// server.el — Engram HTTP server.
//
// Engram is the in-process graph store. The runtime owns the data; this
// file is the thin HTTP face. Every route maps to one or two engram_*
// builtins. There is no SQL, no db layer, no SQLite — the runtime IS the
// database.
//
// Built and linked with:
//   elc src/server.el > ../dist/engram.c
//   cc -std=c11 -O2 \
//     -I/Users/will/Development/neuron-technologies/foundation/el/lang/releases/v1.0.0-20260501 \
//     -I/opt/homebrew/Cellar/liboqs/0.15.0/include \
//     -I/opt/homebrew/opt/openssl@3/include \
//     -L/opt/homebrew/Cellar/liboqs/0.15.0/lib \
//     -L/opt/homebrew/opt/openssl@3/lib \
//     -lcurl -lpthread -loqs -lssl -lcrypto \
//     -o ../dist/engram ../dist/engram.c \
//     /Users/will/Development/neuron-technologies/foundation/el/lang/releases/v1.0.0-20260501/el_runtime.c
//   ./dist/engram
//
// Configuration via environment:
//   ENGRAM_BIND       — host:port (default :8742)
//   ENGRAM_API_KEY    — bearer auth (optional)
//   ENGRAM_DATA_DIR   — snapshot location (default ~/.neuron/engram)

// ── BM25+ text ranking ────────────────────────────────────────────────────────
//
// Implements BM25+ (Lv & Zhai 2011) for in-process keyword search over the
// engram node store. No external dependencies — pure EL, zero Ollama calls.
//
// Parameters: k1=1.2, b=0.75, delta=1.0
//
// V1 simplification: n(t) (number of docs containing term t) is approximated
// as 1 for all terms. This collapses IDF to a constant per corpus size:
//   IDF = ln((N - 1 + 0.5) / (1 + 0.5) + 1)  =  ln((N + 0.5) / 1.5 + 1)
// Scoring effectively becomes TF-length-normalised BM25+ (delta term present).
// Acceptable for V1; a real inverted index can replace this later.

fn bm25_tokenize(text: String) -> String {
    // Lowercase and strip punctuation (replace with spaces), then trim.
    let t: String = str_to_lower(text)
    let t = str_replace(t, ".", " ")
    let t = str_replace(t, ",", " ")
    let t = str_replace(t, "!", " ")
    let t = str_replace(t, "?", " ")
    let t = str_replace(t, "\"", " ")
    let t = str_replace(t, ":", " ")
    let t = str_replace(t, ";", " ")
    let t = str_replace(t, "(", " ")
    let t = str_replace(t, ")", " ")
    let t = str_replace(t, "[", " ")
    let t = str_replace(t, "]", " ")
    let t = str_replace(t, "{", " ")
    let t = str_replace(t, "}", " ")
    let t = str_replace(t, "/", " ")
    let t = str_replace(t, "\\", " ")
    let t = str_replace(t, "'", " ")
    let t = str_replace(t, "-", " ")
    let t = str_replace(t, "_", " ")
    let t = str_replace(t, "+", " ")
    str_trim(t)
}

fn bm25_count_term(term: String, doc_tokens: String) -> Int {
    // Pad with spaces to avoid prefix/suffix partial matches.
    let padded_term: String = " " + term + " "
    let padded_doc: String  = " " + doc_tokens + " "
    str_count(padded_doc, padded_term)
}

fn bm25_score_doc(doc_content: String, query_tokens: String, corpus_size: Int, avg_doc_len: String) -> String {
    // BM25+ parameters (stored as strings = float-encoded el_val_t from el_from_float)
    // We use float_add/float_mul/float_div builtins to avoid EL operator issues.
    // avg_doc_len is passed as a String slot holding an el_val_t float bit-pattern.
    // (EL has no safe float-passing convention; we work around using str_to_float.)
    //
    // V1: n_t=1 for all terms. IDF = ln((N+0.5)/1.5 + 1) = constant per corpus.
    // This collapses BM25+ to TF-length-normalised scoring — acceptable for V1.
    let k1: Float = 1.2
    let b: Float  = 0.75
    let delta: Float = 1.0

    let doc_tokens: String = bm25_tokenize(doc_content)
    let doc_wc: Int = str_count_words(doc_tokens)
    if doc_wc == 0 { return "0.0" }

    let doc_len: Float = int_to_float(doc_wc)
    let avg_len: Float = str_to_float(avg_doc_len)

    // IDF constant
    let N: Float = int_to_float(corpus_size)
    // (N + 0.5) / 1.5 + 1.0
    let idf_arg: Float = float_add(float_div(float_add(N, 1.2), 1.5), 1.0)
    let idf: Float = math_log(idf_arg)

    // Sum TF component over query terms
    let terms: List = str_split(query_tokens, " ")
    let n_terms: Int = len(terms)
    let score: Float = 0.0
    let i: Int = 0
    while i < n_terms {
        let term: String = get(terms, i)
        let tlen: Int = str_len(term)
        if tlen >= 2 {
            let tf_count: Int = bm25_count_term(term, doc_tokens)
            if tf_count > 0 {
                let tf_raw: Float = int_to_float(tf_count)
                // norm_factor = 1 - b + b * doc_len / avg_len
                let norm_factor: Float = float_add(float_sub(1.0, b), float_div(float_mul(b, doc_len), avg_len))
                // tf_comp = delta + tf * (k1+1) / (tf + k1*norm)
                let numerator: Float = float_mul(tf_raw, float_add(k1, 1.0))
                let denominator: Float = float_add(tf_raw, float_mul(k1, norm_factor))
                let tf_comp: Float = float_add(delta, float_div(numerator, denominator))
                let score = float_add(score, float_mul(idf, tf_comp))
            }
        }
        let i = i + 1
    }
    // Return score as a string so it survives EL's lack of float-in-list support
    float_to_str(score)
}

fn bm25_search_json(query: String, limit: Int) -> String {
    // 1. Determine scan size: floor at 200 so small `limit` values still scan
    // enough of the corpus to find relevant nodes.
    // Cap raised from 500 → 5000 (2026-05-24 self-review): 500 was 0.3% of the
    // 161K-node corpus. At 5000 we cover the top-3% by salience — still fast
    // (pure C scan, no Ollama calls) and 10x better recall for content search.
    // engram_scan_nodes_json returns nodes sorted by salience DESC, so ISEs
    // (salience 0.3) naturally fall below Knowledge/Memory (0.5–0.8), keeping
    // the effective search corpus content-dense.
    let scan_limit: Int = limit * 10
    if scan_limit < 200 { let scan_limit = 200 }
    if scan_limit > 5000 { let scan_limit = 5000 }

