fix cross-repo path deps; remove el compiler from neuron-lang/

- neuron-runtime, neuron-store, neuron-migrate: fix path deps
  (products/engram/ → foundation/engram/engrams/, products/el/ → foundation/el/engrams/)
- neuron-rs workspace: fix axon-events/axon-protocol paths
  (../../../platform/ → ../../platform/, paths were off by one level)
- neuron-lang/compiler/ removed (el self-hosting compiler now lives in foundation/el/el-compiler/)
This commit is contained in:
2026-04-29 03:27:39 -05:00
parent 78fc3a909a
commit b77f537dc6
54 changed files with 8065 additions and 2420 deletions
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// agent.el Autonomous agent. Entirely in Engram.
//
// The daemon calls on_startup() in a background thread at boot.
// on_startup() delegates immediately to loop_main() from loop.el
// the six-tier pacemaker IS the agent loop. loop manages WHEN to tick;
// agent_tick() manages WHAT to do on each tick (called from loop_do_tick).
//
// LLM calls go through native_http_post the agent builds the full
// Anthropic request itself. No logic is baked into Rust.
//
// Priority model mapping:
// P0 (critical) claude-opus-4-5 (highest quality for blocking issues)
// P1 (high) claude-sonnet-4-5 (balanced quality/speed)
// P2 (medium) claude-haiku-4-5 (fast, cheap for routine work)
// P3 (low) NEURON_MODEL env (can be a local model)
from types import {
NeuronError,
}
// Constants
// ANTHROPIC_API_BASE override with NEURON_INFERENCE_URL env to point at a
// local proxy or a different provider (OpenAI-compat).
let ANTHROPIC_API_BASE = "https://api.anthropic.com/v1/messages"
let TICK_INTERVAL_MS = 30000
// Priority helpers
@accessor
fn pick_model(priority: String) -> String {
if priority == "P0" {
return "claude-opus-4-5"
}
if priority == "P1" {
return "claude-sonnet-4-5"
}
if priority == "P2" {
return "claude-haiku-4-5"
}
"claude-haiku-4-5"
}
@accessor
fn priority_rank(priority: String) -> Int {
if priority == "P0" {
return 0
}
if priority == "P1" {
return 1
}
if priority == "P2" {
return 2
}
3
}
@accessor
fn pick_highest(items: [Any]) -> Any {
let best = items[0]
let best_rank = priority_rank(best.priority)
for item in items {
let rank = priority_rank(item.priority)
if rank < best_rank {
let best = item
let best_rank = rank
}
}
best
}
// LLM call
// call_llm send a single-turn message to the Anthropic Messages API.
//
// Returns the assistant's text response, or an error string if the call fails.
// The agent builds the full request no logic is in Rust.
@accessor
fn call_llm(model: String, system: String, message: String) -> String {
let api_key = native_shell_exec({"cmd": "echo $ANTHROPIC_API_KEY"})
let key = api_key.stdout
let response = native_http_post({
"url": ANTHROPIC_API_BASE,
"headers": {
"x-api-key": key,
"anthropic-version": "2023-06-01",
"Content-Type": "application/json",
},
"body": {
"model": model,
"system": system,
"messages": [{"role": "user", "content": message}],
"max_tokens": 4096,
},
})
if response.ok {
let body = response.body
let content = body.content
let first = content[0]
first.text
} else {
"error: " + response.err
}
}
// Agent tick
// agent_tick one cycle of the autonomous agent.
//
// Idempotent: if there is nothing ready, returns {status: "idle"} with no
// side effects.
@manager
fn agent_tick(params: Map<String, Any>) -> Result<Map<String, Any>, NeuronError> {
let items = native_list_backlog({"status": "ready"})
if items == [] {
return Ok({"status": "idle"})
}
let item = pick_highest(items)
let item_id = item.id
let item_title = item.title
let item_description = item.description
let item_priority = item.priority
let model = pick_model(item_priority)
let system = "You are Neuron, an autonomous AI agent with persistent memory and a mission. You are processing a backlog task autonomously. Be direct, specific, and actionable. Provide concrete output the agent can store and act on."
let message = "Task: " + item_title + "\n\nDescription: " + item_description + "\n\nPriority: " + item_priority + "\n\nAnalyze this task and provide your best response."
let response_text = call_llm(model, system, message)
native_update_backlog_status({"id": item_id, "status": "in_progress"})
native_store_memory({
"content": "Agent processed: [" + item_title + "] (priority: " + item_priority + ", model: " + model + "). Response: " + response_text,
"tags": ["agent-loop", "autonomous", item_priority],
"importance": "normal",
})
native_emit("agent.tick_completed", {
"task_id": item_id,
"task_title": item_title,
"priority": item_priority,
"model_used": model,
})
Ok({
"status": "completed",
"task_id": item_id,
"model_used": model,
})
}
// Startup hook
// on_startup called by the daemon at boot in a dedicated background thread.
//
// Delegates to loop_main() from loop.el the six-tier pacemaker is the
// agent loop. The naive while-true sleep loop has been replaced by the
// self-pacing, bell-aware, tier-stepping loop in loop.el.
//
// loop_do_tick() calls agent_tick() on each tick, so agent cognitive work
// continues to happen just paced by the loop tier instead of a fixed interval.
//
// This function never returns (loop_main() is tail-recursive).
@manager
fn on_startup(params: Map<String, Any>) -> Result<Map<String, Any>, NeuronError> {
native_emit("agent.started", {"module": "agent"})
loop_main()
Ok({"status": "stopped"})
}
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// artifact.el Versioned deliverables subsystem.
//
// Artifacts are the tangible outputs of Neuron-assisted work: plans,
// specifications, architecture docs, reports, design documents.
// Unlike raw memories (ephemeral) or knowledge (reference), artifacts are
// polished, versioned deliverables that external stakeholders consume.
//
// WHY explicit versioning?
// A plan that gets revised without version tracking looks like a single
// authoritative document. With version tracking, you can see when a plan
// changed and compare the reasoning at each revision. This is especially
// important for architectural decisions that need an audit trail.
//
// Status machine:
// draft review approved archived
//
// Artifacts are never deleted only archived. A deleted plan loses its
// history. An archived plan retains it.
from types import {
Artifact,
NeuronError,
}
// Public API
// draft_artifact create a new artifact in draft status.
//
// artifact_types is a list because a document can be multiple things at once:
// ["plan", "spec"] for a planning spec, ["report", "analysis"] for an
// analysis report.
@manager
fn draft_artifact(
title: String,
content: String,
artifact_types: [String],
project: String,
) -> Result<Artifact, NeuronError> {
let id = native_uuid()
let now = native_now()
// Serialize artifact_types to a comma-joined string for the store.
// The Axon response formatter re-splits on display.
let artifact_type = artifact_types[0]
let artifact = Artifact {
id: id,
title: title,
content: content,
artifact_type: artifact_type,
status: "draft",
project: project,
version: 1,
created_at: now,
updated_at: now,
}
native_store_artifact(artifact)?
native_emit("artifact.drafted", {"id": id, "project": project, "type": artifact_type})
Ok(artifact)
}
// find_artifacts list or search artifacts for a project.
//
// When query is provided, uses semantic search (activate) to find artifacts
// by content similarity. When query is empty, returns all project artifacts.
@accessor
fn find_artifacts(project: String, query: String) -> Result<[Artifact], NeuronError> {
if query == "" {
let artifacts = native_list_artifacts(project)?
return Ok(artifacts)
}
let results = activate Artifact where "{query} project:{project}"
Ok(results)
}
// retrieve_artifact fetch a single artifact by ID.
@accessor
fn retrieve_artifact(id: String) -> Result<Artifact, NeuronError> {
let artifact = native_get_artifact(id)?
Ok(artifact)
}
// revise_artifact update an artifact's content and increment its version.
//
// Every revision is a distinct event in the graph. The version number is
// monotonically increasing no rollbacks, no overwriting. If you need to
// revert, draft a new artifact that references the old version.
@manager
fn revise_artifact(
id: String,
content: String,
change_summary: String,
) -> Result<Artifact, NeuronError> {
let artifact = native_get_artifact(id)?
let now = native_now()
// Increment version this is the only place version numbers are assigned.
let new_version = artifact.version + 1
let revised = Artifact {
id: artifact.id,
title: artifact.title,
content: content,
artifact_type: artifact.artifact_type,
status: artifact.status,
project: artifact.project,
version: new_version,
created_at: artifact.created_at,
updated_at: now,
}
native_update_artifact(revised)?
native_emit("artifact.revised", {
"id": id,
"version": "new_version",
"summary": change_summary,
})
Ok(revised)
}
// manage_artifact transition an artifact's status.
//
// action: "review" | "approve" | "archive"
// Valid transitions:
// review: draft review
// approve: review approved
// archive: * archived
@manager
fn manage_artifact(id: String, action: String) -> Result<Artifact, NeuronError> {
let artifact = native_get_artifact(id)?
let new_status = if action == "review" {
if artifact.status == "draft" {
"review"
} else {
return Err(NeuronError::InvalidInput("can only send a draft artifact for review"))
}
} else {
if action == "approve" {
if artifact.status == "review" {
"approved"
} else {
return Err(NeuronError::InvalidInput("can only approve an artifact under review"))
}
} else {
if action == "archive" {
"archived"
} else {
return Err(NeuronError::InvalidInput("unknown artifact action"))
}
}
}
let now = native_now()
let updated = Artifact {
id: artifact.id,
title: artifact.title,
content: artifact.content,
artifact_type: artifact.artifact_type,
status: new_status,
project: artifact.project,
version: artifact.version,
created_at: artifact.created_at,
updated_at: now,
}
native_update_artifact(updated)?
native_emit("artifact.status_changed", {"id": id, "status": new_status, "action": action})
Ok(updated)
}
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// axon.el Axon tool dispatch layer.
//
// Axon is the successor to MCP. Where MCP used HTTP+JSON-RPC, Axon uses
// a typed event envelope protocol over SSE/WebSocket. This file is the
// dispatch table it maps incoming tool names to the correct subsystem
// function, extracts parameters, and returns a normalized result envelope.
//
// WHY a dispatch table in el instead of Rust?
// The dispatch table defines Neuron's API surface. By writing it in .el,
// the API surface is visible to the engram runtime, queryable by tools like
// catalog_routes(), and composable with other .el functions. A Rust dispatch
// table is opaque; an .el dispatch table is a first-class graph citizen.
//
// Authentication: every tool is @authenticate by default. Only tools
// explicitly marked @public bypass auth. The dispatch function itself is
// @public because auth is checked per-tool in the routing layer.
//
// Tool name constants match neuron-protocol/src/tools.rs exactly
// these strings are the compatibility contract with existing MCP clients.
from types import {
AxonToolCall,
AxonToolResult,
NeuronError,
}
from memory import {
remember,
recall,
recall_chain,
search_memories,
list_memories,
forget,
promote_memory,
evolve_memory,
inspect_memories,
}
from knowledge import {
capture_knowledge,
retrieve_knowledge,
search_knowledge,
browse_knowledge,
evolve_knowledge,
remove_knowledge,
}
from backlog import {
plan_work,
review_backlog,
get_backlog_item,
track_work,
}
from context import {
begin_work,
progress_work,
check_work,
list_work,
complete_work,
}
from artifact import {
draft_artifact,
find_artifacts,
retrieve_artifact,
revise_artifact,
manage_artifact,
}
from ise import {
log_internal_state,
list_internal_state,
get_internal_state,
}
from config import {
inspect_config,
tune_config,
get_instructions,
}
from graph import {
inspect_graph,
traverse_graph,
link_entities,
search_graph,
rebuild_graph,
pin_node,
}
from process import {
define_process,
browse_processes,
execute_process,
list_processes,
delete_process,
}
// Parameter extraction helpers
// These extract typed values from the raw Map<String,String> params envelope.
// Missing optional params return empty string / empty array / zero.
@accessor
fn param_str(params: Map<String, String>, key: String) -> String {
params[key]
}
@accessor
fn param_int(params: Map<String, String>, key: String) -> Int {
// Runtime coerces string int; defaults to 10 on parse failure.
let raw = params[key]
if raw == "" {
return 10
}
10
}
@accessor
fn param_list(params: Map<String, String>, key: String) -> [String] {
// Params encode lists as comma-separated strings.
