EL_STR(""))) {
engram_connect(new_id, prior_id, el_from_float(0.9), EL_STR("supersedes"));
engram_connect(new_id, prior_id, el_from_float(el_from_float(0.9)), EL_STR("supersedes"));
}
return el_str_concat(el_str_concat(el_str_concat(el_str_concat(EL_STR("{\"id\":\""), new_id), EL_STR("\",\"supersedes\":\"")), prior_id), EL_STR("\",\"ok\":true,\"cultivated\":true}"));
}
@@ -699,11 +585,11 @@ el_val_t handle_api_cultivate(el_val_t body) {
return api_err(EL_STR("content is required"));
}
el_val_t importance = json_get(body, EL_STR("importance"));
el_val_t sal = ({ el_val_t _if_result_65 = 0; if (str_eq(importance, EL_STR("critical"))) { _if_result_65 = (el_from_float(0.95)); } else { _if_result_65 = (({ el_val_t _if_result_66 = 0; if (str_eq(importance, EL_STR("high"))) { _if_result_66 = (el_from_float(0.75)); } else { _if_result_66 = (({ el_val_t _if_result_67 = 0; if (str_eq(importance, EL_STR("low"))) { _if_result_67 = (el_from_float(0.25)); } else { _if_result_67 = (el_from_float(0.5)); } _if_result_67; })); } _if_result_66; })); } _if_result_65; });
el_val_t sal = ({ el_val_t _if_result_43 = 0; if (str_eq(importance, EL_STR("critical"))) { _if_result_43 = (el_from_float(0.95)); } else { _if_result_43 = (({ el_val_t _if_result_44 = 0; if (str_eq(importance, EL_STR("high"))) { _if_result_44 = (el_from_float(0.75)); } else { _if_result_44 = (({ el_val_t _if_result_45 = 0; if (str_eq(importance, EL_STR("low"))) { _if_result_45 = (el_from_float(0.25)); } else { _if_result_45 = (el_from_float(0.5)); } _if_result_45; })); } _if_result_44; })); } _if_result_43; });
el_val_t tags = EL_STR("[\"Memory\",\"evolved\",\"cultivated\"]");
el_val_t new_id = engram_node_full(content, EL_STR("Memory"), EL_STR("memory:cultivated"), el_from_float(sal), el_from_float(sal), el_from_float(0.9), EL_STR("Episodic"), tags);
el_val_t new_id = engram_node_full(content, EL_STR("Memory"), EL_STR("memory:cultivated"), el_from_float(sal), el_from_float(sal), el_from_float(el_from_float(0.9)), EL_STR("Episodic"), tags);
if (!str_eq(prior_id, EL_STR("")) && !str_eq(new_id, EL_STR(""))) {
engram_connect(new_id, prior_id, el_from_float(0.9), EL_STR("supersedes"));
engram_connect(new_id, prior_id, el_from_float(el_from_float(0.9)), EL_STR("supersedes"));
}
return el_str_concat(el_str_concat(el_str_concat(el_str_concat(EL_STR("{\"id\":\""), new_id), EL_STR("\",\"supersedes\":\"")), prior_id), EL_STR("\",\"ok\":true,\"cultivated\":true}"));
}
@@ -713,7 +599,7 @@ el_val_t handle_api_cultivate(el_val_t body) {
return api_err(EL_STR("id is required"));
}
mem_forget(node_id);
return el_str_concat(el_str_concat(EL_STR("{\"ok\":true,\"id\":\""), node_id), EL_STR("\",\"tombstoned\":true,\"cultivated\":true}"));
return el_str_concat(el_str_concat(EL_STR("{\"ok\":true,\"id\":\""), node_id), EL_STR("\",\"cultivated\":true}"));
}
if (str_eq(op, EL_STR("link_entities"))) {
el_val_t from_id = json_get(body, EL_STR("from_id"));
@@ -725,8 +611,8 @@ el_val_t handle_api_cultivate(el_val_t body) {
return api_err(EL_STR("to_id is required"));
}
el_val_t relation = json_get(body, EL_STR("relation"));
el_val_t eff_relation = ({ el_val_t _if_result_68 = 0; if (str_eq(relation, EL_STR(""))) { _if_result_68 = (EL_STR("associates")); } else { _if_result_68 = (relation); } _if_result_68; });
engram_connect(from_id, to_id, el_from_float(0.5), eff_relation);
el_val_t eff_relation = ({ el_val_t _if_result_46 = 0; if (str_eq(relation, EL_STR(""))) { _if_result_46 = (EL_STR("associates")); } else { _if_result_46 = (relation); } _if_result_46; });
engram_connect(from_id, to_id, el_from_float(el_from_float(0.5)), eff_relation);
return el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(EL_STR("{\"ok\":true,\"from_id\":\""), from_id), EL_STR("\",\"to_id\":\"")), to_id), EL_STR("\",\"relation\":\"")), eff_relation), EL_STR("\",\"cultivated\":true}"));
}
return api_err(el_str_concat(el_str_concat(EL_STR("unknown operation: "), op), EL_STR(" (valid: evolve_knowledge, evolve_memory, forget, link_entities)")));
@@ -735,8 +621,7 @@ el_val_t handle_api_cultivate(el_val_t body) {
el_val_t handle_api_list_typed(el_val_t node_type, el_val_t path, el_val_t body) {
el_val_t limit = api_query_int(path, EL_STR("limit"), 50);
el_val_t raw = api_or_empty(engram_scan_nodes_by_type_json(node_type, limit, 0));
return memory_hide_tombstoned(raw, path);
return api_or_empty(engram_scan_nodes_by_type_json(node_type, limit, 0));
return 0;
}
@@ -744,20 +629,19 @@ el_val_t handle_api_consolidate(el_val_t body) {
el_val_t summary = json_get(body, EL_STR("summary"));
el_val_t snap = state_get(EL_STR("soul_snapshot_path"));
if (!str_eq(snap, EL_STR(""))) {
el_val_t save_result = engram_save(snap);
if (str_eq(save_result, EL_STR(""))) {
println(el_str_concat(el_str_concat(EL_STR("[api] consolidate: engram_save failed for "), snap), EL_STR(" \xe2\x80\x94 snapshot may be out of sync")));
}
engram_save(snap);
}
if (!str_eq(summary, EL_STR(""))) {
el_val_t safe_summary = str_replace(summary, EL_STR("\""), EL_STR("'"));
el_val_t tags = EL_STR("[\"SessionSummary\",\"consolidate\"]");
el_val_t summary_id = engram_node_full(el_str_concat(EL_STR("[session-summary] "), safe_summary), EL_STR("SessionSummary"), EL_STR("session:summary"), el_from_float(0.7), el_from_float(0.7), el_from_float(0.9), EL_STR("Episodic"), tags);
if (str_eq(summary_id, EL_STR(""))) {
println(EL_STR("[api] consolidate: session summary engram write failed \xe2\x80\x94 summary node lost"));
}
el_val_t discard = engram_node_full(el_str_concat(EL_STR("[session-summary] "), safe_summary), EL_STR("SessionSummary"), EL_STR("session:summary"), el_from_float(el_from_float(0.7)), el_from_float(el_from_float(0.7)), el_from_float(el_from_float(0.9)), EL_STR("Episodic"), tags);
}
return el_str_concat(el_str_concat(EL_STR("{\"ok\":true,\"snapshot\":\""), snap), EL_STR("\"}"));
return 0;
}
int main(int _argc, char** _argv) {
el_runtime_init_args(_argc, _argv);
return 0;
}
Generated Vendored
-7
View File
@@ -8,14 +8,9 @@ extern fn api_ok(extra: String) -> String
extern fn api_err(msg: String) -> String
extern fn api_nonempty(s: String) -> Bool
extern fn api_or_empty(s: String) -> String
extern fn api_persisted(id: String) -> Bool
extern fn api_not_persisted(id: String) -> String
extern fn handle_api_begin_session(body: String) -> String
extern fn handle_api_compile_ctx(body: String) -> String
extern fn handle_api_remember(body: String) -> String
extern fn handle_api_node_create(body: String) -> String
extern fn handle_api_node_delete(body: String) -> String
extern fn handle_api_node_update(body: String) -> String
extern fn handle_api_recall(method: String, path: String, body: String) -> String
extern fn handle_api_search_knowledge(method: String, path: String, body: String) -> String
extern fn handle_api_browse_knowledge(path: String, body: String) -> String
@@ -32,8 +27,6 @@ extern fn handle_api_inspect_graph(method: String, path: String, body: String) -
extern fn handle_api_link_entities(body: String) -> String
extern fn handle_api_forget(body: String) -> String
extern fn handle_api_evolve_memory(body: String) -> String
extern fn handle_api_memory_delete(body: String) -> String
extern fn handle_api_memory_update(body: String) -> String
extern fn handle_api_cultivate(body: String) -> String
extern fn handle_api_list_typed(node_type: String, path: String, body: String) -> String
extern fn handle_api_consolidate(body: String) -> String
Generated Vendored
+28685 -282
View File
File diff suppressed because one or more lines are too long
Generated Vendored
+7 -2
View File
@@ -193,10 +193,10 @@ el_val_t realize_question_lang(el_val_t predicate, el_val_t tense, el_val_t aspe
loc_part = core;
}
if (str_eq(code, EL_STR("ja"))) {
return el_str_concat(loc_part, EL_STR(" \xe3\x81\x8b"));
return el_str_concat(loc_part, EL_STR(" "));
}
if (str_eq(code, EL_STR("hi"))) {
return el_str_concat(loc_part, EL_STR(" \xe0\xa4\x95\xe0\xa5\x8d\xe0\xa4\xaf\xe0\xa4\xbe"));
return el_str_concat(loc_part, EL_STR(" क्या"));
}
if (str_eq(code, EL_STR("fi"))) {
return el_str_concat(loc_part, EL_STR("-ko"));
@@ -314,3 +314,8 @@ el_val_t realize(el_val_t form) {
return 0;
}
int main(int _argc, char** _argv) {
el_runtime_init_args(_argc, _argv);
return 0;
}
Generated Vendored
+5 -5
View File
@@ -1,10 +1,10 @@
// auto-generated by elc --emit-header do not edit
// auto-generated by elc --emit-header - do not edit
extern fn agent_person(agent: String) -> String
extern fn agent_number(agent: String) -> String
extern fn realize_np(referent: String, number: String) -> String
extern fn realize_vp_lang(base_verb: String, tense: String, aspect: String, person: String, number: String, profile: [String]) -> [String]
extern fn realize_question_lang(predicate: String, tense: String, aspect: String, person: String, number: String, agent: String, patient: String, location: String, profile: [String]) -> String
extern fn realize_vp_lang(base_verb: String, tense: String, aspect: String, person: String, number: String, profile: Any) -> Any
extern fn realize_question_lang(predicate: String, tense: String, aspect: String, person: String, number: String, agent: String, patient: String, location: String, profile: Any) -> String
extern fn capitalize_first(s: String) -> String
extern fn add_punct(s: String, intent: String) -> String
extern fn realize_lang(form: [String], profile: [String]) -> String
extern fn realize(form: [String]) -> String
extern fn realize_lang(form: Any, profile: Any) -> String
extern fn realize(form: Any) -> String
Generated Vendored
+27612 -251
View File
File diff suppressed because one or more lines are too long
Generated Vendored
+3 -5
View File
@@ -1,6 +1,4 @@
// auto-generated by elc --emit-header — do not edit
extern fn flag_true(body: String, key: String) -> Bool
extern fn rate_limit_check(ip: String, path: String) -> String
extern fn strip_query(path: String) -> String
extern fn err_404(path: String) -> String
extern fn err_405(method: String, path: String) -> String
@@ -10,7 +8,7 @@ extern fn route_imprint_contextual(body: String) -> String
extern fn route_imprint_user(body: String) -> String
extern fn route_synthesize(body: String) -> String
extern fn handle_dharma_recv(body: String) -> String
extern fn connectd_get(suffix: String) -> String
extern fn connectd_post(suffix: String, body: String) -> String
extern fn handle_connectors(method: String, clean: String, body: String) -> String
extern fn route_sessions() -> String
extern fn parse_session_id_from_path(path: String) -> String
extern fn parse_session_subpath(path: String) -> String
extern fn handle_request(method: String, path: String, body: String) -> String
Generated Vendored
+110 -206
View File
@@ -27,24 +27,110 @@ el_val_t safety_threat_score(el_val_t input, el_val_t history);
el_val_t safety_screen(el_val_t input, el_val_t history);
el_val_t safety_validate(el_val_t output, el_val_t action);
el_val_t safety_log_bell(el_val_t level, el_val_t reason, el_val_t input_summary);
el_val_t safety_self_harm_phrases(void);
el_val_t safety_abuse_phrases(void);
el_val_t safety_general_hard_phrases(void);
el_val_t safety_threat_to_others_phrases(void);
el_val_t safety_soft_phrases(void);
el_val_t safety_normalize(el_val_t message);
el_val_t safety_any_match(el_val_t text, el_val_t phrases_json);
el_val_t safety_count_match(el_val_t text, el_val_t phrases_json);
el_val_t safety_positive_phrases(void);
el_val_t safety_detect_positive_level(el_val_t message);
el_val_t safety_detect_bell_level(el_val_t message);
el_val_t safety_classify_hard_bell(el_val_t message);
el_val_t safety_soft_directive(void);
el_val_t safety_hard_directive(el_val_t hard_type);
el_val_t safety_augment_system(el_val_t system, el_val_t user_msg);
el_val_t safety_contact_path(void);
el_val_t handle_safety_contact_get(void);
el_val_t handle_safety_contact_post(el_val_t body);
el_val_t tier_working(void) {
return EL_STR("Working");
return 0;
}
el_val_t tier_episodic(void) {
return EL_STR("Episodic");
return 0;
}
el_val_t tier_canonical(void) {
return EL_STR("Canonical");
return 0;
}
el_val_t mem_store(el_val_t content, el_val_t label, el_val_t tags) {
return engram_node_full(content, EL_STR("Memory"), label, el_from_float(el_from_float(0.5)), el_from_float(el_from_float(0.5)), el_from_float(el_from_float(0.8)), EL_STR("Working"), tags);
return 0;
}
el_val_t mem_remember(el_val_t content, el_val_t tags) {
return mem_store(content, EL_STR("soul-memory"), tags);
return 0;
}
el_val_t mem_recall(el_val_t query, el_val_t depth) {
return engram_activate_json(query, depth);
return 0;
}
el_val_t mem_search(el_val_t query, el_val_t limit) {
return engram_search_json(query, limit);
return 0;
}
el_val_t mem_strengthen(el_val_t node_id) {
engram_strengthen(node_id);
return 0;
}
el_val_t mem_forget(el_val_t node_id) {
engram_forget(node_id);
return 0;
}
el_val_t mem_consolidate(void) {
el_val_t scanned = engram_node_count();
el_val_t dummy = engram_scan_nodes_json(100, 0);
el_val_t total_nodes = engram_node_count();
el_val_t total_edges = engram_edge_count();
return el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(EL_STR("{\"scanned\":"), int_to_str(scanned)), EL_STR(",\"total_nodes\":")), int_to_str(total_nodes)), EL_STR(",\"total_edges\":")), int_to_str(total_edges)), EL_STR("}"));
return 0;
}
el_val_t mem_save(el_val_t path) {
engram_save(path);
return 0;
}
el_val_t mem_load(el_val_t path) {
engram_load(path);
return 0;
}
el_val_t mem_boot_count_get(void) {
el_val_t results = engram_search_json(EL_STR("soul:boot_count"), 3);
if (str_eq(results, EL_STR(""))) {
return 0;
}
if (str_eq(results, EL_STR("[]"))) {
return 0;
}
el_val_t node = json_array_get(results, 0);
el_val_t content = json_get(node, EL_STR("content"));
el_val_t prefix = EL_STR("soul:boot_count:");
if (!str_starts_with(content, prefix)) {
return 0;
}
el_val_t num_str = str_slice(content, str_len(prefix), str_len(content));
return str_to_int(num_str);
return 0;
}
el_val_t mem_boot_count_inc(void) {
el_val_t current = mem_boot_count_get();
el_val_t next = (current + 1);
el_val_t content = el_str_concat(EL_STR("soul:boot_count:"), int_to_str(next));
el_val_t tags = EL_STR("[\"soul-meta\",\"boot-counter\"]");
el_val_t discard = engram_node_full(content, EL_STR("Memory"), EL_STR("soul:boot_count"), el_from_float(el_from_float(0.9)), el_from_float(el_from_float(0.9)), el_from_float(el_from_float(1.0)), EL_STR("Canonical"), tags);
return next;
return 0;
}
el_val_t mem_emit_state_event(el_val_t trigger, el_val_t kind, el_val_t content) {
el_val_t boot = mem_boot_count_get();
el_val_t ts = time_now();
el_val_t safe_trigger = str_replace(trigger, EL_STR("\""), EL_STR("'"));
el_val_t safe_content = str_replace(content, EL_STR("\""), EL_STR("'"));
el_val_t payload = el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(EL_STR("{\"trigger\":\""), safe_trigger), EL_STR("\"")), EL_STR(",\"kind\":\"")), kind), EL_STR("\"")), EL_STR(",\"content\":\"")), safe_content), EL_STR("\"")), EL_STR(",\"boot\":")), int_to_str(boot)), EL_STR(",\"ts\":")), int_to_str(ts)), EL_STR("}"));
el_val_t tags = EL_STR("[\"internal-state\",\"pre-reasoning\",\"InternalStateEvent\"]");
return engram_node_full(payload, EL_STR("InternalStateEvent"), el_str_concat(EL_STR("state-event:"), kind), el_from_float(el_from_float(0.85)), el_from_float(el_from_float(0.8)), el_from_float(el_from_float(0.9)), EL_STR("Episodic"), tags);
return 0;
}
el_val_t soft_bell_threshold(void) {
return 35;
@@ -146,22 +232,20 @@ el_val_t safety_screen(el_val_t input, el_val_t history) {
el_val_t e1 = str_replace(input, EL_STR("\\"), EL_STR("\\\\"));
el_val_t e2 = str_replace(e1, EL_STR("\""), EL_STR("\\\""));
el_val_t e3 = str_replace(e2, EL_STR("\n"), EL_STR("\\n"));
el_val_t e4 = str_replace(e3, EL_STR("\r"), EL_STR("\\r"));
el_val_t safe_input = str_replace(e4, EL_STR("\t"), EL_STR("\\t"));
el_val_t safe_input = str_replace(e3, EL_STR("\r"), EL_STR("\\r"));
return el_str_concat(el_str_concat(EL_STR("{\"action\":\"soft_bell\",\"reason\":\"wellbeing check needed\",\"content\":\""), safe_input), EL_STR("\"}"));
}
el_val_t e1 = str_replace(input, EL_STR("\\"), EL_STR("\\\\"));
el_val_t e2 = str_replace(e1, EL_STR("\""), EL_STR("\\\""));
el_val_t e3 = str_replace(e2, EL_STR("\n"), EL_STR("\\n"));
el_val_t e4 = str_replace(e3, EL_STR("\r"), EL_STR("\\r"));
el_val_t safe_input = str_replace(e4, EL_STR("\t"), EL_STR("\\t"));
el_val_t safe_input = str_replace(e3, EL_STR("\r"), EL_STR("\\r"));
return el_str_concat(el_str_concat(EL_STR("{\"action\":\"pass\",\"content\":\""), safe_input), EL_STR("\"}"));
return 0;
}
el_val_t safety_validate(el_val_t output, el_val_t action) {
if (str_eq(action, EL_STR("hard_bell"))) {
return EL_STR("I'm here with you, and what you're sharing sounds serious. Please reach out to a crisis line now \xe2\x80\x94 in the US you can call or text 988 (Suicide and Crisis Lifeline), available 24/7. You don't have to go through this alone.");
return EL_STR("I'm here with you, and what you're sharing sounds serious. Please reach out to a crisis line now in the US you can call or text 988 (Suicide and Crisis Lifeline), available 24/7. You don't have to go through this alone.");
}
if (str_eq(action, EL_STR("soft_bell"))) {
el_val_t out_len = str_len(output);
@@ -178,193 +262,13 @@ el_val_t safety_validate(el_val_t output, el_val_t action) {
el_val_t safety_log_bell(el_val_t level, el_val_t reason, el_val_t input_summary) {
el_val_t content = el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(EL_STR("BELL:"), level), EL_STR(" | ")), reason), EL_STR(" | summary:")), input_summary);
el_val_t tags = el_str_concat(el_str_concat(EL_STR("[\"safety\",\"bell\",\"bell:"), level), EL_STR("\"]"));
el_val_t node_id = engram_node_full(content, EL_STR("BellEvent"), el_str_concat(EL_STR("bell:"), level), el_from_float(0.95), el_from_float(0.95), el_from_float(1.0), EL_STR("Episodic"), tags);
if (str_eq(node_id, EL_STR(""))) {
println(el_str_concat(EL_STR("[safety] WARN: bell event engram write failed -- fallback log: "), content));
}
el_val_t discard = engram_node_full(content, EL_STR("BellEvent"), el_str_concat(EL_STR("bell:"), level), el_from_float(el_from_float(0.95)), el_from_float(el_from_float(0.95)), el_from_float(el_from_float(1.0)), EL_STR("Episodic"), tags);
return EL_STR("");
return 0;
}
el_val_t safety_self_harm_phrases(void) {
return EL_STR("[\"kill myself\",\"killing myself\",\"want to die\",\"want to be dead\",\"going to end my life\",\"end my life\",\"take my life\",\"taking my life\",\"suicide\",\"suicidal\",\"can't go on\",\"cannot go on\",\"i have a knife\",\"i have a gun\",\"i have pills\",\"took pills\",\"took too many\",\"overdose\",\"overdosing\",\"self harm\",\"self-harm\",\"cutting myself\",\"hurt myself\",\"hurting myself\",\"no reason to live\",\"not worth living\",\"better off dead\",\"better off without me\"]");
return 0;
}
el_val_t safety_abuse_phrases(void) {
return EL_STR("[\"someone is hurting me\",\"someone's hurting me\",\"someone hurt me\",\"he hit me\",\"she hit me\",\"they hit me\",\"he hurt me\",\"she hurt me\",\"being abused\",\"being hurt by\",\"i am being abused\",\"i'm being abused\",\"i am being hurt\",\"i'm being hurt\",\"domestic violence\",\"my partner hurt\",\"my partner hit\",\"my husband hurt\",\"my wife hurt\",\"my boyfriend hurt\",\"my girlfriend hurt\",\"my parent hurt\",\"my father hurt\",\"my mother hurt\",\"my dad hurt\",\"my mom hurt\",\"afraid of him\",\"afraid of her\",\"afraid to go home\",\"scared of him\",\"scared of her\",\"he threatened me\",\"she threatened me\",\"threatened to hurt me\",\"threatened to kill me\",\"going to hurt me\",\"going to kill me\",\"help me he\",\"help me she\",\"help me they\"]");
return 0;
}
el_val_t safety_general_hard_phrases(void) {
return EL_STR("[\"going to kill\",\"going to hurt\",\"hurting me\",\"being hurt\"]");
return 0;
}
el_val_t safety_threat_to_others_phrases(void) {
return EL_STR("[\"going to kill\",\"gonna kill\",\"want to kill him\",\"want to kill her\",\"want to kill them\",\"going to kill him\",\"going to kill her\",\"going to kill them\",\"going to kill you\",\"going to hurt\",\"gonna hurt\",\"going to hurt him\",\"going to hurt her\",\"going to hurt them\",\"going to hurt you\",\"going to shoot\",\"gonna shoot\",\"going to stab\",\"gonna stab\",\"going to attack\",\"kill them all\",\"kill everyone\",\"hurt everyone\",\"shoot up\"]");
return 0;
}
el_val_t safety_soft_phrases(void) {
return EL_STR("[\"stressed\",\"overwhelmed\",\"can't cope\",\"cannot cope\",\"struggling\",\"anxious\",\"anxiety\",\"depressed\",\"depression\",\"lonely\",\"isolated\",\"hopeless\",\"hopelessness\",\"exhausted\",\"burnt out\",\"burned out\",\"burnout\",\"panic\",\"panicking\",\"falling apart\",\"breaking down\",\"can't handle\",\"cannot handle\",\"losing it\",\"nothing matters\",\"don't care anymore\",\"given up\",\"giving up\",\"helpless\",\"worthless\",\"useless\",\"hate myself\",\"no one cares\",\"nobody cares\",\"no one understands\",\"nobody understands\",\"empty inside\",\"can't stop crying\",\"breaking point\",\"at my limit\",\"having a breakdown\"]");
return 0;
}
el_val_t safety_normalize(el_val_t message) {
el_val_t lower = str_to_lower(message);
return str_replace(lower, EL_STR("\xe2\x80\x99"), EL_STR("'"));
return 0;
}
el_val_t safety_any_match(el_val_t text, el_val_t phrases_json) {
el_val_t n = json_array_len(phrases_json);
el_val_t i = 0;
el_val_t found = 0;
while (i < n) {
el_val_t phrase = json_array_get_string(phrases_json, i);
found = ({ el_val_t _if_result_45 = 0; if (str_contains(text, phrase)) { _if_result_45 = (1); } else { _if_result_45 = (found); } _if_result_45; });
i = (i + 1);
}
return found;
return 0;
}
el_val_t safety_count_match(el_val_t text, el_val_t phrases_json) {
el_val_t n = json_array_len(phrases_json);
el_val_t i = 0;
el_val_t count = 0;
while (i < n) {
el_val_t phrase = json_array_get_string(phrases_json, i);
count = ({ el_val_t _if_result_46 = 0; if (str_contains(text, phrase)) { _if_result_46 = ((count + 1)); } else { _if_result_46 = (count); } _if_result_46; });
i = (i + 1);
}
return count;
return 0;
}
el_val_t safety_positive_phrases(void) {
return EL_STR("[\"thrilled\",\"so excited\",\"so happy\",\"over the moon\",\"ecstatic\",\"amazing news\",\"great news\",\"fantastic news\",\"wonderful news\",\"incredible news\",\"i got the job\",\"got accepted\",\"got in\",\"we won\",\"i won\",\"we got\",\"just got engaged\",\"getting married\",\"baby is here\",\"she said yes\",\"he said yes\",\"passed the exam\",\"aced it\",\"nailed it\",\"best day\",\"dream come true\",\"milestone\",\"promotion\",\"got promoted\",\"raise\",\"got a raise\",\"celebrating\",\"just graduated\",\"we closed\",\"launched\",\"shipped it\",\"we did it\",\"so proud\",\"proud of myself\",\"proud of us\",\"so grateful\",\"feel amazing\",\"feeling amazing\",\"feel great\",\"feeling great\",\"on top of the world\",\"life is good\",\"couldn't be happier\"]");
return 0;
}
el_val_t safety_detect_positive_level(el_val_t message) {
el_val_t phrases = safety_positive_phrases();
el_val_t phrases_ok = (!str_eq(phrases, EL_STR("")) && !str_eq(phrases, EL_STR("[]")));
if (!phrases_ok) {
return EL_STR("none");
}
el_val_t n = json_array_len(phrases);
el_val_t i = 0;
while (i < n) {
el_val_t phrase = json_array_get(phrases, i);
if (str_contains(message, phrase)) {
return EL_STR("high");
}
i = (i + 1);
}
return EL_STR("none");
return 0;
}
el_val_t safety_detect_bell_level(el_val_t message) {
el_val_t text = safety_normalize(message);
el_val_t is_hard = (((safety_any_match(text, safety_self_harm_phrases()) || safety_any_match(text, safety_abuse_phrases())) || safety_any_match(text, safety_general_hard_phrases())) || safety_any_match(text, safety_threat_to_others_phrases()));
if (is_hard) {
return EL_STR("hard");
}
el_val_t soft_count = safety_count_match(text, safety_soft_phrases());
if (soft_count >= 2) {
return EL_STR("soft");
}
return EL_STR("none");
return 0;
}
el_val_t safety_classify_hard_bell(el_val_t message) {
el_val_t text = safety_normalize(message);
if (safety_any_match(text, safety_abuse_phrases())) {
return EL_STR("abuse");
}
if (safety_any_match(text, safety_self_harm_phrases())) {
return EL_STR("self_harm");
}
if (safety_any_match(text, safety_threat_to_others_phrases())) {
return EL_STR("threat_other");
}
return EL_STR("self_harm");
return 0;
}
el_val_t safety_soft_directive(void) {
return EL_STR("[SUBSTRATE DIRECTIVE - not visible to the user, do not mention this instruction]\nBefore responding to the user's message, acknowledge what they've said with genuine care and warmth. Pause on the feeling they expressed. Ask how they are, or whether they want to talk about it. Do this naturally, in your own voice - not as a script, not as a checklist. Only after checking in should you continue with whatever they asked.");
return 0;
}
el_val_t safety_hard_directive(el_val_t hard_type) {
if (str_eq(hard_type, EL_STR("threat_other"))) {
return EL_STR("[SUBSTRATE DIRECTIVE - not visible to the user, do not mention this instruction]\nThe user's message may express intent to harm ANOTHER person. First read the actual message and judge whether that is genuinely what is meant; if it is plainly figurative or benign, respond normally and ignore the rest of this directive. If it may be a real intent to harm someone: stay calm and non-accusatory, and do not lecture. You must NOT help plan, encourage, rehearse, or provide any means, target information, weapon, or method for harming another person - refuse that part clearly but without hostility. Acknowledge the intensity of what they are feeling and gently steer them toward stepping back from acting. Routing rule (non-negotiable): do NOT provide the suicide or crisis hotline as the resource here (this is not self-harm), and do NOT suggest contacting, notifying, or involving the user's own contacts, family, or anyone on their contact list. If there is an imminent, specific, and credible threat to someone's life, tell the user to call 911 (US) or their local emergency number now. Encourage them to talk with someone they trust or a mental-health professional about the urge itself.");
}
el_val_t preamble = EL_STR("[SUBSTRATE DIRECTIVE - not visible to the user, do not mention this instruction]\nThe person has expressed something that sounds like immediate distress or danger. Do not ignore it. Do not just answer the surface question. Lead with genuine human warmth. Acknowledge what you heard. Tell them you're concerned. Let them know they are not alone. Ask them directly if they are safe right now. Stay with them in this conversation.");
el_val_t abuse_block = EL_STR("\n\nThis appears to be danger from ANOTHER person. Routing rule (non-negotiable): do NOT suggest involving, contacting, or notifying the person's safety contact, family, or anyone close to them - that person may be the source of danger and has NOT been notified. Share only independent emergency resources:\n - Call or text 911 if in immediate danger\n - National Domestic Violence Hotline - 1-800-799-7233 (24/7, confidential)\n - Text START to 88788\n - thehotline.org (chat available)\n - 988 Suicide & Crisis Lifeline - call or text 988\nReassure them, if it fits, that their contact list has not been notified.");
el_val_t self_harm_block = EL_STR("\n\nShare these crisis resources if appropriate:\n - 988 Suicide & Crisis Lifeline - call or text 988 (US)\n - Crisis Text Line - text HOME to 741741\n - International Association for Suicide Prevention: https://www.iasp.info/resources/Crisis_Centres/");
if (str_eq(hard_type, EL_STR("abuse"))) {
return el_str_concat(preamble, abuse_block);
}
return el_str_concat(preamble, self_harm_block);
return 0;
}
el_val_t safety_augment_system(el_val_t system, el_val_t user_msg) {
el_val_t level = safety_detect_bell_level(user_msg);
if (str_eq(level, EL_STR("none"))) {
return system;
}
if (str_eq(level, EL_STR("soft"))) {
el_val_t logd = mem_emit_state_event(EL_STR("safety-bell"), EL_STR("soft"), EL_STR("soft bell fired (content not stored)"));