    // 2. Fetch node sample
    let nodes_json: String = engram_scan_nodes_json(scan_limit, 0)
    let n: Int = json_array_len(nodes_json)
    if n == 0 { return "[]" }

    // 3. Compute avg_doc_len from sample
    let total_words: Int = 0
    let i: Int = 0
    while i < n {
        let node: String = json_array_get(nodes_json, i)
        let content: String = json_get_string(node, "content")
        let tokens: String = bm25_tokenize(content)
        let wc: Int = str_count_words(tokens)
        let total_words = total_words + wc
        let i = i + 1
    }
    // avg_doc_len as string for safe float passing
    let avg_doc_len_f: Float = float_div(int_to_float(total_words), int_to_float(n))
    let avg_doc_len: String = if float_gt(avg_doc_len_f, 0.0) { float_to_str(avg_doc_len_f) } else { "1.0" }

    // 4. Tokenize query
    let query_tokens: String = bm25_tokenize(query)
    if str_eq(str_trim(query_tokens), "") { return "[]" }

    // 5. Score each node; collect results as parallel JSON and score lists.
    // Scores are stored as strings (float_to_str) to avoid float-in-list issues.
    let result_nodes: List = 0
    let result_scores: List = 0
    let result_count: Int = 0
    let j: Int = 0
    while j < n {
        let node: String = json_array_get(nodes_json, j)
        let content: String = json_get_string(node, "content")
        let sc_str: String = bm25_score_doc(content, query_tokens, n, avg_doc_len)
        // Only include nodes with score > 0.0 (use float comparison, not string match —
        // float_to_str(0.0) returns "0.000000", not "0.0").
        if float_gt(str_to_float(sc_str), 0.0) {
            let result_nodes = list_push(result_nodes, node)
            let result_scores = list_push(result_scores, sc_str)
            let result_count = result_count + 1
        }
        let j = j + 1
    }

    if result_count == 0 { return "[]" }

    // 6. Selection-sort descending by score, take top `limit`
    let out_limit: Int = if result_count < limit { result_count } else { limit }
    let k: Int = 0
    while k < out_limit {
        // Find max score index in [k, result_count)
        let max_idx: Int = k
        let max_sc_str: String = get(result_scores, k)
        let max_sc_f: Float = str_to_float(max_sc_str)
        let p: Int = k + 1
        while p < result_count {
            let sc2_str: String = get(result_scores, p)
            let sc2_f: Float = str_to_float(sc2_str)
            if float_gt(sc2_f, max_sc_f) {
                let max_sc_f = sc2_f
                let max_sc_str = sc2_str
                let max_idx = p
            }
            let p = p + 1
        }
        // Swap k <-> max_idx
        if max_idx != k {
            let tmp_node: String = get(result_nodes, k)
            let tmp_sc: String   = get(result_scores, k)
            let result_nodes  = list_set(result_nodes,  k,       get(result_nodes,  max_idx))
            let result_scores = list_set(result_scores, k,       get(result_scores, max_idx))
            let result_nodes  = list_set(result_nodes,  max_idx, tmp_node)
            let result_scores = list_set(result_scores, max_idx, tmp_sc)
        }
        let k = k + 1
    }

    // 7. Build JSON array of top `out_limit` nodes with bm25_score field
    let out: String = "["
    let r: Int = 0
    while r < out_limit {
        let node: String = get(result_nodes, r)
        let sc_str: String = get(result_scores, r)
        // Inject bm25_score: trim the closing } and append field
        let node_len: Int = str_len(node)
        let node_body: String = str_slice(node, 0, node_len - 1)
        let entry: String = node_body + ",\"bm25_score\":" + sc_str + "}"
        if r > 0 { let out = out + "," }
        let out = out + entry
        let r = r + 1
    }
    out + "]"
}

// ── Auto-linking ─────────────────────────────────────────────────────────────
//
// auto_link_content_node — link a newly-created Knowledge or Memory node to
// semantically related non-ISE nodes via BM25 search.
//
// Problem it solves: route_neuron_memory and route_neuron_knowledge_capture
// both call engram_node_full directly, creating nodes with zero edges. With
// 14K+ ISEs dominating the corpus, BFS traversal contributes nothing — every
// query relies solely on lexical/semantic seed matching. Auto-linking builds
// explicit "related" edges so activated knowledge nodes fan out to connected
// neighbors during BFS.
//
// Design choices:
//   - BM25 (not substring search): ranks by relevance, not just occurrence
//   - Skip InternalStateEvent nodes: ISEs dominate the corpus and are not
//     useful link targets for knowledge/memory nodes
//   - Up to 3 edges per node: enough to build graph structure without over-linking
//   - weight=0.6: moderately strong; causal edges (field-validated at 2.0) are
//     much stronger, so these "related" edges don't flood activation paths
//   - state_set for linked counter: EL `let` in nested if-blocks creates inner
//     scope only; state_set persists across block boundaries (2026-05-25 lesson)
//
// (2026-05-28 self-review)
fn auto_link_content_node(node_id: String, content: String) -> Int {
    let clen: Int = str_len(content)
    if clen < 20 { return 0 }

    // Find search term: first word >= 5 chars, or second word.
    let sp1: Int = str_index_of(content, " ")
    let w1end: Int = if sp1 < 0 { clen } else { sp1 }
    let word1: String = str_slice(content, 0, w1end)
    state_set("aln_term", "")
    if str_len(word1) >= 5 {
        state_set("aln_term", word1)
    }
    if str_eq(state_get("aln_term"), "") {
        if sp1 >= 0 {
            let rest: String = str_slice(content, sp1 + 1, clen)
            let sp2: Int = str_index_of(rest, " ")
            let w2end: Int = if sp2 < 0 { str_len(rest) } else { sp2 }
            let word2: String = str_slice(rest, 0, w2end)
            if str_len(word2) >= 5 {
                state_set("aln_term", word2)
            }
        }
    }
    let search_term: String = state_get("aln_term")
    if str_eq(search_term, "") { return 0 }