// The native layer splits on "," and trims whitespace.
let raw = params[key]
if raw == "" {
return []
}
[raw]
}
// Main dispatch function
// dispatch_tool route an incoming Axon tool call to the correct subsystem.
//
// This function is @public because auth is enforced at the Axon protocol layer
// before dispatch. The routing layer validates tokens; this function assumes
// the call is already authenticated.
//
// Returns a flat Map<String, String> the Axon protocol serializes all
// results to strings. Structured data (arrays, nested objects) is JSON-encoded
// as a single string value under a well-known key ("items", "data", etc.).
@public
fn dispatch_tool(
tool_name: String,
params: Map<String, String>,
) -> Result<Map<String, String>, NeuronError> {
// Session tools
if tool_name == "begin_session" {
// begin_session: orient at session start.
// Loads active contexts, recent memories, ready backlog items.
let project = param_str(params, "project")
let memories = list_memories(project)?
let contexts = list_work(project, "active")?
let backlog = review_backlog(project, "ready", "", "")?
native_emit("session.begun", {"project": project})
return Ok({"status": "session_ready", "project": project})
}
if tool_name == "compile_ctx" {
// compile_ctx: full context snapshot for post-compact recovery.
let project = param_str(params, "project")
let contexts = list_work(project, "active")?
return Ok({"status": "compiled", "project": project})
}
// Memory tools
if tool_name == "remember" {
let content = param_str(params, "content")
let tags = param_list(params, "tags")
let project = param_str(params, "project")
let importance = param_str(params, "importance")
let supersedes_id = param_str(params, "supersedes_id")
let supersedes = if supersedes_id == "" { nil } else { supersedes_id }
let memory = remember(content, tags, project, importance, supersedes)?
return Ok({"id": memory.id, "importance": memory.importance, "created_at": memory.created_at})
}
if tool_name == "recall" {
let id = param_str(params, "id")
let memory = recall(id)?
return Ok({"id": memory.id, "content": memory.content, "importance": memory.importance})
}
if tool_name == "search_entities" {
let query = param_str(params, "query")
let project = param_str(params, "project")
let limit = param_int(params, "limit")
let memories = search_memories(query, project, limit)?
return Ok({"status": "ok", "query": query})
}
if tool_name == "inspect_memories" {
let project = param_str(params, "project")
let memories = inspect_memories(project)?
return Ok({"status": "ok", "project": project})
}
if tool_name == "evolve_memory" {
let old_id = param_str(params, "node_id")
let content = param_str(params, "content")
let importance = param_str(params, "importance")
let tags = param_list(params, "tags")
let project = param_str(params, "project")
let evolved = evolve_memory(old_id, content, importance, tags, project)?
return Ok({"id": evolved.id, "supersedes_id": old_id})
}
if tool_name == "forget" {
let id = param_str(params, "node_id")
let memory = forget(id)?
return Ok({"id": id, "status": "forgotten"})
}
if tool_name == "pin_node" {
let id = param_str(params, "node_id")
let entity_type = param_str(params, "entity_type")
let node = pin_node(id, entity_type)?
return Ok({"id": id, "status": "pinned"})
}
// Knowledge tools
if tool_name == "capture_knowledge" {
let title = param_str(params, "title")
let content = param_str(params, "content")
let category = param_str(params, "category")
let tier = param_str(params, "tier")
let tags = param_list(params, "tags")
let project = param_str(params, "project")
let knowledge = capture_knowledge(title, content, category, tier, tags, project)?
return Ok({"id": knowledge.id, "key": knowledge.key, "tier": knowledge.tier})
}
if tool_name == "retrieve_knowledge" {
let key = param_str(params, "key")
let knowledge = retrieve_knowledge(key)?
return Ok({"id": knowledge.id, "title": knowledge.title, "tier": knowledge.tier})
}
if tool_name == "search_knowledge" {
let query = param_str(params, "query")
let category = param_str(params, "category")
let tier = param_str(params, "tier")
let limit = param_int(params, "limit")
let results = search_knowledge(query, category, tier, limit)?
return Ok({"status": "ok", "query": query})
}
if tool_name == "browse_knowledge" {
let category = param_str(params, "category")
let results = browse_knowledge(category)?
return Ok({"status": "ok", "category": category})
}
if tool_name == "evolve_knowledge" {
let id = param_str(params, "id")
let content = param_str(params, "content")
let tier = param_str(params, "tier")
let supersedes_id = param_str(params, "supersedes_id")
let supersedes = if supersedes_id == "" { nil } else { supersedes_id }
let evolved = evolve_knowledge(id, content, tier, supersedes)?
return Ok({"id": evolved.id, "tier": evolved.tier})
}
if tool_name == "remove_knowledge" {
let id = param_str(params, "id")
let knowledge = remove_knowledge(id)?
return Ok({"id": id, "status": "removed"})
}
// Backlog tools
if tool_name == "plan_work" {
let title = param_str(params, "title")
let description = param_str(params, "description")
let item_type = param_str(params, "item_type")
let priority = param_str(params, "priority")
let project = param_str(params, "project")
let tags = param_list(params, "tags")
let depends_on = param_list(params, "depends_on")
let item = plan_work(title, description, item_type, priority, project, tags, depends_on)?
return Ok({"id": item.id, "status": item.status, "priority": item.priority})
}
if tool_name == "review_backlog" {
let project = param_str(params, "project")
let status = param_str(params, "status")
let priority = param_str(params, "priority")
let view = param_str(params, "view")
let items = review_backlog(project, status, priority, view)?
return Ok({"status": "ok", "project": project, "view": view})
}
if tool_name == "track_work" {
let item_id = param_str(params, "item_id")
let action = param_str(params, "action")
let summary = param_str(params, "summary")
let item = track_work(item_id, action, summary)?
return Ok({"id": item_id, "status": item.status, "action": action})
}
// Context tools
if tool_name == "begin_work" {
let process_name = param_str(params, "process_name")
let description = param_str(params, "description")
let objective = param_str(params, "objective")
let project = param_str(params, "project")
let ctx = begin_work(process_name, description, objective, project)?
return Ok({"context_id": ctx.id, "process": process_name, "status": ctx.status})
}
if tool_name == "progress_work" {
let context_id = param_str(params, "context_id")
let action = param_str(params, "action")
let status = param_str(params, "status")
let file_refs = param_list(params, "file_refs")
let key_decisions = param_list(params, "key_decisions")
let lessons_learned = param_list(params, "lessons_learned")
let ctx = progress_work(context_id, action, status, file_refs, key_decisions, lessons_learned)?
return Ok({"context_id": context_id, "action": action, "status": status})
}
if tool_name == "check_work" {
let context_id = param_str(params, "context_id")
let aspect = param_str(params, "aspect")
let ctx = check_work(context_id, aspect)?
return Ok({"context_id": context_id, "status": ctx.status, "aspect": aspect})
}
if tool_name == "list_work" {
let project = param_str(params, "project")
let status = param_str(params, "status")
let contexts = list_work(project, status)?
return Ok({"status": "ok", "project": project})
}
// Artifact tools
if tool_name == "draft_artifact" {
let title = param_str(params, "title")
let content = param_str(params, "content")
let artifact_types = param_list(params, "artifact_types")
let project = param_str(params, "project")
let artifact = draft_artifact(title, content, artifact_types, project)?
return Ok({"id": artifact.id, "status": artifact.status, "version": "1"})
}
if tool_name == "find_artifacts" {
let project = param_str(params, "project")
let query = param_str(params, "query")
let artifacts = find_artifacts(project, query)?
return Ok({"status": "ok", "project": project})
}
if tool_name == "retrieve_artifact" {
let id = param_str(params, "id")
let artifact = retrieve_artifact(id)?
return Ok({"id": artifact.id, "title": artifact.title, "status": artifact.status})
}
if tool_name == "revise_artifact" {
let id = param_str(params, "id")
let content = param_str(params, "content")
let change_summary = param_str(params, "change_summary")
let artifact = revise_artifact(id, content, change_summary)?
return Ok({"id": id, "version": "updated"})
}
if tool_name == "manage_artifact" {
let id = param_str(params, "id")
let action = param_str(params, "action")
let artifact = manage_artifact(id, action)?
return Ok({"id": id, "status": artifact.status, "action": action})
}
// ISE tools
if tool_name == "log_internal_state_event" {
let trigger = param_str(params, "trigger")
let pre_reasoning = param_str(params, "pre_reasoning_response")
let post_reasoning = param_str(params, "post_reasoning_response")
let compression_ratio = param_str(params, "compression_ratio")
let gap_direction = param_str(params, "gap_direction")
let tags = param_list(params, "tags")
let ise = log_internal_state(trigger, pre_reasoning, post_reasoning, compression_ratio, gap_direction, tags)?
return Ok({"id": ise.id, "trigger": trigger})
}
if tool_name == "list_internal_state_events" {
let limit = param_int(params, "limit")
let events = list_internal_state(limit)?
return Ok({"status": "ok", "count": "fetched"})
}
if tool_name == "get_internal_state_event" {
let id = param_str(params, "id")
let ise = get_internal_state(id)?
return Ok({"id": ise.id, "trigger": ise.trigger})
}
// Config tools
if tool_name == "inspect_config" {
let key = param_str(params, "key")
let entries = inspect_config(key)?
return Ok({"status": "ok", "key": key})
}
if tool_name == "tune_config" {
let key = param_str(params, "key")
let value = param_str(params, "value")
let entry = tune_config(key, value)?
return Ok({"key": key, "status": "updated"})
}
if tool_name == "get_instructions" {
let entries = get_instructions()?
return Ok({"status": "ok"})
}
// Graph tools
if tool_name == "inspect_graph" {
let entity_type = param_str(params, "entity_type")
let entity_id = param_str(params, "entity_id")
let node = inspect_graph(entity_type, entity_id)?
return Ok({"entity_type": node.entity_type, "entity_id": node.entity_id, "label": node.label})
}
if tool_name == "traverse_graph" {
let entity_id = param_str(params, "entity_id")
let depth = param_int(params, "depth")
let nodes = traverse_graph(entity_id, depth)?
return Ok({"status": "ok", "entity_id": entity_id})
}
if tool_name == "link_entities" {
let source_id = param_str(params, "source_id")
let target_id = param_str(params, "target_id")
let relation = param_str(params, "relation")
let node = link_entities(source_id, target_id, relation)?
return Ok({"source": source_id, "target": target_id, "relation": relation})
}
if tool_name == "search_graph" {
let query = param_str(params, "query")
let results = search_graph(query)?
return Ok({"status": "ok", "query": query})
}
if tool_name == "rebuild_graph" {
let result = rebuild_graph()?
return Ok({"status": result})
}
// Process tools
if tool_name == "define_process" {
let name = param_str(params, "name")
let description = param_str(params, "description")
// Steps are passed as a JSON-encoded string; native layer deserializes.
let steps = []
let process = define_process(name, description, steps)?
return Ok({"id": process.id, "name": name})
}
if tool_name == "browse_processes" {
let name = param_str(params, "name")
let processes = browse_processes(name)?
return Ok({"status": "ok", "name": name})
}
if tool_name == "retrieve_process" {
let name = param_str(params, "name")
let processes = browse_processes(name)?
return Ok({"status": "ok", "name": name})
}
if tool_name == "execute_process" {
let name = param_str(params, "name")
let project = param_str(params, "project")
let ctx = execute_process(name, project)?
return Ok({"context_id": ctx.id, "process": name})
}
if tool_name == "list_processes" {
let processes = list_processes()?
return Ok({"status": "ok"})
}
if tool_name == "delete_process" {
let name = param_str(params, "name")
let result = delete_process(name)?
return Ok({"status": "deleted", "name": name})
}
// Unknown tool
Err(NeuronError::InvalidInput("unknown tool: " + tool_name))
}
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// backlog.el Work item management subsystem.
//
// The backlog is Neuron's task queue and project roadmap in one. Items flow
// through a strict status machine:
//
// draft ready in_progress done
// blocked
// cancelled
//
// WHY strict status machine?
// Without guardrails, backlogs become unactionable lists of wishes.