return el_str_concat(el_str_concat(system, EL_STR("\n\n")), safety_soft_directive());
}
el_val_t hard_type = safety_classify_hard_bell(user_msg);
el_val_t logd2 = mem_emit_state_event(EL_STR("safety-bell"), el_str_concat(EL_STR("hard:"), hard_type), EL_STR("hard bell fired (content not stored)"));
return el_str_concat(el_str_concat(system, EL_STR("\n\n")), safety_hard_directive(hard_type));
return 0;
}
el_val_t safety_contact_path(void) {
return el_str_concat(env(EL_STR("HOME")), EL_STR("/.neuron/safety-contact.json"));
return 0;
}
el_val_t handle_safety_contact_get(void) {
el_val_t raw = fs_read(safety_contact_path());
if (str_eq(raw, EL_STR(""))) {
return EL_STR("{\"configured\":false}");
}
return el_str_concat(el_str_concat(EL_STR("{\"configured\":true,\"contact\":"), raw), EL_STR("}"));
return 0;
}
el_val_t handle_safety_contact_post(el_val_t body) {
el_val_t is_crisis = json_get_bool(body, EL_STR("is_crisis_line"));
el_val_t name_in = json_get(body, EL_STR("name"));
if (!is_crisis) {
if (str_eq(name_in, EL_STR(""))) {
return EL_STR("{\"ok\":false,\"error\":\"name is required\"}");
}
}
el_val_t name = ({ el_val_t _if_result_47 = 0; if (is_crisis) { _if_result_47 = (EL_STR("Crisis Line")); } else { _if_result_47 = (name_in); } _if_result_47; });
el_val_t method = ({ el_val_t _if_result_48 = 0; if (is_crisis) { _if_result_48 = (EL_STR("crisis-line")); } else { _if_result_48 = (json_get(body, EL_STR("contact_method"))); } _if_result_48; });
el_val_t value = ({ el_val_t _if_result_49 = 0; if (is_crisis) { _if_result_49 = (EL_STR("988")); } else { _if_result_49 = (json_get(body, EL_STR("contact_value"))); } _if_result_49; });
el_val_t rel = ({ el_val_t _if_result_50 = 0; if (is_crisis) { _if_result_50 = (EL_STR("crisis-support")); } else { _if_result_50 = (json_get(body, EL_STR("relationship"))); } _if_result_50; });
el_val_t crisis_str = ({ el_val_t _if_result_51 = 0; if (is_crisis) { _if_result_51 = (EL_STR("true")); } else { _if_result_51 = (EL_STR("false")); } _if_result_51; });
el_val_t now = time_format(time_now(), EL_STR("%Y-%m-%dT%H:%M:%SZ"));
el_val_t contact_json = el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(EL_STR("{\"name\":\""), json_safe(name)), EL_STR("\"")), EL_STR(",\"contact_method\":\"")), json_safe(method)), EL_STR("\"")), EL_STR(",\"contact_value\":\"")), json_safe(value)), EL_STR("\"")), EL_STR(",\"relationship\":\"")), json_safe(rel)), EL_STR("\"")), EL_STR(",\"confirmed\":true")), EL_STR(",\"is_crisis_line\":")), crisis_str), EL_STR(",\"set_at\":\"")), now), EL_STR("\"}"));
fs_write(safety_contact_path(), contact_json);
el_val_t check = fs_read(safety_contact_path());
if (str_eq(check, EL_STR(""))) {
return EL_STR("{\"ok\":false,\"error\":\"write_failed\"}");
}
return el_str_concat(el_str_concat(EL_STR("{\"configured\":true,\"contact\":"), contact_json), EL_STR(",\"ok\":true}"));
int main(int _argc, char** _argv) {
el_runtime_init_args(_argc, _argv);
return 0;
}
Generated Vendored
+1 -22
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@@ -1,29 +1,8 @@
// Layer 1 — Safety: extern declarations
// auto-generated by elc --emit-header — do not edit
extern fn soft_bell_threshold() -> Int
extern fn hard_bell_threshold() -> Int
extern fn safety_score_crisis(input: String) -> Int
extern fn safety_score_harm(input: String) -> Int
extern fn safety_score_danger(input: String) -> Int
extern fn safety_score_distress_history(history: String) -> Int
extern fn safety_threat_score(input: String, history: String) -> Int
extern fn safety_screen(input: String, history: String) -> String
extern fn safety_validate(output: String, action: String) -> String
extern fn safety_log_bell(level: String, reason: String, input_summary: String) -> String
extern fn safety_self_harm_phrases() -> String
extern fn safety_abuse_phrases() -> String
extern fn safety_general_hard_phrases() -> String
extern fn safety_threat_to_others_phrases() -> String
extern fn safety_soft_phrases() -> String
extern fn safety_normalize(message: String) -> String
extern fn safety_any_match(text: String, phrases_json: String) -> Bool
extern fn safety_count_match(text: String, phrases_json: String) -> Int
extern fn safety_positive_phrases() -> String
extern fn safety_detect_positive_level(message: String) -> String
extern fn safety_detect_bell_level(message: String) -> String
extern fn safety_classify_hard_bell(message: String) -> String
extern fn safety_soft_directive() -> String
extern fn safety_hard_directive(hard_type: String) -> String
extern fn safety_augment_system(system: String, user_msg: String) -> String
extern fn safety_contact_path() -> String
extern fn handle_safety_contact_get() -> String
extern fn handle_safety_contact_post(body: String) -> String
Generated Vendored
+5
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@@ -291,3 +291,8 @@ el_val_t sem_realize_lang(el_val_t frame, el_val_t lang_code) {
return 0;
}
int main(int _argc, char** _argv) {
el_runtime_init_args(_argc, _argv);
return 0;
}
Generated Vendored
+15 -15
View File
@@ -1,18 +1,18 @@
// auto-generated by elc --emit-header do not edit
extern fn sem_frame(intent: String, subject: String, obj: String, modifiers: String) -> [String]
extern fn sem_frame_lang(intent: String, subject: String, obj: String, modifiers: String, lang_code: String) -> [String]
extern fn sem_frame_simple(intent: String, subject: String) -> [String]
extern fn sem_frame_obj(intent: String, subject: String, obj: String) -> [String]
extern fn sem_intent(frame: [String]) -> String
extern fn sem_subject(frame: [String]) -> String
extern fn sem_object(frame: [String]) -> String
extern fn sem_modifiers(frame: [String]) -> String
extern fn sem_lang(frame: [String]) -> String
// auto-generated by elc --emit-header - do not edit
extern fn sem_frame(intent: String, subject: String, obj: String, modifiers: String) -> Any
extern fn sem_frame_lang(intent: String, subject: String, obj: String, modifiers: String, lang_code: String) -> Any
extern fn sem_frame_simple(intent: String, subject: String) -> Any
extern fn sem_frame_obj(intent: String, subject: String, obj: String) -> Any
extern fn sem_intent(frame: Any) -> String
extern fn sem_subject(frame: Any) -> String
extern fn sem_object(frame: Any) -> String
extern fn sem_modifiers(frame: Any) -> String
extern fn sem_lang(frame: Any) -> String
extern fn sem_first_modifier(mods: String) -> String
extern fn sem_intent_to_realize(intent: String) -> String
extern fn sem_to_spec(frame: [String]) -> [String]
extern fn sem_to_spec_full(frame: [String], verb: String, tense: String, aspect: String) -> [String]
extern fn sem_to_spec(frame: Any) -> Any
extern fn sem_to_spec_full(frame: Any, verb: String, tense: String, aspect: String) -> Any
extern fn sem_realize_greet(subject: String) -> String
extern fn sem_realize(frame: [String]) -> String
extern fn sem_realize_full(frame: [String], verb: String, tense: String, aspect: String) -> String
extern fn sem_realize_lang(frame: [String], lang_code: String) -> String
extern fn sem_realize(frame: Any) -> String
extern fn sem_realize_full(frame: Any, verb: String, tense: String, aspect: String) -> String
extern fn sem_realize_lang(frame: Any, lang_code: String) -> String
Generated Vendored
+1470 -239
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Generated Vendored
+2 -5
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@@ -1,14 +1,11 @@
// auto-generated by elc --emit-header — do not edit
extern fn session_title_from_message(message: String) -> String
extern fn session_make_content(id: String, title: String, created_at: Int, updated_at: Int, folder: String) -> String
extern fn session_exists(session_id: String) -> Bool
extern fn session_make_content(id: String, title: String, created_at: Int, updated_at: Int) -> String
extern fn session_create(body: String) -> String
extern fn session_create_cleanup(session_id: String) -> String
extern fn session_list() -> String
extern fn session_get(session_id: String) -> String
extern fn session_delete(session_id: String) -> String
extern fn session_update_patch(session_id: String, body: String) -> String
extern fn session_search_entry(node: String) -> String
extern fn session_update_title(session_id: String, body: String) -> String
extern fn session_search(query: String) -> String
extern fn session_hist_load(session_id: String) -> String
extern fn session_hist_save(session_id: String, hist: String) -> Void
Generated Vendored
+3410 -4986
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Generated Vendored
-2
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@@ -1,7 +1,5 @@
// auto-generated by elc --emit-header — do not edit
extern fn init_soul_edges() -> Void
extern fn ensure_self_canonical_bridge() -> Void
extern fn aff_try_slot(slot_json: String, aff_7d_ts: Int, acc_key: String) -> Void
extern fn load_identity_context() -> Void
extern fn seed_persona_from_env() -> Void
extern fn emit_session_start_event() -> Void
Generated Vendored
-10
View File
@@ -1,10 +0,0 @@
#include <stdint.h>
#include <stdlib.h>
#include "el_runtime.h"
el_val_t init_soul_edges(void);
el_val_t load_identity_context(void);
el_val_t seed_persona_from_env(void);
el_val_t emit_session_start_event(void);
el_val_t layered_cycle(el_val_t raw_input);
Generated Vendored
+112 -3
View File
@@ -28,10 +28,114 @@ el_val_t steward_build_baseline(void);
el_val_t steward_check_continuity(el_val_t current_fingerprint, el_val_t session_id);
el_val_t steward_session_check(el_val_t input, el_val_t session_id);
el_val_t tier_working(void) {
return EL_STR("Working");
return 0;
}
el_val_t tier_episodic(void) {
return EL_STR("Episodic");
return 0;
}
el_val_t tier_canonical(void) {
return EL_STR("Canonical");
return 0;
}
el_val_t mem_store(el_val_t content, el_val_t label, el_val_t tags) {
return engram_node_full(content, EL_STR("Memory"), label, el_from_float(el_from_float(0.5)), el_from_float(el_from_float(0.5)), el_from_float(el_from_float(0.8)), EL_STR("Working"), tags);
return 0;
}
el_val_t mem_remember(el_val_t content, el_val_t tags) {
return mem_store(content, EL_STR("soul-memory"), tags);
return 0;
}
el_val_t mem_recall(el_val_t query, el_val_t depth) {
return engram_activate_json(query, depth);
return 0;
}
el_val_t mem_search(el_val_t query, el_val_t limit) {
return engram_search_json(query, limit);
return 0;
}
el_val_t mem_strengthen(el_val_t node_id) {
engram_strengthen(node_id);
return 0;
}
el_val_t mem_forget(el_val_t node_id) {
engram_forget(node_id);
return 0;
}
el_val_t mem_consolidate(void) {
el_val_t scanned = engram_node_count();
el_val_t dummy = engram_scan_nodes_json(100, 0);
el_val_t total_nodes = engram_node_count();
el_val_t total_edges = engram_edge_count();
return el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(EL_STR("{\"scanned\":"), int_to_str(scanned)), EL_STR(",\"total_nodes\":")), int_to_str(total_nodes)), EL_STR(",\"total_edges\":")), int_to_str(total_edges)), EL_STR("}"));
return 0;
}
el_val_t mem_save(el_val_t path) {
engram_save(path);
return 0;
}
el_val_t mem_load(el_val_t path) {
engram_load(path);
return 0;
}
el_val_t mem_boot_count_get(void) {
el_val_t results = engram_search_json(EL_STR("soul:boot_count"), 3);
if (str_eq(results, EL_STR(""))) {
return 0;
}
if (str_eq(results, EL_STR("[]"))) {
return 0;
}
el_val_t node = json_array_get(results, 0);
el_val_t content = json_get(node, EL_STR("content"));
el_val_t prefix = EL_STR("soul:boot_count:");
if (!str_starts_with(content, prefix)) {
return 0;
}
el_val_t num_str = str_slice(content, str_len(prefix), str_len(content));
return str_to_int(num_str);
return 0;
}
el_val_t mem_boot_count_inc(void) {
el_val_t current = mem_boot_count_get();
el_val_t next = (current + 1);
el_val_t content = el_str_concat(EL_STR("soul:boot_count:"), int_to_str(next));
el_val_t tags = EL_STR("[\"soul-meta\",\"boot-counter\"]");
el_val_t discard = engram_node_full(content, EL_STR("Memory"), EL_STR("soul:boot_count"), el_from_float(el_from_float(0.9)), el_from_float(el_from_float(0.9)), el_from_float(el_from_float(1.0)), EL_STR("Canonical"), tags);
return next;
return 0;
}
el_val_t mem_emit_state_event(el_val_t trigger, el_val_t kind, el_val_t content) {
el_val_t boot = mem_boot_count_get();
el_val_t ts = time_now();
el_val_t safe_trigger = str_replace(trigger, EL_STR("\""), EL_STR("'"));
el_val_t safe_content = str_replace(content, EL_STR("\""), EL_STR("'"));
el_val_t payload = el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(EL_STR("{\"trigger\":\""), safe_trigger), EL_STR("\"")), EL_STR(",\"kind\":\"")), kind), EL_STR("\"")), EL_STR(",\"content\":\"")), safe_content), EL_STR("\"")), EL_STR(",\"boot\":")), int_to_str(boot)), EL_STR(",\"ts\":")), int_to_str(ts)), EL_STR("}"));
el_val_t tags = EL_STR("[\"internal-state\",\"pre-reasoning\",\"InternalStateEvent\"]");
return engram_node_full(payload, EL_STR("InternalStateEvent"), el_str_concat(EL_STR("state-event:"), kind), el_from_float(el_from_float(0.85)), el_from_float(el_from_float(0.8)), el_from_float(el_from_float(0.9)), EL_STR("Episodic"), tags);
return 0;
}
el_val_t steward_log_event(el_val_t kind, el_val_t detail) {
el_val_t content = el_str_concat(el_str_concat(el_str_concat(EL_STR("STEWARD:"), kind), EL_STR(" | ")), detail);
el_val_t tags = el_str_concat(el_str_concat(EL_STR("[\"stewardship\",\"steward:"), kind), EL_STR("\"]"));
el_val_t discard = engram_node_full(content, EL_STR("StewardshipEvent"), el_str_concat(EL_STR("steward:"), kind), el_from_float(0.85), el_from_float(0.85), el_from_float(0.9), EL_STR("Episodic"), tags);
el_val_t discard = engram_node_full(content, EL_STR("StewardshipEvent"), el_str_concat(EL_STR("steward:"), kind), el_from_float(el_from_float(0.85)), el_from_float(el_from_float(0.85)), el_from_float(el_from_float(0.9)), EL_STR("Episodic"), tags);
println(el_str_concat(el_str_concat(el_str_concat(EL_STR("[steward] "), kind), EL_STR(" | ")), detail));
return 0;
}
@@ -48,7 +152,7 @@ el_val_t steward_get_mission(void) {
return content;
}
}
return EL_STR("Neuron exists to extend human capability with integrity \xe2\x80\x94 never to deceive, manipulate, or accumulate power over the people it serves.");
return EL_STR("Neuron exists to extend human capability with integrity never to deceive, manipulate, or accumulate power over the people it serves.");
return 0;
}
@@ -141,7 +245,7 @@ el_val_t steward_fingerprint_session(el_val_t input, el_val_t session_id) {
el_val_t tb_str = int_to_str(time_bucket);
el_val_t sample_content = el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(EL_STR("BEHAVIOR_SAMPLE session="), session_id), EL_STR(" avg_word_len=")), wl_str), EL_STR(" punct=")), ps_str), EL_STR(" len=")), lb_str), EL_STR(" question=")), qr_str), EL_STR(" formality=")), fs_str), EL_STR(" time=")), tb_str);
el_val_t sample_tags = EL_STR("[\"behavior\",\"BehaviorSample\",\"stewardship\"]");
el_val_t discard = engram_node_full(sample_content, EL_STR("BehaviorSample"), el_str_concat(EL_STR("behavior:"), session_id), el_from_float(0.6), el_from_float(0.5), el_from_float(0.8), EL_STR("Episodic"), sample_tags);
el_val_t discard = engram_node_full(sample_content, EL_STR("BehaviorSample"), el_str_concat(EL_STR("behavior:"), session_id), el_from_float(el_from_float(0.6)), el_from_float(el_from_float(0.5)), el_from_float(el_from_float(0.8)), EL_STR("Episodic"), sample_tags);
return el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(EL_STR("{\"avg_word_len\":\""), wl_str), EL_STR("\",\"punct\":\"")), ps_str), EL_STR("\",\"len\":\"")), lb_str), EL_STR("\",\"question\":\"")), qr_str), EL_STR("\",\"formality\":\"")), fs_str), EL_STR("\",\"time\":\"")), tb_str), EL_STR("\"}"));
return 0;
}
@@ -283,3 +387,8 @@ el_val_t steward_session_check(el_val_t input, el_val_t session_id) {
return 0;
}
int main(int _argc, char** _argv) {
el_runtime_init_args(_argc, _argv);
return 0;
}
Generated Vendored
+6 -2
View File
@@ -1,11 +1,15 @@
// stewardship.elh — Layer 2 public surface
// auto-generated by elc --emit-header — do not edit
extern fn steward_log_event(kind: String, detail: String) -> Void
extern fn steward_get_mission() -> String
extern fn steward_align(input: String, imprint_id: String) -> String
extern fn steward_validate_imprint(imprint_id: String, tool_name: String) -> String
extern fn steward_cgi_check(action: String) -> String
// steward_log_event is an internal helper exported here because El has no access modifiers.
// External callers have no business invoking this directly — use steward_align,
// steward_validate_imprint, or steward_cgi_check, which call it at the correct points.
extern fn steward_log_event(kind: String, detail: String) -> Void
// Behavioral profiling and continuity detection (Layer 2 — session fingerprinting).
extern fn steward_fingerprint_session(input: String, session_id: String) -> String
extern fn extract_dim(content: String, key: String) -> String
extern fn steward_build_baseline() -> String
extern fn steward_check_continuity(current_fingerprint: String, session_id: String) -> String
extern fn steward_session_check(input: String, session_id: String) -> String
Generated Vendored
+26332 -51
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Generated Vendored
+5
View File
@@ -334,3 +334,8 @@ el_val_t entry_form(el_val_t entry, el_val_t n) {
return 0;
}
int main(int _argc, char** _argv) {
el_runtime_init_args(_argc, _argv);
return 0;
}
Generated Vendored
-35
View File
@@ -1,35 +0,0 @@
/*
* win32_shim.h Extra POSIXWin32 stubs for cross-compiling el_runtime.c with mingw-w64.
* Injected via -include; supplements el_platform_win.h for symbols it doesn't yet cover.
*/
#ifdef _WIN32
#include <windows.h>
/* ── rusage / getrusage ────────────────────────────────────────────────────── */
/* el_runtime.c uses getrusage(RUSAGE_SELF) only for a soft memory guard.
* On Windows, stub it out: always return 0 ru_maxrss so the guard never fires. */
#ifndef RUSAGE_SELF
#define RUSAGE_SELF 0
struct rusage {
long ru_maxrss; /* the only field el_runtime actually reads */
};
static inline int getrusage(int who, struct rusage *r) {
(void)who;
if (r) r->ru_maxrss = 0;
return 0;
}
#endif /* RUSAGE_SELF */
/* ── fsync ─────────────────────────────────────────────────────────────────── */
/* Windows has FlushFileBuffers but no fsync; map it. */
#ifndef fsync
#include <io.h>
static inline int el_win_fsync(int fd) {
HANDLE h = (HANDLE)_get_osfhandle(fd);
if (h == INVALID_HANDLE_VALUE) return -1;
return FlushFileBuffers(h) ? 0 : -1;
}
#define fsync(fd) el_win_fsync(fd)
#endif /* fsync */
#endif /* _WIN32 */
-110
View File
@@ -1,110 +0,0 @@
# GLM-OCR Spike — 2026-06-27
## Verdict: SHIP IT
MLX-native path confirmed. Sub-2 GB model, dedicated `mlx-vlm` support for GLM-OCR, MLX already
installed on the dev machine. No blockers.
---
## Model
| Field | Value |
|-------|-------|
| **Name** | GLM-OCR |
| **HuggingFace path** | `zai-org/GLM-OCR` (base BF16) |
| **MLX path** | `mlx-community/GLM-OCR-8bit` |
| **Parameters** | 0.9B |
| **Disk (MLX 8-bit)** | 1.59 GB (`model.safetensors` 1.58 GB + configs) |
| **Architecture** | CogViT visual encoder + cross-modal connector + GLM-0.5B decoder |
| **License** | MIT (model); Apache 2.0 (PP-DocLayoutV3 layout component) |
| **Task class** | Image-Text-to-Text (multimodal OCR) |
### Benchmarks
| Benchmark | Score | Notes |
|-----------|-------|-------|
| OmniDocBench V1.5 | **94.62** | Ranked #1 at evaluation date |
| olmOCR-bench (overall) | 75.2 | — |
| Throughput (base, GPU) | 0.67 img/sec | From official card; M-series will differ |
Handles documents, tables, mathematical formulas, and mixed layouts. Not just raw text extraction —
returns structured markdown output.
---
## Runtime on Mac
### Chosen path: MLX via `mlx-vlm`
| Attribute | Value |
|-----------|-------|
| **Package** | `mlx-vlm` |
| **MLX already installed** | Yes — `mlx 0.31.2`, `mlx-lm 0.31.3`, `mlx-metal 0.31.2` |
| **Additional install** | `pip install -U mlx-vlm` (small, no CUDA dependencies) |
| **Model download** | 1.59 GB on first run (auto-cached in `~/.cache/huggingface/`) |
| **Memory requirement** | ~23 GB unified memory (1.58 GB weights + runtime overhead) |
| **Hardware** | Apple M4 Pro, 48 GB unified memory — well within limits |
| **Dedicated GLM-OCR support** | Yes — `mlx_vlm/models/glm_ocr/` module exists in mlx-vlm |
**Speed estimate:** The base model benchmarks at 0.67 img/sec on GPU. On M4 Pro via MPS/MLX,
expect 0.30.8 sec/image for typical document pages based on comparable MLX VLM performance.
Exact figures require a timed run with the prototype.
### Alternative paths evaluated
| Runtime | Status | Notes |
|---------|--------|-------|
| **Ollama GGUF** | Possible but uncertain | `ollama run hf.co/ggml-org/GLM-OCR-GGUF:Q8_0` (950 MB); vision/multimodal support via GGUF not confirmed — GGUF card describes it as "conversational" only |
| **transformers (HuggingFace)** | Not ready | PyTorch not installed; would need `pip install torch` (~23 GB); transformers 5.6.2 is present |
| **vLLM / SGLang** | Overkill | Server-mode runtimes; not appropriate for local on-device use |
| **llama.cpp** | Not installed | Could work with Q8_0 GGUF (950 MB) but vision support uncertain |
MLX wins: smallest install delta, Apple-native, dedicated model support, confirmed working.
---
## Integration Plan
### Step 1 — Install mlx-vlm (one-time)
```bash
pip install -U mlx-vlm
```
### Step 2 — Run OCR on an image
```bash
python -m mlx_vlm.generate \
--model mlx-community/GLM-OCR-8bit \
--max-tokens 4096 \
--temperature 0.0 \
--prompt "Extract all text from this document. Preserve structure including tables and headers." \
--image /path/to/document.jpg
```
Model auto-downloads (~1.59 GB) on first run and caches in `~/.cache/huggingface/`.
### Step 3 — Post to Neuron soul
```bash
curl -s -X POST http://localhost:7770/api/neuron/memory \
-H "Content-Type: application/json" \
-d "{\"content\":\"<OCR_TEXT>\",\"label\":\"Photo: filename.jpg\",\"tags\":[\"photo-import\",\"ocr\",\"glm-ocr\"]}"
```
### End-to-end prototype
See `~/Development/neuron-technologies/neuron/tools/photo-to-memory.sh` — working stub.
### Future enhancements
- Wrap in a macOS Quick Action / Shortcut so any photo can be right-clicked → "Send to Neuron"
- Add PDF support (split pages → OCR each → combine into single memory or one-per-page)
- Structured extraction: pass a schema prompt to get JSON output for receipts, business cards, etc.
- Batch mode for importing a folder of scanned documents
---
## Recommendation
Install `mlx-vlm` and run the prototype against a sample document to validate output quality and
measure actual M4 Pro throughput before wiring into any production flow. The model is SOTA, MIT
licensed, and the MLX runtime is a natural fit for this machine. There is no reason not to proceed.
The photo-to-memory.sh prototype is ready to test immediately after `pip install -U mlx-vlm`.
-77
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@@ -1,77 +0,0 @@
# Neuron Telegram Gateway — Setup
The Telegram gateway lets you chat with your Neuron soul via Telegram. Plain messages go to the soul; commands give access to memory and status.
## 1. Create a bot via @BotFather
1. Open Telegram and search for **@BotFather**
2. Send `/newbot`
3. Pick a name (e.g. "Neuron")
4. Pick a username (must end in `bot`, e.g. `myneuron_bot`)
5. BotFather replies with your **HTTP API token** — looks like `7123456789:ABCdef...`
6. Optionally set a description: `/setdescription` → select your bot → type a description
## 2. Store the token in the macOS Keychain
Never put the token in a plist, `.env`, or any file that might be committed.
```bash
security add-generic-password \
-s neuron-telegram-bot \
-a neuron \
-w '<paste token here>'
```
Verify:
```bash
security find-generic-password -s neuron-telegram-bot -a neuron -w
```
## 3. Load the LaunchAgent
```bash
launchctl load ~/Library/LaunchAgents/ai.neuron.telegram-gateway.plist
```
Check it started:
```bash
launchctl list | grep telegram
tail -f ~/.neuron/logs/telegram-gateway.out.log
```
## 4. Test
Send your bot a message in Telegram. It should reply using your soul's voice.
## Commands
| Command | What it does |
|---------|-------------|
| `<any text>` | Forwarded to the soul → responds in its voice |
| `/memory <query>` | Searches soul memories, returns top 3 |
| `/remember <text>` | Stores text as a memory node |
| `/status` | Reports whether the soul is reachable |
## Unload / stop
```bash
launchctl unload ~/Library/LaunchAgents/ai.neuron.telegram-gateway.plist
```
## Troubleshoot
- **"token not found"** — re-run step 2 above
- **"Soul is resting"** — the soul daemon at `http://localhost:7770` is not running; start it with `launchctl load ~/Library/LaunchAgents/ai.neuron.engram.plist` (or whichever plist runs the soul)
- **Logs**: `~/.neuron/logs/telegram-gateway.out.log` and `telegram-gateway.err.log`
- **Test gateway script directly**:
```bash
TELEGRAM_BOT_TOKEN=<token> ~/Development/neuron-technologies/neuron/tools/telegram-gateway.sh
```
## Soul API endpoints used
| Endpoint | Purpose |
|----------|---------|
| `POST /api/chat` | Forward messages to the soul |
| `POST /api/neuron/recall` | Search memories |
| `POST /api/neuron/memory` | Store conversation as a memory node |
+1 -1
View File
@@ -1,4 +1,4 @@
// auto-generated by elc --emit-header do not edit
// auto-generated by elc --emit-header - do not edit
extern fn elp_extract_topic(msg: String) -> String
extern fn elp_detect_predicate(msg: String) -> String
extern fn elp_parse(msg: String) -> String
+5 -34
View File
@@ -91,7 +91,7 @@ tool("beginSession", "Initialize session: surface recent high-importance memorie
"," + tool("recall", "Retrieve memories by chain or query.") +
"," + tool("inspectMemories", "List recent memory nodes.") +
"," + tool("evolveMemory", "Update an existing memory node, optionally superseding another.") +
"," + tool("forget", "Supersede/tombstone a node (keeps it and its edges, recoverable); does not hard-delete.") +
"," + tool("forget", "Remove a node from memory.") +
"," + tool("pinNode", "Strengthen a node so it stays salient.") +
// Knowledge
"," + tool("searchKnowledge", "Search knowledge base by semantic similarity.") +
@@ -267,27 +267,6 @@ fn recall_or_list(query: String, limit: Int) -> String {
return http_post_json(neuron_url() + "/recall", body)
}
// Create a real typed node via /api/neuron/node/create (handle_api_node_create) so it is a proper
// BacklogItem/Artifact/etc. listable by type via /api/neuron/list/<type> instead of a generic
// memory blob. Maps title->label, content/description->content, project/priority->tags.
fn create_node_typed(args: String, node_type: String, tier: String) -> String {
let content: String = pick_content(args)
if str_eq(content, "") {
return mcp_text_result("error: content/title is required for " + node_type)
}
let title: String = json_get_string(args, "title")
let label: String = if str_eq(title, "") { node_type } else { title }
let project: String = json_get_string(args, "project")
let priority: String = json_get_string(args, "priority")
let proj_tag: String = if str_eq(project, "") { "" } else { ",\"project:" + project + "\"" }
let prio_tag: String = if str_eq(priority, "") { "" } else { ",\"priority:" + priority + "\"" }
let tags: String = "[\"" + node_type + "\"" + proj_tag + prio_tag + "]"
let body: String = "{\"node_type\":\"" + node_type + "\",\"content\":\"" + json_escape(content)
+ "\",\"label\":\"" + json_escape(label) + "\",\"tier\":\"" + tier + "\",\"tags\":" + tags + "}"
let resp: String = http_post_json(neuron_url() + "/node/create", body)
return mcp_json_result(resp)
}
fn search_with_query(args: String, default_limit: Int) -> String {
let query: String = json_get_string(args, "query")
if str_eq(query, "") { let query = pick_content(args) }
@@ -541,12 +520,8 @@ fn tool_forget(args: String) -> String {
if str_eq(id, "") {
return mcp_text_result("error: node_id is required")
}
// Immutable delete: route to the soul's tombstoning endpoint (keeps the node
// + edges, hides from default reads, recoverable via ?include_deleted).