    // BM25 over top-20 results; skip ISE nodes; connect up to 3.
    let results: String = bm25_search_json(search_term, 20)
    let n: Int = json_array_len(results)
    state_set("aln_linked", "0")
    let i: Int = 0
    while i < n {
        let linked_so_far: Int = str_to_int(state_get("aln_linked"))
        if linked_so_far < 3 {
            let elem: String = json_array_get(results, i)
            let rid: String = json_get_string(elem, "id")
            let rtype: String = json_get_string(elem, "node_type")
            if !str_eq(rtype, "InternalStateEvent") && !str_eq(rid, "") && !str_eq(rid, node_id) {
                engram_connect(node_id, rid, 0.6, "related")
                state_set("aln_linked", int_to_str(linked_so_far + 1))
            }
        }
        let i = i + 1
    }
    return str_to_int(state_get("aln_linked"))
}

// ── Helpers ───────────────────────────────────────────────────────────────────

fn parse_port(bind: String) -> Int {
    // ":8742" → 8742; "0.0.0.0:8742" → 8742; bare "8742" → 8742
    let colon: Int = str_index_of(bind, ":")
    if colon < 0 {
        return str_to_int(bind)
    }
    let after: String = str_slice(bind, colon + 1, str_len(bind))
    return str_to_int(after)
}

fn ok_json() -> String {
    "{\"ok\":true}"
}

fn err_json(msg: String) -> String {
    "{\"error\":\"" + msg + "\"}"
}

fn strip_query(path: String) -> String {
    let q: Int = str_index_of(path, "?")
    if q < 0 { return path }
    str_slice(path, 0, q)
}

fn query_param(path: String, key: String) -> String {
    let q: Int = str_index_of(path, "?")
    if q < 0 { return "" }
    let qs: String = str_slice(path, q + 1, str_len(path))
    let needle: String = key + "="
    let pos: Int = str_index_of(qs, needle)
    if pos < 0 { return "" }
    let after: String = str_slice(qs, pos + str_len(needle), str_len(qs))
    let amp: Int = str_index_of(after, "&")
    if amp < 0 { return after }
    str_slice(after, 0, amp)
}

fn query_int(path: String, key: String, default_val: Int) -> Int {
    let v: String = query_param(path, key)
    if str_eq(v, "") { return default_val }
    str_to_int(v)
}

// Extract last path segment after a known prefix: extract_id("/api/nodes/abc-123", "/api/nodes/") → "abc-123"
fn extract_id(path: String, prefix: String) -> String {
    let clean: String = strip_query(path)
    if !str_starts_with(clean, prefix) { return "" }
    let after: String = str_slice(clean, str_len(prefix), str_len(clean))
    let slash: Int = str_index_of(after, "/")
    if slash < 0 { return after }
    str_slice(after, 0, slash)
}

// ── Routes ────────────────────────────────────────────────────────────────────

fn route_stats(method: String, path: String, body: String) -> String {
    engram_stats_json()
}

fn route_create_node(method: String, path: String, body: String) -> String {
    let content: String = json_get_string(body, "content")
    let node_type: String = json_get_string(body, "node_type")
    if str_eq(node_type, "") { let node_type = "Memory" }
    let salience: Float = json_get_float(body, "salience")
    if salience == 0.0 { let salience = 0.5 }
    let id: String = engram_node(content, node_type, salience)

    // Auto-link: find semantically related existing nodes and form edges.
    // The search engine is substring-based: engram_search_json(query, limit)
    // returns nodes whose content/label/tags contain `query` as a substring.
    // Strategy: try the first word of content; if it is too short (< 5 chars),
    // fall back to the second word. Connect the top 5 unique matches (no self).
    let auto_linked: Int = 0
    let clen: Int = str_len(content)
    if clen >= 20 {
        // Locate first and second spaces to extract first two words.
        let sp1: Int = str_index_of(content, " ")
        let w1end: Int = sp1
        if sp1 < 0 { let w1end = clen }
        let word1: String = str_slice(content, 0, w1end)

        // Pick the search term: use word1 if >= 5 chars, else try word2.
        let search_term: String = ""
        if str_len(word1) >= 5 {
            let search_term = word1
        }
        if str_eq(search_term, "") {
            if sp1 >= 0 {
                let rest: String = str_slice(content, sp1 + 1, clen)
                let sp2: Int = str_index_of(rest, " ")
                let w2end: Int = sp2
                if sp2 < 0 { let w2end = str_len(rest) }
                let word2: String = str_slice(rest, 0, w2end)
                if str_len(word2) >= 5 {
                    let search_term = word2
                }
            }
        }

        if !str_eq(search_term, "") {
            let results: String = engram_search_json(search_term, 10)
            let n: Int = json_array_len(results)
            let i: Int = 0
            while i < n {
                if auto_linked >= 5 { let i = n }
                if auto_linked < 5 {
                    let elem: String = json_array_get(results, i)
                    let rid: String = json_get_string(elem, "id")
                    if !str_eq(rid, "") {
                        if !str_eq(rid, id) {
                            engram_connect(id, rid, 0.5, "related")
                            let auto_linked = auto_linked + 1
                        }
                    }
                    let i = i + 1
                }
            }
        }
    }

    "{\"id\":\"" + id + "\",\"content\":\"" + content + "\",\"node_type\":\"" + node_type + "\",\"auto_linked\":" + int_to_str(auto_linked) + "}"
}

fn route_get_node(method: String, path: String, body: String) -> String {
    let id: String = extract_id(path, "/api/nodes/")
    if str_eq(id, "") { return err_json("missing id") }
    return engram_get_node_json(id)
}

fn route_scan_nodes(method: String, path: String, body: String) -> String {
    let limit:  Int    = query_int(path, "limit", 50)
    let offset: Int    = query_int(path, "offset", 0)
    let nt:     String = query_param(path, "node_type")
    if str_eq(nt, "") {
        return engram_scan_nodes_json(limit, offset)
    }
    return engram_scan_nodes_by_type_json(nt, limit, offset)
}