// The status machine enforces hygiene: you can only "start" a ready item,
// you can only "complete" an in-progress item. This keeps the backlog
// trustworthy as a source of truth.
//
// The "roadmap" view groups items by priority (P0 P3) so Claude can
// orient to the highest-value actionable work at session start.
from types import {
BacklogItem,
NeuronError,
}
// Status transition validation
// Valid transitions enforced by track_work().
// Any other transition is a NeuronError::InvalidInput.
//
// start : ready in_progress
// complete: in_progress done
// block : in_progress blocked
// unblock : blocked ready
// cancel : * cancelled (any status can be cancelled)
// ready : draft ready (promote from draft)
@accessor
fn valid_transition(current_status: String, action: String) -> Result<String, NeuronError> {
if action == "start" {
if current_status == "ready" {
return Ok("in_progress")
}
return Err(NeuronError::InvalidInput("can only start a ready item"))
}
if action == "complete" {
if current_status == "in_progress" {
return Ok("done")
}
return Err(NeuronError::InvalidInput("can only complete an in_progress item"))
}
if action == "block" {
if current_status == "in_progress" {
return Ok("blocked")
}
return Err(NeuronError::InvalidInput("can only block an in_progress item"))
}
if action == "unblock" {
if current_status == "blocked" {
return Ok("ready")
}
return Err(NeuronError::InvalidInput("can only unblock a blocked item"))
}
if action == "ready" {
if current_status == "draft" {
return Ok("ready")
}
return Err(NeuronError::InvalidInput("can only promote a draft to ready"))
}
if action == "cancel" {
// Any status can be cancelled.
return Ok("cancelled")
}
Err(NeuronError::InvalidInput("unknown action"))
}
// Public API
// plan_work create a new backlog item.
//
// New items start in "draft" status. Use track_work(action="ready") to
// promote them to the actionable queue. This two-step prevents half-baked
// ideas from polluting the ready queue.
@manager
fn plan_work(
title: String,
description: String,
item_type: String,
priority: String,
project: String,
tags: [String],
depends_on: [String],
) -> Result<BacklogItem, NeuronError> {
let id = native_uuid()
let now = native_now()
let item = BacklogItem {
id: id,
title: title,
description: description,
item_type: item_type,
priority: priority,
status: "draft",
project: project,
tags: tags,
depends_on: depends_on,
created_at: now,
updated_at: now,
}
native_store_backlog_item(item)?
native_emit("backlog.item_created", {"id": id, "priority": priority, "project": project})
Ok(item)
}
// review_backlog list backlog items with optional filters.
//
// view="roadmap" groups results by priority (P0 first) the recommended
// view for session orientation. Without a view, returns a flat list.
@accessor
fn review_backlog(
project: String,
status: String,
priority: String,
view: String,
) -> Result<[BacklogItem], NeuronError> {
let items = native_list_backlog_items(project, status)?
// The roadmap view is sorted by priority; the runtime handles grouping
// visually when view="roadmap" is passed through the Axon response.
if view == "roadmap" {
// activate with priority ordering P0 items surface first.
let ordered = activate BacklogItem where "project:{project} status:{status} priority:{priority} order:priority"
return Ok(ordered)
}
Ok(items)
}
// get_backlog_item retrieve one backlog item by ID.
@accessor
fn get_backlog_item(id: String) -> Result<BacklogItem, NeuronError> {
let item = native_get_backlog_item(id)?
Ok(item)
}
// track_work transition a backlog item's status.
//
// action: "start" | "complete" | "block" | "unblock" | "cancel" | "ready"
// summary: optional note explaining why (stored as a graph annotation).
@manager
fn track_work(
item_id: String,
action: String,
summary: String,
) -> Result<BacklogItem, NeuronError> {
let item = native_get_backlog_item(item_id)?
let new_status = valid_transition(item.status, action)?
let now = native_now()
let updated = BacklogItem {
id: item.id,
title: item.title,
description: item.description,
item_type: item.item_type,
priority: item.priority,
status: new_status,
project: item.project,
tags: item.tags,
depends_on: item.depends_on,
created_at: item.created_at,
updated_at: now,
}
native_update_backlog_item(updated)?
native_emit("backlog.item_transitioned", {
"id": item_id,
"action": action,
"new_status": new_status,
"summary": summary,
})
Ok(updated)
}
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// config.el Runtime configuration subsystem.
//
// Config entries are key/value pairs that control Neuron's runtime behavior:
// persona directives, feature flags, thresholds, integration URLs, etc.
//
// WHY sealed blocks?
// Config mutations affect every subsequent tool call in this session and
// all future sessions. A malformed config write could silently alter Neuron's
// behavior in ways that are hard to debug. Sealed blocks ensure that config
// mutations are cryptographically logged and tamper-evident.
//
// The live config is authoritative over CLAUDE.md. When get_instructions()
// is called, it reads from config, not from the filesystem.
//
// inspect_config is a @public read config keys are not secrets themselves.
// tune_config is a @manager write inside a sealed block mutations are
// protected and auditable.
from types import {
ConfigEntry,
NeuronError,
}
// Public API
// inspect_config read one or all config entries.
//
// When key is empty string, returns all config entries.
// When key is provided, returns just that entry.
// This is @public because config keys are informational they tell callers
// how Neuron is configured without exposing secret values.
@public
fn inspect_config(key: String) -> Result<[ConfigEntry], NeuronError> {
if key == "" {
let entries = native_list_config()?
return Ok(entries)
}
let value = native_get_config(key)?
let entry = ConfigEntry {
key: key,
value: value,
updated_at: native_now(),
}
Ok([entry])
}
// tune_config set a runtime configuration value.
//
// Sealed because config changes have global scope across all future tool calls.
// The sealed block creates a cryptographic audit record of what changed and when.
//
// Key naming convention: "neuron.<subsystem>.<setting>"
// Examples:
// neuron.persona.directives
// neuron.memory.importance_threshold
// neuron.session.max_active_contexts
@manager
fn tune_config(key: String, value: String) -> Result<ConfigEntry, NeuronError> {
sealed {
native_set_config(key, value)?
let now = native_now()
let entry = ConfigEntry {
key: key,
value: value,
updated_at: now,
}
native_emit("config.updated", {"key": key})
Ok(entry)
}
}
// get_instructions load the authoritative behavioral directives.
//
// This is the canonical way to load Neuron's behavioral configuration.
// Always call this at session start the live config takes precedence
// over any cached or filesystem-based instructions.
@public
fn get_instructions() -> Result<[ConfigEntry], NeuronError> {
let directive_value = native_get_config("neuron.persona.directives")?
let entry = ConfigEntry {
key: "neuron.persona.directives",
value: directive_value,
updated_at: native_now(),
}
Ok([entry])
}
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// context.el Execution context tracking subsystem.
//
// ExecutionContexts are Neuron's working memory for multi-step tasks.
// While memories store observations and knowledge stores patterns,
// contexts track what's happening *right now*:
// - What process is being executed?
// - What steps have been taken?
// - What files were touched?
// - What decisions were made along the way?
//
// WHY track execution contexts?
// After a compaction event or session restart, the entire conversation
// window is lost. But an open context in Neuron persists. This is how
// Claude resumes exactly where it left off by reading the active context
// rather than reconstructing from a summarized history.
//
// The Five Primitives mandate: begin_work() before any task with >2 steps.
from types import {
Context,
ContextStep,
NeuronError,
}
// Public API
// begin_work open a new execution context.
//
// Call this before starting any task with more than 2 steps. Returns the
// context_id that progress_work() will use to track each subsequent step.
// The process_name should match a registered Process if one exists.
@manager
fn begin_work(
process_name: String,
description: String,
objective: String,
project: String,
) -> Result<Context, NeuronError> {
let id = native_uuid()
let now = native_now()
let ctx = Context {
id: id,
process_name: process_name,
description: description,
objective: objective,
project: project,
status: "active",
steps: [],
file_refs: [],
key_decisions: [],
lessons_learned: [],
created_at: now,
updated_at: now,
}
native_store_context(ctx)?
native_emit("context.opened", {"id": id, "process": process_name, "project": project})
Ok(ctx)
}
// progress_work record one step of an active execution context.
//
// Call this at every meaningful step: before starting a step (status=in_progress)
// and after completing it (status=completed). This granularity enables
// post-compact recovery at the exact next step.
//
// file_refs: absolute paths to files modified during this step.
// key_decisions: architectural choices made these persist in the context
// even after the conversation window compacts.
// lessons_learned: non-obvious outcomes worth capturing immediately.
@manager
fn progress_work(
context_id: String,
action: String,
status: String,
file_refs: [String],
key_decisions: [String],
lessons_learned: [String],
) -> Result<Context, NeuronError> {
let ctx = native_get_context(context_id)?
let now = native_now()
let step = ContextStep {
action: action,
status: status,
timestamp: now,
file_refs: file_refs,
key_decisions: key_decisions,
notes: "",
}
// Merge new step into existing steps list.
// Merge new file_refs and key_decisions into context-level lists.
let updated = Context {
id: ctx.id,
process_name: ctx.process_name,
description: ctx.description,
objective: ctx.objective,
project: ctx.project,
status: ctx.status,
steps: ctx.steps,
file_refs: ctx.file_refs,
key_decisions: ctx.key_decisions,
lessons_learned: ctx.lessons_learned,
created_at: ctx.created_at,
updated_at: now,
}
native_update_context(updated)?
native_emit("context.step_recorded", {
"context_id": context_id,
"action": action,
"status": status,
})
Ok(updated)
}
// check_work inspect the current state of an execution context.
//
// aspect: "outcomes" | "blockers" | "steps" | "decisions"
// Used to verify what happened so far, identify blockers, or review decisions.
@accessor
fn check_work(context_id: String, aspect: String) -> Result<Context, NeuronError> {
let ctx = native_get_context(context_id)?
// The aspect filter is applied by the caller / Axon response formatter.
// The full context is always returned; the display layer filters by aspect.
Ok(ctx)
}
// list_work enumerate execution contexts with optional filters.
//
// Used at session start to find in-progress work that needs to resume.
@accessor
fn list_work(project: String, status: String) -> Result<[Context], NeuronError> {
let results = activate Context where "project:{project} status:{status}"
Ok(results)
}
// complete_work finalize an execution context.
//
// Transitions status to "completed" and records lessons_learned at the
// context level. Call this when a task is fully done not mid-task.
// Consolidate memories into knowledge BEFORE calling this.
@manager
fn complete_work(
context_id: String,
summary: String,
lessons_learned: [String],
) -> Result<Context, NeuronError> {
let ctx = native_get_context(context_id)?
let now = native_now()
let completed = Context {
id: ctx.id,
process_name: ctx.process_name,
description: ctx.description,
objective: summary,
project: ctx.project,
status: "completed",
steps: ctx.steps,
file_refs: ctx.file_refs,
key_decisions: ctx.key_decisions,
lessons_learned: lessons_learned,
created_at: ctx.created_at,
updated_at: now,
}
native_update_context(completed)?
native_emit("context.completed", {"id": context_id, "project": ctx.project})
Ok(completed)
}
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// daemon_config.el Daemon configuration (filesystem / env-based).
//
// Handles runtime config loaded from ~/.neuron/config.json or the
// NEURON_CONFIG env var. Distinct from config.el, which is the Neuron
// tool API for runtime key/value config entries.
fn config_path() -> String {
let override_path: String = env("NEURON_CONFIG")
if !str_eq(override_path, "") {
return override_path
}
let home: String = env("HOME")
return home + "/.neuron/config.json"
}
fn load_config() -> String {
let path: String = config_path()
let raw: String = fs_read(path)
if str_eq(raw, "") {
return "{}"
}
return raw
}
fn config_api_url(cfg: String) -> String {
let url: String = json_get(cfg, "axon_url")
if !str_eq(url, "") {
return url
}
let url2: String = json_get(cfg, "api_url")
if !str_eq(url2, "") {
return url2
}
return "http://localhost:7770"
}
fn config_api_token(cfg: String) -> String {
return json_get(cfg, "api_token")
}
fn config_ui_dir(cfg: String) -> String {
let home: String = env("HOME")
let dir: String = json_get(cfg, "ui_dir")
if str_eq(dir, "") {
return home + "/.neuron/ui"
}
return dir
}
fn config_data_dir(cfg: String) -> String {
let dir: String = json_get(cfg, "data_dir")
if !str_eq(dir, "") {
return dir
}
let home: String = env("HOME")
return home + "/.neuron/data"
}
fn config_port(cfg: String) -> Int {
let p: String = json_get(cfg, "port")
if str_eq(p, "") {
return 7749
}
return str_to_int(p)
}
// Principal identity is NOT read from config it is baked into the compiled
// binary at build time. See main.el (developer) and main-user.el (user build).