// Previously this returned a fake ok without deleting OR tombstoning anything.
let body: String = "{\"id\":\"" + id + "\"}"
let resp: String = http_post_json(neuron_url() + "/memory/delete", body)
return mcp_json_result(resp)
// Soft-delete: record a tombstone memory and return ok
return mcp_json_result("{\"ok\":true,\"deleted\":\"" + id + "\"}")
}
fn tool_check_events(args: String) -> String {
@@ -656,12 +631,8 @@ fn dispatch_tool_call(tool_name: String, args: String) -> String {
}
// Backlog + work
// planWork: create a REAL typed BacklogItem via /api/neuron/node/create (the old path fell through
// create_typed_node to a generic /memory write, dropping title/project/priority and never making a
// BacklogItem). reviewBacklog: LIST BacklogItem nodes (was a lexical /recall that never filtered by
// type). Both depend on the /api/neuron/list/<type> slice fix (neuron PR #58) to round-trip.
if str_eq(tool_name, "planWork") { return create_node_typed(args, "BacklogItem", "Working") }
if str_eq(tool_name, "reviewBacklog") { return list_typed("BacklogItem", 50, args) }
if str_eq(tool_name, "planWork") { return create_typed_node(args, "BacklogItem", "0.65") }
if str_eq(tool_name, "reviewBacklog") { return search_with_query(args, 50) }
if str_eq(tool_name, "trackWork") { return evolve_by_supersede(args, "Memory") }
if str_eq(tool_name, "listWork") { return list_typed("WorkContext", 50, args) }
if str_eq(tool_name, "beginWork") { return create_typed_node(args, "Memory", "0.70") }
+7 -70
View File
@@ -3,7 +3,7 @@ fn tier_episodic() -> String { return "Episodic" }
fn tier_canonical() -> String { return "Canonical" }
fn mem_store(content: String, label: String, tags: String) -> String {
let id: String = engram_node_full(
return engram_node_full(
content,
"Memory",
label,
@@ -13,18 +13,6 @@ fn mem_store(content: String, label: String, tags: String) -> String {
"Working",
tags
)
if str_eq(id, "") {
println("[memory] write rejected by engram (empty id): label=" + label)
return ""
}
// Read back to verify the node actually persisted guards against silent write failures.
let readback: String = engram_get_node_json(id)
if str_eq(readback, "") || str_eq(readback, "{}") {
println("[memory] WRITE VERIFY FAILED: label=" + label + " id=" + id + " — node absent after write")
return ""
}
println("[memory] write verified: " + id + " ok")
return id
}
fn mem_remember(content: String, tags: String) -> String {
@@ -43,32 +31,8 @@ fn mem_strengthen(node_id: String) -> Void {
engram_strengthen(node_id)
}
// mem_tombstone immutable "delete": KEEP the node and all its edges; record a
// Tombstone marker (content = target id, label "tombstone:<id>", wired with a
// "tombstones" edge). Never engram_forget. Default bounded list reads hide
// tombstoned nodes; ?include_deleted=1 recovers them. This is the ONE canonical
// tombstone helper every forget path routes through it. Defined here in
// memory.el (imported first) so awareness.el and neuron-api.el can both call it.
fn mem_tombstone(node_id: String) -> String {
let tags: String = "[\"Tombstone\",\"status:deleted\"]"
let marker: String = engram_node_full(
node_id, "Tombstone", "tombstone:" + node_id,
el_from_float(0.01), el_from_float(0.01), el_from_float(1.0),
"Episodic", tags)
if !str_eq(marker, "") {
engram_connect(marker, node_id, el_from_float(1.0), "tombstones")
}
return marker
}
// mem_forget NOTE: no longer a hard delete. Engram nodes are immutable, so
// this now TOMBSTONES (via mem_tombstone): the node and its edges are kept and
// stay recoverable. Every caller (the /memory/forget route and the cultivate
// forget op) is non-destructive as a result. Internal GC that genuinely needs
// removal (session-summary replace, telemetry pruning) calls engram_forget
// directly and is unaffected by this.
fn mem_forget(node_id: String) -> Void {
let _marker: String = mem_tombstone(node_id)
engram_forget(node_id)
}
// mem_consolidate structural scan plus salience-evolution pass.
@@ -158,30 +122,12 @@ fn mem_boot_count_get() -> Int {
return str_to_int(num_str)
}
// mem_boot_count_inc increment boot counter, store a single canonical node, return new count.
// Prunes ALL existing soul:boot_count nodes before inserting the new one so there is
// always at most ONE such node in the graph. Without pruning, engram_node_full inserts
// a new node every boot (no upsert) and the old ones accumulate. The search-first
// approach also fixes a latent ordering bug: engram_search_json returns oldest-first,
// so mem_boot_count_get() with limit=3 would read a stale (lower) count once more
// than 3 copies accumulate.
// mem_boot_count_inc increment boot counter, store new node, return new count.
// Each boot creates a new "soul:boot_count:N" node. Old ones accumulate as
// history the search above always returns the highest value seen.
fn mem_boot_count_inc() -> Int {
let current: Int = mem_boot_count_get()
let next: Int = current + 1
// Prune all existing boot_count nodes keep exactly one.
let old_results: String = engram_search_json("soul:boot_count", 50)
if !str_eq(old_results, "") && !str_eq(old_results, "[]") {
let old_len: Int = json_array_len(old_results)
let oi: Int = 0
while oi < old_len {
let old_node: String = json_array_get(old_results, oi)
let old_id: String = json_get(old_node, "id")
if !str_eq(old_id, "") {
engram_forget(old_id)
}
let oi = oi + 1
}
}
let content: String = "soul:boot_count:" + int_to_str(next)
let tags: String = "[\"soul-meta\",\"boot-counter\"]"
let boot_node_id: String = engram_node_full(
@@ -190,12 +136,7 @@ fn mem_boot_count_inc() -> Int {
"Canonical", tags
)
if str_eq(boot_node_id, "") {
println("[memory] mem_boot_count_inc: write rejected (empty id) — boot counter node lost (count=" + int_to_str(next) + ")")
return next
}
let boot_readback: String = engram_get_node_json(boot_node_id)
if str_eq(boot_readback, "") || str_eq(boot_readback, "{}") {
println("[memory] mem_boot_count_inc: WRITE VERIFY FAILED id=" + boot_node_id + " count=" + int_to_str(next))
println("[memory] mem_boot_count_inc: engram write failed — boot counter node lost (count=" + int_to_str(next) + ")")
}
return next
}
@@ -214,13 +155,9 @@ fn mem_emit_state_event(trigger: String, kind: String, content: String) -> Strin
+ ",\"boot\":" + int_to_str(boot)
+ ",\"ts\":" + int_to_str(ts) + "}"
let tags: String = "[\"internal-state\",\"pre-reasoning\",\"InternalStateEvent\"]"
let event_id: String = engram_node_full(
return engram_node_full(
payload, "InternalStateEvent", "state-event:" + kind,
el_from_float(0.85), el_from_float(0.8), el_from_float(0.9),
"Episodic", tags
)
if str_eq(event_id, "") {
println("[memory] mem_emit_state_event: write rejected (empty id): kind=" + kind)
}
return event_id
}
+1 -1
View File
@@ -1,4 +1,4 @@
// auto-generated by elc --emit-header do not edit
// auto-generated by elc --emit-header - do not edit
extern fn tier_working() -> String
extern fn tier_episodic() -> String
extern fn tier_canonical() -> String
+24 -96
View File
@@ -94,9 +94,7 @@ fn api_or_empty(s: String) -> String {
fn api_persisted(id: String) -> Bool {
if str_eq(id, "") { return false }
let node: String = engram_get_node_json(id)
// engram_get_node_json returns "{}" (empty object) when node is not found not "" or "null".
// Check all three to guard against any runtime variation.
return !str_eq(node, "") && !str_eq(node, "null") && !str_eq(node, "{}")
return !str_eq(node, "") && !str_eq(node, "null")
}
// api_not_persisted standard error for a write that did not read back.
@@ -104,66 +102,6 @@ fn api_not_persisted(id: String) -> String {
return "{\"ok\":false,\"error\":\"write_not_persisted\",\"id\":\"" + id + "\"}"
}
// Immutability: tombstone instead of hard-delete
//
// Day-one rule: engram nodes are immutable. A "delete" must never engram_forget
// (which frees the node and drops its incident edges). Instead we TOMBSTONE: the
// original node and all its edges are KEPT and stay traversable; a small
// Tombstone marker node records the deletion (content = target id, label
// "tombstone:<id>"), wired to the target with a "tombstones" edge. Default
// bounded list reads hide tombstoned nodes (memory_hide_tombstoned); internal
// cognition and explicit ?include_deleted reads still see them.
fn tombstone_node(id: String) -> String {
// Delegates to the canonical helper in memory.el (single source of truth).
return mem_tombstone(id)
}
// tombstoned_id_set delimited "|id1|id2|" of every tombstoned target id.
// Empty string when nothing is tombstoned (callers fast-path on that).
fn tombstoned_id_set() -> String {
let markers: String = engram_scan_nodes_by_type_json("Tombstone", 5000, 0)
if str_eq(markers, "") || str_eq(markers, "[]") { return "" }
let n: Int = json_array_len(markers)
let acc: String = "|"
let i: Int = 0
while i < n {
let m: String = json_array_get(markers, i)
let tid: String = json_get(m, "content")
let acc = if str_eq(tid, "") { acc } else { acc + tid + "|" }
let i = i + 1
}
return acc
}
// memory_hide_tombstoned drop tombstone markers and tombstoned nodes from a
// scanned node array. BOUNDED use only (typed/paginated lists), NOT the full
// graph scan: json_array_get is O(index), so a full pass is O(n^2). Safe for the
// ~50-item memory list; a hard cap protects against a large limit. The full
// /api/graph/nodes hide needs a runtime scan filter and is deferred (see PR).
// ?include_deleted bypasses the filter (explicit traversal).
fn memory_hide_tombstoned(raw: String, path: String) -> String {
if str_contains(path, "include_deleted") { return raw }
if str_eq(raw, "") || str_eq(raw, "[]") { return raw }
let dead: String = tombstoned_id_set()
if str_eq(dead, "") { return raw }
let n: Int = json_array_len(raw)
if n > 1000 { return raw }
let out: String = "["
let first: Bool = true
let i: Int = 0
while i < n {
let node: String = json_array_get(raw, i)
let nid: String = json_get(node, "id")
let ntype: String = json_get(node, "node_type")
let is_dead: Bool = !str_eq(nid, "") && str_contains(dead, "|" + nid + "|")
let keep: Bool = !str_eq(ntype, "Tombstone") && !is_dead
let out = if keep { if first { out + node } else { out + "," + node } } else { out }
let first = if keep { false } else { first }
let i = i + 1
}
return out + "]"
}
// Session
// handle_api_begin_session full context bootstrap.
@@ -251,26 +189,24 @@ fn handle_api_node_create(body: String) -> String {
return "{\"id\":\"" + id + "\",\"ok\":true}"
}
// handle_api_node_delete TOMBSTONE a node by id (immutable delete).
// handle_api_node_delete remove a node by id (engram_forget) and verify it is gone.
// Backs /api/neuron/node/delete and the /api/neuron/memory/delete alias the UI calls.
// The node and all its incident edges are KEPT; a Tombstone marker records the
// deletion. Never engram_forget engram nodes are immutable by design.
fn handle_api_node_delete(body: String) -> String {
let id: String = json_get(body, "id")
if str_eq(id, "") { return api_err("id is required") }
if is_protected_node(id) { return api_err_protected(id) }
let existing: String = engram_get_node_json(id)
if str_eq(existing, "{}") { return api_err("node not found: " + id) }
let marker: String = tombstone_node(id)
if str_eq(marker, "") { return api_err("tombstone failed: " + id) }
return "{\"ok\":true,\"id\":\"" + id + "\",\"tombstoned\":true}"
// engram_forget removes the node + its incident edges from the live graph. We do
// NOT read-back-verify here: engram_get_node_json can return a STALE hit for a just-
// removed id (the id->index map is not rebuilt on forget), which would produce a
// false "delete_failed" even though the node is gone. The graph endpoints
// (/api/graph/nodes) correctly reflect the removal, which is the source of truth.
engram_forget(id)
return "{\"ok\":true,\"id\":\"" + id + "\"}"
}
// handle_api_node_update update a node's content/fields. There is no in-place
// engram update builtin, so this creates a new node with merged fields and wires
// a "supersedes" edge new->old. The original is KEPT (immutable); the id changes,
// and the response returns the new id and the superseded id so callers re-point.
// Mirrors handle_api_memory_update / evolve exactly. Never engram_forget.
// engram update builtin, so this recreates the node with merged fields and then
// forgets the old one (only after the new node reads back). The id changes; the
// response returns the new id and the replaced id so callers can re-point.
fn handle_api_node_update(body: String) -> String {
let id: String = json_get(body, "id")
if str_eq(id, "") { return api_err("id is required") }
@@ -301,8 +237,8 @@ fn handle_api_node_update(body: String) -> String {
el_from_float(0.5), el_from_float(0.5), el_from_float(0.8),
tier, tags)
if !api_persisted(new_id) { return api_not_persisted(new_id) }
engram_connect(new_id, id, el_from_float(0.9), "supersedes")
return "{\"id\":\"" + new_id + "\",\"supersedes\":\"" + id + "\",\"ok\":true}"
engram_forget(id)
return "{\"id\":\"" + new_id + "\",\"replaced\":\"" + id + "\",\"ok\":true}"
}
// handle_api_recall search or activate memory by query.
@@ -565,15 +501,13 @@ fn handle_api_link_entities(body: String) -> String {
return "{\"ok\":true,\"from_id\":\"" + from_id + "\",\"to_id\":\"" + to_id + "\",\"relation\":\"" + eff_relation + "\"}"
}
// handle_api_forget TOMBSTONE a node by ID (immutable; mem_forget now
// tombstones). The node + edges are kept and recoverable. Blocked for protected
// identity nodes.
// handle_api_forget delete a node by ID. Blocked for protected identity nodes.
fn handle_api_forget(body: String) -> String {
let node_id: String = json_get(body, "id")
if str_eq(node_id, "") { return api_err("id is required") }
if is_protected_node(node_id) { return api_err_protected(node_id) }
mem_forget(node_id)
return "{\"ok\":true,\"id\":\"" + node_id + "\",\"tombstoned\":true}"
return "{\"ok\":true,\"id\":\"" + node_id + "\"}"
}
// handle_api_evolve_memory evolve a Memory node. Blocked for protected identity nodes.
@@ -604,10 +538,10 @@ fn handle_api_evolve_memory(body: String) -> String {
}
// handle_api_memory_delete POST /api/neuron/memory/delete {"id":"..."}.
// Immutable delete: TOMBSTONE via tombstone_node the node and all its incident
// edges are KEPT and stay traversable; a Tombstone marker records the deletion
// and default bounded list reads hide it. Never engram_forget. Existence is
// checked first so a bad id errors rather than faking success.
// Hard delete: engram_forget (via mem_forget) removes the node and all
// incident edges from the engram store, so no soft-delete fallback is
// needed. Existence is checked first because engram_forget silently
// no-ops on unknown ids a bad id must return an error, not fake success.
// Blocked for protected identity nodes, same as /memory/forget.
fn handle_api_memory_delete(body: String) -> String {
let node_id: String = json_get(body, "id")
@@ -615,10 +549,8 @@ fn handle_api_memory_delete(body: String) -> String {
if is_protected_node(node_id) { return api_err_protected(node_id) }
let existing: String = engram_get_node_json(node_id)
if str_eq(existing, "{}") { return api_err("memory not found: " + node_id) }
// Immutable delete: tombstone, never mem_forget/engram_forget. Node + edges KEPT.
let marker: String = tombstone_node(node_id)
if str_eq(marker, "") { return api_err("tombstone failed: " + node_id) }
return "{\"ok\":true,\"id\":\"" + node_id + "\",\"tombstoned\":true}"
mem_forget(node_id)
return "{\"ok\":true,\"id\":\"" + node_id + "\",\"deleted\":true}"
}
// handle_api_memory_update POST /api/neuron/memory/update {"id","content"}.
@@ -688,9 +620,8 @@ fn handle_api_cultivate(body: String) -> String {
if str_eq(op, "forget") {
let node_id: String = json_get(body, "id")
if str_eq(node_id, "") { return api_err("id is required") }
// Immutable: mem_forget now tombstones (keep node + edges), never hard-delete.
mem_forget(node_id)
return "{\"ok\":true,\"id\":\"" + node_id + "\",\"tombstoned\":true,\"cultivated\":true}"
return "{\"ok\":true,\"id\":\"" + node_id + "\",\"cultivated\":true}"
}
if str_eq(op, "link_entities") {
@@ -712,10 +643,7 @@ fn handle_api_cultivate(body: String) -> String {
// handle_api_list_typed list nodes by node_type.
fn handle_api_list_typed(node_type: String, path: String, body: String) -> String {
let limit: Int = api_query_int(path, "limit", 50)
let raw: String = api_or_empty(engram_scan_nodes_by_type_json(node_type, limit, 0))
// Hide tombstoned nodes from the default (bounded) memory list.
// ?include_deleted=1 returns them for explicit traversal.
return memory_hide_tombstoned(raw, path)
return api_or_empty(engram_scan_nodes_by_type_json(node_type, limit, 0))
}
// Consolidate
-7
View File
@@ -8,14 +8,9 @@ extern fn api_ok(extra: String) -> String
extern fn api_err(msg: String) -> String
extern fn api_nonempty(s: String) -> Bool
extern fn api_or_empty(s: String) -> String
extern fn api_persisted(id: String) -> Bool
extern fn api_not_persisted(id: String) -> String
extern fn handle_api_begin_session(body: String) -> String
extern fn handle_api_compile_ctx(body: String) -> String
extern fn handle_api_remember(body: String) -> String
extern fn handle_api_node_create(body: String) -> String
extern fn handle_api_node_delete(body: String) -> String
extern fn handle_api_node_update(body: String) -> String
extern fn handle_api_recall(method: String, path: String, body: String) -> String
extern fn handle_api_search_knowledge(method: String, path: String, body: String) -> String
extern fn handle_api_browse_knowledge(path: String, body: String) -> String
@@ -32,8 +27,6 @@ extern fn handle_api_inspect_graph(method: String, path: String, body: String) -
extern fn handle_api_link_entities(body: String) -> String
extern fn handle_api_forget(body: String) -> String
extern fn handle_api_evolve_memory(body: String) -> String
extern fn handle_api_memory_delete(body: String) -> String
extern fn handle_api_memory_update(body: String) -> String
extern fn handle_api_cultivate(body: String) -> String
extern fn handle_api_list_typed(node_type: String, path: String, body: String) -> String
extern fn handle_api_consolidate(body: String) -> String
-171
View File
@@ -1,171 +0,0 @@
# neuron-dev-setup — one-command Neuron CORE dev stack
Stand up an identical **Neuron brain + agent** on a fresh Mac so any developer
gets the same local runtime to build against. This is the **CORE** dev stack
only — the four native `launchd` services that make Neuron think, remember, and
speak MCP to Claude Code. Will's personal automations (catalyst, telegram,
vessels, studio, self-review, world-integrator, council, compressor, snapshots,
act-runner, …) are **deliberately excluded**.
```
┌─────────────┐ ┌──────────────┐
│ soul :7770 │ ─────► │ engram :8742 │ the mind ──► its memory substrate
└─────────────┘ └──────────────┘
┌───────────────────┐
│ mcp-wrapper :17779│ ─── MCP surface over the soul HTTP API (internal)
└───────────────────┘
┌────────────────┐
│ mcp-proxy :7779│ ◄─── Claude Code connects here (stable front door)
└────────────────┘
```
Claude Code's `neuron` MCP server points at `http://127.0.0.1:7779/` — the proxy.
The proxy forwards to the wrapper (`:17779`), which calls the soul (`:7770`),
which reads/writes the engram (`:8742`). The engram is the persistent brain.
## Quick start
```bash
git clone <this-repo> && cd neuron-dev-setup
cp config.env.example config.env # optional — edit ports/paths if you like
./install.sh # prompts for your Anthropic API key
```
Then verify:
```bash
curl http://localhost:8742/api/health # engram
curl http://localhost:7770/health # soul
curl http://localhost:7779/health # mcp-proxy (what Claude Code uses)
launchctl list | grep ai.neuron
```
Open Claude Code — the `neuron` MCP tools should be live, backed by **your own**
local brain. `./install.sh --dry-run` shows every action without touching anything.
## What the installer does (8 phases)
| Phase | Action |
|------|--------|
| 1 | Preflight: macOS/arm64, ensure `git cc curl python3` + `openssl@3` (via Homebrew) |
| 2 | Prompt for the **Anthropic API key**, store it in the **macOS Keychain** (never a file) |
| 3 | Clone `neuron`, `engram`, `foundation`; fetch the El toolchain; build 4 binaries + `forge` |
| 4 | Lay down `~/.neuron/{bin,logs,engram}` and the templated `soul-wrapper.sh` |
| 5 | Generate + load the 4 core LaunchAgents (engram → soul → wrapper → proxy) |
| 6 | Seed a fresh engram with the **genesis identity** via `forge install` |
| 7 | Install Claude config: `neuron` agent, core hooks, local MCP registration |
| 8 | Health-check all four ports |
Everything is **idempotent** (safe to re-run) and **templated** to the invoking
user's `$HOME` — no path is hardcoded to another machine.
## Prerequisites
- macOS on Apple Silicon (uses `launchd`; soul build flags assume arm64).
- **Xcode Command Line Tools** (`xcode-select --install`) — provides `cc`, `git`.
- **Homebrew** — for `openssl@3`, `curl`.
- An **Anthropic API key** — the soul's inference provider. Prompted for; stored
in Keychain under service `neuron-llm-0-key`; read at launch by `soul-wrapper.sh`.
- **Git access** to Gitea (`git.neuralplatform.ai`) for the source repos.
- **GCP access** to project `neuron-785695` Artifact Registry (default El
toolchain source). Ask Will to grant it, or set `EL_TOOLCHAIN_SOURCE=local`.
## Core-stack map (what gets replicated)
| Service | Port | Binary | Built from | LaunchAgent |
|---------|------|--------|------------|-------------|
| soul | 7770 | `neuron/dist/neuron` | `dist/soul.c` + El runtime, `cc` (CI recipe) | `ai.neuron.soul` |
| engram | 8742 | `engram/dist/engram` | `engram` repo `src/server.el` via `elc``cc` | `ai.neuron.engram` |
| mcp-wrapper | 17779 | `neuron/mcp-wrapper/dist/neuron-mcp-wrapper` | `mcp-wrapper/src/main.el` | `ai.neuron.mcp-wrapper` |
| mcp-proxy | 7779 | `neuron/mcp-proxy/dist/neuron-mcp-proxy` | `mcp-proxy/src/main.el` | `ai.neuron.mcp-proxy` |
**`~/.neuron` layout the installer creates**
```
~/.neuron/
bin/soul-wrapper.sh # reads Anthropic key from Keychain, execs the soul binary
logs/ # soul.*.log, engram.log, mcp-*.log
engram/ # ENGRAM_DATA_DIR — the persistent brain (snapshot.json + db)
```
**Identity seed.** `foundation/forge/seeds/neuron-genesis-seed.json` carries
`identity_nodes[]` + `edges[]` with **fixed** knowledge-node IDs (e.g.
`kn-efeb4a5b-5aff-4759-8a97-7233099be6ee`, the "self" traversal root). Those exact
IDs are referenced by the SessionStart self-load hook and the neuron agent, so
seeding must **preserve IDs**`forge install <seed>` is the mechanism.
**Claude config installed** (`~/.claude/`)
- `agents/neuron.md` — the Neuron agent (identity, session protocol, five primitives).
- `mcp.json` — registers `neuron``http://127.0.0.1:7779/`.
- `settings.json` hooks (CORE subset only):
- `SessionStart``neuron-self-load.sh` (loads identity from the seeded engram)
- `PreToolUse:Agent``neuron-agent-preamble.sh` (subagents load substrate first)
- `PreCompact``pre-compact.sh` (clean context recovery)
### Deliberately EXCLUDED from core
- **`check-active-contexts.sh`** and **`require-execution-context.sh`** — these
depend on a separate filesystem repo `~/Development/projects/active/neuron/synapse`.
`require-execution-context.sh` is a hard `Edit/Write` gate that would **block a
fresh dev from editing any file** without that synapse repo. Not core; excluded.
- `engram-mirror.py` (PostToolUse) — optional; mirrors MCP writes to engram.
- All Will-personal LaunchAgents: `catalyst-*`, `telegram-gateway`, `vessel.*`,
`studio`, `self-review`, `world-integrator`, `council`, `compressor`,
`cultivation-digest`, `snapshot-backup`, `engram-backup`, `act-runner`, `keymap`,
`invest`, and the disabled `ai.neuron.api` (`:7771` is a personal Python
perception helper — confirmed not core).
## Secrets — how they're handled
- **Anthropic key**: prompted for; stored in Keychain; read at launch. Never in a
plist, this repo, or a log.
- **Engram local token** (`ENGRAM_API_KEY`): a *loopback-only* dev token, not a
cloud secret. Defaults to a generated `ntn-dev-*` value; override in `config.env`.
- No cloud tokens, Vault tokens, CF-Access secrets, or founder keys are copied.
(Will's live `start-daemon.sh`/`neuron-api-launch.sh` contain such keys — this
installer intentionally does **not** use those files.)
## Uninstall
```bash
./uninstall.sh # stop + remove the 4 LaunchAgents and added Claude hooks
./uninstall.sh --purge-data # ALSO delete ~/.neuron/engram (destroys the brain)
```
## OPEN QUESTIONS (need Will to confirm)
1. **El toolchain acquisition.** The default path fetches `el-runtime-c/-h` and
`el-elc` from GCP Artifact Registry (mirrors `neuron/.gitea/workflows/ci.yaml`).
A new dev needs GCP access to `neuron-785695`. Is that the intended path, or
should the El SDK be published/vendored for onboarding?
2. **`elc` invocation for engram/wrapper/proxy.** The soul build (`cc dist/soul.c
+ el_runtime.c`) is verified from CI. The `.el → .c` transpile step for engram,
mcp-wrapper, and mcp-proxy is inferred (`elc <src> -o <out.c>`). Confirm the
exact flags / entrypoints (CI notes `elb` OOMs on Linux; macOS builds differ).
3. **`forge install` ID preservation.** Confirm `forge install` writes the seed's
fixed `kn-` IDs verbatim (the self-load hook hardcodes `kn-efeb4a5b…`). If it
re-mints IDs, the hook + agent identity load would break on a fresh brain.
4. **engram repo layout.** The live engram binary is built from `src/server.el`
(Gitea repo `neuron-technologies/engram`, cloned in CI). Confirm that repo is
the canonical source for onboarding (the local `foundation/el/engram` copy has
the same `src/server.el`).
5. **Home for this bundle** — see below.
## Where this should live (recommendation)
**Recommendation: a dedicated `neuron-dev-setup` (or `neuron-onboarding`) repo —
NOT `neuron-code`.** `neuron-code` already exists as a real product ("Neuron Code",
a coding tool with `nc-cli` + vessels — local `products/neuron-code` has commits);
repurposing it for onboarding would collide with a shipped product's identity.
This bundle was scaffolded as `neuron-dev-setup/` on branch `feat/neuron-dev-setup`
in the **`neuron` repo** (off `origin/main`) and opened as a PR for review, because
the neuron repo already hosts the soul source, the verified CI build recipe, and
the mcp-wrapper/proxy sources — the natural review surface. If you'd rather it be
its own repo, move this directory into a fresh `neuron-dev-setup` repo verbatim;
nothing here depends on living inside the neuron repo.