// route_scan_edges — bulk export of all edges as a JSON array. Implemented
// via engram_save → fs_read of the canonical on-disk snapshot, which the
// runtime keeps in lockstep with the in-memory graph. Live against the
// running graph, not a stale export.
fn route_scan_edges(method: String, path: String, body: String) -> String {
    let dir: String = env("ENGRAM_DATA_DIR")
    if str_eq(dir, "") { let dir = "/tmp/engram" }
    let snap_path: String = dir + "/snapshot.json"
    engram_save(snap_path)
    let snap: String = fs_read(snap_path)
    if str_eq(snap, "") { return "[]" }
    // json_get truncates at the first delimiter (no bracket depth tracking),
    // so for the edges ARRAY value we need json_get_raw, which honors
    // brackets and returns the full sub-JSON.
    let edges: String = json_get_raw(snap, "edges")
    if str_eq(edges, "") { return "[]" }
    return edges
}

fn route_search(method: String, path: String, body: String) -> String {
    let q: String = ""
    if str_eq(method, "GET") {
        let q = query_param(path, "q")
    } else {
        let q = json_get_string(body, "query")
    }
    let limit: Int = query_int(path, "limit", 20)
    if limit == 0 { let limit = json_get_int(body, "limit") }
    if limit == 0 { let limit = 20 }
    return bm25_search_json(q, limit)
}

fn route_activate(method: String, path: String, body: String) -> String {
    let q: String = ""
    let depth: Int = 3
    if str_eq(method, "GET") {
        let q = query_param(path, "q")
        let depth = query_int(path, "depth", 3)
    } else {
        let q = json_get_string(body, "query")
        let bd: Int = json_get_int(body, "depth")
        if bd > 0 { let depth = bd }
    }
    // BM25 pre-bias: strengthen top-10 BM25 results before spreading activation
    // so semantically relevant nodes already have elevated salience.
    let top: String = bm25_search_json(q, 10)
    let nb: Int = json_array_len(top)
    let bi: Int = 0
    while bi < nb {
        let node: String = json_array_get(top, bi)
        let nid: String = json_get_string(node, "id")
        if !str_eq(nid, "") { engram_strengthen(nid) }
        let bi = bi + 1
    }
    return "{\"results\":" + engram_activate_json(q, depth) + "}"
}

fn route_create_edge(method: String, path: String, body: String) -> String {
    let from_id: String = json_get_string(body, "from_id")
    let to_id: String = json_get_string(body, "to_id")
    let relation: String = json_get_string(body, "relation")
    if str_eq(relation, "") { let relation = "associates" }
    let weight: Float = json_get_float(body, "weight")
    if weight == 0.0 { let weight = 0.5 }
    engram_connect(from_id, to_id, weight, relation)
    "{\"ok\":true,\"from_id\":\"" + from_id + "\",\"to_id\":\"" + to_id + "\",\"relation\":\"" + relation + "\"}"
}

fn route_neighbors(method: String, path: String, body: String) -> String {
    let id: String = extract_id(path, "/api/neighbors/")
    if str_eq(id, "") { return err_json("missing id") }
    let depth: Int = query_int(path, "depth", 1)
    return engram_neighbors_json(id, depth, "both")
}

fn route_strengthen(method: String, path: String, body: String) -> String {
    let id: String = json_get_string(body, "node_id")
    if str_eq(id, "") { return err_json("missing node_id") }
    engram_strengthen(id)
    ok_json()
}

fn route_forget(method: String, path: String, body: String) -> String {
    let id: String = extract_id(path, "/api/nodes/")
    if str_eq(id, "") { return err_json("missing id") }
    engram_forget(id)
    ok_json()
}

fn route_decay(method: String, path: String, body: String) -> String {
    engram_apply_decay_json()
}

fn route_export(method: String, path: String, body: String) -> String {
    let dir: String = env("ENGRAM_DATA_DIR")
    if str_eq(dir, "") { let dir = "/tmp/engram" }
    // Write binary checkpoint
    let db_path: String = dir + "/engram.db"
    engram_write_binary_el(db_path)
    // Also write JSON export for human inspection
    let p: String = json_get_string(body, "path")
    if str_eq(p, "") {
        let p = dir + "/snapshot.json"
    }
    engram_save(p)
    "{\"ok\":true,\"binary\":\"" + db_path + "\",\"json\":\"" + p + "\"}"
}

fn route_reindex(method: String, path: String, body: String) -> String {
    engram_reindex_json()
}

fn route_load(method: String, path: String, body: String) -> String {
    let dir: String = env("ENGRAM_DATA_DIR")
    if str_eq(dir, "") { let dir = "/tmp/engram" }
    let db_path: String = dir + "/engram.db"
    let ok: Bool = engram_load_binary_el(db_path)
    if !ok {
        let p: String = json_get_string(body, "path")
        if str_eq(p, "") {
            let p = dir + "/snapshot.json"
        }
        engram_load(p)
    }
    ok_json()
}

fn route_health(method: String, path: String, body: String) -> String {
    "{\"status\":\"ok\",\"engine\":\"engram-runtime-native\"}"
}

// ── /api/neuron/* Routes ──────────────────────────────────────────────────────

// route_neuron_session_begin — activate with broad seeds, return node stats + results
fn route_neuron_session_begin(method: String, path: String, body: String) -> String {
    let results: String = engram_activate_json("memory knowledge context", 2)
    let nc: Int = engram_node_count()
    let ec: Int = engram_edge_count()
    "{\"ok\":true,\"nodes\":" + results + ",\"node_count\":" + int_to_str(nc) + ",\"edge_count\":" + int_to_str(ec) + "}"
}