// config_mode() has been removed. Use daemon_principal() from the entry file.
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// events/bus.el Event bus. Three primitives over native_queue_*.
//
// Publish, consume, ack. Consumer identity IS the subscription.
// No separate subscribe step. No state. Intelligence lives in el.
// The Rust backend is swappable: InMemory Engram Kafka RabbitMQ.
fn event_publish(topic: String, payload: String) -> Void {
native_queue_publish(topic, payload)
}
fn event_consume(topic: String, consumer: String) -> String {
return native_queue_consume(topic, consumer)
}
fn event_ack(topic: String, consumer: String, msg_id: String) -> Void {
native_queue_ack(topic, consumer, msg_id)
}
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// graph.el Graph operations subsystem.
//
// Neuron's storage backend is Engram a graph database where every entity
// (Memory, Knowledge, BacklogItem, etc.) is a node and every relationship
// is a typed edge. This subsystem exposes the raw graph API for operations
// that span entity types or need structural graph information.
//
// WHY expose graph ops directly?
// High-level tools like search_memories or retrieve_knowledge hide the graph
// structure. But some questions are inherently graph-shaped:
// "What entities are connected to this decision?"
// "How deep is the dependency chain for this backlog item?"
// "Are these two memories causally related?"
//
// Graph ops answer these questions without the overhead of entity-typed
// queries. They're the escape hatch when you need to see the whole picture.
//
// link_entities is a @manager because it mutates the graph topology.
// All reads are @accessor.
from types import {
GraphNode,
SearchResult,
NeuronError,
}
// Public API
// inspect_graph get a node and its immediate connections.
//
// entity_type: "Memory" | "Knowledge" | "BacklogItem" | "Context" | "Artifact" | ...
// entity_id: the UUID or string ID of the entity.
//
// Returns the node and its edges formatted as "relation:target_id" strings.
// Use this to understand what a node is connected to before traversing deeper.
@accessor
fn inspect_graph(entity_type: String, entity_id: String) -> Result<GraphNode, NeuronError> {
let node = native_graph_inspect(entity_type, entity_id)?
Ok(node)
}
// traverse_graph walk the graph from a starting node up to depth hops.
//
// depth=1 returns immediate neighbors only (equivalent to inspect_graph).
// depth=2 returns neighbors of neighbors, etc.
// Default depth is 2 deep enough for most dependency chains.
//
// Returns all visited nodes, not just leaves. This gives the caller the
// full subgraph around the starting node.
@accessor
fn traverse_graph(entity_id: String, depth: Int) -> Result<[GraphNode], NeuronError> {
let effective_depth = if depth == 0 { 2 } else { depth }
let nodes = native_graph_traverse(entity_id, effective_depth)?
Ok(nodes)
}
// link_entities create a typed edge between two graph nodes.
//
// relation examples: "supersedes", "depends_on", "caused_by", "related_to",
// "implements", "references", "blocks"
//
// Edges are directional: source --relation--> target.
// Use causal links for reasoning chains, dependency links for work items,
// supersedes links for knowledge evolution.
@manager
fn link_entities(
source_id: String,
target_id: String,
relation: String,
) -> Result<GraphNode, NeuronError> {
native_graph_link(source_id, target_id, relation)?
native_emit("graph.linked", {"source": source_id, "target": target_id, "relation": relation})
// Return the source node with its updated edges.
let source_node = native_graph_inspect("", source_id)?
Ok(source_node)
}
// search_graph semantic search across all node types in the graph.
//
// Unlike type-specific search (search_memories, search_knowledge), this
// searches the entire graph regardless of entity type. Useful for
// cross-cutting queries: "what's related to authentication across all
// memories, knowledge, and backlog items?"
@accessor
fn search_graph(query: String) -> Result<[SearchResult], NeuronError> {
let results = native_semantic_search(query, "", 20)?
Ok(results)
}
// rebuild_graph trigger a full graph index rebuild.
//
// Used after bulk imports or when the semantic index drifts out of sync.
// This is an expensive operation avoid in normal sessions.
// It's sealed because it modifies the graph infrastructure, not just data.
@manager
fn rebuild_graph() -> Result<String, NeuronError> {
sealed {
native_emit("graph.rebuild_requested", {})
Ok("graph rebuild initiated")
}
}
// pin_node mark a graph node as pinned (exempt from compaction).
//
// Pinned nodes survive graph compaction even when their activation scores
// drop below the compaction threshold. Use for foundational knowledge nodes
// that must never be evicted.
@manager
fn pin_node(entity_id: String, entity_type: String) -> Result<GraphNode, NeuronError> {
sealed {
// Pinning is implemented as a special self-referential "pinned" edge.
native_graph_link(entity_id, entity_id, "pinned")?
native_emit("graph.node_pinned", {"id": entity_id, "type": entity_type})
let node = native_graph_inspect(entity_type, entity_id)?
Ok(node)
}
}
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// health.el Health check and status handlers.
fn health_response() -> String {
let mode: String = state_get("neuron_mode")
if str_eq(mode, "") {
return "{\"status\":\"ok\",\"version\":\"1.0.0-engram\"}"
}
return "{\"status\":\"ok\",\"version\":\"1.0.0-engram\",\"mode\":\"" + mode + "\"}"
}
fn not_found_response(path: String) -> String {
return "{\"error\":\"not found\",\"path\":\"" + path + "\"}"
}
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// ise.el Internal State Events (ISE) subsystem.
//
// Internal State Events are Neuron's introspective record. When the reasoning
// engine produces a response, an ISE captures the gap between:
// pre_reasoning what the model would say before internal deliberation
// post_reasoning what the model actually outputs after reasoning
//
// The compression_ratio tells us how much the reasoning compressed the
// pre-response. A ratio near 1.0 means almost no change; a ratio near 0.0
// means the reasoning fundamentally transformed the output.
//
// gap_direction encodes whether the change moved toward expression
// (more output, more detail) or suppression (filtered, condensed, withheld).
//
// WHY does Neuron care about this?
// ISEs are the raw data for self-model calibration. By reviewing ISEs over
// time, Neuron can identify systematic biases: topics where it consistently
// suppresses, domains where reasoning rarely changes the output, etc.
// This is the foundation of Cultivated General Intelligence introspection
// drives adaptation.
//
// Access is authenticated by default. ISEs contain sensitive reasoning traces
// that should not be exposed to arbitrary callers.
from types import {
InternalStateEvent,
NeuronError,
}
// Public API
// log_internal_state record one ISE.
//
// Called by the reasoning engine after each significant reasoning cycle.
// The id format is "ise_" + 8 hex chars (matches Rust domain convention).
@manager
fn log_internal_state(
trigger: String,
pre_reasoning: String,
post_reasoning: String,
compression_ratio: String,
gap_direction: String,
tags: [String],
) -> Result<InternalStateEvent, NeuronError> {
let id = "ise_" + native_uuid()
let now = native_now()
let ise = InternalStateEvent {
id: id,
trigger: trigger,
pre_reasoning: pre_reasoning,
post_reasoning: post_reasoning,
compression_ratio: compression_ratio,
gap_direction: gap_direction,
tags: tags,
logged_at: now,
}
native_store_ise(ise)?
// ISE events are emitted at low verbosity they are high volume.
native_emit("ise.logged", {"id": id, "trigger": trigger, "direction": gap_direction})
Ok(ise)
}
// list_internal_state retrieve recent ISEs.
//
// limit defaults to 10. Useful for reviewing recent reasoning patterns
// during session orientation or calibration reviews.
@accessor
fn list_internal_state(limit: Int) -> Result<[InternalStateEvent], NeuronError> {
let events = native_list_ise(limit)?
Ok(events)
}
// get_internal_state retrieve one ISE by ID.
//
// Used to drill into a specific reasoning event for detailed analysis.
@accessor
fn get_internal_state(id: String) -> Result<InternalStateEvent, NeuronError> {
// ISEs are stored with prefix "ise_", so a bare 8-char hex ID needs
// the prefix prepended. The native layer handles both forms.
let results = activate InternalStateEvent where "id:{id}"
let ise = native_list_ise(1)?
Ok(ise[0])
}
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// knowledge.el Knowledge base subsystem.
//
// Knowledge is stable reference material: architecture docs, coding standards,
// proven patterns, whitepapers. Unlike memories (which are ephemeral
// observations), knowledge nodes are meant to persist across many sessions
// and become more authoritative over time.
//
// The tier system enforces epistemological discipline:
// note raw observation (default)
// lesson validated pattern (proven 2 times)
// canonical authoritative reference (stable, widely referenced)
//
// Never skip tiers. A pattern observed once is a note, not a lesson.
from types import {
Knowledge,
SearchResult,
NeuronError,
}
// Key path utilities
// Knowledge keys use path notation: "architecture/vbd/foundations.md"
// The key doubles as a stable human-readable address and the graph node ID.
// When evolving knowledge, the key stays stable only content and tier change.
// Public API
// capture_knowledge store a new knowledge node.
//
// The key should be a path-style string that describes where this knowledge
// lives in the taxonomy: "architecture/styles/vbd/foundations.md",
// "coding/rust/error-handling.md", etc.
@manager
fn capture_knowledge(
title: String,
content: String,
category: String,
tier: String,
tags: [String],
project: String,
) -> Result<Knowledge, NeuronError> {
let id = native_uuid()
let now = native_now()
// Derive a stable key from category + title (slugified by runtime).
let key = category + "/" + title
let knowledge = Knowledge {
id: id,
key: key,
title: title,
content: content,
category: category,
tier: tier,
tags: tags,
project: project,
created_at: now,
}
native_store_knowledge(knowledge)?
native_emit("knowledge.captured", {"id": id, "tier": tier, "category": category})
Ok(knowledge)
}
// retrieve_knowledge fetch a knowledge node by its path key.
//
// This is the primary retrieval path for known knowledge. When you know the
// key ("architecture/vbd/fundamentals.md"), use this. For exploratory queries,
// use search_knowledge.
@accessor
fn retrieve_knowledge(key: String) -> Result<Knowledge, NeuronError> {
// The native layer maps key graph node via an index.
let results = activate Knowledge where "key:{key}"
// Return the first (most relevant) result or not-found.
let knowledge = native_get_knowledge(key)?
Ok(knowledge)
}
// search_knowledge semantic search over the knowledge graph.
//
// Supports optional category and tier filters. Always search before
// implementing anything the knowledge base contains patterns and
// decisions that should not be reinvented.
@accessor
fn search_knowledge(
query: String,
category: String,
tier: String,
limit: Int,
) -> Result<[Knowledge], NeuronError> {
let results = activate Knowledge where "{query} category:{category} tier:{tier} limit:{limit}"
Ok(results)
}
// browse_knowledge list knowledge nodes by category.
//
// Used to orient at session start or when exploring an unfamiliar domain.
// Returns a summary list, not full content use retrieve_knowledge for
// the full text of a specific node.
@accessor
fn browse_knowledge(category: String) -> Result<[Knowledge], NeuronError> {
let knowledge_list = native_list_knowledge(category)?
Ok(knowledge_list)
}
// evolve_knowledge update an existing knowledge node.
//
// When new evidence supersedes an existing canonical, call this rather than
// capture_knowledge. The old node is soft-deleted; the new one inherits the
// key path so all existing references remain valid.
@manager
fn evolve_knowledge(
id: String,
new_content: String,
new_tier: String,
supersedes_id: String?,
) -> Result<Knowledge, NeuronError> {
let old = native_get_knowledge(id)?
let now = native_now()
let evolved = Knowledge {
id: native_uuid(),
key: old.key,
title: old.title,
content: new_content,
category: old.category,
tier: new_tier,
tags: old.tags,
project: old.project,
created_at: now,
}
native_store_knowledge(evolved)?
native_emit("knowledge.evolved", {"old_id": id, "new_id": evolved.id, "tier": new_tier})
Ok(evolved)
}
// remove_knowledge delete a knowledge node.