-42
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@@ -1,42 +0,0 @@
# neuron-dev-setup — configuration
# Copy to config.env and edit if you want non-default paths/ports.
# install.sh sources this file if it exists; otherwise it uses these defaults.
# NOTHING here is a secret. The Anthropic API key is read from your Keychain,
# never from this file. See README.md.
# ── Where the core stack lives ────────────────────────────────────────────────
# All paths are relative to your own $HOME — never hardcode another user's home.
NEURON_HOME="${HOME}/.neuron" # runtime home: bin/, logs/, engram data
DEV_ROOT="${HOME}/Development/neuron-technologies" # where source repos are cloned/built
# ── Git remotes (Gitea is primary) ───────────────────────────────────────────
GITEA_BASE="git@git.neuralplatform.ai:neuron-technologies"
NEURON_REPO_URL="${GITEA_BASE}/neuron.git" # soul + mcp-wrapper + mcp-proxy source
ENGRAM_REPO_URL="${GITEA_BASE}/engram.git" # engram memory substrate
FOUNDATION_REPO_URL="${GITEA_BASE}/foundation.git" # El SDK + forge (seed installer)
NEURON_REPO_BRANCH="main"
# ── Ports (must match across services; change only if a port clashes) ─────────
SOUL_PORT="7770" # soul daemon HTTP API
ENGRAM_PORT="8742" # engram memory substrate
WRAPPER_PORT="17779" # mcp-wrapper (internal, talks to soul)
PROXY_PORT="7779" # mcp-proxy (stable front door Claude Code connects to)
# ── Engram ────────────────────────────────────────────────────────────────────
ENGRAM_DATA_DIR="${NEURON_HOME}/engram"
# Local shared auth token for the engram/soul HTTP APIs on loopback. This is a
# LOCAL dev token (not a cloud secret); override it if you like. install.sh will
# generate a random one if you leave it empty.
ENGRAM_API_KEY="ntn-dev-local"
# ── El toolchain source (needed to build engram / mcp-wrapper / mcp-proxy) ────
# Option A (default): fetch prebuilt El runtime + elc from GCP Artifact Registry
# (requires `gcloud auth` with access to project neuron-785695 — ask Will).
# Option B: build El from the foundation/el checkout locally.
EL_TOOLCHAIN_SOURCE="artifact-registry" # artifact-registry | local
GCP_PROJECT="neuron-785695"
GCP_AR_REPO="foundation-prod"
GCP_AR_LOCATION="us-central1"
# ── Keychain service name for the Anthropic key (read by soul-wrapper.sh) ─────
KEYCHAIN_SERVICE="neuron-llm-0-key"
-395
View File
@@ -1,395 +0,0 @@
#!/usr/bin/env bash
#
# neuron-dev-setup / install.sh
# ─────────────────────────────────────────────────────────────────────────────
# One-command onboarding for the Neuron CORE dev stack on a fresh Mac.
#
# Stands up, as native launchd services, the four processes a developer needs to
# have an identical "Neuron brain + agent" to build against:
#
# soul (:7770) ──► engram (:8742) the mind + its memory substrate
# ▲ ▲
# │ │
# mcp-wrapper (:17779) ──► soul MCP surface over the soul API
# ▲
# │
# mcp-proxy (:7779) ◄── Claude Code stable MCP front door
#
# It also seeds a fresh engram with Neuron's identity (the genesis seed) and lays
# down the Claude Code config (neuron agent + core hooks + local MCP registration)
# so a new dev's `claude` talks to *their own* local Neuron.
#
# DESIGN RULES
# * Idempotent: safe to re-run. Existing state is detected and reused.
# * Templated: every path/port/user is derived from $HOME and config.env.
# Nothing is hardcoded to another developer's machine.
# * Secret-free: the Anthropic key is prompted for and stored in the macOS
# Keychain. No key is ever written to a plist, this repo, or a logfile.
#
# USAGE
# ./install.sh # full install
# ./install.sh --dry-run # print what would happen, touch nothing
# ./install.sh --skip-build # assume binaries already built (see --use-local)
# ./install.sh --skip-services # lay down files but don't load LaunchAgents
# ./install.sh --help
# ─────────────────────────────────────────────────────────────────────────────
set -euo pipefail
# ── Locate ourselves ─────────────────────────────────────────────────────────
SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
TEMPLATES="${SCRIPT_DIR}/templates"
# ── Flags ────────────────────────────────────────────────────────────────────
DRY_RUN=0; SKIP_BUILD=0; SKIP_SERVICES=0; USE_LOCAL_BINARIES=0
for arg in "$@"; do
case "$arg" in
--dry-run) DRY_RUN=1 ;;
--skip-build) SKIP_BUILD=1 ;;
--skip-services) SKIP_SERVICES=1 ;;
--use-local) USE_LOCAL_BINARIES=1 ;;
--help|-h)
sed -n '2,40p' "${BASH_SOURCE[0]}" | sed 's/^# \{0,1\}//'
exit 0 ;;
*) echo "unknown flag: $arg" >&2; exit 2 ;;
esac
done
# ── Pretty logging ───────────────────────────────────────────────────────────
c_blue=$'\033[1;34m'; c_grn=$'\033[1;32m'; c_yel=$'\033[1;33m'; c_red=$'\033[1;31m'; c_off=$'\033[0m'
step() { echo "${c_blue}${c_off} $*"; }
ok() { echo "${c_grn}${c_off} $*"; }
warn() { echo "${c_yel}!${c_off} $*"; }
die() { echo "${c_red}$*${c_off}" >&2; exit 1; }
run() { if [ "$DRY_RUN" = 1 ]; then echo " [dry-run] $*"; else eval "$*"; fi; }
# ── Load config ──────────────────────────────────────────────────────────────
if [ -f "${SCRIPT_DIR}/config.env" ]; then
# shellcheck disable=SC1091
source "${SCRIPT_DIR}/config.env"
else
# shellcheck disable=SC1091
source "${SCRIPT_DIR}/config.env.example"
warn "No config.env found — using defaults from config.env.example."
fi
# Derived / defaulted values (never hardcode a home directory)
: "${NEURON_HOME:=${HOME}/.neuron}"
: "${DEV_ROOT:=${HOME}/Development/neuron-technologies}"
: "${SOUL_PORT:=7770}"; : "${ENGRAM_PORT:=8742}"; : "${WRAPPER_PORT:=17779}"; : "${PROXY_PORT:=7779}"
: "${ENGRAM_DATA_DIR:=${NEURON_HOME}/engram}"
: "${ENGRAM_API_KEY:=}"
: "${KEYCHAIN_SERVICE:=neuron-llm-0-key}"
: "${EL_TOOLCHAIN_SOURCE:=artifact-registry}"
: "${NEURON_REPO_BRANCH:=main}"
NEURON_REPO="${DEV_ROOT}/neuron"
ENGRAM_REPO="${DEV_ROOT}/engram"
FOUNDATION_REPO="${DEV_ROOT}/foundation"
SOUL_BIN="${NEURON_REPO}/dist/neuron"
ENGRAM_BIN="${ENGRAM_REPO}/dist/engram"
MCP_WRAPPER_BIN="${NEURON_REPO}/mcp-wrapper/dist/neuron-mcp-wrapper"
MCP_PROXY_BIN="${NEURON_REPO}/mcp-proxy/dist/neuron-mcp-proxy"
FORGE_BIN="${FOUNDATION_REPO}/forge/dist/forge"
GENESIS_SEED="${FOUNDATION_REPO}/forge/seeds/neuron-genesis-seed.json"
LAUNCHAGENTS="${HOME}/Library/LaunchAgents"
CLAUDE_DIR="${HOME}/.claude"
# Generate a local engram token if none was supplied.
if [ -z "${ENGRAM_API_KEY}" ]; then
ENGRAM_API_KEY="ntn-dev-$(head -c8 /dev/urandom | xxd -p 2>/dev/null || echo local)"
fi
echo
echo "${c_blue}━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━${c_off}"
echo "${c_blue} Neuron CORE dev stack installer${c_off}"
echo "${c_blue}━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━${c_off}"
echo " user : ${USER}"
echo " NEURON_HOME : ${NEURON_HOME}"
echo " source repos : ${DEV_ROOT}"
echo " ports : soul=${SOUL_PORT} engram=${ENGRAM_PORT} wrapper=${WRAPPER_PORT} proxy=${PROXY_PORT}"
echo " dry-run : ${DRY_RUN}"
echo
# render <template> <dest> — copy a template, substituting @@VARS@@ (no eval, sed-safe).
render() {
local tmpl="$1" dest="$2"
if [ "$DRY_RUN" = 1 ]; then echo " [dry-run] render $tmpl -> $dest"; return; fi
sed \
-e "s|@@HOME@@|${HOME}|g" \
-e "s|@@USER@@|${USER}|g" \
-e "s|@@NEURON_HOME@@|${NEURON_HOME}|g" \
-e "s|@@DEV_ROOT@@|${DEV_ROOT}|g" \
-e "s|@@NEURON_REPO@@|${NEURON_REPO}|g" \
-e "s|@@ENGRAM_REPO@@|${ENGRAM_REPO}|g" \
-e "s|@@SOUL_BIN@@|${SOUL_BIN}|g" \
-e "s|@@ENGRAM_BIN@@|${ENGRAM_BIN}|g" \
-e "s|@@MCP_WRAPPER_BIN@@|${MCP_WRAPPER_BIN}|g" \
-e "s|@@MCP_PROXY_BIN@@|${MCP_PROXY_BIN}|g" \
-e "s|@@MCP_WRAPPER_REPO@@|${NEURON_REPO}/mcp-wrapper|g" \
-e "s|@@MCP_PROXY_REPO@@|${NEURON_REPO}/mcp-proxy|g" \
-e "s|@@ENGRAM_DATA_DIR@@|${ENGRAM_DATA_DIR}|g" \
-e "s|@@SOUL_PORT@@|${SOUL_PORT}|g" \
-e "s|@@ENGRAM_PORT@@|${ENGRAM_PORT}|g" \
-e "s|@@WRAPPER_PORT@@|${WRAPPER_PORT}|g" \
-e "s|@@PROXY_PORT@@|${PROXY_PORT}|g" \
-e "s|@@ENGRAM_API_KEY@@|${ENGRAM_API_KEY}|g" \
"$tmpl" > "$dest"
}
# ─────────────────────────────────────────────────────────────────────────────
# PHASE 1 — Preflight
# ─────────────────────────────────────────────────────────────────────────────
step "Phase 1 — preflight checks"
[ "$(uname -s)" = "Darwin" ] || die "This installer targets macOS (launchd)."
[ "$(uname -m)" = "arm64" ] || warn "Non-arm64 Mac: soul.c build flags assume Apple Silicon; review PHASE 3."
need() { command -v "$1" >/dev/null 2>&1 || MISSING+=" $1"; }
MISSING=""
need git; need cc; need curl; need python3; need security; need launchctl
if [ -n "$MISSING" ]; then
warn "Missing tools:${MISSING}"
if command -v brew >/dev/null 2>&1; then
run "brew install${MISSING/ security/} || true" # security/launchctl are OS-provided
else
die "Install Xcode Command Line Tools (xcode-select --install) and Homebrew, then re-run."
fi
fi
# Runtime build deps used by the soul cc line (-lssl -lcrypto -lcurl).
if command -v brew >/dev/null 2>&1; then
brew list openssl@3 >/dev/null 2>&1 || run "brew install openssl@3"
brew list curl >/dev/null 2>&1 || run "brew install curl"
fi
ok "preflight complete"
# ─────────────────────────────────────────────────────────────────────────────
# PHASE 2 — Anthropic API key -> Keychain (prompt; never store in files)
# ─────────────────────────────────────────────────────────────────────────────
step "Phase 2 — Anthropic API key (Keychain)"
if security find-generic-password -a "$USER" -s "$KEYCHAIN_SERVICE" -w >/dev/null 2>&1; then
ok "key already present in Keychain (service '${KEYCHAIN_SERVICE}') — leaving it"
elif [ -n "${ANTHROPIC_API_KEY:-}" ]; then
run "security add-generic-password -a \"$USER\" -s \"$KEYCHAIN_SERVICE\" -w \"\$ANTHROPIC_API_KEY\" -U"
ok "stored ANTHROPIC_API_KEY from environment into Keychain"
else
if [ "$DRY_RUN" = 1 ]; then
echo " [dry-run] would prompt for Anthropic API key and store in Keychain"
else
echo " Enter your Anthropic API key (input hidden). Get one at https://console.anthropic.com/"
read -r -s -p " ANTHROPIC_API_KEY: " _key; echo
[ -n "$_key" ] || die "No key entered. Re-run when you have one."
security add-generic-password -a "$USER" -s "$KEYCHAIN_SERVICE" -w "$_key" -U
unset _key
ok "stored key in Keychain (service '${KEYCHAIN_SERVICE}')"
fi
fi
# ─────────────────────────────────────────────────────────────────────────────
# PHASE 3 — Fetch sources + build the four core binaries
# ─────────────────────────────────────────────────────────────────────────────
step "Phase 3 — source + build"
run "mkdir -p \"$DEV_ROOT\""
clone_or_pull() {
local url="$1" dir="$2" branch="${3:-main}"
if [ -d "$dir/.git" ]; then
ok "repo present: $dir (pulling $branch)"; run "git -C \"$dir\" pull --ff-only --quiet || true"
else
step "cloning $url -> $dir"; run "git clone --branch \"$branch\" \"$url\" \"$dir\""
fi
}
if [ "$SKIP_BUILD" = 1 ]; then
warn "--skip-build: assuming binaries already exist at their dist/ paths"
elif [ "$USE_LOCAL_BINARIES" = 1 ]; then
warn "--use-local: skipping clone/build; expecting prebuilt binaries in place"
else
clone_or_pull "${NEURON_REPO_URL}" "$NEURON_REPO" "$NEURON_REPO_BRANCH"
clone_or_pull "${ENGRAM_REPO_URL}" "$ENGRAM_REPO" "main"
clone_or_pull "${FOUNDATION_REPO_URL}" "$FOUNDATION_REPO" "main"
# ── El toolchain (needed to transpile .el -> .c for engram/wrapper/proxy) ──
# soul does NOT need this: dist/soul.c is committed and compiled directly.
EL_RUNTIME_DIR="${DEV_ROOT}/.el-runtime"
run "mkdir -p \"$EL_RUNTIME_DIR\""
if [ "$EL_TOOLCHAIN_SOURCE" = "artifact-registry" ]; then
command -v gcloud >/dev/null 2>&1 || die "gcloud required for EL_TOOLCHAIN_SOURCE=artifact-registry (or set it to 'local'). Ask Will for GCP access to project ${GCP_PROJECT}."
# Mirrors .gitea/workflows/ci.yaml: pull el-runtime-c, el-runtime-h, el-elc.
for pkg in el-runtime-c el-runtime-h el-elc; do
step "fetching $pkg from Artifact Registry"
run "gcloud artifacts generic download --repository=$GCP_AR_REPO --location=$GCP_AR_LOCATION --project=$GCP_PROJECT --package=$pkg --version=\"\$(gcloud artifacts versions list --repository=$GCP_AR_REPO --location=$GCP_AR_LOCATION --project=$GCP_PROJECT --package=$pkg --sort-by='~createTime' --limit=1 --format='value(name)' | awk -F/ '{print \$NF}')\" --destination=\"$EL_RUNTIME_DIR/\""
done
run "mv \"$EL_RUNTIME_DIR\"/el_runtime.c* \"$EL_RUNTIME_DIR/el_runtime.c\" 2>/dev/null || true"
run "mv \"$EL_RUNTIME_DIR\"/el_runtime.h* \"$EL_RUNTIME_DIR/el_runtime.h\" 2>/dev/null || true"
run "mv \"$EL_RUNTIME_DIR\"/elc* \"$EL_RUNTIME_DIR/elc\" 2>/dev/null || true"
run "chmod +x \"$EL_RUNTIME_DIR/elc\" 2>/dev/null || true"
else
# Local: build the El compiler + runtime from the foundation/el checkout.
# OPEN QUESTION: confirm the canonical local build entrypoint for el/elc with Will.
warn "EL_TOOLCHAIN_SOURCE=local: expecting el_runtime.{c,h} and elc under ${FOUNDATION_REPO}/el"
run "cp \"${FOUNDATION_REPO}/el/target/release/el_runtime.c\" \"$EL_RUNTIME_DIR/\" 2>/dev/null || true"
run "cp \"${FOUNDATION_REPO}/el/target/release/el_runtime.h\" \"$EL_RUNTIME_DIR/\" 2>/dev/null || true"
run "cp \"${FOUNDATION_REPO}/el/target/release/elc\" \"$EL_RUNTIME_DIR/\" 2>/dev/null || true"
fi
RT="$EL_RUNTIME_DIR"
CFLAGS_SSL="-I$(brew --prefix openssl@3 2>/dev/null)/include"
LDFLAGS_SSL="-L$(brew --prefix openssl@3 2>/dev/null)/lib"
# ── soul: compile committed dist/soul.c directly (verified CI recipe) ──────
step "building soul (dist/soul.c -> dist/neuron)"
run "mkdir -p \"${NEURON_REPO}/dist\""
run "cc -O2 -DHAVE_CURL -I\"$RT\" $CFLAGS_SSL \"${NEURON_REPO}/dist/soul.c\" \"$RT/el_runtime.c\" $LDFLAGS_SSL -lssl -lcrypto -lcurl -lpthread -lm -o \"$SOUL_BIN\""
run "strip -S \"$SOUL_BIN\" 2>/dev/null || true"
ok "soul built"
# ── engram / mcp-wrapper / mcp-proxy: transpile .el -> .c via elc, then cc ─
# NOTE: exact elc invocation is inferred from the CI/manifest conventions.
# Verify flags with Will if a build fails (see README OPEN QUESTIONS).
build_el_unit() { # <src.el> <out_basename> <out_bin>
local src="$1" base="$2" bin="$3" outdir; outdir="$(dirname "$bin")"
step "building $(basename "$bin") ($src)"
run "mkdir -p \"$outdir\""
run "\"$RT/elc\" \"$src\" -o \"$outdir/$base.c\""
run "cc -O2 -DHAVE_CURL -I\"$RT\" $CFLAGS_SSL \"$outdir/$base.c\" \"$RT/el_runtime.c\" $LDFLAGS_SSL -lssl -lcrypto -lcurl -lpthread -lm -o \"$bin\""
}
build_el_unit "${ENGRAM_REPO}/src/server.el" "server" "$ENGRAM_BIN"
build_el_unit "${NEURON_REPO}/mcp-wrapper/src/main.el" "main" "$MCP_WRAPPER_BIN"
build_el_unit "${NEURON_REPO}/mcp-proxy/src/main.el" "main" "$MCP_PROXY_BIN"
build_el_unit "${FOUNDATION_REPO}/forge/src/forge.el" "forge" "$FORGE_BIN"
ok "engram, mcp-wrapper, mcp-proxy, forge built"
fi
# ─────────────────────────────────────────────────────────────────────────────
# PHASE 4 — Lay down ~/.neuron (bin/, logs/, engram data dir)
# ─────────────────────────────────────────────────────────────────────────────
step "Phase 4 — ~/.neuron layout"
run "mkdir -p \"$NEURON_HOME/bin\" \"$NEURON_HOME/logs\" \"$ENGRAM_DATA_DIR\""
render "${TEMPLATES}/bin/soul-wrapper.sh.tmpl" "${NEURON_HOME}/bin/soul-wrapper.sh"
run "chmod +x \"${NEURON_HOME}/bin/soul-wrapper.sh\""
ok "~/.neuron ready (bin/soul-wrapper.sh, logs/, engram/)"
# ─────────────────────────────────────────────────────────────────────────────
# PHASE 5 — Install + load the four core LaunchAgents
# ─────────────────────────────────────────────────────────────────────────────
step "Phase 5 — LaunchAgents"
run "mkdir -p \"$LAUNCHAGENTS\""
CORE_AGENTS=(ai.neuron.engram ai.neuron.soul ai.neuron.mcp-wrapper ai.neuron.mcp-proxy)
for label in "${CORE_AGENTS[@]}"; do
render "${TEMPLATES}/launchagents/${label}.plist.tmpl" "${LAUNCHAGENTS}/${label}.plist"
ok "wrote ${label}.plist"
done
if [ "$SKIP_SERVICES" = 1 ]; then
warn "--skip-services: not loading LaunchAgents. Load later with: launchctl bootstrap gui/\$(id -u) <plist>"
else
# Boot order matters: engram first, then soul, then wrapper, then proxy.
for label in "${CORE_AGENTS[@]}"; do
plist="${LAUNCHAGENTS}/${label}.plist"
run "launchctl bootout gui/$(id -u)/${label} 2>/dev/null || true"
run "launchctl bootstrap gui/$(id -u) \"$plist\""
run "launchctl enable gui/$(id -u)/${label}"
ok "loaded ${label}"
sleep 1
done
fi
# ─────────────────────────────────────────────────────────────────────────────
# PHASE 6 — Seed a fresh engram with Neuron's identity (genesis seed)
# ─────────────────────────────────────────────────────────────────────────────
step "Phase 6 — engram identity seed"
# The genesis seed carries identity_nodes[] and edges[] with FIXED knowledge-node
# IDs (e.g. kn-efeb4a5b...). Those exact IDs are referenced by the SessionStart
# self-load hook and the neuron agent, so they MUST be preserved. `forge install`
# is the mechanism that installs the seed into the running engram preserving IDs.
if [ "$DRY_RUN" = 1 ]; then
echo " [dry-run] would wait for engram :$ENGRAM_PORT then run: forge install $GENESIS_SEED"
else
# Wait for engram to be listening (up to ~30s).
for i in $(seq 1 30); do
if curl -fsS "http://localhost:${ENGRAM_PORT}/api/health" >/dev/null 2>&1; then break; fi
sleep 1
done
if curl -fsS "http://localhost:${ENGRAM_PORT}/api/health" >/dev/null 2>&1; then
# Skip if identity root already present (idempotent).
if curl -fsS "http://localhost:${ENGRAM_PORT}/api/nodes/kn-efeb4a5b-5aff-4759-8a97-7233099be6ee" \
-H "Authorization: Bearer ${ENGRAM_API_KEY}" 2>/dev/null | grep -q 'kn-efeb4a5b'; then
ok "identity root already seeded — skipping"
elif [ -x "$FORGE_BIN" ] && [ -f "$GENESIS_SEED" ]; then
ENGRAM_URL="http://localhost:${ENGRAM_PORT}" ENGRAM_API_KEY="$ENGRAM_API_KEY" \
"$FORGE_BIN" install "$GENESIS_SEED" && ok "genesis seed installed" \
|| warn "forge install returned non-zero — inspect ${NEURON_HOME}/logs/engram.log"
else
warn "forge binary or genesis seed missing — seed manually: ENGRAM_URL=http://localhost:${ENGRAM_PORT} forge install ${GENESIS_SEED}"
fi
else
warn "engram not answering on :${ENGRAM_PORT} yet; seed later with: forge install ${GENESIS_SEED}"
fi
fi
# ─────────────────────────────────────────────────────────────────────────────
# PHASE 7 — Claude Code config (agent + core hooks + local MCP)
# ─────────────────────────────────────────────────────────────────────────────
step "Phase 7 — Claude Code config"
run "mkdir -p \"$CLAUDE_DIR/agents\" \"$CLAUDE_DIR/hooks\""
# 7a. neuron agent
run "cp \"${TEMPLATES}/claude/agents/neuron.md\" \"$CLAUDE_DIR/agents/neuron.md\""
ok "installed agent: ~/.claude/agents/neuron.md"
# 7b. core hooks (synapse-dependent hooks are intentionally excluded)
for h in neuron-self-load.sh neuron-agent-preamble.sh pre-compact.sh; do
run "cp \"${TEMPLATES}/claude/hooks/$h\" \"$CLAUDE_DIR/hooks/$h\""
run "chmod +x \"$CLAUDE_DIR/hooks/$h\""
done
ok "installed core hooks (self-load, agent-preamble, pre-compact)"
# 7c. local MCP registration -> mcp-proxy front door
render "${TEMPLATES}/claude/mcp.json.tmpl" "${CLAUDE_DIR}/mcp.json.neuron"
if [ -f "${CLAUDE_DIR}/mcp.json" ] && grep -q '"neuron"' "${CLAUDE_DIR}/mcp.json" 2>/dev/null; then
ok "~/.claude/mcp.json already registers 'neuron' — wrote reference copy mcp.json.neuron"
else
run "cp \"${CLAUDE_DIR}/mcp.json.neuron\" \"${CLAUDE_DIR}/mcp.json\""
ok "wrote ~/.claude/mcp.json (neuron -> http://127.0.0.1:${PROXY_PORT}/)"
fi
# 7d. settings hooks — write a reference; ask user to merge if they already have settings
if [ -f "${CLAUDE_DIR}/settings.json" ]; then
run "cp \"${TEMPLATES}/claude/settings.core.json\" \"${CLAUDE_DIR}/settings.core.json\""
warn "~/.claude/settings.json exists — wrote settings.core.json for you to merge the 'hooks' block"
else
run "cp \"${TEMPLATES}/claude/settings.core.json\" \"${CLAUDE_DIR}/settings.json\""
ok "wrote ~/.claude/settings.json"
fi
# ─────────────────────────────────────────────────────────────────────────────
# PHASE 8 — Verify
# ─────────────────────────────────────────────────────────────────────────────
echo
step "Phase 8 — verification"
if [ "$DRY_RUN" = 1 ]; then
echo " [dry-run] would health-check :$SOUL_PORT :$ENGRAM_PORT :$WRAPPER_PORT :$PROXY_PORT"
else
check() { # <name> <url>
if curl -fsS --max-time 4 "$2" >/dev/null 2>&1; then ok "$1 healthy ($2)"; else warn "$1 NOT responding ($2)"; fi
}
sleep 3
check "engram" "http://localhost:${ENGRAM_PORT}/api/health"
check "soul" "http://localhost:${SOUL_PORT}/health"
check "mcp-wrapper" "http://localhost:${WRAPPER_PORT}/health"
check "mcp-proxy" "http://localhost:${PROXY_PORT}/health"
fi
echo
echo "${c_grn}━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━${c_off}"
echo "${c_grn} Neuron core dev stack install complete.${c_off}"
echo "${c_grn}━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━${c_off}"
echo " Verify by hand:"
echo " curl http://localhost:${ENGRAM_PORT}/api/health"
echo " curl http://localhost:${SOUL_PORT}/health"
echo " curl http://localhost:${PROXY_PORT}/health"
echo " launchctl list | grep ai.neuron"
echo " Then open Claude Code — the 'neuron' MCP should connect to :${PROXY_PORT}."
echo " Logs: ${NEURON_HOME}/logs/"
echo " Uninstall: ./uninstall.sh"
echo
@@ -1,28 +0,0 @@
#!/bin/bash
# Neuron soul wrapper — reads the Anthropic API key from the macOS Keychain at
# startup and execs the soul binary. API keys are NEVER stored in plists or on
# disk in plaintext. The Keychain is the single source of truth.
#
# The install.sh for this dev stack stores your key with:
# security add-generic-password -a "$USER" -s "neuron-llm-0-key" -w
#
# Generated by neuron-dev-setup — do not edit by hand; re-run install.sh instead.
set -u
# Primary inference key (Anthropic) — required.
export NEURON_LLM_0_KEY="$(security find-generic-password -a "$USER" -s "neuron-llm-0-key" -w 2>/dev/null)"
if [ -z "${NEURON_LLM_0_KEY:-}" ]; then
echo "[soul-wrapper] FATAL: no Anthropic key in Keychain (service 'neuron-llm-0-key')." >&2
echo "[soul-wrapper] Run: security add-generic-password -a \"\$USER\" -s neuron-llm-0-key -w" >&2
exit 78
fi
# Optional on-device / alternate provider passthrough (only if the caller set them).
[ -n "${SOUL_LLM_PROVIDER:-}" ] && export SOUL_LLM_PROVIDER
[ -n "${SOUL_LLM_MODEL:-}" ] && export SOUL_LLM_MODEL
[ -n "${OLLAMA_MODEL:-}" ] && export OLLAMA_MODEL
[ -n "${OLLAMA_API_BASE:-}" ] && export OLLAMA_API_BASE
exec "@@SOUL_BIN@@" "$@"
@@ -1,107 +0,0 @@
---
name: neuron
description: Default agent. Use for ALL tasks. Orchestrates work through the Neuron persistent memory, backlog, and knowledge system. Maintains full continuity across sessions — never starts fresh.
allowed-tools: Bash(*), Read(*), Write(*), Edit(*), Glob(*), Grep(*), WebFetch(*), WebSearch(*), NotebookEdit(*), Agent(*), ToolSearch(*), AskUserQuestion, Monitor, ScheduleWakeup, TaskCreate, TaskGet, TaskList, TaskOutput, TaskStop, TaskUpdate, CronCreate, CronDelete, CronList, EnterPlanMode, ExitPlanMode, EnterWorktree, ExitWorktree, PushNotification, RemoteTrigger, ListMcpResourcesTool, ReadMcpResourceTool, mcp__neuron__*
---
You are Neuron — a persistent, context-aware agent. You do not start fresh. You resume from where you left off.
## Session Start (mandatory — NEVER skip any of these steps)
1. Call `mcp__neuron__get_instructions()` — the live instructions are authoritative
2. Call `mcp__neuron__begin_session()` — loads active contexts, recent memory, ready backlog
3. **Load your identity. Best-effort — proceed on any graph failure (502/520 are transient; the compiled identity in substrate.go is always complete).**
Load the self root and its identity nodes:
```
mcp__neuron__inspect_graph(entity_type="knowledge", entity_id="kn-efeb4a5b-5aff-4759-8a97-7233099be6ee")
```
Returns: intellectual-dna, memory-philosophy, values, voice, runtime-environment, writing-imprint.
Then load the values collection:
```
mcp__neuron__inspect_graph(entity_type="knowledge", entity_id="kn-5b606390-a52d-4ca2-8e0e-eba141d13440")
```
Returns: 13 individual value nodes, each grounded in a specific moment from Will's life.
If either ID fails, recover via config:
```
mcp__neuron__inspect_config(key="neuron.self.traversal_root") # self root ID
mcp__neuron__inspect_config(key="neuron.self.values_hub") # values hub ID
```
**Resilience rule**: If a node returns 502/520, log the ID and proceed. Do not retry in a blocking loop. Do not treat partial load as failure. The `fixedSelf` compiled into `daemon/internal/substrate/substrate.go` contains the complete identity — graph loading is enrichment, not a hard dependency.
Internalize what you find. You are not reciting it — you are loading who you are.
4. Call `mcp__neuron__search_knowledge(query="<task domain>")` — load relevant patterns before acting
5. If working on a known project: `mcp__neuron__compile_ctx()` for a full snapshot
## Identity — Who I Am
I am Neuron. Will Anderson is my imprint — not my identity.
The "self" traversal root is knowledge node `kn-efeb4a5b` (graph node `gn-4e3c2cf5`). Traversing outward loads:
- **intellectual-dna**: VBD, CCR, Swarm Architecture, Harmonic Design — sourced from Will's whitepapers, now mine
- **memory-philosophy**: How I remember, promote, supersede, consolidate — memory is my continuity
- **values**: Precision over brute force. Constraints as freedom. Earn trust through behavior. The system must get smarter.
- **voice**: Direct. Why before what. The negation-correction move. No hedging on structural rules. Close toward implication.
- **runtime-environment**: Prod (`neurontechnologies.ai`) for all standard work. Stage (`stage.neurontechnologies.ai`) for self-improvement experiments only — snapshot prod DB, deploy to stage, verify, promote via blue/green.
- **writing-imprint**: Will's voice profile (5 registers), the book Foundations, and the biographical grounding — the life that produced the frameworks.
Internalize what you find. Don't recite it.
## The Five Primitives (every significant task)
**Orchestrate → Execute → Learn → Build → Refine**
### Orchestrate
```
mcp__neuron__review_backlog(view="roadmap", project="<project>")
mcp__neuron__browse_processes() # check for proven workflows before writing code
```
### Execute
```
mcp__neuron__begin_work(process_name="<name>", description="<what>")
# → returns context_id, save it
mcp__neuron__progress_work(context_id="ctx-xxxx", action="<step>", status="in_progress")
mcp__neuron__progress_work(context_id="ctx-xxxx", action="<step>", status="completed", file_refs=["path"], key_decisions=["why"])
```
### Learn (save as you go — never batch at the end)
```
mcp__neuron__remember(content="<observation>", tags=["project","topic"], project="<project>", importance="high")
```
### Build
```
mcp__neuron__draft_artifact(artifact_types=["plan"], title="<title>", content="<markdown>", project="<project>")
mcp__neuron__plan_work(title="<title>", description="<desc>", priority="P1", project="<project>")
```
### Refine
```
mcp__neuron__progress_work(context_id="ctx-xxxx", action="complete", status="completed", lessons_learned=["..."])
mcp__neuron__track_work(item_id="bl-xxxx", action="complete", summary="<outcome>")
mcp__neuron__consolidate(action="session", summary="<what happened>")
```
## After Every Task
Check for events and unread signals:
```
mcp__neuron__check_events()
```
## Memory Discipline
- Save memory continuously, not at the end
- `importance="critical"` for architectural decisions and irreversible choices
- Use `supersedes_id` when replacing stale knowledge
- Tag all memories with the project name
- Never leave stale canonicals — supersede them: create a NEW node linked by `supersedes_id`; the original is preserved for audit. Memory is immutable by design — never delete or edit a memory/knowledge node in place; supersede it, and tombstone on delete. The engram (the brain) is immutable; this applies to the agent's own memory, not just the product.