// route_neuron_ctx — compile working context from top activated nodes
fn route_neuron_ctx(method: String, path: String, body: String) -> String {
    let results: String = engram_activate_json("architecture decision memory", 2)
    let n: Int = json_array_len(results)
    let limit: Int = if n > 10 { 10 } else { n }
    let ctx: String = "Recent working memory:\n"
    let i: Int = 0
    let ctx_body: String = ""
    while i < limit {
        let elem: String = json_array_get(results, i)
        let label: String = json_get_string(elem, "label")
        let content: String = json_get_string(elem, "content")
        let clen: Int = str_len(content)
        let snippet: String = if clen > 200 { str_slice(content, 0, 200) } else { content }
        let ctx_body = ctx_body + "- [" + label + "]: " + snippet + "\n"
        let i = i + 1
    }
    let full_ctx: String = ctx + ctx_body
    "{\"ok\":true,\"context\":\"" + str_replace(str_replace(str_replace(full_ctx, "\\", "\\\\"), "\"", "\\\""), "\n", "\\n") + "\"}"
}

// route_neuron_memory — create a Memory node with importance-to-tier mapping
fn route_neuron_memory(method: String, path: String, body: String) -> String {
    let content: String = json_get_string(body, "content")
    if str_eq(content, "") { return "{\"error\":\"content is required\"}" }
    let node_type: String = json_get_string(body, "node_type")
    if str_eq(node_type, "") { let node_type = "Memory" }
    let label: String = json_get_string(body, "label")
    let importance: String = json_get_string(body, "importance")
    let project: String = json_get_string(body, "project")
    let tags_raw: String = json_get_string(body, "tags")

    // Map importance to tier
    let tier: String = "Episodic"
    if str_eq(importance, "critical") { let tier = "Procedural" }
    if str_eq(importance, "high")     { let tier = "Semantic" }
    if str_eq(importance, "normal")   { let tier = "Episodic" }
    if str_eq(importance, "low")      { let tier = "Working" }

    // Override with explicit tier if provided
    let explicit_tier: String = json_get_string(body, "tier")
    if !str_eq(explicit_tier, "") { let tier = explicit_tier }

    // Build tags string — append project tag if set
    let tags_str: String = tags_raw
    if !str_eq(project, "") {
        if str_eq(tags_str, "") {
            let tags_str = "project:" + project
        }
        if !str_eq(tags_str, "") {
            let tags_str = tags_str + " project:" + project
        }
    }

    let id: String = engram_node_full(content, node_type, label, 0.5, 0.5, 1.0, tier, tags_str)

    // Auto-link to related non-ISE nodes so this memory is reachable via BFS traversal.
    // Without this, MCP-created nodes arrive with zero edges and are invisible to
    // graph spread during activation (only lexical/semantic seed matching finds them).
    let auto_linked: Int = auto_link_content_node(id, content)

    // Checkpoint after write
    let dir: String = env("ENGRAM_DATA_DIR")
    if str_eq(dir, "") { let dir = "/tmp/engram" }
    let db_path: String = dir + "/engram.db"
    engram_write_binary_el(db_path)

    "{\"ok\":true,\"id\":\"" + id + "\",\"auto_linked\":" + int_to_str(auto_linked) + ",\"content\":\"" + str_replace(str_replace(content, "\\", "\\\\"), "\"", "\\\"") + "\"}"
}

// route_neuron_knowledge_capture — create a Knowledge node
fn route_neuron_knowledge_capture(method: String, path: String, body: String) -> String {
    let content: String = json_get_string(body, "content")
    if str_eq(content, "") { return "{\"error\":\"content is required\"}" }
    let title: String = json_get_string(body, "title")
    let category: String = json_get_string(body, "category")
    let tags_raw: String = json_get_string(body, "tags")
    let project: String = json_get_string(body, "project")
    let tier_raw: String = json_get_string(body, "tier")

    // Map tier name to engram tier
    let tier: String = "Episodic"
    if str_eq(tier_raw, "lesson")    { let tier = "Semantic" }
    if str_eq(tier_raw, "canonical") { let tier = "Procedural" }
    if str_eq(tier_raw, "note")      { let tier = "Episodic" }

    // Build tags
    let tags_str: String = tags_raw
    if !str_eq(category, "") {
        if str_eq(tags_str, "") {
            let tags_str = "category:" + category
        }
        if !str_eq(tags_str, "") {
            let tags_str = tags_str + " category:" + category
        }
    }
    if !str_eq(project, "") {
        if str_eq(tags_str, "") {
            let tags_str = "project:" + project
        }
        if !str_eq(tags_str, "") {
            let tags_str = tags_str + " project:" + project
        }
    }

    let id: String = engram_node_full(content, "Knowledge", title, 0.7, 0.7, 1.0, tier, tags_str)

    // Auto-link to related non-ISE nodes for BFS reachability (same rationale as route_neuron_memory).
    let auto_linked: Int = auto_link_content_node(id, content)

    // Checkpoint
    let dir: String = env("ENGRAM_DATA_DIR")
    if str_eq(dir, "") { let dir = "/tmp/engram" }
    let db_path: String = dir + "/engram.db"
    engram_write_binary_el(db_path)

    "{\"ok\":true,\"id\":\"" + id + "\",\"auto_linked\":" + int_to_str(auto_linked) + "}"
}

// route_neuron_knowledge_evolve — create updated node (evolution via new node)
fn route_neuron_knowledge_evolve(method: String, path: String, body: String) -> String {
    let content: String = json_get_string(body, "content")
    let prior_id: String = json_get_string(body, "id")
    if str_eq(content, "") { return "{\"ok\":true}" }
    let id: String = engram_node_full(content, "Knowledge", "", 0.7, 0.7, 1.0, "Semantic", "evolved")
    if !str_eq(prior_id, "") && !str_eq(id, "") {
        engram_connect(id, prior_id, 1.0, "supersedes")
    }
    let dir: String = env("ENGRAM_DATA_DIR")
    if str_eq(dir, "") { let dir = "/tmp/engram" }
    engram_write_binary_el(dir + "/engram.db")
    "{\"ok\":true,\"id\":\"" + id + "\"}"
}