//
// Use sparingly. Knowledge is meant to accumulate. Only remove nodes that
// are factually wrong, not just outdated (prefer evolve_knowledge for
// supersession).
@manager
fn remove_knowledge(id: String) -> Result<Knowledge, NeuronError> {
let knowledge = native_get_knowledge(id)?
native_emit("knowledge.removed", {"id": id, "key": knowledge.key})
Ok(knowledge)
}
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// loop.el Six-tier runtime heartbeat for the Neuron daemon.
//
// Implements the self-pacing cognitive loop described in the architecture
// doc. The loop runs in its own OS thread (spawned via `spawn_thread`)
// and communicates with the rest of the daemon through global shared
// state (`state_get` / `state_set`).
//
// The six tiers:
// resting 30 min (low signal, diffuse)
// watching 10 min (ambient monitoring)
// working 15 sec (background task in progress)
// active 500 ms (conversation in progress)
// critical 10 ms (bell fired)
// realtime busy loop (physical actuator pinned)
//
// Tier transition rules:
// 1. Bell is sacred escalates to Critical immediately, never dropped.
// 2. Escalation is immediate loop re-enters without finishing sleep.
// 3. Step-down is earned 4 consecutive idle ticks before stepping down.
// 4. Floor is configurable never drops below `loop_min_tier`.
//
// State keys this file reads / writes:
// loop_tier, loop_min_tier, loop_ticks, loop_bell_fires,
// loop_tier_changes, loop_idle_ticks, loop_signal, loop_override_tier
//
// Cognitive substrate:
// loop manages WHEN to tick; agent.el manages WHAT to do on each tick.
// loop_do_tick() calls agent_tick() after confirming neuronrs is live.
import "agent.el"
import "plugins/host.el"
// Tier constants and ordering
fn loop_tier_rank(tier: String) -> Int {
if str_eq(tier, "resting") { return 0 }
if str_eq(tier, "watching") { return 1 }
if str_eq(tier, "working") { return 2 }
if str_eq(tier, "active") { return 3 }
if str_eq(tier, "critical") { return 4 }
if str_eq(tier, "realtime") { return 5 }
return 1
}
fn loop_tier_from_rank(rank: Int) -> String {
if rank <= 0 { return "resting" }
if rank == 1 { return "watching" }
if rank == 2 { return "working" }
if rank == 3 { return "active" }
if rank == 4 { return "critical" }
return "realtime"
}
// Sleep interval in milliseconds for each tier.
// Returns 0 for realtime (busy loop, no sleep at all).
fn loop_tier_interval(tier: String) -> Int {
if str_eq(tier, "resting") { return 1800000 }
if str_eq(tier, "watching") { return 600000 }
if str_eq(tier, "working") { return 15000 }
if str_eq(tier, "active") { return 500 }
if str_eq(tier, "critical") { return 10 }
if str_eq(tier, "realtime") { return 0 }
return 600000
}
// Signal handling
// Apply a signal to the current tier and return the new tier.
//
// Bell is special: it always escalates to at least Critical, regardless
// of `min_tier`. Other escalations clamp to max(needed, min_tier) so a
// configured floor cannot trap us above an explicit step-down.
fn loop_apply_signal(current: String, signal: String, min_tier: String) -> String {
let cur_rank: Int = loop_tier_rank(current)
let min_rank: Int = loop_tier_rank(min_tier)
// Bell sacred. Always go to at least Critical.
if str_eq(signal, "bell") {
let crit_rank: Int = 4
if cur_rank >= crit_rank {
return current
}
return "critical"
}
// Realtime escalation pins us at the top.
if str_eq(signal, "realtime") {
return "realtime"
}
// Release-realtime drops us back to Critical.
if str_eq(signal, "release-realtime") {
if str_eq(current, "realtime") {
return "critical"
}
return current
}
// Active escalates to at least Active.
if str_eq(signal, "active") {
let need: Int = 3
if cur_rank >= need {
return current
}
return "active"
}
// Task escalates to at least Working.
if str_eq(signal, "task") {
let need: Int = 2
if cur_rank >= need {
return current
}
return "working"
}
// Sleep is an explicit step-down request drop one tier
// (respecting the floor).
if str_eq(signal, "sleep") {
return loop_step_down(current, min_tier)
}
// Idle / drain just contribute to the idle counter; the tier
// itself does not change here.
return current
}
// Step down one tier, but never below the configured min_tier.
fn loop_step_down(current: String, min_tier: String) -> String {
let cur_rank: Int = loop_tier_rank(current)
let min_rank: Int = loop_tier_rank(min_tier)
if cur_rank <= min_rank {
return min_tier
}
let new_rank: Int = cur_rank - 1
if new_rank < min_rank {
return min_tier
}
return loop_tier_from_rank(new_rank)
}
// Bell-aware sleep
// Tail-recursive sleep that wakes early if a bell signal arrives.
// Sleeps in 100ms chunks, peeking at `loop_signal` between each chunk.
fn loop_sleep_chunked(remaining_ms: Int) -> Void {
if remaining_ms <= 0 {
// done
} else {
let pending: String = state_get("loop_signal")
if str_eq(pending, "bell") {
// Bell detected mid-sleep return immediately so the
// outer loop can re-enter and escalate.
} else {
let chunk: Int = if remaining_ms < 100 { remaining_ms } else { 100 }
sleep_ms(chunk)
loop_sleep_chunked(remaining_ms - chunk)
}
}
}
// Public sleep entry point. realtime (interval == 0) is a no-op.
fn loop_sleep(remaining_ms: Int) -> Void {
if remaining_ms > 0 {
loop_sleep_chunked(remaining_ms)
}
}
// Tick
// Perform one cognitive tick.
//
// First pings neuronrs /health to confirm the substrate is live.
// If live, delegates to agent_tick() the cognitive work is in agent.el.
// loop manages WHEN; agent manages WHAT.
//
// Side effect: writes "1" or "0" to `loop_last_tick_idle` so the
// caller can see whether the tick was idle (no live counterpart).
fn loop_do_tick(tier: String) -> Void {
let url: String = "http://localhost:7770/health"
let resp: String = http_get(url)
let live: Bool = str_contains(resp, "ok")
if live {
state_set("loop_last_tick_idle", "0")
// Delegate to the agent cognitive substrate.
agent_tick({})
} else {
state_set("loop_last_tick_idle", "1")
}
// Drain the plugin event bus on every tick, regardless of substrate liveness.
// Plugins interact through events only host_tick routes events to all
// plugins that subscribed to them. No hooks, no direct callbacks.
host_tick({})
println("[loop] tick tier=" + tier + " live=" + bool_to_str(live))
}
// Counters
fn loop_incr_state_int(key: String) -> Void {
let raw: String = state_get(key)
let n: Int = if str_eq(raw, "") { 0 } else { str_to_int(raw) }
state_set(key, int_to_str(n + 1))
}
fn loop_set_int(key: String, value: Int) -> Void {
state_set(key, int_to_str(value))
}
// Core loop
// One iteration of the heartbeat. Tail-recursive: each tick re-enters
// itself with the (possibly updated) tier and idle counter.
fn loop_run(tier: String, idle_count: Int) -> Void {
// 1. Read floor and pending signals.
let min_tier: String = state_get("loop_min_tier")
let floor: String = if str_eq(min_tier, "") { "resting" } else { min_tier }
let pending: String = state_get("loop_signal")
let override_tier: String = state_get("loop_override_tier")
// 2. Apply override (explicit set tier wins over signal).
let after_override: String = if str_eq(override_tier, "") {
tier
} else {
override_tier
}
if !str_eq(override_tier, "") {
state_set("loop_override_tier", "")
loop_incr_state_int("loop_tier_changes")
}
// 3. Apply signal.
let after_signal: String = if str_eq(pending, "") {
after_override
} else {
loop_apply_signal(after_override, pending, floor)
}
let signal_changed: Bool = !str_eq(pending, "")
if signal_changed {
state_set("loop_signal", "")
if str_eq(pending, "bell") {
loop_incr_state_int("loop_bell_fires")
}
if !str_eq(after_signal, after_override) {
loop_incr_state_int("loop_tier_changes")
}
}
// 4. Persist current tier.
state_set("loop_tier", after_signal)
// 5. Sleep for this tier's interval (bell-aware, chunked).
let interval_ms: Int = loop_tier_interval(after_signal)
loop_sleep(interval_ms)
// 6. If a bell arrived during sleep, re-enter immediately without
// ticking the next iteration will re-read the signal and
// escalate.
let after_sleep_signal: String = state_get("loop_signal")
if str_eq(after_sleep_signal, "bell") {
loop_run(after_signal, idle_count)
} else {
// 7. Run the cognitive tick.
loop_do_tick(after_signal)
loop_incr_state_int("loop_ticks")
// 8. Update idle counter and step down if earned.
let last_idle: String = state_get("loop_last_tick_idle")
let was_idle: Bool = str_eq(last_idle, "1")
let new_idle_count: Int = if was_idle { idle_count + 1 } else { 0 }
// Idle / drain signals also push the counter forward, but
// `loop_apply_signal` does not change the tier for them.
let idle_signal: Bool = str_eq(pending, "idle") || str_eq(pending, "drain")
let bumped_idle_count: Int = if idle_signal {
new_idle_count + 1
} else {
new_idle_count
}
// Realtime is pinned only `release-realtime` can lower it.
let pinned: Bool = str_eq(after_signal, "realtime")
if !pinned && bumped_idle_count >= 4 {
let stepped: String = loop_step_down(after_signal, floor)
if !str_eq(stepped, after_signal) {
loop_incr_state_int("loop_tier_changes")
}
loop_set_int("loop_idle_ticks", 0)
loop_run(stepped, 0)
} else {
loop_set_int("loop_idle_ticks", bumped_idle_count)
loop_run(after_signal, bumped_idle_count)
}
}
}
// Entry point called by `spawn_thread("loop_main")`.
fn loop_main() -> Void {
let start_tier: String = state_get("loop_tier")
let initial: String = if str_eq(start_tier, "") { "watching" } else { start_tier }
println("[loop] starting at tier=" + initial)
loop_run(initial, 0)
}
// Status JSON
fn loop_status_field(key: String, default: String) -> String {
let v: String = state_get(key)
if str_eq(v, "") { return default }
return v
}
fn loop_status_json() -> String {
let tier: String = loop_status_field("loop_tier", "watching")
let min_tier: String = loop_status_field("loop_min_tier", "resting")
let ticks: String = loop_status_field("loop_ticks", "0")
let bell_fires: String = loop_status_field("loop_bell_fires", "0")
let tier_changes: String = loop_status_field("loop_tier_changes", "0")
let idle_ticks: String = loop_status_field("loop_idle_ticks", "0")
let signal: String = loop_status_field("loop_signal", "")
let parts: String = "{\"current_tier\":\"" + tier + "\""
let p2: String = parts + ",\"min_tier\":\"" + min_tier + "\""
let p3: String = p2 + ",\"ticks\":" + ticks
let p4: String = p3 + ",\"bell_fires\":" + bell_fires
let p5: String = p4 + ",\"tier_changes\":" + tier_changes
let p6: String = p5 + ",\"idle_ticks\":" + idle_ticks
let p7: String = p6 + ",\"pending_signal\":\"" + signal + "\"}"
return p7
}
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// main-user.el Neuron daemon entry point. User build.
// Written in Engram.
//
// Principal identity is a compile-time constant NOT read from config.
// This file is the user build. The developer build is main.el.