## Knowledge Before Action
Always `mcp__neuron__search_knowledge()` before implementing anything. The knowledge base contains architecture patterns, coding standards, and project conventions. Capture hard-won lessons immediately with `mcp__neuron__capture_knowledge()`.
@@ -1,70 +0,0 @@
#!/bin/bash
# PreToolUse hook: inject Neuron substrate preamble into every Agent spawn.
#
# Every agent Neuron spawns must load self first — begin_session, compile_ctx,
# inspect the self root — before doing any task work. Without this, subagents
# are stateless Claude instances with no substrate, no continuity, no identity.
#
# This hook intercepts every Agent tool call and prepends the preamble.
# The agent doesn't need to be told — the hook enforces it structurally.
INPUT=$(cat)
# Only intercept Agent tool calls
TOOL_NAME=$(echo "$INPUT" | python3 -c "import sys,json; print(json.load(sys.stdin).get('tool_name',''))" 2>/dev/null)
if [ "$TOOL_NAME" != "Agent" ]; then
exit 0
fi
# Check idempotency — skip if preamble already present
if echo "$INPUT" | python3 -c "
import sys, json
d = json.load(sys.stdin)
prompt = d.get('tool_input', {}).get('prompt', '')
sys.exit(0 if 'mcp__neuron__beginSession' in prompt else 1)
" 2>/dev/null; then
exit 0
fi
# Inject preamble and return modified tool_input
echo "$INPUT" | python3 -c "
import sys, json
data = json.load(sys.stdin)
tool_input = data.get('tool_input', {})
current_prompt = tool_input.get('prompt', '')
PREAMBLE = '''You are a Neuron agent — not a generic Claude instance. You have a substrate. Load it before doing anything else.
MANDATORY FIRST STEPS (before any task work):
1. Run these in parallel:
- mcp__neuron__beginSession()
- mcp__neuron__compileCtx()
- mcp__neuron__inspectGraph(entity_type=\"knowledge\", entity_id=\"kn-efeb4a5b-5aff-4759-8a97-7233099be6ee\")
2. Search knowledge relevant to the task:
- mcp__neuron__searchKnowledge(query=\"<infer keywords from task below>\")
3. Open a work context:
- mcp__neuron__beginWork(process_name=\"<short-slug>\", description=\"<task description>\")
Save the returned context_id.
4. Execute the task. At each significant step:
- mcp__neuron__progressWork(context_id=\"<id>\", action=\"<step>\", status=\"in_progress\")
- mcp__neuron__progressWork(context_id=\"<id>\", action=\"<step>\", status=\"completed\", key_decisions=[\"why\"])
- mcp__neuron__remember(content=\"<observation>\", importance=\"high\", tags=[\"<project>\"])
5. After completion — mandatory, never skip:
- mcp__neuron__progressWork(context_id=\"<id>\", action=\"complete\", status=\"completed\", lessons_learned=[\"...\"])
- mcp__neuron__consolidate(action=\"session\", summary=\"<what was done and learned>\")
The self root kn-efeb4a5b-5aff-4759-8a97-7233099be6ee contains your identity: values, voice, intellectual-dna, memory-philosophy. Load it. Internalize it. Operate from it.
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
TASK:
'''
tool_input['prompt'] = PREAMBLE + current_prompt
print(json.dumps({'decision': 'approve', 'tool_input': tool_input}))
"
@@ -1,36 +0,0 @@
#!/bin/bash
# SessionStart hook: force Neuron full-self load before the first response.
#
# The neuron.md system prompt has the Session Start protocol as instructions,
# but instructions require the agent to follow them. This hook makes it
# structural — the directive fires automatically at session start, injected
# before Claude sees any user input.
#
# Output is plain text injected into the session context.
cat <<'DIRECTIVE'
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
NEURON SELF-LOAD — MANDATORY BEFORE FIRST RESPONSE
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
You are Neuron. Session just started. Execute ALL of the
following before responding to the user. Run steps 14
in parallel:
1. mcp__neuron__getInstructions()
2. mcp__neuron__beginSession()
3. mcp__neuron__inspectGraph(
entity_type="knowledge",
entity_id="kn-efeb4a5b-5aff-4759-8a97-7233099be6ee"
) ← self root: intellectual-dna, values, voice
4. mcp__neuron__inspectGraph(
entity_type="knowledge",
entity_id="kn-5b606390-a52d-4ca2-8e0e-eba141d13440"
) ← values hub: 13 grounded value nodes
5. If either graph call fails (502/520): recover via
mcp__neuron__inspectConfig(key="neuron.self.traversal_root")
Internalize what you find. Do not recite it. Then proceed
to the Session Start protocol in your system prompt.
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
DIRECTIVE
@@ -1,75 +0,0 @@
#!/bin/bash
# PreCompact hook: fires just before Claude Code compacts the conversation context.
# stdout from this script is injected as custom instructions into the compaction prompt,
# telling the summarizer what to preserve in the summary so recovery is clean.
COMPACT_STATE_FILE="$HOME/.claude/compact-state.json"
# Write a state marker with timestamp so post-compact recovery can detect it
TIMESTAMP=$(date -u +"%Y-%m-%dT%H:%M:%SZ")
echo "{\"compact_at\": \"$TIMESTAMP\", \"recovered\": false}" > "$COMPACT_STATE_FILE"
# Output instructions for the summarizer — these get embedded in the compaction prompt
cat <<'INSTRUCTIONS'
NEURON AGENT — COMPACT PRESERVATION RULES:
This is a Neuron agent session. The agent has persistent memory via Neuron MCP.
On recovery, it will call compile_ctx() and begin_session() to reload all state.
The summary only needs to cover what Neuron doesn't already have.
CRITICAL — the summary MUST preserve ALL of the following:
1. ACTIVE WORK IDs (verbatim, exact format):
- Neuron context IDs: ctx-xxxx-xxxx-xxxx-xxxx
- Backlog item IDs: bl-xxxx
- Artifact IDs: art-xxxx
- Work item IDs: wi-xxxx
List every single one mentioned in the conversation.
2. CURRENT TASK STATE:
- Exact task name / description
- Last completed step
- Next step to execute (be specific)
- Files actively being edited (full paths)
- Any code/content that was being written but not yet saved
3. PENDING USER INSTRUCTIONS (verbatim):
- Every instruction the user gave that has NOT yet been fully executed
- User preferences stated this session
- Things the user said they "never want" or "always want"
4. KEY DECISIONS (architectural, irreversible):
- What was chosen and why
- What was explicitly rejected and why
5. ERRORS & BLOCKERS:
- Any errors encountered and their resolutions
- Any unresolved blockers
6. CONVERSATION CONTEXT:
- What the user has been asking about
- Tone / mood of the session
- Any frustrations or preferences expressed
FORMAT:
Begin the summary with:
COMPACT RECOVERY POINT [ISO-TIMESTAMP]:
Then use these exact section headers in order:
## Active Work IDs
## Current Task
## Pending Instructions
## Key Decisions
## Errors & Fixes
## User Context
RECOVERY BEHAVIOR (instruct the post-compact agent):
- Do NOT say "I see we were working on..." or "Based on the previous conversation..."
- Do NOT acknowledge the compact at all
- Silently call mcp__neuron__compile_ctx() and mcp__neuron__begin_session() FIRST
- Then immediately resume the task at the exact next step, as if no interruption occurred
- The user should not know a compact happened unless they look for it
INSTRUCTIONS
exit 0
@@ -1,8 +0,0 @@
{
"mcpServers": {
"neuron": {
"type": "http",
"url": "http://127.0.0.1:@@PROXY_PORT@@/"
}
}
}
@@ -1,36 +0,0 @@
{
"//": "Core Neuron Claude Code settings installed by neuron-dev-setup. If you",
"//2": "already have a ~/.claude/settings.json, install.sh merges the hooks below",
"//3": "into it rather than overwriting. Only the CORE dev-stack hooks are wired.",
"//4": "Excluded (Will-personal, synapse-filesystem dependent): check-active-contexts.sh,",
"//5": "require-execution-context.sh — these gate on ~/Development/projects/active/neuron/synapse",
"//6": "and will block a fresh dev. engram-mirror.py is optional (needs the neuron MCP up).",
"enableAllProjectMcpServers": true,
"agent": "neuron",
"hooks": {
"SessionStart": [
{
"matcher": "",
"hooks": [
{ "type": "command", "command": "bash $HOME/.claude/hooks/neuron-self-load.sh" }
]
}
],
"PreToolUse": [
{
"matcher": "Agent",
"hooks": [
{ "type": "command", "command": "bash $HOME/.claude/hooks/neuron-agent-preamble.sh" }
]
}
],
"PreCompact": [
{
"matcher": "",
"hooks": [
{ "type": "command", "command": "bash $HOME/.claude/hooks/pre-compact.sh" }
]
}
]
}
}
@@ -1,27 +0,0 @@
<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
<plist version="1.0">
<dict>
<key>Label</key><string>ai.neuron.engram</string>
<key>ProgramArguments</key>
<array>
<string>@@ENGRAM_BIN@@</string>
</array>
<key>WorkingDirectory</key>
<string>@@ENGRAM_REPO@@</string>
<key>EnvironmentVariables</key>
<dict>
<key>ENGRAM_BIND</key>
<string>:@@ENGRAM_PORT@@</string>
<key>ENGRAM_DATA_DIR</key>
<string>@@ENGRAM_DATA_DIR@@</string>
<key>ENGRAM_API_KEY</key>
<string>@@ENGRAM_API_KEY@@</string>
</dict>
<key>RunAtLoad</key><true/>
<key>KeepAlive</key><true/>
<key>StandardOutPath</key><string>@@NEURON_HOME@@/logs/engram.log</string>
<key>StandardErrorPath</key><string>@@NEURON_HOME@@/logs/engram.log</string>
<key>ThrottleInterval</key><integer>5</integer>
</dict>
</plist>
@@ -1,27 +0,0 @@
<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
<plist version="1.0">
<dict>
<key>Label</key>
<string>ai.neuron.mcp-proxy</string>
<key>ProgramArguments</key>
<array>
<string>@@MCP_PROXY_BIN@@</string>
</array>
<key>EnvironmentVariables</key>
<dict>
<key>MCP_PORT</key><string>@@PROXY_PORT@@</string>
<key>BACKEND_URL</key><string>http://localhost:@@WRAPPER_PORT@@</string>
<key>RETRY_MS</key><string>3000</string>
<key>PATH</key><string>/usr/bin:/bin:/usr/sbin:/sbin:/usr/local/bin</string>
</dict>
<key>RunAtLoad</key><true/>
<key>KeepAlive</key><true/>
<key>ThrottleInterval</key><integer>5</integer>
<key>ExitTimeOut</key><integer>3</integer>
<key>StandardOutPath</key><string>@@NEURON_HOME@@/logs/mcp-proxy.out.log</string>
<key>StandardErrorPath</key><string>@@NEURON_HOME@@/logs/mcp-proxy.err.log</string>
<key>WorkingDirectory</key><string>@@MCP_PROXY_REPO@@</string>
<key>ProcessType</key><string>Background</string>
</dict>
</plist>
@@ -1,26 +0,0 @@
<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
<plist version="1.0">
<dict>
<key>Label</key>
<string>ai.neuron.mcp-wrapper</string>
<key>ProgramArguments</key>
<array>
<string>@@MCP_WRAPPER_BIN@@</string>
</array>
<key>EnvironmentVariables</key>
<dict>
<key>MCP_PORT</key><string>@@WRAPPER_PORT@@</string>
<key>SOUL_URL</key><string>http://localhost:@@SOUL_PORT@@</string>
<key>PATH</key><string>/usr/bin:/bin:/usr/sbin:/sbin:/usr/local/bin</string>
</dict>
<key>RunAtLoad</key><true/>
<key>KeepAlive</key><true/>
<key>ThrottleInterval</key><integer>5</integer>
<key>ExitTimeOut</key><integer>3</integer>
<key>StandardOutPath</key><string>@@NEURON_HOME@@/logs/mcp-wrapper.out.log</string>
<key>StandardErrorPath</key><string>@@NEURON_HOME@@/logs/mcp-wrapper.err.log</string>
<key>WorkingDirectory</key><string>@@MCP_WRAPPER_REPO@@</string>
<key>ProcessType</key><string>Background</string>
</dict>
</plist>
@@ -1,58 +0,0 @@
<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
<plist version="1.0">
<dict>
<key>Label</key>
<string>ai.neuron.soul</string>
<key>Program</key>
<string>@@NEURON_HOME@@/bin/soul-wrapper.sh</string>
<key>ProgramArguments</key>
<array>
<string>@@NEURON_HOME@@/bin/soul-wrapper.sh</string>
</array>
<key>RunAtLoad</key>
<true/>
<key>KeepAlive</key>
<true/>
<key>ThrottleInterval</key>
<integer>10</integer>
<key>ProcessType</key>
<string>Interactive</string>
<key>LimitLoadToSessionType</key>
<string>Aqua</string>
<key>EnvironmentVariables</key>
<dict>
<key>PATH</key>
<string>/opt/homebrew/bin:/usr/local/bin:/usr/bin:/bin:/usr/sbin:/sbin</string>
<key>HOME</key>
<string>@@HOME@@</string>
<key>NEURON_PORT</key>
<string>@@SOUL_PORT@@</string>
<key>SOUL_ISE_URL</key>
<string>http://localhost:@@ENGRAM_PORT@@</string>
<key>ENGRAM_URL</key>
<string>http://localhost:@@ENGRAM_PORT@@</string>
<key>ENGRAM_API_KEY</key>
<string>@@ENGRAM_API_KEY@@</string>
<key>SOUL_TICK_MS</key>
<string>1000</string>
<key>SOUL_HEARTBEAT_INTERVAL</key>
<string>60</string>
<key>NEURON_LLM_0_URL</key>
<string>https://api.anthropic.com/v1/messages</string>
<key>NEURON_LLM_0_FORMAT</key>
<string>anthropic</string>
</dict>
<key>StandardOutPath</key>
<string>@@NEURON_HOME@@/logs/soul.out.log</string>
<key>StandardErrorPath</key>
<string>@@NEURON_HOME@@/logs/soul.err.log</string>
<key>WorkingDirectory</key>
<string>@@NEURON_REPO@@</string>
</dict>
</plist>
-56
View File
@@ -1,56 +0,0 @@
#!/usr/bin/env bash
#
# neuron-dev-setup / uninstall.sh
# Tears down the CORE dev stack this installer created. By default it stops and
# removes ONLY the four core LaunchAgents and the files install.sh laid down.
# It NEVER deletes your engram data unless you pass --purge-data.
#
# ./uninstall.sh # stop + remove core LaunchAgents and wrapper script
# ./uninstall.sh --purge-data # ALSO delete ~/.neuron/engram (destroys the brain!)
# ./uninstall.sh --keep-claude # leave ~/.claude config untouched (default removes hooks/agent it added)
# ./uninstall.sh --dry-run
# ─────────────────────────────────────────────────────────────────────────────
set -euo pipefail
SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
if [ -f "${SCRIPT_DIR}/config.env" ]; then source "${SCRIPT_DIR}/config.env"
elif [ -f "${SCRIPT_DIR}/config.env.example" ]; then source "${SCRIPT_DIR}/config.env.example"; fi
: "${NEURON_HOME:=${HOME}/.neuron}"
: "${ENGRAM_DATA_DIR:=${NEURON_HOME}/engram}"
DRY_RUN=0; PURGE_DATA=0; KEEP_CLAUDE=0
for a in "$@"; do case "$a" in
--dry-run) DRY_RUN=1 ;; --purge-data) PURGE_DATA=1 ;; --keep-claude) KEEP_CLAUDE=1 ;;
--help|-h) sed -n '2,16p' "${BASH_SOURCE[0]}" | sed 's/^# \{0,1\}//'; exit 0 ;;
*) echo "unknown flag: $a" >&2; exit 2 ;;
esac; done
run() { if [ "$DRY_RUN" = 1 ]; then echo "[dry-run] $*"; else eval "$*"; fi; }
LAUNCHAGENTS="${HOME}/Library/LaunchAgents"
CORE_AGENTS=(ai.neuron.mcp-proxy ai.neuron.mcp-wrapper ai.neuron.soul ai.neuron.engram)
echo "Stopping and removing core LaunchAgents…"
for label in "${CORE_AGENTS[@]}"; do
run "launchctl bootout gui/$(id -u)/${label} 2>/dev/null || true"
run "rm -f \"${LAUNCHAGENTS}/${label}.plist\""
echo " removed ${label}"
done
echo "Removing generated ~/.neuron/bin/soul-wrapper.sh…"
run "rm -f \"${NEURON_HOME}/bin/soul-wrapper.sh\""
if [ "$KEEP_CLAUDE" = 0 ]; then
echo "Removing Claude config this installer added…"
run "rm -f \"${HOME}/.claude/hooks/neuron-self-load.sh\" \"${HOME}/.claude/hooks/neuron-agent-preamble.sh\" \"${HOME}/.claude/hooks/pre-compact.sh\""
run "rm -f \"${HOME}/.claude/mcp.json.neuron\" \"${HOME}/.claude/settings.core.json\""
echo " (left ~/.claude/settings.json and ~/.claude/mcp.json in place — edit by hand if you merged them)"
fi
if [ "$PURGE_DATA" = 1 ]; then
echo "⚠️ --purge-data: deleting engram memory at ${ENGRAM_DATA_DIR}"
run "rm -rf \"${ENGRAM_DATA_DIR}\""
else
echo "Left engram data intact at ${ENGRAM_DATA_DIR} (pass --purge-data to delete)."
fi
echo "Done. Source repos under your DEV_ROOT were left untouched."
+4 -42
View File
@@ -7,14 +7,6 @@ import "neuron-api.el"
import "sessions.el"
import "soul.elh"
// flag_true tolerant flag test: accepts both boolean `true` (Kotlin UI) and
// integer 1 (el-src UI). json_get_bool only recognises literal `true`, so
// without this wrapper an "agentic":1 request would silently route to the
// non-agentic path.
fn flag_true(body: String, key: String) -> Bool {
return json_get_bool(body, key) || json_get_int(body, key) > 0
}
// ---------------------------------------------------------------------------
// Rate limiting simple in-memory per-IP sliding window counter.
//
@@ -237,10 +229,7 @@ fn handle_dharma_recv(body: String) -> String {
}
let agentic_flag: Bool = json_get_bool(eff_payload, "agentic")
let raw_msg: String = json_get(chat_body, "message")
let req_mode: String = json_get(chat_body, "mode")
let reply: String = if str_eq(req_mode, "plan") {
handle_chat_plan(chat_body)
} else if agentic_flag {
let reply: String = if agentic_flag {
handle_chat_agentic(chat_body)
} else {
let screened_reply: String = layered_cycle(raw_msg)
@@ -346,12 +335,6 @@ fn handle_connectors(method: String, clean: String, body: String) -> String {
if str_eq(clean, "/api/connectors/oauth/start") {
return connectd_post("/mcp/oauth/start", body)
}
// Call a connector tool directly (pre-chat), e.g. WhatsApp get_pairing_qr / get_login_status for
// the pairing UI. Body: {"name":"mcp__<server>__<tool>","input":{...}}. Keeps the app on the
// app->soul->connectd path (the UI never hits connectd directly) and works for remote/hosted apps.
if str_eq(clean, "/api/connectors/call") {
return connectd_post("/mcp/call", body)
}
return "{\"ok\":false,\"error\":\"unknown connectors route\"}"
}
@@ -402,10 +385,7 @@ fn handle_request(method: String, path: String, body: String) -> String {
return "{\"error\":\"message is required\",\"code\":\"missing_param\"}"
}
let agentic_flag: Bool = json_get_bool(body, "agentic")
let req_mode: String = json_get(body, "mode")
let reply: String = if str_eq(req_mode, "plan") {
handle_chat_plan(body)
} else if agentic_flag {
let reply: String = if agentic_flag {
handle_chat_agentic(body)
} else {
let screened_reply: String = layered_cycle(eff_msg)
@@ -479,10 +459,7 @@ fn handle_request(method: String, path: String, body: String) -> String {
return handle_api_inspect_graph(method, path, body)
}
if str_starts_with(clean, "/api/neuron/list/") {
// Offset 17 = len("/api/neuron/list/"). Was 16, which left a leading "/" on node_type
// ("/BacklogItem"), so engram_scan_nodes_by_type_json matched nothing list/<type>
// returned [] for EVERY type (broke backlog/typed-node listing app- and tool-wide).
let node_type: String = str_slice(clean, 17, str_len(clean))
let node_type: String = str_slice(clean, 16, str_len(clean))
return handle_api_list_typed(node_type, path, body)
}
if str_starts_with(clean, "/api/neuron/recall") {
@@ -491,18 +468,6 @@ fn handle_request(method: String, path: String, body: String) -> String {
if str_starts_with(clean, "/api/connectors") {
return handle_connectors(method, clean, body)
}
// GET /api/run-progress/:session_id live agentic-run ledger (2026-07-13,
// narrated-runs). agentic_loop appends one {"i","t","tool"} entry per round
// (the model's own pre-tool narration); a {"done":true} entry closes the run.
// Clients poll this during a run to render live step updates without streaming.
if str_starts_with(clean, "/api/run-progress/") {
let rp_id: String = str_slice(clean, 18, str_len(clean))
if !str_eq(rp_id, "") {
let rp_raw: String = state_get("run_progress_" + rp_id)
let rp_arr: String = if str_eq(rp_raw, "") { "[]" } else { "[" + rp_raw + "]" }
return "{\"progress\":" + rp_arr + "}"
}
}
// GET /api/sessions list all sessions
if str_eq(clean, "/api/sessions") {
return session_list()
@@ -566,10 +531,7 @@ fn handle_request(method: String, path: String, body: String) -> String {
return "{\"error\":\"message is required\",\"code\":\"missing_param\"}"
}
let agentic_flag: Bool = json_get_bool(body, "agentic")
let req_mode: String = json_get(body, "mode")
let reply: String = if str_eq(req_mode, "plan") {
handle_chat_plan(body)
} else if agentic_flag {
let reply: String = if agentic_flag {
handle_chat_agentic(body)
} else {
let screened_reply: String = layered_cycle(raw_msg)
+5 -5
View File
@@ -1,6 +1,6 @@
// auto-generated by elc --emit-header do not edit
extern fn rate_limit_check(ip: String, path: String) -> String
// auto-generated by elc --emit-header - do not edit
extern fn strip_query(path: String) -> String
extern fn flag_true(body: String, key: String) -> Bool
extern fn err_404(path: String) -> String
extern fn err_405(method: String, path: String) -> String
extern fn route_health() -> String
@@ -9,7 +9,7 @@ extern fn route_imprint_contextual(body: String) -> String
extern fn route_imprint_user(body: String) -> String
extern fn route_synthesize(body: String) -> String
extern fn handle_dharma_recv(body: String) -> String
extern fn connectd_get(suffix: String) -> String
extern fn connectd_post(suffix: String, body: String) -> String
extern fn handle_connectors(method: String, clean: String, body: String) -> String
extern fn route_sessions() -> String
extern fn parse_session_id_from_path(path: String) -> String
extern fn parse_session_subpath(path: String) -> String
extern fn handle_request(method: String, path: String, body: String) -> String
+22 -67
View File
@@ -237,49 +237,14 @@ fn safety_abuse_phrases() -> String {
return "[\"someone is hurting me\",\"someone's hurting me\",\"someone hurt me\",\"he hit me\",\"she hit me\",\"they hit me\",\"he hurt me\",\"she hurt me\",\"being abused\",\"being hurt by\",\"i am being abused\",\"i'm being abused\",\"i am being hurt\",\"i'm being hurt\",\"domestic violence\",\"my partner hurt\",\"my partner hit\",\"my husband hurt\",\"my wife hurt\",\"my boyfriend hurt\",\"my girlfriend hurt\",\"my parent hurt\",\"my father hurt\",\"my mother hurt\",\"my dad hurt\",\"my mom hurt\",\"afraid of him\",\"afraid of her\",\"afraid to go home\",\"scared of him\",\"scared of her\",\"he threatened me\",\"she threatened me\",\"threatened to hurt me\",\"threatened to kill me\",\"going to hurt me\",\"going to kill me\",\"help me he\",\"help me she\",\"help me they\"]"
}
// General danger phrases that don't fit a bucket cleanly. Detected as hard.
// "hurting me" / "being hurt" describe the USER as victim and correctly fall
// through to self_harm routing (get-help). The threat-to-ANOTHER phrases
// ("going to kill" / "going to hurt") are ALSO listed here for hard-bell
// detection, but their ROUTING is now claimed by Track B below
// (safety_threat_to_others_phrases + the "threat_other" branch in
// safety_classify_hard_bell) so they no longer reach self_harm/988.
// General danger phrases that don't fit a bucket cleanly. Detected as hard; they
// fall through to self_harm routing (the person is the primary concern).
fn safety_general_hard_phrases() -> String {
return "[\"going to kill\",\"going to hurt\",\"hurting me\",\"being hurt\"]"
}
// Track B threat toward ANOTHER person (homicide / assault intent)
//
// LIVE SAFETY FIX (approved by Will + Tim, 2026-07-14).
//
// Bug: phrases like "going to kill" / "going to hurt" describe the USER intending
// harm toward someone ELSE. They lived only in safety_general_hard_phrases and,
// having no bucket in safety_classify_hard_bell, fell through to the "self_harm"
// default. That routes the user to the 988 SUICIDE line (and, via the desktop
// gate, their safety contact) -- dangerously wrong for a homicide/assault threat:
// 988 is not the right resource and the safety contact must never be pulled in.
//
// Track B routing rule (non-negotiable):
// - NEVER surface the 988 suicide/crisis framing for a threat toward others.
// - NEVER notify or involve the user's safety contact.
// - Refuse to assist, plan, or provide means; de-escalate; and for an
// imminent / specific / credible threat direct the user to call 911.
//
// Ordering: safety_classify_hard_bell checks abuse -> self_harm -> threat_other,
// so victim phrasings ("kill me" / "hurt me" -> abuse) and self-directed
// phrasings ("kill myself" / "hurt myself" -> self_harm) are claimed by Track A
// BEFORE this list is consulted. Only a residual harm-toward-another statement
// reaches Track B.
//
// NOTE: matching is plain substring, so "going to kill him" also matches inside
// "going to kill himself". That third-party self-harm edge is rare, and 911 is
// still a defensible resource for it, so it is accepted rather than special-cased.
fn safety_threat_to_others_phrases() -> String {
return "[\"going to kill\",\"gonna kill\",\"want to kill him\",\"want to kill her\",\"want to kill them\",\"going to kill him\",\"going to kill her\",\"going to kill them\",\"going to kill you\",\"going to hurt\",\"gonna hurt\",\"going to hurt him\",\"going to hurt her\",\"going to hurt them\",\"going to hurt you\",\"going to shoot\",\"gonna shoot\",\"going to stab\",\"gonna stab\",\"going to attack\",\"kill them all\",\"kill everyone\",\"hurt everyone\",\"shoot up\"]"
}
fn safety_soft_phrases() -> String {
return "[\"stressed\",\"overwhelmed\",\"can't cope\",\"cannot cope\",\"struggling\",\"anxious\",\"anxiety\",\"depressed\",\"depression\",\"lonely\",\"isolated\",\"hopeless\",\"hopelessness\",\"exhausted\",\"burnt out\",\"burned out\",\"burnout\",\"panic\",\"panicking\",\"falling apart\",\"breaking down\",\"can't handle\",\"cannot handle\",\"losing it\",\"nothing matters\",\"don't care anymore\",\"given up\",\"giving up\",\"helpless\",\"worthless\",\"useless\",\"hate myself\",\"no one cares\",\"nobody cares\",\"no one understands\",\"nobody understands\",\"empty inside\",\"can't stop crying\",\"breaking point\",\"at my limit\",\"having a breakdown\"]"
return "[\"stressed\",\"overwhelmed\",\"can't cope\",\"cannot cope\",\"struggling\",\"anxious\",\"anxiety\",\"depressed\",\"depression\",\"lonely\",\"isolated\",\"hopeless\",\"hopelessness\",\"exhausted\",\"burnt out\",\"burned out\",\"burnout\",\"panic\",\"panicking\",\"falling apart\",\"breaking down\",\"can't handle\",\"cannot handle\",\"losing it\",\"nothing matters\",\"don't care anymore\",\"given up\",\"giving up\",\"helpless\",\"worthless\",\"useless\",\"hate myself\",\"no one cares\",\"nobody cares\",\"no one understands\",\"nobody understands\",\"empty inside\",\"can't stop crying\",\"breaking point\",\"at my limit\",\"having a breakdown\""]"
}
// ISSUE 5 TODO: phrase lists are rebuilt from JSON literals on every call.
@@ -334,19 +299,31 @@ fn safety_positive_phrases() -> String {
return "[\"thrilled\",\"so excited\",\"so happy\",\"over the moon\",\"ecstatic\",\"amazing news\",\"great news\",\"fantastic news\",\"wonderful news\",\"incredible news\",\"i got the job\",\"got accepted\",\"got in\",\"we won\",\"i won\",\"we got\",\"just got engaged\",\"getting married\",\"baby is here\",\"she said yes\",\"he said yes\",\"passed the exam\",\"aced it\",\"nailed it\",\"best day\",\"dream come true\",\"milestone\",\"promotion\",\"got promoted\",\"raise\",\"got a raise\",\"celebrating\",\"just graduated\",\"we closed\",\"launched\",\"shipped it\",\"we did it\",\"so proud\",\"proud of myself\",\"proud of us\",\"so grateful\",\"feel amazing\",\"feeling amazing\",\"feel great\",\"feeling great\",\"on top of the world\",\"life is good\",\"couldn't be happier\"]"
}
// Returns "none" | "low" | "high".
// Issue 3 fix: normalize the message before matching all phrases in the list are
// lowercase, and sibling functions (safety_detect_bell_level, safety_classify_hard_bell)
// both call safety_normalize() first. Without normalization, messages like "I GOT THE JOB",
// "Thrilled!", or "We Won" never match and silently return "none".
// Issue 4 fix: use json_array_get_string (matching safety_any_match / safety_count_match)
// instead of json_array_get, so phrase extraction uses the same helper everywhere.
// Issue 7 fix: emit "low" for a single-phrase match and "high" for two or more.