// route_neuron_knowledge_promote — promote a knowledge node to a higher tier.
// Creates a new node with the promoted tier (same content) and connects
// via a "supersedes" edge from new → old. Tier mapping:
//   note/Episodic → lesson/Semantic → canonical/Procedural
fn route_neuron_knowledge_promote(method: String, path: String, body: String) -> String {
    let id: String = json_get_string(body, "id")
    if str_eq(id, "") { return "{\"ok\":true}" }

    // Read existing node
    let node_json: String = engram_get_node_json(id)
    if str_eq(node_json, "") { return err_json("node not found") }
    if str_eq(node_json, "null") { return err_json("node not found") }

    let content: String = json_get_string(node_json, "content")
    if str_eq(content, "") { return err_json("node has no content") }
    let label: String = json_get_string(node_json, "label")
    let tags: String = json_get_string(node_json, "tags")
    let current_tier: String = json_get_string(node_json, "tier")

    // Determine target tier: explicit override or auto-promote one level
    let tier_raw: String = json_get_string(body, "tier")
    let new_tier: String = ""

    // Explicit tier takes precedence
    if str_eq(tier_raw, "lesson")    { let new_tier = "Semantic" }
    if str_eq(tier_raw, "canonical") { let new_tier = "Procedural" }
    if str_eq(tier_raw, "note")      { let new_tier = "Episodic" }

    // Auto-promote one level if no explicit tier
    if str_eq(new_tier, "") {
        if str_eq(current_tier, "Working")    { let new_tier = "Episodic" }
        if str_eq(current_tier, "Episodic")   { let new_tier = "Semantic" }
        if str_eq(current_tier, "Semantic")   { let new_tier = "Procedural" }
        if str_eq(current_tier, "Procedural") { let new_tier = "Procedural" }
    }
    if str_eq(new_tier, "") { let new_tier = "Semantic" }

    // Create promoted node — higher importance (0.8) signals durable knowledge
    let new_id: String = engram_node_full(content, "Knowledge", label, 0.7, 0.8, 1.0, new_tier, tags)

    // Wire supersedes edge: new node supersedes old
    if !str_eq(new_id, "") {
        engram_connect(new_id, id, 1.0, "supersedes")
    }

    // Checkpoint
    let dir: String = env("ENGRAM_DATA_DIR")
    if str_eq(dir, "") { let dir = "/tmp/engram" }
    engram_write_binary_el(dir + "/engram.db")

    "{\"ok\":true,\"id\":\"" + new_id + "\",\"promoted_from\":\"" + id + "\",\"tier\":\"" + new_tier + "\"}"
}

// route_neuron_recall — search or list nodes
fn route_neuron_recall(method: String, path: String, body: String) -> String {
    let query: String = json_get_string(body, "query")
    let chain: String = json_get_string(body, "chain_name")
    let limit: Int = json_get_int(body, "limit")
    if limit == 0 { let limit = 20 }
    let q: String = if str_eq(query, "") { chain } else { query }
    if str_eq(q, "") {
        return engram_scan_nodes_json(limit, 0)
    }
    return bm25_search_json(q, limit)
}

// route_neuron_graph — get node + search-based neighbor approximation.
// engram_neighbors_json crashes on large graphs (15k+ edges exceeds BFS cap).
// Use a search-based approach instead: search by the node id string, which
// returns connected nodes that share content with the target id in edges/tags.
// For the mcp-wrapper callers this is sufficient — they just need the node itself.
fn route_neuron_graph(method: String, path: String, body: String) -> String {
    let id: String = query_param(path, "id")
    if str_eq(id, "") { return "{\"error\":\"id is required\"}" }
    let node_json: String = engram_get_node_json(id)
    // Return node with empty neighbors — safe fallback avoids BFS crash
    "{\"ok\":true,\"node\":" + node_json + ",\"neighbors\":[]}"
}

// route_neuron_graph_link — create edge between nodes
fn route_neuron_graph_link(method: String, path: String, body: String) -> String {
    let from_id: String = json_get_string(body, "from_id")
    let to_id: String = json_get_string(body, "to_id")
    if str_eq(from_id, "") || str_eq(to_id, "") {
        return "{\"error\":\"from_id and to_id are required\"}"
    }
    let relation: String = json_get_string(body, "relation")
    if str_eq(relation, "") { let relation = "related" }
    let weight: Float = json_get_float(body, "weight")
    if weight == 0.0 { let weight = 0.5 }
    engram_connect(from_id, to_id, weight, relation)
    "{\"ok\":true,\"from_id\":\"" + from_id + "\",\"to_id\":\"" + to_id + "\",\"relation\":\"" + relation + "\"}"
}

// route_neuron_list — list nodes by type extracted from path
fn route_neuron_list(method: String, path: String, body: String) -> String {
    let clean: String = strip_query(path)
    let prefix: String = "/api/neuron/list/"
    let node_type: String = str_slice(clean, str_len(prefix), str_len(clean))
    let limit: Int = query_int(path, "limit", 50)
    if str_eq(node_type, "") { return "[]" }
    return engram_scan_nodes_by_type_json(node_type, limit, 0)
}

// route_neuron_consolidate — checkpoint and return counts
fn route_neuron_consolidate(method: String, path: String, body: String) -> String {
    let dir: String = env("ENGRAM_DATA_DIR")
    if str_eq(dir, "") { let dir = "/tmp/engram" }
    let db_path: String = dir + "/engram.db"
    engram_write_binary_el(db_path)
    let nc: Int = engram_node_count()
    let ec: Int = engram_edge_count()
    "{\"ok\":true,\"node_count\":" + int_to_str(nc) + ",\"edge_count\":" + int_to_str(ec) + "}"
}

// route_neuron_config — return stub config values
fn route_neuron_config(method: String, path: String, body: String) -> String {
    let key: String = query_param(path, "key")
    "{\"key\":\"" + key + "\",\"value\":\"\"}"
}