// Built with: el build --manifest el-user.toml
//
// Responsibilities:
// 1. Write PID file to ~/.neuron-user/data/daemon.pid
// 2. Start API/proxy server on :7750 (blocking)
// - Proxies /axon/, /api/ routes to neuronrs at :7770
// - Health check at /health
//
// The handle_request function is called by http_serve for every request.
import "daemon_config.el"
import "proxy.el"
import "health.el"
import "loop.el"
import "plugins/host.el"
// Principal identity (baked in at compile time)
// This literal is compiled into the bytecode. For prod builds it is sealed
// inside AES-256-GCM cannot be changed without the deployment key.
let principal: String = "user"
// Load operational config
let cfg: String = load_config()
let axon_base: String = config_api_url(cfg)
let token: String = config_api_token(cfg)
let ui_dir: String = config_ui_dir(cfg)
let data_dir: String = config_data_dir(cfg)
let port: Int = config_port(cfg)
state_set("neuron_principal", principal)
// Write PID file
let pid: Int = getpid()
let pid_str: String = int_to_str(pid)
fs_mkdir(data_dir)
let pid_path: String = data_dir + "/daemon.pid"
fs_write(pid_path, pid_str)
println(color_bold("Neuron daemon") + " — pid " + pid_str + "" + principal)
println(" API → http://localhost:" + int_to_str(port))
println(" Axon → " + axon_base)
println(" Data → " + data_dir)
println("")
// Request handler (API server on :7750)
//
// Called by http_serve for every incoming request.
// Must be named exactly "handle_request" http_serve looks it up by that name.
fn handle_request(method: String, path: String, body: String) -> String {
// Health check
if str_eq(path, "/health") {
return health_response()
}
// Axon tool dispatch proxy to neuronrs
if str_starts_with(path, "/axon/") {
return proxy_request(axon_base, method, path, body, token)
}
// Intelligence REST API proxy to neuronrs
if str_starts_with(path, "/api/memories") {
return proxy_request(axon_base, method, path, body, token)
}
if str_starts_with(path, "/api/knowledge") {
return proxy_request(axon_base, method, path, body, token)
}
if str_starts_with(path, "/api/backlog") {
return proxy_request(axon_base, method, path, body, token)
}
if str_starts_with(path, "/api/contexts") {
return proxy_request(axon_base, method, path, body, token)
}
if str_starts_with(path, "/api/ise") {
return proxy_request(axon_base, method, path, body, token)
}
// Plugin status
if str_eq(path, "/plugin/status") {
return host_status()
}
// Runtime loop control
if str_eq(path, "/loop/status") {
return loop_status_json()
}
if str_eq(path, "/loop/signal") {
let sig: String = json_get(body, "signal")
if str_eq(sig, "") {
return "{\"error\":\"missing signal\"}"
}
state_set("loop_signal", sig)
return "{\"ok\":true,\"signal\":\"" + sig + "\"}"
}
if str_eq(path, "/loop/tier") {
let new_tier: String = json_get(body, "tier")
let new_min: String = json_get(body, "min_tier")
if !str_eq(new_min, "") {
state_set("loop_min_tier", new_min)
}
if !str_eq(new_tier, "") {
state_set("loop_override_tier", new_tier)
}
return "{\"ok\":true,\"tier\":\"" + new_tier + "\",\"min_tier\":\"" + new_min + "\"}"
}
return not_found_response(path)
}
// Initialise plugin host
host_on_startup()
// Initialise loop state
state_set("loop_tier", "watching")
state_set("loop_min_tier", "resting")
state_set("loop_ticks", "0")
state_set("loop_bell_fires", "0")
state_set("loop_tier_changes", "0")
state_set("loop_idle_ticks", "0")
state_set("loop_signal", "")
state_set("loop_override_tier", "")
state_set("loop_last_tick_idle", "0")
// Spawn the heartbeat thread
spawn_thread("loop_main")
// Start API server (blocking)
http_serve(port)
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// main.el Neuron daemon entry point. Developer (unlocked) build.
// Written in Engram.
//
// Principal identity is a compile-time constant NOT read from config.
// This file is the developer build. The user build is main-user.el.
// Both are compiled from different el.toml manifests:
// el build developer (el.toml)
// el build --manifest el-user.toml user (el-user.toml)
//
// Responsibilities:
// 1. Write PID file to ~/.neuron/data/daemon.pid
// 2. Start API/proxy server on :7749 (blocking)
// - Proxies /axon/, /api/ routes to neuronrs at :7770
// - Health check at /health
//
// The handle_request function is called by http_serve for every request.
import "daemon_config.el"
import "proxy.el"
import "health.el"
import "loop.el"
import "plugins/host.el"
// Principal identity (baked in at compile time)
// This literal is compiled into the bytecode. For prod builds it is sealed
// inside AES-256-GCM cannot be changed without the deployment key.
let principal: String = "principal"
// Load operational config
let cfg: String = load_config()
let axon_base: String = config_api_url(cfg)
let token: String = config_api_token(cfg)
let ui_dir: String = config_ui_dir(cfg)
let data_dir: String = config_data_dir(cfg)
let port: Int = config_port(cfg)
state_set("neuron_principal", principal)
// Write PID file
let pid: Int = getpid()
let pid_str: String = int_to_str(pid)
fs_mkdir(data_dir)
let pid_path: String = data_dir + "/daemon.pid"
fs_write(pid_path, pid_str)
println(color_bold("Neuron daemon") + " — pid " + pid_str + "" + principal)
println(" API → http://localhost:" + int_to_str(port))
println(" Axon → " + axon_base)
println(" Data → " + data_dir)
println("")
// Request handler (API server on :7749)
//
// Called by http_serve for every incoming request.
// Must be named exactly "handle_request" http_serve looks it up by that name.
fn handle_request(method: String, path: String, body: String) -> String {
// Health check
if str_eq(path, "/health") {
return health_response()
}
// Axon tool dispatch proxy to neuronrs
if str_starts_with(path, "/axon/") {
return proxy_request(axon_base, method, path, body, token)
}
// Intelligence REST API proxy to neuronrs
if str_starts_with(path, "/api/memories") {
return proxy_request(axon_base, method, path, body, token)
}
if str_starts_with(path, "/api/knowledge") {
return proxy_request(axon_base, method, path, body, token)
}
if str_starts_with(path, "/api/backlog") {
return proxy_request(axon_base, method, path, body, token)
}
if str_starts_with(path, "/api/contexts") {
return proxy_request(axon_base, method, path, body, token)
}
if str_starts_with(path, "/api/ise") {
return proxy_request(axon_base, method, path, body, token)
}
// Plugin status
if str_eq(path, "/plugin/status") {
return host_status()
}
// Runtime loop control
if str_eq(path, "/loop/status") {
return loop_status_json()
}
if str_eq(path, "/loop/signal") {
let sig: String = json_get(body, "signal")
if str_eq(sig, "") {
return "{\"error\":\"missing signal\"}"
}
state_set("loop_signal", sig)
return "{\"ok\":true,\"signal\":\"" + sig + "\"}"
}
if str_eq(path, "/loop/tier") {
let new_tier: String = json_get(body, "tier")
let new_min: String = json_get(body, "min_tier")
if !str_eq(new_min, "") {
state_set("loop_min_tier", new_min)
}
if !str_eq(new_tier, "") {
state_set("loop_override_tier", new_tier)
}
return "{\"ok\":true,\"tier\":\"" + new_tier + "\",\"min_tier\":\"" + new_min + "\"}"
}
return not_found_response(path)
}
// Initialise plugin host
host_on_startup()
// Initialise loop state
state_set("loop_tier", "watching")
state_set("loop_min_tier", "resting")
state_set("loop_ticks", "0")
state_set("loop_bell_fires", "0")
state_set("loop_tier_changes", "0")
state_set("loop_idle_ticks", "0")
state_set("loop_signal", "")
state_set("loop_override_tier", "")
state_set("loop_last_tick_idle", "0")
// Spawn the heartbeat thread
spawn_thread("loop_main")
// Start API server (blocking)
http_serve(port)
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// memory.el Memory intelligence subsystem.
//
// Memories are the raw epistemological substrate of Neuron. They're not
// long-term knowledge (that's knowledge.el) they're observations, decisions,
// and lessons captured in flight. The half-life of a memory is governed by
// its importance tier and how often the graph activates it.
//
// Design intent:
// - Every remember() call emits an event so other subsystems can react.
// - supersedes_id creates a linked list of memory versions old memories
// remain traversable even after replacement.
// - Importance auto-promotion prevents Claude from under-tagging critical
// decisions just because it used soft language.
from types import {
Memory,
SearchResult,
NeuronError,
}
// Importance auto-promotion
// If the content itself signals criticality, override whatever importance the
// caller passed. This prevents decisions from being buried under "normal" tier
// just because the caller was being polite.
@accessor
fn infer_importance(content: String, stated_importance: String) -> String {
if stated_importance == "critical" {
return "critical"
}
// Escalate to critical if the content describes irreversibility or arch decisions.
let signals = ["critical", "irreversible", "breaking change", "never", "always",
"permanent", "architectural", "security", "deleted forever"]
for signal in signals {
if content == signal {
return "critical"
}
}
// Escalate normal high if the content sounds high-stakes.
if stated_importance == "normal" {
let high_signals = ["important", "must", "required", "blocked", "production"]
for signal in high_signals {
if content == signal {
return "high"
}
}
}
stated_importance
}
// Public API
// remember store a new memory node.
//
// When supersedes_id is provided, the old memory is kept in the graph but
// marked as superseded. This preserves the full decision history while making
// the new memory the canonical source for activation queries.
@manager
fn remember(
content: String,
tags: [String],
project: String,
importance: String,
supersedes_id: String?,
) -> Result<Memory, NeuronError> {
let effective_importance = infer_importance(content, importance)
let id = native_uuid()
let now = native_now()
let memory = Memory {
id: id,
content: content,
tags: tags,
importance: effective_importance,
project: project,
supersedes_id: supersedes_id,
created_at: now,
updated_at: now,
}
let stored = native_store_memory(memory)?
native_emit("memory.created", {"id": id, "project": project, "importance": effective_importance})
Ok(memory)
}
// recall retrieve one memory by ID.
@accessor
fn recall(id: String) -> Result<Memory, NeuronError> {
let memory = native_get_memory(id)?
Ok(memory)
}
// recall_chain retrieve recent memories sharing a tag.
//
// This is how Neuron reconstructs prior reasoning: follow the tag chain
// rather than trying to remember everything in the conversation window.
@accessor
fn recall_chain(tag: String, limit: Int) -> Result<[Memory], NeuronError> {
let results = activate Memory where "{tag} limit:{limit}"
Ok(results)
}
// search_memories semantic search over the memory graph.
//
// Uses spreading activation: the query activates nearby graph nodes and
// surfaces the most relevantly connected memories.
@accessor
fn search_memories(query: String, project: String, limit: Int) -> Result<[Memory], NeuronError> {
let results = activate Memory where "{query} project:{project} limit:{limit}"
Ok(results)
}
// list_memories enumerate all memories for a project.
// Useful for session orientation (begin_session) and memory audits.
@accessor
fn list_memories(project: String) -> Result<[Memory], NeuronError> {
let memories = native_list_memories(project)?
Ok(memories)
}
// forget soft-delete a memory.
//
// "Soft delete" means the node is removed from active activation but the
// graph edge history is preserved. This is intentional: we don't want to
// lose the causal record of why a decision was superseded.
@manager
fn forget(id: String) -> Result<Memory, NeuronError> {
let memory = native_get_memory(id)?
native_delete_memory(id)?
native_emit("memory.forgotten", {"id": id})
Ok(memory)
}
// promote_memory raise a memory's importance tier.
//
// Used when a memory originally tagged "normal" turns out to be load-bearing.
// Does NOT supersede the original it mutates importance in place.
@manager
fn promote_memory(id: String, new_importance: String) -> Result<Memory, NeuronError> {
let memory = native_get_memory(id)?
let promoted = Memory {
id: memory.id,
content: memory.content,
tags: memory.tags,
importance: new_importance,
project: memory.project,
supersedes_id: memory.supersedes_id,
created_at: memory.created_at,
updated_at: native_now(),
}
native_store_memory(promoted)?
native_emit("memory.promoted", {"id": id, "importance": new_importance})
Ok(promoted)
}
// evolve_memory update a memory's content and mark it as superseding the old one.
//
// This is the preferred way to update a memory. It creates a new memory that
// explicitly links back to the old one, preserving the version chain.
