poimen-memory/DESIGN.md

Poimen Memory — gated recurrent knowledge extraction from agent context

Target repo: /Users/rockliang/workplace/Poimen/memory (empty git repo, no remote, no commits).

Context

Agent sessions accumulate faster than they can be read, and almost all of the volume is noise. One pi session in this project measured:

assistant   1445
toolResult  1261   <- 43%, mostly evidence-free (ls output, file reads)
user         196
+ 8 compaction events

Compaction already fires 8 times per session, which means context is being discarded rather than retained — the knowledge produced (root causes, decisions, gotchas) evaporates when the window rolls. Meanwhile the corpus is already project-partitioned on disk:

• ~/.pi/agent/sessions/--Users-rockliang-workplace-Poimen--/*.jsonl — cwd in the session header gives the project key
• ~/.claude/projects/<dash-encoded-cwd>/<uuid>.jsonl — 15 MB, 7 transcripts
• Poimen/agent-rust/tasks/artifacts/<TaskId>/ — coder/reviewer JSONL per attempt

Intended outcome: a durable, queryable project memory built by reading that history chunk-by-chunk and keeping only what answers standing questions — surfaced as an Obsidian vault a human reads and a pgvector index an agent queries.

The mechanism is GRU-Mem (arXiv 2602.10560). Its two gates map directly onto the problem: the update gate refuses to write evidence-free chunks into memory (the 43% of toolResults), and the exit gate stops the scan once evidence is sufficient. The paper reports up to 400% speedup and better accuracy than ungated recurrent memory, because unchecked memory growth actively degrades later updates.

Verified facts this plan depends on

Fact	Value	How known
Embedding dims	768	probed /v1/embeddings with nomic-ai/nomic-embed-text-v2-moe
pgvector in CNPG	available, v0.7.0, not yet installed	pg_available_extensions on forgejo-db-2, stock image ghcr.io/cloudnative-pg/postgresql:16.2
CNPG operator	1.30.0, supports declarative Database.spec.extensions	kubectl explain database.spec.extensions
Ollama context cap	32768 (OLLAMA_CONTEXT_LENGTH)	k8s/apps/llm-serving/ornith.yaml
Controller model	qwen2.5:3b-instruct	is Qwen2.5-3B-Instruct — the paper's exact 3B backbone
Gateway auth	apikey: header, not Authorization: Bearer	Kong key-auth compares whole header value

The 32K cap is the binding constraint and it fits: paper uses 5000-token chunks, 8192 max prompt, 2048 max response.

Side fix: ~/.pi/agent/models.json declares contextWindow: 131072 for ornith. Wrong — Ollama caps at 32768, so long prompts truncate silently. Correct to 32768.

The tier model

Memory is levelled, and every event in the log carries its level. The paper has one flat memory; a project knowledge base needs three.

Level	What it is	Produced by	Bounded
L0	evidence chunk — the verbatim source span the update gate accepted	update gate opening at L1	no, but sparse (~17 of 412 chunks)
L1	per-query memory — GRU-Mem's M_t for one standing question	gated loop over L0 chunk stream	1024 tokens
L2	project synthesis — memory across the L1 memories of one project	gated loop over L1 memories	1024 tokens

The tiering is not new machinery. L2 is the same controller, same prompt, same two gates — run with the L1 memories as its chunk stream and a project-level question. The recurrence is the algorithm applied to its own output, so mem-core implements one loop and the level is a parameter. Two consequences worth having on purpose:

• Exit gate flips by level. Off at L1 (see below), reasonably on at L2, where the input is a handful of memories rather than hundreds of chunks and "enough evidence" is actually decidable.
• Levels form a provenance graph, not a pile. Each L1 node records the L0 nodes that produced it; each L2 node records its L1 parents. That graph is the Obsidian link structure and the parent_id edges in Postgres — one relationship expressed in both projections.