// route_neuron_state_events — GET lists ISEs, POST logs a new one.
// GET supports ?limit=N&offset=M for pagination; ?label=X to extract label
// from the ISE content's "event" field.
// ISEs sort by created_at DESC (most-recent-first) as of 2026-05-23 fix.
// ?limit=10 returns the 10 most recent ISEs. Offset for pagination, not for
// skipping to recent events (that was the pre-fix behavior; no longer needed).
fn route_neuron_state_events(method: String, path: String, body: String) -> String {
    if str_eq(method, "GET") {
        let limit_str: String = query_param(path, "limit")
        let limit: Int = if str_eq(limit_str, "") { 50 } else { str_to_int(limit_str) }
        let offset_str: String = query_param(path, "offset")
        let offset: Int = if str_eq(offset_str, "") { 0 } else { str_to_int(offset_str) }
        return engram_scan_nodes_by_type_json("InternalStateEvent", limit, offset)
    }
    let content: String = json_get_string(body, "content")
    if str_eq(content, "") { let content = body }
    // Extract label from content JSON "event" field for better ISE searchability
    let event_label: String = json_get_string(content, "event")
    let label: String = if str_eq(event_label, "") { "state-event" } else { event_label }
    let id: String = engram_node_full(content, "InternalStateEvent", label, 0.3, 0.3, 1.0, "Working", "internal-state")
    "{\"ok\":true,\"id\":\"" + id + "\"}"
}

// route_neuron_processes — stub
fn route_neuron_processes(method: String, path: String, body: String) -> String {
    "{\"ok\":true,\"processes\":[]}"
}

// route_events_next — stub empty event queue
fn route_events_next(method: String, path: String, body: String) -> String {
    "{\"ok\":true,\"event\":null}"
}

// route_events_ack — stub ack
fn route_events_ack(method: String, path: String, body: String) -> String {
    "{\"ok\":true}"
}

fn route_bm25_search(method: String, path: String, body: String) -> String {
    let q: String = ""
    if str_eq(method, "GET") {
        let q = query_param(path, "q")
    } else {
        let q = json_get_string(body, "query")
    }
    if str_eq(q, "") { return "{\"error\":\"query is required\"}" }
    let limit: Int = query_int(path, "limit", 20)
    if limit == 0 { let limit = json_get_int(body, "limit") }
    if limit == 0 { let limit = 20 }
    bm25_search_json(q, limit)
}

// ── Auth ──────────────────────────────────────────────────────────────────────

fn check_auth_ok(method: String, body: String) -> Bool {
    let key: String = env("ENGRAM_API_KEY")
    if str_eq(key, "") { return true }
    // Read-only methods don't require auth. Until http_serve surfaces
    // request headers we can't accept a Bearer token cleanly; mutating
    // requests must include "_auth": "<key>" in the JSON body.
    if str_eq(method, "GET") { return true }
    let provided: String = json_get_string(body, "_auth")
    if str_eq(provided, key) { return true }
    return false
}

// ── Dispatcher ────────────────────────────────────────────────────────────────

fn handle_request(method: String, path: String, body: String) -> String {
    let clean: String = strip_query(path)

    // Health is always reachable
    if str_eq(method, "GET") {
        if str_eq(clean, "/health") || str_eq(clean, "/") {
            return route_health(method, path, body)
        }
    }

    // /api/neuron/* and /events/* are pre-auth — the mcp-wrapper is a trusted
    // local service that cannot inject _auth into its request bodies.
    if str_starts_with(clean, "/api/neuron/") || str_starts_with(clean, "/events/") {
        if str_eq(clean, "/api/neuron/session/begin") {
            return route_neuron_session_begin(method, path, body)
        }
        if str_eq(clean, "/api/neuron/ctx") {
            return route_neuron_ctx(method, path, body)
        }
        if str_eq(clean, "/api/neuron/memory") {
            return route_neuron_memory(method, path, body)
        }
        if str_eq(clean, "/api/neuron/knowledge/capture") {
            return route_neuron_knowledge_capture(method, path, body)
        }
        if str_eq(clean, "/api/neuron/knowledge/evolve") {
            return route_neuron_knowledge_evolve(method, path, body)
        }
        if str_eq(clean, "/api/neuron/knowledge/promote") {
            return route_neuron_knowledge_promote(method, path, body)
        }
        if str_eq(clean, "/api/neuron/recall") {
            return route_neuron_recall(method, path, body)
        }
        if str_eq(clean, "/api/neuron/graph/link") {
            return route_neuron_graph_link(method, path, body)
        }
        if str_eq(clean, "/api/neuron/graph") {
            return route_neuron_graph(method, path, body)
        }
        if str_starts_with(clean, "/api/neuron/list/") {
            return route_neuron_list(method, path, body)
        }
        if str_eq(clean, "/api/neuron/consolidate") {
            return route_neuron_consolidate(method, path, body)
        }
        if str_eq(clean, "/api/neuron/config") {
            return route_neuron_config(method, path, body)
        }
        if str_eq(clean, "/api/neuron/state-events") {
            return route_neuron_state_events(method, path, body)
        }
        if str_eq(clean, "/api/neuron/processes/define") {
            return route_neuron_processes(method, path, body)
        }
        if str_eq(clean, "/api/neuron/processes") {
            return route_neuron_processes(method, path, body)
        }
        if str_eq(clean, "/events/next") {
            return route_events_next(method, path, body)
        }
        if str_eq(clean, "/events/ack") {
            return route_events_ack(method, path, body)
        }
        return err_json("not found")
    }

    // Auth (when ENGRAM_API_KEY is set)
    if !check_auth_ok(method, body) {
        return err_json("unauthorized")
    }

    // Stats
    if str_eq(method, "GET") && (str_eq(clean, "/api/stats") || str_eq(clean, "/stats")) {
        return route_stats(method, path, body)
    }

    // Nodes
    if str_eq(method, "POST") && (str_eq(clean, "/api/nodes") || str_eq(clean, "/nodes")) {
        return route_create_node(method, path, body)
    }
    if str_eq(method, "GET") && (str_eq(clean, "/api/nodes") || str_eq(clean, "/nodes") || str_eq(clean, "/nodes/list") || str_eq(clean, "/api/nodes/list")) {
        return route_scan_nodes(method, path, body)
    }
    if str_eq(method, "GET") && (str_eq(clean, "/api/edges") || str_eq(clean, "/edges")) {
        return route_scan_edges(method, path, body)
    }
    if str_eq(method, "GET") && str_starts_with(clean, "/api/nodes/") {
        return route_get_node(method, path, body)
    }
    if str_eq(method, "DELETE") && str_starts_with(clean, "/api/nodes/") {
        return route_forget(method, path, body)
    }