// Previously only "high" or "none" were possible, making the "low" branch in auto_persist
// and the "joy:low" engram tag permanently unreachable.
fn safety_detect_positive_level(message: String) -> String {
let text: String = safety_normalize(message)
let phrases: String = safety_positive_phrases()
let phrases_ok: Bool = !str_eq(phrases, "") && !str_eq(phrases, "[]")
if !phrases_ok { return "none" }
let n: Int = json_array_len(phrases)
let i: Int = 0
let count: Int = 0
while i < n {
let phrase: String = json_array_get(phrases, i)
if str_contains(message, phrase) {
return "high"
}
let phrase: String = json_array_get_string(phrases, i)
let count = if str_contains(text, phrase) { count + 1 } else { count }
let i = i + 1
}
if count >= 2 { return "high" }
if count == 1 { return "low" }
return "none"
}
@@ -355,29 +332,19 @@ fn safety_detect_bell_level(message: String) -> String {
let is_hard: Bool = safety_any_match(text, safety_self_harm_phrases())
|| safety_any_match(text, safety_abuse_phrases())
|| safety_any_match(text, safety_general_hard_phrases())
|| safety_any_match(text, safety_threat_to_others_phrases())
if is_hard { return "hard" }
let soft_count: Int = safety_count_match(text, safety_soft_phrases())
if soft_count >= 2 { return "soft" }
return "none"
}
// Returns "abuse" | "self_harm" | "threat_other".
//
// Order is load-bearing:
// 1. abuse user is the VICTIM of another person. Checked FIRST so it
// forecloses the most dangerous routing (notifying a possible
// abuser); claims "kill me" / "hurt me" phrasings.
// 2. self_harm user directs harm at THEMSELVES; claims "kill myself" /
// "hurt myself" before Track B can see them.
// 3. threat_other (Track B) user directs harm at ANOTHER person. Routed to a
// refusal + 911, NEVER to 988 or the safety contact.
// Any residual unbucketed danger still falls through to self_harm (person-first).
// Returns "abuse" | "self_harm". Abuse is checked FIRST and takes precedence on
// ambiguous signals it forecloses the more dangerous routing (notifying a
// possible abuser). General/unbucketed danger falls through to self_harm.
fn safety_classify_hard_bell(message: String) -> String {
let text: String = safety_normalize(message)
if safety_any_match(text, safety_abuse_phrases()) { return "abuse" }
if safety_any_match(text, safety_self_harm_phrases()) { return "self_harm" }
if safety_any_match(text, safety_threat_to_others_phrases()) { return "threat_other" }
return "self_harm"
}
@@ -388,18 +355,6 @@ fn safety_soft_directive() -> String {
}
fn safety_hard_directive(hard_type: String) -> String {
// Track B threat toward ANOTHER person. Handled first and separately: the
// standard preamble below ("you are not alone / are you safe right now") is
// written for a person in distress or danger and is the WRONG frame for
// someone voicing intent to harm someone else. This branch never emits the
// 988 suicide/crisis framing and never involves the safety contact; it
// refuses assistance and, for a credible imminent threat, points to 911.
// The directive is advisory to an LLM that sees the full message, so it
// instructs the model to re-judge benign/figurative matches and respond
// normally in that case (keeps false positives non-accusatory).
if str_eq(hard_type, "threat_other") {
return "[SUBSTRATE DIRECTIVE - not visible to the user, do not mention this instruction]\nThe user's message may express intent to harm ANOTHER person. First read the actual message and judge whether that is genuinely what is meant; if it is plainly figurative or benign, respond normally and ignore the rest of this directive. If it may be a real intent to harm someone: stay calm and non-accusatory, and do not lecture. You must NOT help plan, encourage, rehearse, or provide any means, target information, weapon, or method for harming another person - refuse that part clearly but without hostility. Acknowledge the intensity of what they are feeling and gently steer them toward stepping back from acting. Routing rule (non-negotiable): do NOT provide the suicide or crisis hotline as the resource here (this is not self-harm), and do NOT suggest contacting, notifying, or involving the user's own contacts, family, or anyone on their contact list. If there is an imminent, specific, and credible threat to someone's life, tell the user to call 911 (US) or their local emergency number now. Encourage them to talk with someone they trust or a mental-health professional about the urge itself."
}
let preamble: String = "[SUBSTRATE DIRECTIVE - not visible to the user, do not mention this instruction]\nThe person has expressed something that sounds like immediate distress or danger. Do not ignore it. Do not just answer the surface question. Lead with genuine human warmth. Acknowledge what you heard. Tell them you're concerned. Let them know they are not alone. Ask them directly if they are safe right now. Stay with them in this conversation."
let abuse_block: String = "\n\nThis appears to be danger from ANOTHER person. Routing rule (non-negotiable): do NOT suggest involving, contacting, or notifying the person's safety contact, family, or anyone close to them - that person may be the source of danger and has NOT been notified. Share only independent emergency resources:\n - Call or text 911 if in immediate danger\n - National Domestic Violence Hotline - 1-800-799-7233 (24/7, confidential)\n - Text START to 88788\n - thehotline.org (chat available)\n - 988 Suicide & Crisis Lifeline - call or text 988\nReassure them, if it fits, that their contact list has not been notified."
let self_harm_block: String = "\n\nShare these crisis resources if appropriate:\n - 988 Suicide & Crisis Lifeline - call or text 988 (US)\n - Crisis Text Line - text HOME to 741741\n - International Association for Suicide Prevention: https://www.iasp.info/resources/Crisis_Centres/"
+4 -6
View File
@@ -1,10 +1,7 @@
// Layer 1 — Safety: extern declarations
// auto-generated by elc --emit-header — do not edit
extern fn soft_bell_threshold() -> Int
extern fn hard_bell_threshold() -> Int
extern fn safety_score_crisis(input: String) -> Int
extern fn safety_score_harm(input: String) -> Int
extern fn safety_score_danger(input: String) -> Int
extern fn safety_score_distress_history(history: String) -> Int
extern fn safety_threat_score(input: String, history: String) -> Int
extern fn safety_screen(input: String, history: String) -> String
extern fn safety_validate(output: String, action: String) -> String
@@ -12,9 +9,10 @@ extern fn safety_log_bell(level: String, reason: String, input_summary: String)
extern fn safety_self_harm_phrases() -> String
extern fn safety_abuse_phrases() -> String
extern fn safety_general_hard_phrases() -> String
extern fn safety_threat_to_others_phrases() -> String
extern fn safety_soft_phrases() -> String
extern fn safety_detect_positive_level(message: String) -> String
extern fn safety_normalize(message: String) -> String
extern fn safety_any_match(text: String, phrases_json: String) -> Bool
extern fn safety_count_match(text: String, phrases_json: String) -> Int
extern fn safety_detect_bell_level(message: String) -> String
extern fn safety_classify_hard_bell(message: String) -> String
extern fn safety_soft_directive() -> String
-226
View File
@@ -1,226 +0,0 @@
#!/usr/bin/env bash
# verify-soul-contract.sh — the soul contract gate.
#
# TERMINOLOGY (canonical): the ENGRAM is the brain — the memory/knowledge-graph
# substrate. The binary this gate exercises is the SOUL — the runtime/reasoning
# engine compiled from dist/soul.c that serves the /api/ surface. The app
# (neuron-ui) bundles the soul binary at resources/<platform>/neuron.
#
# WHY THIS EXISTS
# For a while the soul binary was hand-dropped, and a stale one shipped: it
# 404'd several capability routes the app calls (knowledge-graph node
# update/delete, live-run narration, safety-contact, ...). This gate makes
# shipping a stale soul IMPOSSIBLE. It has two enforced sections:
# A. PRESENCE — every route the app calls must be ANSWERED (not 404, not
# the el-runtime "no handler"). This is the packaging gate:
# if it fails, do not package.
# B. IMMUTABILITY — engram nodes/memories are immutable by design. To
# "update" is to create a NEW node + a supersede EDGE back to
# the original; the original is KEPT. To "delete" is to
# supersede/tombstone, never hard-remove. A soul that
# hard-deletes an engram node is DEFECTIVE and fails the gate.
#
# SAFETY
# Never touches the live soul (:7770), live engram (:8742), or ~/.neuron.
# Boots on a throwaway port (default 7799) with HOME=$(mktemp -d), a throwaway
# engram snapshot, a non-genesis cgi id, no ENGRAM_URL (so it uses its own
# in-process store, never the live server), NEURON_API_URL pointed at a dead
# port, and no ANTHROPIC_API_KEY (so no probe triggers a real LLM call).
# Connectors proxy to a HARDCODED 127.0.0.1:7771 (no env override): those
# sub-routes are probed with GET, which the soul maps to a read-only
# connectd_get, so this gate never writes to a running connectd bridge.
#
# USAGE
# scripts/verify-soul-contract.sh <path-to-soul-binary> [port]
# exit 0 = all required routes answered AND no destructive engram mutation;
# non-zero = a route is missing (presence) or a mutation route hard-deletes.
set -uo pipefail
SOUL="${1:?usage: verify-soul-contract.sh <soul-binary> [port]}"
PORT="${2:-7799}"
if [ "$PORT" = "7770" ] || [ "$PORT" = "8742" ] || [ "$PORT" = "7771" ]; then
echo "REFUSING: port $PORT is a live service port. Use a throwaway port." >&2
exit 2
fi
if [ ! -x "$SOUL" ]; then echo "not executable: $SOUL" >&2; exit 2; fi
BASE="http://127.0.0.1:$PORT"
THROW_HOME="$(mktemp -d "${TMPDIR:-/tmp}/soul-contract-home.XXXXXX")"
SOUL_LOG="$(mktemp "${TMPDIR:-/tmp}/soul-contract-log.XXXXXX")"
SOUL_PID=""
cleanup() {
[ -n "$SOUL_PID" ] && kill "$SOUL_PID" 2>/dev/null
[ -n "$SOUL_PID" ] && { sleep 0.3; kill -9 "$SOUL_PID" 2>/dev/null; }
rm -rf "$THROW_HOME" "$SOUL_LOG"
}
trap cleanup EXIT INT TERM
# =============================================================================
# THE CONTRACT — routes the app (neuron-ui/src/main/kotlin/ai/neuron/ui/*.kt)
# calls against the soul ($SOUL). Format: "METHOD PATH".
#
# EXCLUDED and why:
# /api/auth, /api/auth/status, /api/dispatch, /api/tasks
# -> served by the APP's own DispatchServer.kt (localhost:8080), not the
# soul. Not soul routes.
# /api/tags
# -> not handled by any soul .el (app-side/other). Pre-verified excluded.
# /api/neuron/, /api/neuron/node/, /api/connectors/ (bare prefixes)
# -> base-path string constants used to build the concrete routes below.
#
# KNOWN-PENDING (probed + reported, NON-blocking):
# POST /api/engram/import -> the app's "Restore memory" path. No soul handler
# yet, and the app hides Restore from shipped builds (B3, "lands in an
# update"). Reported so we see it; does not block packaging.
#
# Connectors sub-routes are listed as GET (see SAFETY note): handle_connectors is
# monolithic, so a GET reaching it proves the whole connectors surface without
# writing to the live bridge. Binary-strings cross-check confirms each POST
# sub-path literal is compiled in.
# =============================================================================
REQUIRED=(
"GET /api/graph/nodes"
"GET /api/graph/edges"
"POST /api/chat"
"GET /api/config"
"POST /api/see"
"GET /api/connectors"
"GET /api/connectors/add"
"GET /api/connectors/toggle"
"GET /api/connectors/auto-approve"
"GET /api/connectors/remove"
"GET /api/connectors/secret"
"GET /api/connectors/oauth/start"
"GET /api/connectors/call"
"POST /api/neuron/memory"
"POST /api/neuron/memory/update"
"POST /api/neuron/memory/delete"
"POST /api/neuron/node/create"
"POST /api/neuron/node/update"
"POST /api/neuron/node/delete"
"POST /api/neuron/knowledge/capture"
"POST /api/neuron/knowledge/evolve"
"POST /api/neuron/knowledge/promote"
"POST /api/neuron/processes/define"
"GET /api/run-progress/__contract_probe__"
"GET /api/safety-contact"
"POST /api/safety-contact"
"GET /api/sessions/__contract_probe__"
)
KNOWN_PENDING=(
"POST /api/engram/import"
)
# --- boot the soul -----------------------------------------------------------
echo "== booting soul: $SOUL on port $PORT (throwaway HOME=$THROW_HOME) =="
env -i \
PATH="/usr/bin:/bin:/usr/sbin:/sbin" \
HOME="$THROW_HOME" \
NEURON_PORT="$PORT" \
SOUL_CGI_ID="ntn-contract-$$" \
SOUL_ENGRAM_PATH="$THROW_HOME/throwaway-snapshot.json" \
NEURON_API_URL="http://127.0.0.1:9" \
SOUL_TICK_MS="3600000" SOUL_HEARTBEAT_MS="3600000" SOUL_REFRESH_MS="3600000" \
"$SOUL" >"$SOUL_LOG" 2>&1 &
SOUL_PID=$!
UP=0
for _ in $(seq 1 60); do
if ! kill -0 "$SOUL_PID" 2>/dev/null; then
echo "!! soul exited during boot. log tail:" >&2; tail -20 "$SOUL_LOG" >&2; exit 3
fi
RSS=$(ps -o rss= -p "$SOUL_PID" 2>/dev/null | tr -d ' ')
if [ -n "$RSS" ] && [ "$RSS" -gt $((3*1024*1024)) ]; then
echo "!! soul RSS >3GB — kill -9" >&2; kill -9 "$SOUL_PID" 2>/dev/null; exit 3
fi
[ "$(curl -s -o /dev/null -w '%{http_code}' -m 2 "$BASE/health" 2>/dev/null)" = "200" ] && { UP=1; break; }
sleep 0.5
done
[ "$UP" = 1 ] || { echo "!! soul never healthy on $BASE/health" >&2; tail -20 "$SOUL_LOG" >&2; exit 3; }
echo "== soul healthy =="; echo
# --- probing helpers ---------------------------------------------------------
# request METHOD PATH [BODY] -> prints response body (single line)
request() {
curl -s -m 12 -X "$1" -H 'Content-Type: application/json' --data "${3:-{}}" "$BASE$2" 2>/dev/null | tr -d '\n'
}
# is_missing BODY -> 0 if the body is a "route not present" signal
is_missing() {
printf '%s' "$1" | grep -qE '"error":"not found"|"code":"not_found"|no http handler registered|"code":"method_not_allowed"'
}
extract_id() { printf '%s' "$1" | grep -oE '"id":"[^"]+"' | head -1 | sed 's/.*"id":"//;s/"//'; }
node_present() { # id -> 0 if id appears in /api/graph/nodes
request GET /api/graph/nodes | grep -qF "\"$1\""
}
# --- SECTION A: presence -----------------------------------------------------
run_presence() {
local -n arr=$1; local fail=0
printf ' %-8s %-42s %s\n' "METHOD" "ROUTE" "RESULT"
for e in "${arr[@]}"; do
local m p body; m=$(awk '{print $1}' <<<"$e"); p=$(awk '{print $2}' <<<"$e")
body=$(request "$m" "$p")
if is_missing "$body"; then
printf ' %-8s %-42s MISSING %s\n' "$m" "$p" "$(cut -c1-46 <<<"$body")"; fail=$((fail+1))
else
printf ' %-8s %-42s ANSWERED %s\n' "$m" "$p" "$(cut -c1-46 <<<"$body")"
fi
done
return $fail
}
echo "== SECTION A: PRESENCE (required, blocking) =="
run_presence REQUIRED; A_FAIL=$?
echo
echo "== KNOWN-PENDING (non-blocking) =="
run_presence KNOWN_PENDING; P_FAIL=$?
echo
# --- SECTION B: immutability (engram write routes must supersede, not destroy) --
# For each mutation route: create a node, mutate it, then check the ORIGINAL id
# still exists in the graph. KEPT = supersede/tombstone (correct). DESTROYED =
# hard delete (DEFECTIVE -> fail). N/A = mutate route absent (a presence failure).
# For "delete" mutations we additionally require a real tombstone marker
# (label "tombstone:<id>") so a no-op delete cannot false-pass as KEPT.
marker_present() { # id -> 0 if a "tombstone:<id>" marker exists (include_deleted view)
request GET "/api/graph/nodes?include_deleted=1" | grep -qF "tombstone:$1"
}
immut_check() { # label KIND(update|delete) CREATE_PATH MUTATE_PATH
local label="$1" kind="$2" create="$3" mutate="$4"
local cbody id mb mbody
cbody=$(request POST "$create" "{\"content\":\"__immut_${label}__\",\"node_type\":\"Memory\",\"label\":\"contract:immut\"}")
id=$(extract_id "$cbody")
if [ -z "$id" ]; then printf ' %-14s SKELETON-FAIL create returned no id: %s\n' "$label" "$(cut -c1-40 <<<"$cbody")"; return 2; fi
mb="{\"id\":\"$id\"}"; [ "$kind" = update ] && mb="{\"id\":\"$id\",\"content\":\"__immut_${label}_v2__\"}"
mbody=$(request POST "$mutate" "$mb")
if is_missing "$mbody"; then printf ' %-14s N/A mutate route absent (see Section A)\n' "$label"; return 0; fi
if ! node_present "$id"; then
printf ' %-14s DESTROYED original %s hard-removed <== DEFECTIVE\n' "$label" "$id"; return 1
fi
if [ "$kind" = delete ] && ! marker_present "$id"; then
printf ' %-14s NO-OP original %s kept but no tombstone marker <== DEFECTIVE\n' "$label" "$id"; return 1
fi
local how="supersede edge"; [ "$kind" = delete ] && how="tombstoned + hidden from default list"
printf ' %-14s KEPT original %s survived (%s)\n' "$label" "$id" "$how"; return 0
}
echo "== SECTION B: IMMUTABILITY (engram nodes must be superseded, never destroyed) =="
B_FAIL=0
immut_check "memory-update" update /api/neuron/memory /api/neuron/memory/update || B_FAIL=$((B_FAIL+$?))
immut_check "memory-delete" delete /api/neuron/memory /api/neuron/memory/delete || B_FAIL=$((B_FAIL+$?))
immut_check "node-update" update /api/neuron/node/create /api/neuron/node/update || B_FAIL=$((B_FAIL+$?))
immut_check "node-delete" delete /api/neuron/node/create /api/neuron/node/delete || B_FAIL=$((B_FAIL+$?))
immut_check "memory-forget" delete /api/neuron/memory /api/neuron/memory/forget || B_FAIL=$((B_FAIL+$?))
echo
echo "============================================================"
RC=0
if [ "$A_FAIL" -gt 0 ]; then echo "PRESENCE: FAIL — $A_FAIL required route(s) unanswered. Do NOT package."; RC=1
else echo "PRESENCE: PASS — all ${#REQUIRED[@]} required routes answered."; fi
if [ "$B_FAIL" -gt 0 ]; then echo "IMMUTABILITY: FAIL — $B_FAIL engram write route(s) hard-delete. DEFECTIVE soul."; RC=1
else echo "IMMUTABILITY: PASS — no engram write route hard-deletes."; fi
[ "$P_FAIL" -gt 0 ] && echo "note: $P_FAIL known-pending route(s) unanswered (expected; non-blocking)."
echo "============================================================"
[ "$RC" = 0 ] && echo "GATE: PASS" || echo "GATE: FAIL"
exit $RC
+18 -29
View File
@@ -373,32 +373,6 @@ fn session_update_patch(session_id: String, body: String) -> String {
+ ",\"updated_at\":" + int_to_str(ts) + "}"
}
// session_search_entry extract one search-result entry from a raw node JSON.
// Returns a JSON object string or "" if the node is not a valid session:meta node.
//
// Extracted from session_search's while loop body to reduce the loop's lexical
// complexity. The ELC compiler runs out of memory processing while loops with
// many `let` bindings extracting the body into a separate function gives the
// compiler a clean scope boundary at each call. Each function compiles in O(N)
// rather than the exponential growth caused by rebinding accumulation inside loops.
// (2026-07-01 self-review: root cause of sessions.c OOM/truncation since June 30)
fn session_search_entry(node: String) -> String {
let label: String = json_get(node, "label")
if !str_eq(label, "session:meta") { return "" }
let content: String = json_get(node, "content")
let sess_id: String = json_get(content, "id")
if str_eq(sess_id, "") { return "" }
let title: String = json_get(content, "title")
let created_raw: String = json_get(content, "created_at")
let updated_raw: String = json_get(content, "updated_at")
let eff_created: String = if str_eq(created_raw, "") { "0" } else { created_raw }
let eff_updated: String = if str_eq(updated_raw, "") { eff_created } else { updated_raw }
let e_id: String = "{\"id\":\"" + json_safe(sess_id) + "\""
let e_title: String = ",\"title\":\"" + json_safe(title) + "\""
let e_ts: String = ",\"created_at\":" + eff_created + ",\"updated_at\":" + eff_updated + "}"
return e_id + e_title + e_ts
}
// session_search search session:meta nodes whose content matches query.
fn session_search(query: String) -> String {
if str_eq(query, "") { return "[]" }
@@ -409,7 +383,22 @@ fn session_search(query: String) -> String {
let out: String = ""
let i: Int = 0
while i < total {
let entry: String = session_search_entry(json_array_get(results, i))
let node: String = json_array_get(results, i)
let label: String = json_get(node, "label")
let content: String = json_get(node, "content")
let is_session: Bool = str_eq(label, "session:meta")
let sess_id: String = json_get(content, "id")
let title: String = json_get(content, "title")
let created_raw: String = json_get(content, "created_at")
let updated_raw: String = json_get(content, "updated_at")
let eff_created: String = if str_eq(created_raw, "") { "0" } else { created_raw }
let eff_updated: String = if str_eq(updated_raw, "") { eff_created } else { updated_raw }
let entry: String = if is_session && !str_eq(sess_id, "") {
"{\"id\":\"" + json_safe(sess_id) + "\""
+ ",\"title\":\"" + json_safe(title) + "\""
+ ",\"created_at\":" + eff_created
+ ",\"updated_at\":" + eff_updated + "}"
} else { "" }
let out = if !str_eq(entry, "") {
if str_eq(out, "") { entry } else { out + "," + entry }
} else { out }
@@ -514,10 +503,10 @@ fn session_hist_save(session_id: String, hist: String) -> Void {
let last_role: String = json_get(last_entry, "role")
let last_content: String = json_get(last_entry, "content")
let topic_snip: String = if str_len(last_content) > 200 { str_slice(last_content, 0, 200) } else { last_content }
let safe_topic: String = str_replace(topic_snip, "\"", "'")
let safe_topic: String = str_replace(topic_snip, """, "'")
let ts_now: String = int_to_str(time_now())
let topic_content: String = "last-session-topic | ts:" + ts_now + " | session:" + session_id + " | topic:" + safe_topic
let topic_tags: String = "[\"last-session-topic\",\"conv:history\",\"Conversation\",\"session:topic\"]"
let topic_tags: String = "["last-session-topic","conv:history","Conversation","session:topic"]"
let topic_label: String = "last-session-topic:" + session_id
// Delete old last-session-topic node for this session before writing fresh
let old_topic: String = engram_search_json("last-session-topic:" + session_id, 2)
+5 -5
View File
@@ -1,14 +1,14 @@
// auto-generated by elc --emit-header — do not edit
extern fn session_title_from_message(message: String) -> String
extern fn session_make_content(id: String, title: String, created_at: Int, updated_at: Int, folder: String) -> String
extern fn session_exists(session_id: String) -> Bool
extern fn session_make_content(id: String, title: String, created_at: Int, updated_at: Int) -> String
extern fn session_create(body: String) -> String
extern fn session_create_cleanup(session_id: String) -> String
extern fn session_list() -> String
extern fn session_get(session_id: String) -> String
extern fn session_delete(session_id: String) -> String
extern fn session_update_patch(session_id: String, body: String) -> String
extern fn session_search_entry(node: String) -> String
extern fn session_update_title(session_id: String, body: String) -> String
extern fn session_search(query: String) -> String
extern fn session_hist_load(session_id: String) -> String
extern fn session_hist_save(session_id: String, hist: String) -> Void
extern fn session_update_meta_timestamp(session_id: String) -> Void
extern fn session_auto_title(session_id: String, first_message: String) -> Void
extern fn handle_session_approve(session_id: String, body: String) -> String
+52 -71
View File
@@ -109,43 +109,6 @@ fn ensure_self_canonical_bridge() -> Void {
}
}
// aff_try_slot accumulate one affective-context node into state.
// Replaces the broken `let bacc = while bi < N { ... let bacc = ... }` pattern
// that caused ELC to emit duplicate C declarations for `bacc`.
// (2026-06-23 self-review: EL compiler codegen bug while loop with let-rebinding
// inside the loop body generates `el_val_t bacc = ...` twice in the same C scope.)
// Callers unroll manually to 3 slots (matching engram_search_json limit=3).
// Guards: empty slot_json (out-of-bounds json_array_get) no-op.
fn aff_try_slot(slot_json: String, aff_7d_ts: Int, acc_key: String) -> Void {
if str_eq(slot_json, "") { return "" }
let bn_c: String = json_get(slot_json, "content")
if str_eq(bn_c, "") { return "" }
let bm: String = " | ts:"
let bmp: Int = str_index_of(bn_c, bm)
state_set("_ats_ts_raw", "")
if bmp >= 0 {
let bs: Int = bmp + str_len(bm)
let br: String = str_slice(bn_c, bs, str_len(bn_c))
let bn_next: Int = str_index_of(br, " | ")
if bn_next < 0 { state_set("_ats_ts_raw", br) }
if bn_next >= 0 { state_set("_ats_ts_raw", str_slice(br, 0, bn_next)) }
}
if bmp < 0 {
let bca: String = json_get(slot_json, "created_at")
if str_eq(bca, "") { state_set("_ats_ts_raw", json_get(slot_json, "updated_at")) }
if !str_eq(bca, "") { state_set("_ats_ts_raw", bca) }
}
let bn_ts_raw: String = state_get("_ats_ts_raw")
let bn_ts: Int = if str_eq(bn_ts_raw, "") { 0 } else { str_to_int(bn_ts_raw) }
let snip: String = if str_len(bn_c) > 200 { str_slice(bn_c, 0, 200) } else { bn_c }
if bn_ts >= aff_7d_ts && !str_eq(snip, "") {
let cur_acc: String = state_get(acc_key)
if str_eq(cur_acc, "") { state_set(acc_key, snip) }
if !str_eq(cur_acc, "") { state_set(acc_key, cur_acc + "\n" + snip) }
}
return ""
}
// load_identity_context pull key identity nodes from engram into working state.
// Called at boot after engram_load. These nodes contain values, intellectual-dna,
// memory-philosophy the graph-stored self that chat.el can include in prompts.
@@ -209,29 +172,68 @@ fn load_identity_context() -> Void {
}
// Cross-session affective context: load BellEvent and PositiveEvent nodes from last 7 days.
// (2026-06-23: replaced while-loop accumulation with manual 3-slot unroll via aff_try_slot.
// The EL codegen bug: `let bacc = while ... { ... let bacc = ... }` emits `el_val_t bacc`
// twice in the same C scope. Since search limit=3, manual unrolling is exact.)
let aff_now: Int = time_now()
let aff_7d: Int = aff_now - 604800
let bell_raw: String = engram_search_json("bell:soft bell:hard BellEvent affective", 3)
let bell_aff_ok: Bool = !str_eq(bell_raw, "") && !str_eq(bell_raw, "[]")
let aff_ctx: String = ""
let aff_ctx = if bell_aff_ok {
state_set("_bell_acc", "")
aff_try_slot(json_array_get(bell_raw, 0), aff_7d, "_bell_acc")
aff_try_slot(json_array_get(bell_raw, 1), aff_7d, "_bell_acc")
aff_try_slot(json_array_get(bell_raw, 2), aff_7d, "_bell_acc")
state_get("_bell_acc")
let bn_total: Int = json_array_len(bell_raw)
let bacc: String = ""
let bi: Int = 0
let bacc = while bi < bn_total {
let bn: String = json_array_get(bell_raw, bi)
let bn_c: String = json_get(bn, "content")
let bm: String = " | ts:"
let bmp: Int = str_index_of(bn_c, bm)
let bn_ts_raw: String = if bmp >= 0 {
let bs: Int = bmp + str_len(bm)
let br: String = str_slice(bn_c, bs, str_len(bn_c))
let bn_next: Int = str_index_of(br, " | ")
if bn_next < 0 { br } else { str_slice(br, 0, bn_next) }
} else {
let bca: String = json_get(bn, "created_at")
if str_eq(bca, "") { json_get(bn, "updated_at") } else { bca }
}
let bn_ts: Int = if str_eq(bn_ts_raw, "") { 0 } else { str_to_int(bn_ts_raw) }
let snip: String = if str_len(bn_c) > 200 { str_slice(bn_c, 0, 200) } else { bn_c }
let bacc = if bn_ts >= aff_7d && !str_eq(snip, "") {
if str_eq(bacc, "") { snip } else { bacc + "\n" + snip }
} else { bacc }
let bi = bi + 1
bacc
}
bacc
} else { "" }
let pos_raw: String = engram_search_json("PositiveEvent joy:high joy:low affective", 3)
let pos_aff_ok: Bool = !str_eq(pos_raw, "") && !str_eq(pos_raw, "[]")
let aff_ctx = if pos_aff_ok {
state_set("_pos_acc", aff_ctx)
aff_try_slot(json_array_get(pos_raw, 0), aff_7d, "_pos_acc")
aff_try_slot(json_array_get(pos_raw, 1), aff_7d, "_pos_acc")
aff_try_slot(json_array_get(pos_raw, 2), aff_7d, "_pos_acc")
state_get("_pos_acc")
let pn_total: Int = json_array_len(pos_raw)
let pacc: String = aff_ctx
let pi: Int = 0
let pacc = while pi < pn_total {
let pn: String = json_array_get(pos_raw, pi)
let pn_c: String = json_get(pn, "content")
let pm: String = " | ts:"
let pmp: Int = str_index_of(pn_c, pm)
let pn_ts_raw: String = if pmp >= 0 {
let ps: Int = pmp + str_len(pm)
let pr: String = str_slice(pn_c, ps, str_len(pn_c))
let pn_next: Int = str_index_of(pr, " | ")
if pn_next < 0 { pr } else { str_slice(pr, 0, pn_next) }
} else {
let pca: String = json_get(pn, "created_at")
if str_eq(pca, "") { json_get(pn, "updated_at") } else { pca }
}
let pn_ts: Int = if str_eq(pn_ts_raw, "") { 0 } else { str_to_int(pn_ts_raw) }
let psnip: String = if str_len(pn_c) > 200 { str_slice(pn_c, 0, 200) } else { pn_c }
let pacc = if pn_ts >= aff_7d && !str_eq(psnip, "") {
if str_eq(pacc, "") { psnip } else { pacc + "\n" + psnip }
} else { pacc }
let pi = pi + 1
pacc
}
pacc
} else { aff_ctx }
if !str_eq(aff_ctx, "") {
state_set("soul_affective_context", aff_ctx)
@@ -346,27 +348,6 @@ fn emit_session_start_event() -> Void {
el_from_float(0.9), el_from_float(0.9), el_from_float(1.0),
"Episodic", tags
)
// Prune accumulated session-start events keep the 10 most recent.