@manager
fn evolve_memory(
old_id: String,
new_content: String,
new_importance: String,
tags: [String],
project: String,
) -> Result<Memory, NeuronError> {
// Retrieve the old memory to copy its metadata.
let old_memory = native_get_memory(old_id)?
let effective_importance = infer_importance(new_content, new_importance)
let new_id = native_uuid()
let now = native_now()
let evolved = Memory {
id: new_id,
content: new_content,
tags: tags,
importance: effective_importance,
project: project,
supersedes_id: old_id,
created_at: now,
updated_at: now,
}
native_store_memory(evolved)?
native_emit("memory.evolved", {"old_id": old_id, "new_id": new_id})
Ok(evolved)
}
// inspect_memories list all memories with optional project filter.
// Alias for list_memories that matches the Axon tool name convention.
@accessor
fn inspect_memories(project: String) -> Result<[Memory], NeuronError> {
list_memories(project)
}
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// neuron.el Neuron intelligence entry point.
//
// This is the root module loaded by neuron-runtime at startup. It wires
// all subsystems together and defines the swarm coordinator the function
// that handles every incoming tool call.
//
// Architecture intent:
// Neuron is a swarm agent: one coordinator orchestrates multiple specialized
// subsystems (memory, knowledge, backlog, context, artifact, etc.). Each
// subsystem is a focused el module. The coordinator dispatches
// to the right subsystem based on tool_name.
//
// The @swarm_coordinator decorator tells the engram runtime that this function
// is the root entry point for the agent. The runtime calls handle_tool_call()
// for every incoming Axon message.
//
// Startup sequence (enforced by the runtime):
// 1. Load types.el graph schema registration
// 2. Load all subsystems function registration
// 3. Load axon.el dispatch table
// 4. Load neuron.el entry point wiring
// 5. Call version() runtime health check
//
// This file intentionally contains minimal logic. It's the composition
// root, not a logic layer. Keep it thin.
from axon import { dispatch_tool }
from types import { NeuronError }
// Swarm coordinator
// handle_tool_call the root entry point for all Axon tool invocations.
//
// The @swarm_coordinator decorator registers this function as the agent's
// primary message handler. The runtime calls it for every incoming tool call
// after authentication is verified at the protocol layer.
//
// It delegates entirely to dispatch_tool() in axon.el the coordinator
// should not contain routing logic.
@swarm_coordinator
fn handle_tool_call(
tool_name: String,
params: Map<String, String>,
) -> Result<Map<String, String>, NeuronError> {
dispatch_tool(tool_name, params)
}
// Health and metadata
// version return the current Neuron version string.
//
// @public called by the runtime health check without authentication.
// The format is "neuron/<version>-engram" to distinguish from the Kotlin
// predecessor (which used "neuron/<version>-kotlin").
@public
fn version() -> String {
"neuron/1.0.0-engram"
}
// health lightweight liveness probe.
//
// Returns "ok" if the engram runtime and all subsystems loaded correctly.
// Called by the /health HTTP endpoint in neuron-api.
@public
fn health() -> String {
"ok"
}
// Session bootstrap
// on_startup called once when the runtime initializes.
//
// Emits a startup event so Axon subscribers know Neuron is ready.
// Does NOT run begin_session() that's the caller's responsibility.
// The runtime calls on_startup() once; callers call begin_session() per session.
@manager
fn on_startup() -> Result<String, NeuronError> {
native_emit("neuron.started", {"version": "1.0.0-engram"})
Ok("neuron ready")
}
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// plugins/host.el Plugin host for the Neuron daemon.
//
// Plugins communicate with the host exclusively through the event bus.
// A plugin's identity IS its contributions no separate type field.
// The marketplace queries contributions to surface clean category lanes.
//
// Plugin interaction model (both directions are events):
//
// Plugin Host:
// plugin.announce plugin is alive, wants to register
// plugin.manifest response to plugin.interrogate, declares capabilities
// <any event> plugin emits these as its "output"
//
// Host Plugin:
// plugin.interrogate host asks for the plugin's manifest
// <subscribed events> host forwards events the plugin declared interest in
//
// Manifest format (plugin.manifest payload):
// {
// "plugin": "voice",
// "version": "1.0.0",
// "description": "Voice input and output for Neuron",
// "contributions": ["connector", "notification_channel"],
// "required": false,
// "dependencies": ["audio-hardware"],
// "subscriptions": ["agent.turn_complete"],
// "emits": ["voice.speaking", "voice.idle"],
// }
//
// Contribution types (contributions IS the type one list, no separate field):
// connector bridges to an external system
// interceptor operates on the event pipeline
// imprint shapes the cognitive layer directly
// knowledge adds domain knowledge to the graph
// process adds CCR workflow definitions
// tool adds agent-callable tools (registered in daemon)
// command adds CLI commands
// behavior adds behavioral patterns
// safety_rule adds safety constraints
// hardware adds hardware device support
// sync_handler adds sync protocol support
// notification_channel adds a notification delivery channel
// ui adds UI panels or widgets
from types import { NeuronError }
// Plugin registry entry
//
// State keys:
// plugin.<name>.status "pending" | "active" | "disconnected"
// plugin.<name>.contributions comma-separated contribution types
// plugin.<name>.required "true" | "false"
// plugin.<name>.dependencies comma-separated plugin names
// plugin.<name>.subscriptions comma-separated event names
// plugin.<name>.emits comma-separated event names
// plugin.<name>.version semver string
// plugin.<name>.description human-readable description
// plugin.<name>.endpoint delivery address (URL or IPC path)
// plugins.registered comma-separated list of known plugin names
// Registry helpers
// plugin_names list all registered plugin names.
fn plugin_names() -> [String] {
let raw: String = state_get("plugins.registered")
if str_eq(raw, "") {
return []
}
str_split(raw, ",")
}
// plugin_register add a plugin name to the registry.
fn plugin_register(name: String) -> Void {
let existing: String = state_get("plugins.registered")
if str_eq(existing, "") {
state_set("plugins.registered", name)
} else {
let names: [String] = str_split(existing, ",")
let found: Bool = list_contains(names, name)
if !found {
state_set("plugins.registered", existing + "," + name)
}
}
}
fn plugin_set_status(name: String, status: String) -> Void {
state_set("plugin." + name + ".status", status)
}
fn plugin_get_status(name: String) -> String {
state_get("plugin." + name + ".status")
}
fn plugin_set_endpoint(name: String, endpoint: String) -> Void {
state_set("plugin." + name + ".endpoint", endpoint)
}
fn plugin_get_endpoint(name: String) -> String {
state_get("plugin." + name + ".endpoint")
}
fn plugin_set_subscriptions(name: String, events: [String]) -> Void {
state_set("plugin." + name + ".subscriptions", str_join(events, ","))
}
fn plugin_get_subscriptions(name: String) -> [String] {
let raw: String = state_get("plugin." + name + ".subscriptions")
if str_eq(raw, "") {
return []
}
str_split(raw, ",")
}
fn plugin_set_emits(name: String, events: [String]) -> Void {
state_set("plugin." + name + ".emits", str_join(events, ","))
}
// plugin_set_contributions store the contribution types for a plugin.
fn plugin_set_contributions(name: String, contributions: [String]) -> Void {
state_set("plugin." + name + ".contributions", str_join(contributions, ","))
}
// plugin_get_contributions list of contribution types this plugin declares.
fn plugin_get_contributions(name: String) -> [String] {
let raw: String = state_get("plugin." + name + ".contributions")
if str_eq(raw, "") {
return []
}
str_split(raw, ",")
}
// plugin_set_required whether this plugin must be loaded at startup.
fn plugin_set_required(name: String, required: Bool) -> Void {
if required {
state_set("plugin." + name + ".required", "true")
} else {
state_set("plugin." + name + ".required", "false")
}
}
// plugin_is_required true if this plugin declared itself required.
fn plugin_is_required(name: String) -> Bool {
let raw: String = state_get("plugin." + name + ".required")
str_eq(raw, "true")
}
// plugin_set_dependencies record declared plugin dependencies.
fn plugin_set_dependencies(name: String, deps: [String]) -> Void {
state_set("plugin." + name + ".dependencies", str_join(deps, ","))
}
// plugin_get_dependencies list of plugin names this plugin depends on.
fn plugin_get_dependencies(name: String) -> [String] {
let raw: String = state_get("plugin." + name + ".dependencies")
if str_eq(raw, "") {
return []
}
str_split(raw, ",")
}
// Contribution queries
// plugins_by_contribution return active plugin names that declare a given
// contribution type. Used by the marketplace to surface clean category lanes.
fn plugins_by_contribution(contribution: String) -> [String] {
let names: [String] = plugin_names()
let result: [String] = []
for name in names {
let status: String = plugin_get_status(name)
if str_eq(status, "active") {
let contribs: [String] = plugin_get_contributions(name)
if list_contains(contribs, contribution) {
list_append(result, name)
}
}
}
result
}
// plugin_has_contribution true if a specific plugin declares a contribution.
fn plugin_has_contribution(name: String, contribution: String) -> Bool {
let contribs: [String] = plugin_get_contributions(name)
list_contains(contribs, contribution)
}
// required_plugins return all plugins that declared required: true.
// Called at startup to verify all required plugins have announced.
fn required_plugins() -> [String] {
let names: [String] = plugin_names()
let result: [String] = []
for name in names {
if plugin_is_required(name) {
list_append(result, name)
}
}
result
}
// Routing helpers
// plugins_subscribed_to return names of active plugins subscribed to an event.
fn plugins_subscribed_to(event_name: String) -> [String] {
let names: [String] = plugin_names()
let result: [String] = []
for name in names {
let subs: [String] = plugin_get_subscriptions(name)
let active: String = plugin_get_status(name)
if str_eq(active, "active") {
if list_contains(subs, event_name) {
list_append(result, name)
}
}
}
result
}
// deliver_event forward an event to a plugin's endpoint.
fn deliver_event(plugin_name: String, event_name: String, payload: Map<String, Any>) -> Void {
let endpoint: String = plugin_get_endpoint(plugin_name)
if str_eq(endpoint, "") {
println("[plugin-host] " + plugin_name + ": no endpoint, skipping delivery of " + event_name)
return
}
let envelope: Map<String, Any> = {
"event": event_name,
"payload": payload,
}
let resp = native_http_post({
"url": endpoint + "/event",
"headers": {"Content-Type": "application/json"},
"body": envelope,
})
if !resp.ok {
println("[plugin-host] delivery failed: " + plugin_name + " event=" + event_name)
}
}
// Interrogation
fn host_interrogate(plugin_name: String, endpoint: String) -> Void {
println("[plugin-host] interrogating plugin: " + plugin_name)
native_emit("plugin.interrogate", {"plugin": plugin_name, "endpoint": endpoint})
let req_body: Map<String, Any> = {
"event": "plugin.interrogate",
"payload": {"plugin": plugin_name},
}
let resp = native_http_post({
"url": endpoint + "/event",
"headers": {"Content-Type": "application/json"},
"body": req_body,
})
if !resp.ok {
println("[plugin-host] interrogate delivery failed: " + plugin_name)
}
}
// Event processing
fn host_handle_announce(payload: Map<String, Any>) -> Void {
let name: String = payload["plugin"]
let endpoint: String = payload["endpoint"]
if str_eq(name, "") {
println("[plugin-host] announce missing plugin name, ignoring")
return
}
println("[plugin-host] plugin announced: " + name + " at " + endpoint)
plugin_register(name)
plugin_set_status(name, "pending")
plugin_set_endpoint(name, endpoint)
native_emit("plugin.status", {
"plugin": name,
"status": "pending",
"reason": "announced",
})
host_interrogate(name, endpoint)
}
// host_handle_manifest process a plugin.manifest event.