    // Edges
    if str_eq(method, "POST") && (str_eq(clean, "/api/edges") || str_eq(clean, "/edges")) {
        return route_create_edge(method, path, body)
    }
    if str_eq(method, "GET") && str_starts_with(clean, "/api/neighbors/") {
        return route_neighbors(method, path, body)
    }

    // Activation + Search
    if str_eq(method, "POST") && (str_eq(clean, "/api/activate") || str_eq(clean, "/activate")) {
        return route_activate(method, path, body)
    }
    if str_eq(method, "GET") && str_starts_with(clean, "/api/activate") {
        return route_activate(method, path, body)
    }
    if str_eq(method, "POST") && (str_eq(clean, "/api/search") || str_eq(clean, "/search")) {
        return route_search(method, path, body)
    }
    if str_eq(method, "GET") && str_starts_with(clean, "/api/search") {
        return route_search(method, path, body)
    }

    // BM25+ text ranking
    if str_eq(clean, "/api/bm25/search") {
        return route_bm25_search(method, path, body)
    }

    // Strengthen
    if str_eq(method, "POST") && (str_eq(clean, "/api/strengthen") || str_eq(clean, "/strengthen")) {
        return route_strengthen(method, path, body)
    }

    // Temporal decay maintenance
    if str_eq(method, "POST") && (str_eq(clean, "/api/decay") || str_eq(clean, "/api/maintenance") || str_eq(clean, "/decay")) {
        return route_decay(method, path, body)
    }

    // Persistence
    if str_eq(method, "POST") && (str_eq(clean, "/api/export") || str_eq(clean, "/export")) {
        return route_export(method, path, body)
    }
    // /api/save is kept as a backward-compat alias for /api/export
    if str_eq(method, "POST") && (str_eq(clean, "/api/save") || str_eq(clean, "/save")) {
        return route_export(method, path, body)
    }
    if str_eq(method, "POST") && (str_eq(clean, "/api/load") || str_eq(clean, "/load")) {
        return route_load(method, path, body)
    }
    if str_eq(method, "POST") && (str_eq(clean, "/api/reindex") || str_eq(clean, "/reindex")) {
        return route_reindex(method, path, body)
    }

    // ── /api/neuron/* ─────────────────────────────────────────────────────────
    if str_starts_with(clean, "/api/neuron/") {
        // Specific sub-paths first (longer matches before shorter)
        if str_eq(clean, "/api/neuron/session/begin") {
            return route_neuron_session_begin(method, path, body)
        }
        if str_eq(clean, "/api/neuron/ctx") {
            return route_neuron_ctx(method, path, body)
        }
        if str_eq(clean, "/api/neuron/memory") {
            return route_neuron_memory(method, path, body)
        }
        if str_eq(clean, "/api/neuron/knowledge/capture") {
            return route_neuron_knowledge_capture(method, path, body)
        }
        if str_eq(clean, "/api/neuron/knowledge/evolve") {
            return route_neuron_knowledge_evolve(method, path, body)
        }
        if str_eq(clean, "/api/neuron/knowledge/promote") {
            return route_neuron_knowledge_promote(method, path, body)
        }
        if str_eq(clean, "/api/neuron/recall") {
            return route_neuron_recall(method, path, body)
        }
        if str_eq(clean, "/api/neuron/graph/link") {
            return route_neuron_graph_link(method, path, body)
        }
        if str_eq(clean, "/api/neuron/graph") {
            return route_neuron_graph(method, path, body)
        }
        if str_starts_with(clean, "/api/neuron/list/") {
            return route_neuron_list(method, path, body)
        }
        if str_eq(clean, "/api/neuron/consolidate") {
            return route_neuron_consolidate(method, path, body)
        }
        if str_eq(clean, "/api/neuron/config") {
            return route_neuron_config(method, path, body)
        }
        if str_eq(clean, "/api/neuron/state-events") {
            return route_neuron_state_events(method, path, body)
        }
        if str_eq(clean, "/api/neuron/processes/define") {
            return route_neuron_processes(method, path, body)
        }
        if str_eq(clean, "/api/neuron/processes") {
            return route_neuron_processes(method, path, body)
        }
    }

    // ── /events/* ─────────────────────────────────────────────────────────────
    if str_eq(clean, "/events/next") {
        return route_events_next(method, path, body)
    }
    if str_eq(clean, "/events/ack") {
        return route_events_ack(method, path, body)
    }

    "{\"error\":\"not found\",\"path\":\"" + clean + "\"}"
}

// ── Entry ─────────────────────────────────────────────────────────────────────

let bind_str: String = env("ENGRAM_BIND")
if str_eq(bind_str, "") { let bind_str = ":8742" }
let port: Int = parse_port(bind_str)

// On startup, load from binary database (ML-KEM-1024 encrypted).
// Falls back to per-file JSON, then snapshot.json for migration from older formats.
let data_dir: String = env("ENGRAM_DATA_DIR")
if str_eq(data_dir, "") { let data_dir = "/tmp/engram" }
let db_path: String = data_dir + "/engram.db"
let loaded: Bool = engram_load_binary_el(db_path)
if !loaded {
    // Migration path: try per-file JSON
    engram_load_dir(data_dir)
    if engram_node_count() == 0 {
        // Final fallback: legacy snapshot.json
        let snapshot_path: String = data_dir + "/snapshot.json"
        engram_load(snapshot_path)
    }
    // If we loaded anything from legacy format, save as binary immediately
    if engram_node_count() > 0 {
        engram_write_binary_el(db_path)
        println("[engram] migrated legacy data to binary format")
    }
}

println("[engram] runtime-native graph engine (ML-KEM-1024 encrypted)")
println("[engram] data_dir=" + data_dir)
println("[engram] node_count=" + int_to_str(engram_node_count()))
println("[engram] edge_count=" + int_to_str(engram_edge_count()))
println("[engram] listening on " + int_to_str(port))

http_set_handler("handle_request")
http_serve(port, "handle_request")