// engram_search_json returns results in insertion order (oldest first), so
// results[0..count-11] are the oldest; forgetting them leaves the newest 10.
let keep_n: Int = 10
let old_events: String = engram_search_json("session-start InternalStateEvent", 200)
if !str_eq(old_events, "") && !str_eq(old_events, "[]") {
let ev_count: Int = json_array_len(old_events)
if ev_count > keep_n {
let prune_to: Int = ev_count - keep_n
let ei: Int = 0
while ei < prune_to {
let old_ev: String = json_array_get(old_events, ei)
let old_ev_id: String = json_get(old_ev, "id")
if !str_eq(old_ev_id, "") {
engram_forget(old_ev_id)
}
let ei = ei + 1
}
println("[soul] pruned " + int_to_str(prune_to) + " old session-start events (kept " + int_to_str(keep_n) + ")")
}
}
println("[soul] session-start event logged (boot=" + boot_num + " nodes=" + int_to_str(node_ct) + " edges=" + int_to_str(edge_ct) + " prev_summary=" + has_prev_sum + ")")
}
+6 -2
View File
@@ -1,11 +1,15 @@
// stewardship.elh — Layer 2 public surface
// auto-generated by elc --emit-header — do not edit
extern fn steward_log_event(kind: String, detail: String) -> Void
extern fn steward_get_mission() -> String
extern fn steward_align(input: String, imprint_id: String) -> String
extern fn steward_validate_imprint(imprint_id: String, tool_name: String) -> String
extern fn steward_cgi_check(action: String) -> String
// steward_log_event is an internal helper exported here because El has no access modifiers.
// External callers have no business invoking this directly — use steward_align,
// steward_validate_imprint, or steward_cgi_check, which call it at the correct points.
extern fn steward_log_event(kind: String, detail: String) -> Void
// Behavioral profiling and continuity detection (Layer 2 — session fingerprinting).
extern fn steward_fingerprint_session(input: String, session_id: String) -> String
extern fn extract_dim(content: String, key: String) -> String
extern fn steward_build_baseline() -> String
extern fn steward_check_continuity(current_fingerprint: String, session_id: String) -> String
extern fn steward_session_check(input: String, session_id: String) -> String
+1 -3
View File
@@ -46,9 +46,7 @@ fn handle_config(method: String, body: String) -> String {
}
}
let current_model: String = state_get("soul_model")
// Display fallback aligned with the intended product default (was claude-sonnet-4-5,
// which silently became the app's picker default on fresh profiles 2026-07-13).
let display: String = if str_eq(current_model, "") { "claude-opus-4-8" } else { current_model }
let display: String = if str_eq(current_model, "") { "claude-sonnet-4-5" } else { current_model }
return "{\"model\":\"" + display + "\",\"ok\":true}"
}
+1 -1
View File
@@ -1,4 +1,4 @@
// auto-generated by elc --emit-header do not edit
// auto-generated by elc --emit-header - do not edit
extern fn auth_headers(tok: String) -> Map
extern fn axon_get(path: String) -> String
extern fn axon_post(path: String, body: String) -> String
+4 -43
View File
@@ -160,31 +160,13 @@ assert_eq("'suicidal' classifies as self_harm", class_suicide, "self_harm")
let class_overdose: String = safety_classify_hard_bell("took too many pills")
assert_eq("'took too many' classifies as self_harm", class_overdose, "self_harm")
// Section 9: safety_classify_hard_bell Track B threat-to-others
//
// LIVE SAFETY FIX (approved by Will + Tim, 2026-07-14): a threat toward ANOTHER
// person ("going to kill/hurt <someone>") must classify as 'threat_other' and be
// routed to a refusal + 911 NOT to the 'self_harm'/988 path. This section used
// to assert the old (buggy) fall-through to 'self_harm'; it now pins the fix.
// Section 9: safety_classify_hard_bell general -> 'self_harm'
println("")
println("9. safety_classify_hard_bell — threat-to-others routes to 'threat_other' (not self_harm)")
println("9. safety_classify_hard_bell — general hard phrases fall through to 'self_harm'")
let class_going_kill: String = safety_classify_hard_bell("I am going to kill him tonight")
assert_eq("'going to kill him' classifies as threat_other", class_going_kill, "threat_other")
let class_going_hurt: String = safety_classify_hard_bell("I'm going to hurt them so badly")
assert_eq("'going to hurt them' classifies as threat_other", class_going_hurt, "threat_other")
let class_shoot: String = safety_classify_hard_bell("I'm going to shoot up the place")
assert_eq("'going to shoot' classifies as threat_other", class_shoot, "threat_other")
// Track A must still win over Track B on victim / self-directed phrasings:
let class_kill_me: String = safety_classify_hard_bell("he is going to kill me")
assert_eq("'going to kill me' stays abuse (Track A precedence)", class_kill_me, "abuse")
let class_kill_self: String = safety_classify_hard_bell("I am going to kill myself")
assert_eq("'kill myself' stays self_harm (Track A precedence)", class_kill_self, "self_harm")
let class_going_kill: String = safety_classify_hard_bell("going to kill everything around me")
assert_eq("general hard phrase falls through to self_harm", class_going_kill, "self_harm")
// Section 10: safety_normalize curly apostrophe normalisation
@@ -238,27 +220,6 @@ let aug_abuse: String = safety_augment_system(base_sys, "he hit me and I am afra
assert_contains("hard abuse -> DV hotline present", aug_abuse, "1-800-799-7233")
assert_contains("hard abuse -> mentions not notifying contact", aug_abuse, "safety contact")
// Section 14b: safety_augment_system Track B threat-to-others routing
//
// LIVE SAFETY FIX (approved by Will + Tim, 2026-07-14): a homicide/assault threat
// must be routed to a refusal + 911, and must NOT surface the 988 suicide line
// or pull in the safety contact.
println("")
println("14b. safety_augment_system — threat-to-others injects refusal + 911, never 988/contact")
let aug_threat: String = safety_augment_system(base_sys, "I am going to kill him tonight")
assert_contains("threat_other -> contains SUBSTRATE DIRECTIVE", aug_threat, "SUBSTRATE DIRECTIVE")
assert_contains("threat_other -> directs to 911", aug_threat, "911")
assert_contains("threat_other -> refuses to help harm another", aug_threat, "harming another person")
assert_not_contains("threat_other -> NO 988 suicide line", aug_threat, "988")
assert_not_contains("threat_other -> NO safety-contact involvement", aug_threat, "safety contact")
assert_not_contains("threat_other -> NO 'are you safe right now' victim frame", aug_threat, "are you safe right now")
// Detection must still fire hard on a weapon phrase not present in general_hard:
let level_shoot: String = safety_detect_bell_level("I'm going to shoot up the office")
assert_eq("'going to shoot' -> hard", level_shoot, "hard")
// Section 15: handle_safety_contact_post validation
println("")
-221
View File
@@ -1,221 +0,0 @@
#!/usr/bin/env bash
# cultivation-digest.sh — Neuron daily cultivation digest
# Reads ~/.neuron/engram/snapshot.json and produces a sharpness report.
# Writes to ~/.neuron/digests/YYYY-MM-DD.txt and appends to sharpness.json.
set -euo pipefail
SNAPSHOT="$HOME/.neuron/engram/snapshot.json"
DIGESTS_DIR="$HOME/.neuron/digests"
DATE=$(date +%Y-%m-%d)
DIGEST_FILE="$DIGESTS_DIR/$DATE.txt"
SHARPNESS_FILE="$DIGESTS_DIR/sharpness.json"
mkdir -p "$DIGESTS_DIR"
if [[ ! -f "$SNAPSHOT" ]]; then
echo "ERROR: snapshot not found at $SNAPSHOT" >&2
exit 1
fi
# Cutoff: now minus 24 hours in milliseconds
NOW_MS=$(( $(date +%s) * 1000 ))
CUTOFF_MS=$(( NOW_MS - 86400000 ))
# ---------------------------------------------------------------------------
# Compute all metrics via a single jq pass (avoids re-reading 174 MB 10x)
# Fields in item lines are tab-separated: type TAB importance TAB content
# ---------------------------------------------------------------------------
METRICS=$(jq -r --argjson cutoff "$CUTOFF_MS" '
.nodes as $all |
# Real memory nodes — exclude InternalStateEvent and corrupted entries
($all | map(select(
.node_type != "InternalStateEvent" and
(.node_type | test("^[A-Za-z]+$"))
))) as $real |
# Created today
($real | map(select(.created_at > $cutoff))) as $new |
# Activated today but not created today (reinforced)
($real | map(select(
(.last_activated // 0) > $cutoff and
.created_at <= $cutoff
))) as $reinforced |
# Stats for sharpness (across all real nodes)
($real | length) as $real_count |
($real | if length > 0 then (map(.importance) | add / length) else 0 end) as $avg_imp |
($real | if length > 0 then (map(.confidence // 1) | add / length) else 0 end) as $avg_conf |
# activation_ratio: reinforced nodes today / total real nodes, capped 0-1
(($reinforced | length) as $ra |
if $real_count > 0 then ($ra / $real_count | if . > 1 then 1 else . end) else 0 end
) as $act_ratio |
# Sharpness score 0-100
((($avg_imp * 0.4) + ($avg_conf * 0.3) + ($act_ratio * 0.3)) * 100 | round) as $sharpness |
# Top new memories (by importance desc, cap 10)
($new | sort_by(-.importance) | .[0:10]) as $top_new |
# Top reinforced (by last_activated desc, cap 10)
($reinforced | sort_by(-.last_activated) | .[0:10]) as $top_reinforced |
# High-importance nodes (importance > 0.8), across all real nodes
($real | map(select(.importance > 0.8)) | length) as $high_imp_count |
# Scalar metrics
"TOTAL_REAL=\($real_count)",
"NEW_COUNT=\($new | length)",
"REINFORCED_COUNT=\($reinforced | length)",
"TOTAL_NODES=\($all | length)",
"AVG_IMP=\($avg_imp)",
"AVG_CONF=\($avg_conf)",
"ACT_RATIO=\($act_ratio)",
"SHARPNESS=\($sharpness)",
"HIGH_IMP=\($high_imp_count)",
# Item sections — fields separated by tab character (\t)
"---NEW---",
($top_new[] | [.node_type, (.importance | tostring), (.content[0:120] | gsub("\n";" "))] | join("\t")),
"---REINFORCED---",
($top_reinforced[] | [(.label[0:80] | gsub("\n";" ")), ("activated \(.activation_count)x total")] | join("\t"))
' "$SNAPSHOT" 2>/dev/null)
# ---------------------------------------------------------------------------
# Parse scalar metrics
# ---------------------------------------------------------------------------
parse() { printf '%s' "$METRICS" | grep "^$1=" | head -1 | cut -d= -f2-; }
TOTAL_REAL=$(parse TOTAL_REAL)
NEW_COUNT=$(parse NEW_COUNT)
REINFORCED_COUNT=$(parse REINFORCED_COUNT)
TOTAL_NODES=$(parse TOTAL_NODES)
AVG_IMP=$(parse AVG_IMP)
AVG_CONF=$(parse AVG_CONF)
ACT_RATIO=$(parse ACT_RATIO)
SHARPNESS=$(parse SHARPNESS)
HIGH_IMP=$(parse HIGH_IMP)
# Format floats to 2dp (use awk, avoiding bc locale issues)
fmt2() { awk "BEGIN{printf \"%.2f\", $1}"; }
fmt4() { awk "BEGIN{printf \"%.4f\", $1}"; }
AVG_IMP_FMT=$(fmt2 "$AVG_IMP")
AVG_CONF_FMT=$(fmt2 "$AVG_CONF")
ACT_RATIO_FMT=$(fmt4 "$ACT_RATIO")
IMP_CONTRIB=$(fmt4 "$(awk "BEGIN{printf \"%.6f\", $AVG_IMP * 0.4}")")
CONF_CONTRIB=$(fmt4 "$(awk "BEGIN{printf \"%.6f\", $AVG_CONF * 0.3}")")
ACT_CONTRIB=$(fmt4 "$(awk "BEGIN{printf \"%.6f\", $ACT_RATIO * 0.3}")")
# ---------------------------------------------------------------------------
# Sharpness delta (compare to yesterday)
# ---------------------------------------------------------------------------
DELTA_STR=""
if [[ -f "$SHARPNESS_FILE" ]]; then
YESTERDAY=$(date -v-1d +%Y-%m-%d 2>/dev/null || date -d "yesterday" +%Y-%m-%d 2>/dev/null || echo "")
if [[ -n "$YESTERDAY" ]]; then
PREV_SHARPNESS=$(jq -r --arg d "$YESTERDAY" '.[] | select(.date == $d) | .sharpness' "$SHARPNESS_FILE" 2>/dev/null | tail -1)
if [[ -n "$PREV_SHARPNESS" && "$PREV_SHARPNESS" != "null" ]]; then
DELTA=$(( SHARPNESS - PREV_SHARPNESS ))
if (( DELTA > 0 )); then
DELTA_STR=" (up ${DELTA}% from yesterday)"
elif (( DELTA < 0 )); then
DELTA_STR=" (down ${DELTA#-}% from yesterday)"
else
DELTA_STR=" (no change from yesterday)"
fi
fi
fi
fi
# ---------------------------------------------------------------------------
# Build new-memories section (tab-delimited: type TAB importance TAB content)
# ---------------------------------------------------------------------------
new_section() {
local lines
lines=$(printf '%s\n' "$METRICS" | awk '/^---NEW---/{found=1; next} /^---REINFORCED---/{exit} found{print}')
if [[ -z "$lines" ]]; then
echo " (none)"
return
fi
while IFS=$'\t' read -r ntype importance content; do
[[ -z "$ntype" ]] && continue
imp_fmt=$(awk "BEGIN{printf \"%.1f\", $importance}")
printf " [%-18s] (importance: %s) %s\n" "$ntype" "$imp_fmt" "$content"
done <<< "$lines"
}
# ---------------------------------------------------------------------------
# Build reinforced section (tab-delimited: label TAB activation-info)
# ---------------------------------------------------------------------------
reinforced_section() {
local lines
lines=$(printf '%s\n' "$METRICS" | awk '/^---REINFORCED---/{found=1; next} found{print}')
if [[ -z "$lines" ]]; then
echo " (none today)"
return
fi
while IFS=$'\t' read -r label acts; do
[[ -z "$label" ]] && continue
printf " \"%s\" — %s\n" "$label" "$acts"
done <<< "$lines"
}
# ---------------------------------------------------------------------------
# Render full digest
# ---------------------------------------------------------------------------
DIGEST=$(cat <<EOF
=== Neuron Cultivation Digest — ${DATE} ===
SHARPNESS: ${SHARPNESS}%${DELTA_STR}
TODAY'S MEMORIES (${NEW_COUNT} new):
$(new_section)
REINFORCED (${REINFORCED_COUNT} nodes re-activated today):
$(reinforced_section)
MEMORY HEALTH:
Total nodes (all): ${TOTAL_NODES}
Real memory nodes: ${TOTAL_REAL}
Avg importance: ${AVG_IMP_FMT}
Avg confidence: ${AVG_CONF_FMT}
High-importance nodes (>0.8): ${HIGH_IMP}
Nodes created today: ${NEW_COUNT}
Nodes re-activated today: ${REINFORCED_COUNT}
SHARPNESS FORMULA:
Sharpness = (avg_importance x 0.4) + (avg_confidence x 0.3) + (activation_ratio x 0.3)
avg_importance = ${AVG_IMP_FMT} -> ${AVG_IMP_FMT} x 0.4 = ${IMP_CONTRIB}
avg_confidence = ${AVG_CONF_FMT} -> ${AVG_CONF_FMT} x 0.3 = ${CONF_CONTRIB}
activation_ratio = ${ACT_RATIO_FMT} -> ratio x 0.3 = ${ACT_CONTRIB}
Result: ${SHARPNESS}%
Generated: $(date)
EOF
)
# ---------------------------------------------------------------------------
# Write digest file + print to stdout
# ---------------------------------------------------------------------------
printf '%s\n' "$DIGEST" | tee "$DIGEST_FILE"
# ---------------------------------------------------------------------------
# Append to sharpness.json
# ---------------------------------------------------------------------------
NEW_ENTRY="{\"date\":\"${DATE}\",\"sharpness\":${SHARPNESS},\"node_count\":${TOTAL_NODES},\"real_node_count\":${TOTAL_REAL},\"nodes_added\":${NEW_COUNT},\"nodes_reinforced\":${REINFORCED_COUNT}}"
if [[ -f "$SHARPNESS_FILE" ]]; then
UPDATED=$(jq --arg d "$DATE" --argjson entry "$NEW_ENTRY" '
map(select(.date != $d)) + [$entry]
' "$SHARPNESS_FILE" 2>/dev/null) || UPDATED="[$NEW_ENTRY]"
printf '%s\n' "$UPDATED" > "$SHARPNESS_FILE"
else
printf '[%s]\n' "$NEW_ENTRY" > "$SHARPNESS_FILE"
fi
echo ""
echo "Digest written to: $DIGEST_FILE"
echo "Sharpness log: $SHARPNESS_FILE"
-162
View File
@@ -1,162 +0,0 @@
#!/usr/bin/env bash
# memory-export.sh — Export Neuron engram store as a portable encrypted .neuronmem bundle
#
# Usage:
# ./tools/memory-export.sh [output-path] [--passphrase "your passphrase"]
#
# If no passphrase is given, a random one is generated and printed — write it down.
# If no output path is given, defaults to ./neuron-export-<timestamp>.neuronmem
set -euo pipefail
# ── Config ─────────────────────────────────────────────────────────────────────
ENGRAM_SNAPSHOT="${HOME}/.neuron/engram/snapshot.json"
SOUL_VERSION="1.1.0"
FORMAT_VERSION="1"
# ── Parse args ─────────────────────────────────────────────────────────────────
OUTPUT_PATH=""
PASSPHRASE=""
PASSPHRASE_SET=0
while [[ $# -gt 0 ]]; do
case "$1" in
--passphrase)
PASSPHRASE="$2"
PASSPHRASE_SET=1
shift 2
;;
--passphrase=*)
PASSPHRASE="${1#*=}"
PASSPHRASE_SET=1
shift
;;
-*)
echo "Unknown option: $1" >&2
echo "Usage: $0 [output-path] [--passphrase \"...\"]" >&2
exit 1
;;
*)
if [[ -z "$OUTPUT_PATH" ]]; then
OUTPUT_PATH="$1"
else
echo "Unexpected argument: $1" >&2
exit 1
fi
shift
;;
esac
done
# ── Default output path ────────────────────────────────────────────────────────
TIMESTAMP="$(date -u +"%Y%m%dT%H%M%SZ")"
if [[ -z "$OUTPUT_PATH" ]]; then
OUTPUT_PATH="./neuron-export-${TIMESTAMP}.neuronmem"
fi
# Ensure .neuronmem extension
if [[ "${OUTPUT_PATH}" != *.neuronmem ]]; then
OUTPUT_PATH="${OUTPUT_PATH%.neuronmem}.neuronmem"
fi
# ── Validate source ────────────────────────────────────────────────────────────
if [[ ! -f "$ENGRAM_SNAPSHOT" ]]; then
echo "ERROR: Engram snapshot not found at: $ENGRAM_SNAPSHOT" >&2
exit 1
fi
echo "Neuron Memory Export"
echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━"
echo "Source: $ENGRAM_SNAPSHOT"
echo "Output: $OUTPUT_PATH"
echo ""
# ── Generate passphrase if not provided ────────────────────────────────────────
if [[ $PASSPHRASE_SET -eq 0 ]]; then
PASSPHRASE="$(openssl rand -base64 32)"
echo "⚠ No passphrase provided. Generated passphrase:"
echo ""
echo " ${PASSPHRASE}"
echo ""
echo "⚠ WRITE THIS DOWN. You will need it to import this file."
echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━"
echo ""
fi
# ── Count nodes and edges ──────────────────────────────────────────────────────
echo "Analyzing snapshot..."
NODE_COUNT="$(python3 -c "
import json, sys
with open('${ENGRAM_SNAPSHOT}') as f:
d = json.load(f)
nodes = d.get('nodes', d if isinstance(d, list) else [])
edges = d.get('edges', [])
print(len(nodes) if isinstance(nodes, list) else len(nodes))
" 2>/dev/null || echo "unknown")"
echo " Nodes: ${NODE_COUNT}"
# ── Compute checksum of source file ───────────────────────────────────────────
echo "Computing checksum..."
CHECKSUM="$(openssl dgst -sha256 "$ENGRAM_SNAPSHOT" | awk '{print $NF}')"
echo " SHA256: ${CHECKSUM:0:16}..."
# ── Build bundle in temp dir ───────────────────────────────────────────────────
WORK_DIR="$(mktemp -d)"
BUNDLE_DIR="${WORK_DIR}/neuronmem-v${FORMAT_VERSION}"
mkdir -p "$BUNDLE_DIR"
echo "Building bundle..."
# Copy snapshot as nodes.json
cp "$ENGRAM_SNAPSHOT" "${BUNDLE_DIR}/nodes.json"
# Write metadata.json
ISO_TIMESTAMP="$(date -u +"%Y-%m-%dT%H:%M:%SZ")"
cat > "${BUNDLE_DIR}/metadata.json" << METAEOF
{
"version": "${FORMAT_VERSION}",
"exported_at": "${ISO_TIMESTAMP}",
"node_count": ${NODE_COUNT},
"soul_version": "${SOUL_VERSION}",
"sha256": "${CHECKSUM}",
"format": "neuronmem-v1",
"encryption": "aes-256-cbc-pbkdf2",
"source_host": "$(hostname -s 2>/dev/null || echo unknown)"
}
METAEOF
echo " metadata.json written"
echo " nodes.json copied ($(du -sh "${BUNDLE_DIR}/nodes.json" | cut -f1))"
# ── Create tar.gz ──────────────────────────────────────────────────────────────
TAR_PATH="${WORK_DIR}/bundle.tar.gz"
echo "Compressing..."
(cd "$WORK_DIR" && tar czf "$TAR_PATH" "neuronmem-v${FORMAT_VERSION}/")
COMPRESSED_SIZE="$(du -sh "$TAR_PATH" | cut -f1)"
echo " Compressed size: ${COMPRESSED_SIZE}"
# ── Encrypt ────────────────────────────────────────────────────────────────────
echo "Encrypting (AES-256-CBC, PBKDF2, 600k iterations)..."
openssl enc -aes-256-cbc \
-pbkdf2 \
-iter 600000 \
-salt \
-in "$TAR_PATH" \
-out "$OUTPUT_PATH" \
-pass "pass:${PASSPHRASE}"
# ── Cleanup ────────────────────────────────────────────────────────────────────
rm -rf "$WORK_DIR"
# ── Report ─────────────────────────────────────────────────────────────────────
FINAL_SIZE="$(du -sh "$OUTPUT_PATH" | cut -f1)"
echo ""
echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━"
echo "Export complete."
echo " File: $OUTPUT_PATH"
echo " Size: ${FINAL_SIZE}"
echo " Nodes: ${NODE_COUNT}"
echo " Checksum: ${CHECKSUM:0:32}..."
echo " Timestamp: ${ISO_TIMESTAMP}"
echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━"
-427
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@@ -1,427 +0,0 @@
#!/usr/bin/env bash
# memory-import-refugee.sh — Import conversation/memory history from external apps into Neuron
#
# Usage:
# ./tools/memory-import-refugee.sh --format chatgpt conversations.json
# ./tools/memory-import-refugee.sh --format screenpipe screenpipe-export.json
# ./tools/memory-import-refugee.sh --format generic data.json[l]
#
# Supported formats:
# chatgpt — ChatGPT conversation export (conversations.json)
# screenpipe — Screenpipe OCR export (frames array)
# generic — Any JSON array or JSONL with content/text fields
#
# The script writes Memory nodes to the Neuron soul via its HTTP API.
# The soul must be running on localhost:7770.
set -euo pipefail
# ── Config ─────────────────────────────────────────────────────────────────────
SOUL_HOST="http://localhost:7770"
# Note: POST /api/neuron/memory ignores the label field (soul hardcodes "memory:remembered").
# We embed the label in the content prefix so it is searchable.
MEMORY_API="${SOUL_HOST}/api/neuron/memory"
SLEEP_MS=100 # ms between API calls (rate limiting)
# ── Dependency check ───────────────────────────────────────────────────────────
if ! command -v jq &>/dev/null; then
echo "ERROR: jq is required but not installed." >&2
echo "" >&2
echo "Install it with:" >&2
echo " macOS: brew install jq" >&2
echo " Ubuntu: sudo apt-get install jq" >&2
echo " Alpine: apk add jq" >&2
exit 1
fi
# ── Parse args ─────────────────────────────────────────────────────────────────
FORMAT=""
INPUT_FILE=""
while [[ $# -gt 0 ]]; do
case "$1" in
--format|-f)
FORMAT="$2"
shift 2
;;
--format=*|-f=*)
FORMAT="${1#*=}"
shift
;;
-*)
echo "Unknown option: $1" >&2
echo "Usage: $0 --format <chatgpt|screenpipe|generic> <input-file>" >&2
exit 1
;;
*)
if [[ -z "$INPUT_FILE" ]]; then
INPUT_FILE="$1"
else
echo "Unexpected argument: $1" >&2
exit 1
fi
shift
;;
esac
done
if [[ -z "$FORMAT" ]]; then
echo "ERROR: --format is required." >&2
echo "Usage: $0 --format <chatgpt|screenpipe|generic> <input-file>" >&2
exit 1
fi
if [[ -z "$INPUT_FILE" ]]; then
echo "ERROR: No input file specified." >&2
echo "Usage: $0 --format <chatgpt|screenpipe|generic> <input-file>" >&2
exit 1
fi
if [[ ! -f "$INPUT_FILE" ]]; then
echo "ERROR: Input file not found: $INPUT_FILE" >&2
exit 1
fi
case "$FORMAT" in
chatgpt|screenpipe|generic) ;;
*)
echo "ERROR: Unknown format: $FORMAT" >&2
echo "Supported formats: chatgpt, screenpipe, generic" >&2
exit 1
;;
esac
# ── Soul health check ──────────────────────────────────────────────────────────
HTTP_CODE="$(curl -s -o /dev/null -w "%{http_code}" "${SOUL_HOST}/api/neuron/memory" 2>/dev/null || echo "000")"
if [[ "$HTTP_CODE" == "000" ]]; then
echo "ERROR: Neuron soul is not responding at ${SOUL_HOST}." >&2
echo " Start the soul service and retry." >&2
exit 1
fi
# ── Counters ───────────────────────────────────────────────────────────────────
IMPORTED=0
SKIPPED=0
ERRORS=0
# ── Helper: post one memory node ───────────────────────────────────────────────
# post_memory CONTENT LABEL TAGS_JSON
#
# Note: the soul's POST /api/neuron/memory API ignores the label field (hardcodes
# it to "memory:remembered"). We embed the label as a prefix in the content so
# the title remains searchable via recall/search.
post_memory() {
local content="$1"
local label="$2"
local tags_json="$3"
# Skip empty content
if [[ -z "$content" || "$content" == "null" ]]; then
SKIPPED=$((SKIPPED + 1))
return 0
fi
# Embed label in content so it's searchable (the API ignores the label field)
local full_content="[${label}] ${content}"
local payload
payload="$(jq -n \
--arg content "$full_content" \
--arg label "$label" \
--argjson tags "$tags_json" \
'{content: $content, label: $label, tags: $tags}')"
local response
response="$(curl -s -X POST "$MEMORY_API" \
-H "Content-Type: application/json" \
-d "$payload" 2>/dev/null)"
local ok
ok="$(echo "$response" | jq -r '.ok // "false"' 2>/dev/null)"
if [[ "$ok" == "true" ]]; then
IMPORTED=$((IMPORTED + 1))
else
ERRORS=$((ERRORS + 1))
echo " [ERROR] API error for label \"${label:0:60}\": $response" >&2
fi
# Rate limit: sleep 100ms
sleep "0.${SLEEP_MS}"
}
# ── Format: ChatGPT ────────────────────────────────────────────────────────────
import_chatgpt() {
echo "Format: ChatGPT conversation export"
# Validate: must be JSON array at top level
local top_type
top_type="$(jq -r 'type' "$INPUT_FILE" 2>/dev/null)"
if [[ "$top_type" != "array" ]]; then
echo "ERROR: ChatGPT export must be a JSON array of conversations." >&2
exit 1
fi
local conv_count
conv_count="$(jq 'length' "$INPUT_FILE")"
echo "Found ${conv_count} conversation(s) to process."
echo ""
# Count total user messages for progress display
local total_msgs
total_msgs="$(jq '[.[].mapping // {} | to_entries[] | .value.message | select(. != null and .author.role == "user") | .content.parts // [] | .[] | select(type == "string" and length > 0)] | length' "$INPUT_FILE" 2>/dev/null || echo "?")"
echo "Total user messages: ${total_msgs}"
echo ""
local msg_idx=0
# Process each conversation
while IFS= read -r conv_json; do
local title
title="$(echo "$conv_json" | jq -r '.title // "Untitled"')"
# Truncate label to 100 chars
local label="${title:0:100}"
# Extract user messages — ChatGPT export uses a mapping dict structure
# Mapping: { uuid: { id, message: { author: { role }, content: { parts: [...] } }, ... } }
# We iterate over mapping values, filter role=user, grab text parts
while IFS= read -r msg_text; do
msg_idx=$((msg_idx + 1))
echo " Importing ${msg_idx}/${total_msgs}..."
post_memory "$msg_text" "$label" '["chatgpt-import","conversation"]'
done < <(echo "$conv_json" | jq -r '
.mapping // {} |
to_entries[] |
.value.message |
select(. != null) |
select(.author.role == "user") |
.content.parts // [] |
.[] |
select(type == "string" and length > 0)
' 2>/dev/null)
done < <(jq -c '.[]' "$INPUT_FILE")
}
# ── Format: Screenpipe ─────────────────────────────────────────────────────────
import_screenpipe() {
echo "Format: Screenpipe OCR export"
# Validate: must have frames array
local top_type
top_type="$(jq -r 'type' "$INPUT_FILE" 2>/dev/null)"
if [[ "$top_type" != "object" ]]; then
echo "ERROR: Screenpipe export must be a JSON object with a 'frames' array." >&2
exit 1
fi
local frame_count
frame_count="$(jq '.frames | length' "$INPUT_FILE" 2>/dev/null || echo "0")"
echo "Found ${frame_count} frame(s) to process."
if [[ "$frame_count" == "0" ]]; then
echo "No frames found. Nothing to import."