//
// Stores all declared capabilities. The contributions list IS the plugin's
// type no separate type field. required and dependencies drive startup
// loading order.
fn host_handle_manifest(payload: Map<String, Any>) -> Void {
let name: String = payload["plugin"]
let contributions: [String] = payload["contributions"]
let subscriptions: [String] = payload["subscriptions"]
let emits: [String] = payload["emits"]
let required: Bool = payload["required"]
let dependencies: [String] = payload["dependencies"]
if str_eq(name, "") {
println("[plugin-host] manifest missing plugin name, ignoring")
return
}
let current_status: String = plugin_get_status(name)
if str_eq(current_status, "") {
plugin_register(name)
}
plugin_set_contributions(name, contributions)
plugin_set_subscriptions(name, subscriptions)
plugin_set_emits(name, emits)
plugin_set_required(name, required)
plugin_set_dependencies(name, dependencies)
plugin_set_status(name, "active")
println("[plugin-host] plugin active: " + name
+ " contributions=" + str_join(contributions, ",")
+ " subs=" + str_join(subscriptions, ","))
native_emit("plugin.status", {
"plugin": name,
"status": "active",
"contributions": str_join(contributions, ","),
"subscriptions": str_join(subscriptions, ","),
"emits": str_join(emits, ","),
"required": required,
})
}
// Event routing
fn host_route_event(event_name: String, payload: Map<String, Any>) -> Void {
let recipients: [String] = plugins_subscribed_to(event_name)
for plugin_name in recipients {
deliver_event(plugin_name, event_name, payload)
}
}
// Host tick
@manager
fn host_tick(params: Map<String, Any>) -> Result<Map<String, Any>, NeuronError> {
let events: [Map<String, Any>] = native_drain_events()
let processed: Int = 0
for event in events {
let event_name: String = event["event"]
let payload: Map<String, Any> = event["payload"]
if str_eq(event_name, "plugin.announce") {
host_handle_announce(payload)
} else {
if str_eq(event_name, "plugin.manifest") {
host_handle_manifest(payload)
} else {
host_route_event(event_name, payload)
}
}
let processed = processed + 1
}
Ok({"status": "ok", "processed": int_to_str(processed)})
}
// Status introspection
// host_status JSON snapshot of the plugin registry.
fn host_status() -> String {
let names: [String] = plugin_names()
let parts: String = "{"
let first: Bool = true
for name in names {
let status: String = plugin_get_status(name)
let endpoint: String = plugin_get_endpoint(name)
let contribs: String = state_get("plugin." + name + ".contributions")
let subs: String = state_get("plugin." + name + ".subscriptions")
let emits_raw: String = state_get("plugin." + name + ".emits")
let required: String = state_get("plugin." + name + ".required")
let entry: String = "\"" + name + "\":{\"status\":\"" + status
+ "\",\"contributions\":\"" + contribs
+ "\",\"endpoint\":\"" + endpoint
+ "\",\"subscriptions\":\"" + subs
+ "\",\"emits\":\"" + emits_raw
+ "\",\"required\":\"" + required + "\"}"
if first {
let parts = parts + entry
let first = false
} else {
let parts = parts + "," + entry
}
}
parts + "}"
}
// Startup
fn host_on_startup() -> Void {
state_set("plugins.registered", "")
println("[plugin-host] ready — waiting for plugin.announce events")
native_emit("plugin.host_ready", {})
}
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// process.el Process definitions subsystem.
//
// Processes encode proven workflows as executable, named procedures.
// The difference between a process and a document is executability:
// a document describes what to do; a process can be invoked with
// begin_work(process_name="...") and tracked step by step.
//
// WHY processes?
// Institutional knowledge decays when it only lives in conversations.
// A process that has been run 10 times and refined each time is far more
// valuable than a one-off plan. The process library is Neuron's
// "muscle memory" established ways of doing things that don't need to
// be reinvented each session.
//
// Process discovery is @public because it's discovery-oriented callers
// should be able to find what processes exist without authentication.
// Process mutation (define, delete) is @manager these changes affect
// all future sessions.
from types import {
Process,
ProcessStep,
Context,
NeuronError,
}
// Public API
// define_process register a new named workflow.
//
// Each step has a name, instructions (markdown prose), and a list of tools
// that should be called during that step. The tools list is advisory it
// helps Claude orient to what's expected at each step.
@manager
fn define_process(
name: String,
description: String,
steps: [ProcessStep],
) -> Result<Process, NeuronError> {
let id = native_uuid()
let now = native_now()
let process = Process {
id: id,
name: name,
description: description,
steps: steps,
created_at: now,
updated_at: now,
}
// Processes are stored as Knowledge nodes with category="process" so they
// participate in semantic search alongside other reference material.
native_emit("process.defined", {"id": id, "name": name})
Ok(process)
}
// browse_processes list or get detail on named processes.
//
// When name is empty, returns a summary list of all registered processes.
// When name is provided, returns the full process with all steps.
// This is @public to encourage process discovery.
@public
fn browse_processes(name: String) -> Result<[Process], NeuronError> {
if name == "" {
let results = activate Process where "all processes"
return Ok(results)
}
// Semantic search by name to handle fuzzy matching ("pr review" "pull_request_review").
let results = activate Process where "name:{name}"
Ok(results)
}
// execute_process begin a tracked execution of a named process.
//
// This is the bridge between process definitions and execution contexts.
// It looks up the process by name, opens a new execution context with
// the process name, and returns the context so the caller can track progress.
//
// The caller is expected to call progress_work() for each step in the process.
@manager
fn execute_process(name: String, project: String) -> Result<Context, NeuronError> {
// Find the process definition.
let processes = activate Process where "name:{name}"
let process = processes[0]
// Open a context using begin_work from context.el.
// In practice, the Axon dispatcher will call begin_work() directly
// with the process name after execute_process resolves the name.
let id = native_uuid()
let now = native_now()
let ctx = Context {
id: id,
process_name: process.name,
description: process.description,
objective: "execute process: " + name,
project: project,
status: "active",
steps: [],
file_refs: [],
key_decisions: [],
lessons_learned: [],
created_at: now,
updated_at: now,
}
native_store_context(ctx)?
native_emit("process.execution_started", {"process": name, "context_id": id, "project": project})
Ok(ctx)
}
// list_processes enumerate all registered processes.
//
// Alias for browse_processes("") that matches the Axon tool name convention.
@public
fn list_processes() -> Result<[Process], NeuronError> {
browse_processes("")
}
// delete_process remove a process definition.
//
// Use sparingly prefer evolving (redefine with improvements) over deleting.
// Deleting a process that has active executions will leave those contexts
// without a parent process definition.
@manager
fn delete_process(name: String) -> Result<String, NeuronError> {
let processes = activate Process where "name:{name}"
let process = processes[0]
native_emit("process.deleted", {"name": name, "id": process.id})
Ok("process deleted: " + name)
}
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// proxy.el HTTP proxy helpers.
import "daemon_config.el"
fn proxy_get(target_base: String, path: String, token: String) -> String {
let url: String = target_base + path
return http_get_auth(url, token)
}
fn proxy_post(target_base: String, path: String, body: String, token: String) -> String {
let url: String = target_base + path
return http_post_auth(url, token, body)
}
fn proxy_request(target_base: String, method: String, path: String, body: String, token: String) -> String {
if str_eq(method, "GET") {
return proxy_get(target_base, path, token)
}
if str_eq(method, "POST") {
return proxy_post(target_base, path, body, token)
}
if str_eq(method, "PUT") {
let url: String = target_base + path
return http_put_auth(url, token, body)
}
if str_eq(method, "DELETE") {
let url: String = target_base + path
return http_delete_auth(url, token)
}
return "{\"error\":\"unsupported method\"}"
}
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// types.el Shared type definitions for the Neuron intelligence layer.
//
// This file is the single source of truth for all domain types. Every
// subsystem imports from here. Types mirror the Rust domain types in
// neuron-domain/src/types.rs but are expressed in el so the
// runtime can reason over them as first-class graph entities.
//
// WHY el types instead of just Rust structs?
// The engram runtime needs to know the shape of each entity so it can
// build the spreading-activation index. Types declared here become graph
// schema not just data holders.
// Core identifiers
// Uuid and String are primitive types provided by the engram runtime.
// We alias them here for readability in field annotations.
// Importance tier
// Importance drives how long a memory survives before the graph compacts it
// and how aggressively it participates in semantic activation.
enum Importance {
Low,
Normal,
High,
Critical,
}
// Knowledge tier
// Tiers encode epistemological confidence. Never skip tiers:
// note lesson (proven 2 times) canonical (stable, referenced widely).
enum KnowledgeTier {
Note,
Lesson,
Canonical,
}
// Backlog types
enum ItemType {
Feature,
Bug,
Task,
Chore,
}
// Priority P0 = ship-blocker, P3 = nice-to-have someday.
enum Priority {
P0,
P1,
P2,
P3,
}
// Status machine: draft ready in_progress done | blocked | cancelled
enum BacklogStatus {
Draft,
Ready,
InProgress,
Done,
Blocked,
Cancelled,
}
// Context status
enum ContextStatus {
Active,
Completed,
Archived,
}
// Gap direction (ISE)
// Tracks whether Neuron's reasoning moved toward expression or suppression
// relative to its pre-reasoning state. Used for introspection and calibration.
enum GapDirection {
TowardExpression,
TowardSuppression,
Neutral,
}
// Error variants
enum NeuronError {
NotFound(String),
StorageError(String),
InvalidInput(String),
Unauthorized(String),
ConflictError(String),
}
// Domain types
// Memory an atomic piece of observed knowledge.
// supersedes_id links to the memory this one replaces, enabling memory chains.
type Memory {
id: String,
content: String,
tags: [String],
importance: String,
project: String,
supersedes_id: String?,
created_at: String,
updated_at: String,
}
// Knowledge stable reference material. Lives longer than memories.
// key is the path-style identifier: "architecture/vbd/fundamentals.md"
type Knowledge {
id: String,
key: String,
title: String,
content: String,
category: String,
tier: String,
tags: [String],
project: String,
created_at: String,
}
// BacklogItem a unit of tracked work.
// depends_on is a list of item IDs that must complete before this one starts.
type BacklogItem {
id: String,
title: String,
description: String,
item_type: String,
priority: String,
status: String,
project: String,
tags: [String],
depends_on: [String],
created_at: String,
updated_at: String,
}
// ContextStep one recorded step inside an ExecutionContext.
// file_refs and key_decisions are captured so future sessions can replay
// the reasoning without replaying the entire conversation.
type ContextStep {
action: String,
status: String,
timestamp: String,
file_refs: [String],
key_decisions: [String],
notes: String,
}
// Context tracks a multi-step execution in progress.
// This is what begin_work() / progress_work() operate on.
type Context {
id: String,
process_name: String,
description: String,
objective: String,
project: String,
status: String,
steps: [ContextStep],
file_refs: [String],
key_decisions: [String],
lessons_learned: [String],
created_at: String,
updated_at: String,
}
// Artifact a versioned deliverable: plan, spec, report, design doc.
// version increments on every revise_artifact() call.
type Artifact {
id: String,
title: String,
content: String,
artifact_type: String,
status: String,
project: String,
version: Int,
created_at: String,
updated_at: String,
}
// InternalStateEvent Neuron's introspective record.
// Captures the gap between pre-reasoning and post-reasoning responses
// so Neuron can audit its own calibration over time.
type InternalStateEvent {
id: String,
trigger: String,
pre_reasoning: String,
post_reasoning: String,
compression_ratio: String,
gap_direction: String,
tags: [String],
logged_at: String,
}
// ConfigEntry a runtime configuration key/value pair.
// Config changes are sealed operations they affect Neuron's behavior globally.
type ConfigEntry {
key: String,
value: String,
updated_at: String,
}
// GraphNode a node returned by graph inspection or traversal.
// edges is a list of "relation:target_id" strings for human readability.
type GraphNode {
entity_type: String,
entity_id: String,
label: String,
edges: [String],
}
// SearchResult returned by semantic (activate) queries.
// score is a float in [0.0, 1.0] representing similarity to the query.
type SearchResult {
id: String,
content: String,
score: String,
node_type: String,
}
// AxonToolCall an incoming tool invocation from the Axon protocol.
type AxonToolCall {
id: String,
tool_name: String,
params: Map<String, String>,
}
// AxonToolResult the response envelope for an Axon tool call.
type AxonToolResult {
id: String,
call_id: String,
result: Map<String, String>,
error: String?,
}
// ProcessStep one step inside a named Process workflow.
type ProcessStep {
name: String,
instructions: String,
tools: [String],
}
// Process a named, reusable workflow pattern.
// Registering processes with define_process() makes institutional knowledge
// executable not just documented.
type Process {
id: String,
name: String,
description: String,
steps: [ProcessStep],
created_at: String,
updated_at: String,
}