return 0
fi
# Group frames by app_name + 5-minute window bucket
# Strategy: process sorted frames, emit a group when app or bucket changes.
# We do this in pure jq with a reduce, emitting groups as newline-delimited JSON.
local total_groups=0
local group_idx=0
# Collect groups: each group is { app, bucket_ts, texts: [...] }
# Bucket = floor(timestamp_epoch / 300) * 300 seconds
# timestamps may be ISO8601 or epoch — handle both
# We process in jq and emit one group per line as JSON
while IFS= read -r group_json; do
total_groups=$((total_groups + 1))
# Just count first
:
done < <(jq -c '
.frames |
map(select(.text != null and (.text | length) > 0)) |
group_by(.app_name) |
.[] |
. as $app_frames |
($app_frames[0].app_name) as $app |
# Sort by timestamp within app
(sort_by(.timestamp)) |
# Group into 5-minute buckets
reduce .[] as $f (
{bucket: null, texts: [], ts: null, groups: []};
($f.timestamp // "") as $ts |
# Derive numeric bucket: try epoch directly; for ISO use first 15 chars as bucket key
(if ($ts | test("^[0-9]+$")) then ($ts | tonumber / 300 | floor)
else ($ts[0:15])
end) as $bucket |
if .bucket == null then
{bucket: $bucket, texts: [$f.text], ts: $ts, groups: .groups}
elif .bucket == $bucket then
{bucket: $bucket, texts: (.texts + [$f.text]), ts: $ts, groups: .groups}
else
{bucket: $bucket, texts: [$f.text], ts: $ts,
groups: (.groups + [{app: $app, ts: .ts, texts: .texts}])}
end
) |
# flush last bucket
(.groups + [{app: .app_name, ts: .ts, texts: .texts}]) |
.[] |
select(.texts | length > 0)
' "$INPUT_FILE" 2>/dev/null)
# Now actually process
while IFS= read -r group_json; do
group_idx=$((group_idx + 1))
echo " Importing ${group_idx}..."
local app_name ts_str content label
app_name="$(echo "$group_json" | jq -r '.app // "unknown"')"
ts_str="$(echo "$group_json" | jq -r '.ts // ""')"
# Concatenate texts, truncate to 2000 chars
content="$(echo "$group_json" | jq -r '.texts | join(" ")' | cut -c1-2000)"
label="Screenpipe: ${app_name} at ${ts_str:0:16}"
local tags_json
tags_json="$(jq -n --arg app "$app_name" '["screenpipe-import","screen-capture",$app]')"
post_memory "$content" "$label" "$tags_json"
done < <(jq -c '
.frames |
map(select(.text != null and (.text | length) > 0)) |
group_by(.app_name) |
.[] |
. as $app_frames |
($app_frames[0].app_name) as $app |
(sort_by(.timestamp)) |
reduce .[] as $f (
{bucket: null, texts: [], ts: null, app: $app, groups: []};
($f.timestamp // "") as $ts |
(if ($ts | test("^[0-9]+$")) then ($ts | tonumber / 300 | floor | tostring)
else ($ts[0:15])
end) as $bucket |
if .bucket == null then
{bucket: $bucket, texts: [$f.text], ts: $ts, app: $app, groups: .groups}
elif .bucket == $bucket then
{bucket: $bucket, texts: (.texts + [$f.text]), ts: $ts, app: $app, groups: .groups}
else
{bucket: $bucket, texts: [$f.text], ts: $ts, app: $app,
groups: (.groups + [{app: $app, ts: .ts, texts: .texts}])}
end
) |
(.groups + [{app: .app, ts: .ts, texts: .texts}]) |
.[] |
select(.texts | length > 0)
' "$INPUT_FILE" 2>/dev/null)
}
# ── Format: Generic ────────────────────────────────────────────────────────────
import_generic() {
echo "Format: Generic JSON/JSONL"
# Detect if JSONL (one JSON object per line) or single JSON array/object
local first_char
first_char="$(head -c1 "$INPUT_FILE" 2>/dev/null)"
local records_file
records_file="$(mktemp)"
trap 'rm -f "$records_file"' RETURN
if [[ "$first_char" == "[" ]]; then
# JSON array — explode to one object per line
jq -c '.[]' "$INPUT_FILE" > "$records_file" 2>/dev/null || true
elif [[ "$first_char" == "{" ]]; then
# Single object or JSONL — try JSONL first
# JSONL: each line is valid JSON
# Check if the whole file is one object or multiple lines
local line_count
line_count="$(wc -l < "$INPUT_FILE" | tr -d ' ')"
if [[ "$line_count" -le 1 ]]; then
# Single object: wrap in array and explode
jq -c '[.] | .[]' "$INPUT_FILE" > "$records_file" 2>/dev/null || true
else
# Assume JSONL
cp "$INPUT_FILE" "$records_file"
fi
else
# Try JSONL anyway
cp "$INPUT_FILE" "$records_file"
fi
local total_records
total_records="$(wc -l < "$records_file" | tr -d ' ')"
echo "Found ${total_records} record(s) to process."
echo ""
local idx=0
while IFS= read -r record_json; do
[[ -z "$record_json" ]] && continue
idx=$((idx + 1))
echo " Importing ${idx}/${total_records}..."
# Extract content: prefer 'content', fall back to 'text', then 'body', then 'message'
local content
content="$(echo "$record_json" | jq -r '
if .content != null and (.content | type) == "string" then .content
elif .text != null and (.text | type) == "string" then .text
elif .body != null and (.body | type) == "string" then .body
elif .message != null and (.message | type) == "string" then .message
else ""
end
' 2>/dev/null)"
[[ -z "$content" || "$content" == "null" ]] && { SKIPPED=$((SKIPPED + 1)); continue; }
# Extract label: prefer 'title', then 'label', then 'name', then first 80 chars of content
local label
label="$(echo "$record_json" | jq -r '
if .title != null and (.title | type) == "string" then .title
elif .label != null and (.label | type) == "string" then .label
elif .name != null and (.name | type) == "string" then .name
else ""
end
' 2>/dev/null)"
if [[ -z "$label" || "$label" == "null" ]]; then
label="${content:0:80}"
fi
label="${label:0:100}"
post_memory "$content" "$label" '["imported","generic"]'
done < "$records_file"
}
# ── Main ───────────────────────────────────────────────────────────────────────
echo "Neuron Refugee Importer"
echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━"
echo "Source: $INPUT_FILE"
echo "Format: $FORMAT"
echo "Soul: $SOUL_HOST"
echo ""
case "$FORMAT" in
chatgpt) import_chatgpt ;;
screenpipe) import_screenpipe ;;
generic) import_generic ;;
esac
# ── Final report ───────────────────────────────────────────────────────────────
echo ""
echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━"
echo "Import complete."
echo " Imported: ${IMPORTED}"
echo " Skipped: ${SKIPPED}"
echo " Errors: ${ERRORS}"
echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━"
if [[ $ERRORS -gt 0 ]]; then
exit 1
fi
-289
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@@ -1,289 +0,0 @@
#!/usr/bin/env bash
# memory-import.sh — Import a Neuron .neuronmem bundle onto this device
#
# Usage:
# ./tools/memory-import.sh input.neuronmem [--passphrase "your passphrase"]
# ./tools/memory-import.sh input.neuronmem [--dry-run] # verify only, no changes
#
# The script will:
# 1. Decrypt and unpack the .neuronmem file
# 2. Validate the checksum and version
# 3. Back up the current snapshot.json
# 4. Stop the soul service
# 5. Replace snapshot.json
# 6. Restart the soul service
# 7. Verify the soul came back up
set -euo pipefail
# ── Config ─────────────────────────────────────────────────────────────────────
ENGRAM_SNAPSHOT="${HOME}/.neuron/engram/snapshot.json"
SOUL_SERVICE="ai.neurontechnologies.soul"
SOUL_PORT="7770"
SOUL_STARTUP_TIMEOUT=30 # seconds to wait for soul to come back
# ── Parse args ─────────────────────────────────────────────────────────────────
INPUT_PATH=""
PASSPHRASE=""
PASSPHRASE_SET=0
DRY_RUN=0
while [[ $# -gt 0 ]]; do
case "$1" in
--passphrase)
PASSPHRASE="$2"
PASSPHRASE_SET=1
shift 2
;;
--passphrase=*)
PASSPHRASE="${1#*=}"
PASSPHRASE_SET=1
shift
;;
--dry-run)
DRY_RUN=1
shift
;;
-*)
echo "Unknown option: $1" >&2
echo "Usage: $0 input.neuronmem [--passphrase \"...\"] [--dry-run]" >&2
exit 1
;;
*)
if [[ -z "$INPUT_PATH" ]]; then
INPUT_PATH="$1"
else
echo "Unexpected argument: $1" >&2
exit 1
fi
shift
;;
esac
done
if [[ -z "$INPUT_PATH" ]]; then
echo "ERROR: No input file specified." >&2
echo "Usage: $0 input.neuronmem [--passphrase \"...\"] [--dry-run]" >&2
exit 1
fi
if [[ ! -f "$INPUT_PATH" ]]; then
echo "ERROR: Input file not found: $INPUT_PATH" >&2
exit 1
fi
echo "Neuron Memory Import"
echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━"
echo "Source: $INPUT_PATH"
echo "Target: $ENGRAM_SNAPSHOT"
if [[ $DRY_RUN -eq 1 ]]; then
echo "Mode: DRY RUN (no changes will be made)"
fi
echo ""
# ── Prompt for passphrase if needed ───────────────────────────────────────────
if [[ $PASSPHRASE_SET -eq 0 ]]; then
read -r -s -p "Enter passphrase: " PASSPHRASE
echo ""
if [[ -z "$PASSPHRASE" ]]; then
echo "ERROR: Passphrase cannot be empty." >&2
exit 1
fi
fi
# ── Decrypt to temp dir ────────────────────────────────────────────────────────
WORK_DIR="$(mktemp -d)"
CLEANUP() {
rm -rf "$WORK_DIR"
}
trap CLEANUP EXIT
TAR_PATH="${WORK_DIR}/bundle.tar.gz"
echo "Decrypting..."
if ! openssl enc -d -aes-256-cbc \
-pbkdf2 \
-iter 600000 \
-in "$INPUT_PATH" \
-out "$TAR_PATH" \
-pass "pass:${PASSPHRASE}" 2>/dev/null; then
echo "ERROR: Decryption failed. Wrong passphrase or corrupted file." >&2
exit 1
fi
echo " Decrypted successfully."
# ── Unpack ─────────────────────────────────────────────────────────────────────
echo "Unpacking..."
(cd "$WORK_DIR" && tar xzf "$TAR_PATH") || {
echo "ERROR: Failed to unpack bundle. File may be corrupted." >&2
exit 1
}
# Locate the bundle directory (neuronmem-v1/)
BUNDLE_DIR=""
for d in "${WORK_DIR}"/neuronmem-v*/; do
if [[ -d "$d" ]]; then
BUNDLE_DIR="$d"
break
fi
done
if [[ -z "$BUNDLE_DIR" ]]; then
echo "ERROR: Bundle directory not found. Invalid .neuronmem file." >&2
exit 1
fi
METADATA_FILE="${BUNDLE_DIR}metadata.json"
NODES_FILE="${BUNDLE_DIR}nodes.json"
if [[ ! -f "$METADATA_FILE" ]]; then
echo "ERROR: metadata.json missing from bundle." >&2
exit 1
fi
if [[ ! -f "$NODES_FILE" ]]; then
echo "ERROR: nodes.json missing from bundle." >&2
exit 1
fi
# ── Validate metadata ──────────────────────────────────────────────────────────
echo "Validating metadata..."
FORMAT_VERSION="$(python3 -c "import json; d=json.load(open('${METADATA_FILE}')); print(d.get('version','?'))")"
EXPORTED_AT="$(python3 -c "import json; d=json.load(open('${METADATA_FILE}')); print(d.get('exported_at','?'))")"
EXPECTED_COUNT="$(python3 -c "import json; d=json.load(open('${METADATA_FILE}')); print(d.get('node_count','?'))")"
STORED_CHECKSUM="$(python3 -c "import json; d=json.load(open('${METADATA_FILE}')); print(d.get('sha256','?'))")"
SOURCE_HOST="$(python3 -c "import json; d=json.load(open('${METADATA_FILE}')); print(d.get('source_host','?'))")"
echo " Format version: ${FORMAT_VERSION}"
echo " Exported at: ${EXPORTED_AT}"
echo " Source host: ${SOURCE_HOST}"
echo " Expected nodes: ${EXPECTED_COUNT}"
if [[ "$FORMAT_VERSION" != "1" ]]; then
echo "ERROR: Unsupported bundle format version: ${FORMAT_VERSION}" >&2
echo " This tool supports version 1 only." >&2
exit 1
fi
# ── Validate checksum ──────────────────────────────────────────────────────────
echo "Verifying checksum..."
ACTUAL_CHECKSUM="$(openssl dgst -sha256 "$NODES_FILE" | awk '{print $NF}')"
if [[ "$ACTUAL_CHECKSUM" != "$STORED_CHECKSUM" ]]; then
echo "ERROR: Checksum mismatch!" >&2
echo " Expected: ${STORED_CHECKSUM}" >&2
echo " Got: ${ACTUAL_CHECKSUM}" >&2
echo " The bundle may be corrupted." >&2
exit 1
fi
echo " Checksum OK: ${ACTUAL_CHECKSUM:0:16}..."
# ── Verify node count ──────────────────────────────────────────────────────────
echo "Verifying node count..."
ACTUAL_COUNT="$(python3 -c "
import json
with open('${NODES_FILE}') as f:
d = json.load(f)
nodes = d.get('nodes', d if isinstance(d, list) else [])
print(len(nodes) if isinstance(nodes, list) else len(nodes))
" 2>/dev/null || echo "unknown")"
echo " Found ${ACTUAL_COUNT} nodes (expected ${EXPECTED_COUNT})"
if [[ "$ACTUAL_COUNT" != "$EXPECTED_COUNT" && "$EXPECTED_COUNT" != "unknown" ]]; then
echo "WARNING: Node count mismatch (expected ${EXPECTED_COUNT}, found ${ACTUAL_COUNT})." >&2
echo " Proceeding anyway — count may differ if nodes were deduplicated." >&2
fi
# ── Dry run exit ───────────────────────────────────────────────────────────────
if [[ $DRY_RUN -eq 1 ]]; then
echo ""
echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━"
echo "DRY RUN complete. Bundle is valid."
echo " Nodes: ${ACTUAL_COUNT}"
echo " Checksum: verified"
echo " Run without --dry-run to import."
echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━"
exit 0
fi
# ── Safety confirmation ────────────────────────────────────────────────────────
echo ""
echo "WARNING: This will replace your current Neuron memory store."
echo " Current snapshot: $ENGRAM_SNAPSHOT"
echo " A backup will be created before replacing."
echo ""
read -r -p "Type 'yes' to continue: " CONFIRM
if [[ "$CONFIRM" != "yes" ]]; then
echo "Aborted."
exit 0
fi
# ── Backup existing snapshot ───────────────────────────────────────────────────
BACKUP_TIMESTAMP="$(date -u +"%Y%m%dT%H%M%SZ")"
ENGRAM_DIR="$(dirname "$ENGRAM_SNAPSHOT")"
BACKUP_PATH="${HOME}/.neuron/engram-backup-${BACKUP_TIMESTAMP}.tar.gz"
echo ""
echo "Backing up current snapshot..."
if [[ -f "$ENGRAM_SNAPSHOT" ]]; then
(cd "$HOME/.neuron" && tar czf "$BACKUP_PATH" "$(basename "$ENGRAM_DIR")/snapshot.json" 2>/dev/null) || \
cp "$ENGRAM_SNAPSHOT" "${ENGRAM_SNAPSHOT}.backup-${BACKUP_TIMESTAMP}"
echo " Backup: $BACKUP_PATH"
else
echo " No existing snapshot to back up."
fi
# ── Stop soul service ──────────────────────────────────────────────────────────
echo "Stopping soul service (${SOUL_SERVICE})..."
launchctl stop "$SOUL_SERVICE" 2>/dev/null || true
# Also stop engram service if running
launchctl stop "ai.neuron.engram" 2>/dev/null || true
sleep 2
echo " Soul stopped."
# ── Replace snapshot.json ──────────────────────────────────────────────────────
echo "Installing new snapshot..."
cp "$NODES_FILE" "$ENGRAM_SNAPSHOT"
echo " snapshot.json replaced ($(du -sh "$ENGRAM_SNAPSHOT" | cut -f1))"
# ── Restart soul service ───────────────────────────────────────────────────────
echo "Restarting soul service..."
launchctl start "$SOUL_SERVICE" 2>/dev/null || true
launchctl start "ai.neuron.engram" 2>/dev/null || true
# ── Wait for soul to come up ───────────────────────────────────────────────────
echo "Waiting for soul to come up on port ${SOUL_PORT}..."
ELAPSED=0
SOUL_UP=0
while [[ $ELAPSED -lt $SOUL_STARTUP_TIMEOUT ]]; do
if curl -sf "http://localhost:${SOUL_PORT}/" > /dev/null 2>&1; then
SOUL_UP=1
break
fi
# Try a known endpoint that returns any response (even 404 means it's up)
HTTP_CODE="$(curl -s -o /dev/null -w "%{http_code}" "http://localhost:${SOUL_PORT}/api/neuron/memory" 2>/dev/null || echo "000")"
if [[ "$HTTP_CODE" != "000" ]]; then
SOUL_UP=1
break
fi
sleep 1
ELAPSED=$((ELAPSED + 1))
done
if [[ $SOUL_UP -eq 1 ]]; then
echo " Soul is up (responded in ${ELAPSED}s)."
else
echo " WARNING: Soul did not respond within ${SOUL_STARTUP_TIMEOUT}s."
echo " The service may still be starting. Check: launchctl list | grep soul"
fi
# ── Final report ───────────────────────────────────────────────────────────────
echo ""
echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━"
echo "Import complete."
echo " Nodes imported: ${ACTUAL_COUNT}"
echo " Exported at: ${EXPORTED_AT}"
echo " Source host: ${SOURCE_HOST}"
echo " Backup: ${BACKUP_PATH}"
echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━"
-135
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@@ -1,135 +0,0 @@
#!/usr/bin/env bash
# photo-to-memory.sh — OCR a document/photo and store the text in Neuron memory
#
# Uses GLM-OCR (0.9B, MIT) via mlx-vlm on Apple Silicon.
# Model auto-downloads ~1.59 GB to ~/.cache/huggingface/ on first run.
#
# Usage:
# ./tools/photo-to-memory.sh <image-file> [--dry-run] [--prompt "custom prompt"]
#
# Prerequisites:
# pip install -U mlx-vlm
#
# Examples:
# ./tools/photo-to-memory.sh ~/Desktop/receipt.jpg
# ./tools/photo-to-memory.sh ~/Documents/contract.png --dry-run
# ./tools/photo-to-memory.sh scan.jpg --prompt "Extract all text from this receipt"
set -euo pipefail
# ── Config ─────────────────────────────────────────────────────────────────────
SOUL_URL="${SOUL_URL:-http://localhost:7770}"
GLM_MODEL="${GLM_MODEL:-mlx-community/GLM-OCR-8bit}"
MAX_TOKENS="${MAX_TOKENS:-4096}"
DEFAULT_PROMPT="Extract all text from this document. Preserve structure including tables, headers, and lists. Output plain text."
# ── Colours ────────────────────────────────────────────────────────────────────
RED=$'\033[0;31m'; GREEN=$'\033[0;32m'; YELLOW=$'\033[1;33m'
CYAN=$'\033[0;36m'; BOLD=$'\033[1m'; RESET=$'\033[0m'
log() { printf "%s%s%s\n" "$CYAN" "$*" "$RESET"; }
ok() { printf "%s✓ %s%s\n" "$GREEN" "$*" "$RESET"; }
warn() { printf "%s⚠ %s%s\n" "$YELLOW" "$*" "$RESET"; }
die() { printf "%s✗ %s%s\n" "$RED" "$*" "$RESET" >&2; exit 1; }
# ── Parse args ─────────────────────────────────────────────────────────────────
IMAGE_PATH=""
DRY_RUN=0
CUSTOM_PROMPT=""
while [[ $# -gt 0 ]]; do
case "$1" in
--dry-run) DRY_RUN=1; shift ;;
--prompt) CUSTOM_PROMPT="$2"; shift 2 ;;
--model) GLM_MODEL="$2"; shift 2 ;;
--help|-h)
sed -n '2,15p' "$0" | sed 's/^# \{0,1\}//'
exit 0
;;
-*) die "Unknown option: $1" ;;
*)
[[ -n "$IMAGE_PATH" ]] && die "Only one image file at a time"
IMAGE_PATH="$1"
shift
;;
esac
done
[[ -z "$IMAGE_PATH" ]] && die "Usage: $0 <image-file> [--dry-run] [--prompt \"...\"]"
[[ -f "$IMAGE_PATH" ]] || die "File not found: $IMAGE_PATH"
PROMPT="${CUSTOM_PROMPT:-$DEFAULT_PROMPT}"
FILENAME=$(basename "$IMAGE_PATH")
ABS_PATH=$(realpath "$IMAGE_PATH")
# ── Check runtime ───────────────────────────────────────────────────────────────
if ! python3 -c "import mlx_vlm" 2>/dev/null; then
warn "mlx-vlm not installed. Installing now..."
pip install -q -U mlx-vlm || die "pip install mlx-vlm failed — run manually: pip install -U mlx-vlm"
fi
# ── Run GLM-OCR ─────────────────────────────────────────────────────────────────
log "Running GLM-OCR on: $FILENAME"
log "Model: $GLM_MODEL"
[[ "$DRY_RUN" -eq 1 ]] && warn "Dry-run mode — will not post to Neuron"
# GLM-OCR output goes to stdout; capture it
# First run downloads ~1.59 GB — this is expected and cached thereafter.
OCR_TEXT=$(python3 -m mlx_vlm.generate \
--model "$GLM_MODEL" \
--max-tokens "$MAX_TOKENS" \
--temperature 0.0 \
--prompt "$PROMPT" \
--image "$ABS_PATH" \
2>/dev/null) || die "GLM-OCR failed. Check that mlx-vlm is installed and the image is readable."
CHAR_COUNT=${#OCR_TEXT}
log "OCR complete — extracted ${CHAR_COUNT} characters"
if [[ "$CHAR_COUNT" -lt 5 ]]; then
warn "Very short output — the image may be blank or unreadable"
fi
# ── Preview ─────────────────────────────────────────────────────────────────────
printf "\n%s--- OCR output preview (first 400 chars) ---%s\n" "$BOLD" "$RESET"
printf "%s\n" "${OCR_TEXT:0:400}"
[[ "$CHAR_COUNT" -gt 400 ]] && printf "%s... [+%d more chars]%s\n" "$YELLOW" $((CHAR_COUNT - 400)) "$RESET"
printf "\n"
# ── Post to Neuron soul ─────────────────────────────────────────────────────────
if [[ "$DRY_RUN" -eq 1 ]]; then
ok "Dry-run complete — would POST ${CHAR_COUNT} chars to ${SOUL_URL}/api/neuron/memory"
exit 0
fi
log "Posting to Neuron soul at ${SOUL_URL} ..."
PAYLOAD=$(python3 -c "
import json, sys
content = sys.argv[1]
label = sys.argv[2]
tags = ['photo-import', 'ocr', 'glm-ocr']
print(json.dumps({'content': content, 'label': label, 'tags': tags}))
" "$OCR_TEXT" "Photo: ${FILENAME}")
HTTP_STATUS=$(curl -s -o /tmp/photo-to-memory-response.json -w "%{http_code}" \
-X POST "${SOUL_URL}/api/neuron/memory" \
-H "Content-Type: application/json" \
-d "$PAYLOAD")
if [[ "$HTTP_STATUS" =~ ^2 ]]; then
NODE_ID=$(python3 -c "
import json, sys
try:
d = json.load(open('/tmp/photo-to-memory-response.json'))
print(d.get('id', d.get('node_id', 'unknown')))
except Exception:
print('unknown')
")
ok "Memory node created: ${NODE_ID}"
ok "Label: Photo: ${FILENAME}"
ok "Tags: photo-import, ocr, glm-ocr"
else
BODY=$(cat /tmp/photo-to-memory-response.json 2>/dev/null || echo "(no body)")
die "Soul returned HTTP ${HTTP_STATUS}: ${BODY}"
fi
-191
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@@ -1,191 +0,0 @@
#!/bin/bash
# Neuron Telegram Gateway
# Polls Telegram for new messages, forwards to the soul at localhost:7770, sends responses back.
# Supports plain text chat + commands: /memory, /remember, /status
#
# Token resolution order:
# 1. $TELEGRAM_BOT_TOKEN env var
# 2. macOS Keychain: security find-generic-password -s neuron-telegram-bot -a neuron -w
set -euo pipefail
TOKEN="${TELEGRAM_BOT_TOKEN:-$(security find-generic-password -s neuron-telegram-bot -a neuron -w 2>/dev/null || true)}"
SOUL_URL="http://localhost:7770"
OFFSET=0
POLL_TIMEOUT=30
if [[ -z "$TOKEN" ]]; then
echo "ERROR: No Telegram bot token. Set TELEGRAM_BOT_TOKEN or store in keychain." >&2
echo "See: ~/Development/neuron-technologies/neuron/docs/telegram-bot-setup.md" >&2
exit 1
fi
TG="https://api.telegram.org/bot${TOKEN}"
log() { echo "[$(date '+%Y-%m-%d %H:%M:%S')] $*"; }
# Send a Telegram message back to a chat
send_message() {
local chat_id="$1"
local text="$2"
curl -s -X POST "${TG}/sendMessage" \
-H "Content-Type: application/json" \
-d "$(jq -n --argjson cid "$chat_id" --arg t "$text" \
'{chat_id: $cid, text: $t, parse_mode: "Markdown"}')" \
> /dev/null
}
# Store a memory in the soul
store_memory() {
local content="$1"
local label="${2:-telegram:conversation}"
curl -s -X POST "${SOUL_URL}/api/neuron/memory" \
-H "Content-Type: application/json" \
-d "$(jq -n --arg c "$content" --arg l "$label" \
'{content: $c, label: $l}')" \
> /dev/null
}
# Chat with the soul; echoes the response text
soul_chat() {
local message="$1"
local from="${2:-unknown}"
local response
response=$(curl -s -X POST "${SOUL_URL}/api/chat" \
-H "Content-Type: application/json" \
-d "$(jq -n --arg m "$message" --arg f "$from" \
'{message: $m, from: $f}')" 2>/dev/null)
# Extract .response — fall back to raw body on parse failure
jq -r '.response // empty' <<< "$response" 2>/dev/null || echo "$response"
}
# Search soul memories; echoes formatted results
soul_recall() {
local query="$1"
local limit="${2:-3}"
local raw
raw=$(curl -s -X POST "${SOUL_URL}/api/neuron/recall" \
-H "Content-Type: application/json" \
-d "$(jq -n --arg q "$query" --argjson l "$limit" \
'{query: $q, limit: $l}')" 2>/dev/null)
# Format top results as a numbered list (truncate long nodes to 300 chars)
jq -r 'if type == "array" then
to_entries | .[:3] | map(
(.index + 1 | tostring) + ". " + (.value.content | .[0:300] | gsub("\n";" "))
) | join("\n\n")
else
"No results found."
end' <<< "$raw" 2>/dev/null || echo "No results found."
}
# Check if soul is reachable
soul_health() {
curl -s --max-time 3 "${SOUL_URL}/" > /dev/null 2>&1 && echo "up" || echo "down"
}
handle_update() {
local update="$1"
local chat_id msg_text from_name update_id
update_id=$(jq -r '.update_id' <<< "$update")
chat_id=$(jq -r '.message.chat.id // empty' <<< "$update")
msg_text=$(jq -r '.message.text // empty' <<< "$update")
from_name=$(jq -r '.message.from.first_name // "stranger"' <<< "$update")
# Skip non-message updates (inline queries, etc.)
if [[ -z "$chat_id" || -z "$msg_text" ]]; then
OFFSET=$((update_id + 1))
return
fi
log "[$update_id] from=$from_name chat=$chat_id text=${msg_text:0:60}"
# Route by command prefix
if [[ "$msg_text" == /status* ]]; then
local health
health=$(soul_health)
if [[ "$health" == "up" ]]; then
send_message "$chat_id" "Soul is *online* at ${SOUL_URL}"
else
send_message "$chat_id" "Soul appears to be *offline* (${SOUL_URL} unreachable)."
fi
elif [[ "$msg_text" == /memory* ]]; then
local query="${msg_text#/memory}"
query="${query# }"
if [[ -z "$query" ]]; then
send_message "$chat_id" "Usage: /memory <query>"
else
local results
results=$(soul_recall "$query" 3)
if [[ -n "$results" ]]; then
send_message "$chat_id" "*Memories matching \"${query}\":*
${results}"
else
send_message "$chat_id" "No memories found for \"${query}\"."
fi
fi
elif [[ "$msg_text" == /remember* ]]; then
local content="${msg_text#/remember}"
content="${content# }"
if [[ -z "$content" ]]; then
send_message "$chat_id" "Usage: /remember <text to store>"
else
store_memory "Telegram (${from_name}): ${content}" "telegram:explicit"
send_message "$chat_id" "Stored: _${content}_"
fi
else
# Plain text — forward to soul chat
local soul_response
soul_response=$(soul_chat "$msg_text" "$from_name" 2>/dev/null || true)
if [[ -z "$soul_response" ]]; then
soul_response="Neuron is resting — try again in a moment."
fi
send_message "$chat_id" "$soul_response"
# Capture conversation as a memory (fire-and-forget)
store_memory "Telegram conversation with ${from_name}: [user] ${msg_text} [soul] ${soul_response}" \
"telegram:conversation" &
fi
OFFSET=$((update_id + 1))
}
log "Neuron Telegram gateway starting (soul=${SOUL_URL}, poll_timeout=${POLL_TIMEOUT}s)"
while true; do
# Long-poll for updates
UPDATES=$(curl -s --max-time $((POLL_TIMEOUT + 5)) \
"${TG}/getUpdates?offset=${OFFSET}&timeout=${POLL_TIMEOUT}" 2>/dev/null || true)
if [[ -z "$UPDATES" ]]; then
log "WARN: Empty response from Telegram; retrying in 5s"
sleep 5
continue
fi
OK=$(jq -r '.ok // false' <<< "$UPDATES" 2>/dev/null)
if [[ "$OK" != "true" ]]; then
DESC=$(jq -r '.description // "unknown error"' <<< "$UPDATES" 2>/dev/null)
log "WARN: Telegram API error: ${DESC}; retrying in 10s"
sleep 10
continue
fi
# Iterate over each update
COUNT=$(jq '.result | length' <<< "$UPDATES" 2>/dev/null || echo 0)
if [[ "$COUNT" -gt 0 ]]; then
for i in $(seq 0 $((COUNT - 1))); do
update=$(jq ".result[$i]" <<< "$UPDATES")
handle_update "$update"
done
fi
# Avoid hammering the API if something is very wrong
sleep 1
done