Architecture

                            api.riotpiao.com (Kong)
                                    │
                  ┌─────────────────┼─────────────────┐
                  │                 │                 │
            /ingest            /query              /skills
              (async)          (sync)           (read-only)
                  │                 │                 │
                  ▼                 ▼                 ▼
        ┌─────────────────────────────────────────────────────┐
        │  API Server (Rust httpd)  mem-store / mem-llm      │
        │  - ingest_id dedup + queue                          │
        │  - query → HNSW + rerank + edge-walk               │
        │  - skill catalog (excludes _drafts)                │
        └──────────────────┬──────────────────────────────────┘
                          │
        pi/claude CLI ─────┼────── agents in-session
        local or CI/CD     │     (embedded queries)
                          │
       ┌──────────────────┴──────────────────┐
       │                                     │
       ▼                                     ▼
  [ Ingest Queue ]                   [ CNPG Cluster ]
  (redis or local)                   (pgvector, HNSW)
       │                                     │
       ├─────────────────────────────────────┤
       │
       ▼
pi sessions / claude transcripts / loop.sh artifacts
        │
        ▼  project resolver (cwd -> project id)
   [ Chunk Parser ]  5000-token chunks, split on message boundaries
        │
        ▼
┌──────────────────────────────────────────────────────┐
│  GRU-Mem Controller   qwen2.5:3b-instruct            │
│  <think>  reason about chunk vs question             │
│  <check>  yes|no   -> update gate U_t                │
│  <update> candidate memory M̂_t                       │
│  <next>   continue|end -> exit gate E_t              │
└───────────────┬──────────────────────────────────────┘
      U_t=yes   │   U_t=no
        ┌───────┴────────┐
        ▼                ▼
  emit L0 evidence   discard chunk
  M_t <- M̂_t         M_t <- M_{t-1}
        │
        ▼  L1 memory per standing query
        └──────► same loop, input = L1 memories ──► L2 project synthesis
        │
        ▼
┌──────────────────────────────────────────────────────┐
│  JSONL event log         AUTHORITATIVE               │
│  Obsidian vault          derived projection          │
│  pgvector index          derived projection          │
└──────────────────────────────────────────────────────┘

Authority invariant unchanged: API is stateless demultiplexer. JSONL log is authoritative; vault and pgvector projections are droppable. API caches read-only state (embeddings, L2 synthesis); ingest writes only to log.

Authority model — the load-bearing decision. The JSONL log is the only source of truth. The vault and the vector index are projections that must be droppable and rebuildable byte-identically from the log. This is poimen's own §1 principle ("nothing derived is authoritative; if it cannot be dropped and rebuilt, it has hidden inputs and that is a bug") applied here, and it buys three things: re-embedding after a model change is a rebuild not a migration, Obsidian edits cannot corrupt the record, and the post-training corpus is the log itself.

Standing queries

The paper's memory agent is φθ(Q, C_t, M_{t-1}) — it requires a question. The update gate is defined as "does this chunk contain useful information about the problem". Without Q the gate has no referent, and r_update becomes undefinable, which forecloses post-training.

So each project declares durable questions in YAML. One query = one L1 memory = one Obsidian note.

# queries/poimen.yaml
project: poimen
sources:
  - pi:--Users-rockliang-workplace-Poimen-agent-rust--
  - claude:-Users-rockliang-workplace-Poimen
queries:
  - id: architecture-decisions
    question: What architectural decisions were made, with reasoning and rejected alternatives?
  - id: infra-root-causes
    question: What infrastructure bugs were found, what was the root cause, how was it isolated?
  - id: open-questions
    question: What questions were raised and left unresolved?
synthesis:                      # the L2 pass
  question: What is the current state of this project, and what should someone know before working on it?
  exit_gate: true

Exit gate defaults off at L1. Paper §3.3 makes this call itself: for "what are all the X" questions you cannot know evidence is sufficient without reading everything, so they provide a w/o-EG inference mode. L1 extraction is exactly that shape. Keep the gate recorded (its signal is needed for RL) but do not act on it. Enable at L2 and for interactive retrieval, where the paper measures 4× speedup.

Separate weights — yes, specifically a LoRA adapter

Confirming the instinct, with the reason that actually matters here:

• Base: Qwen2.5-3B-Instruct, already resident as qwen2.5:3b-instruct
• Memory policy: LoRA adapter, rank 16–32, ~30–60 MB

Why an adapter rather than a fine-tuned model:

1. VRAM. One GPU, OLLAMA_MAX_LOADED_MODELS=2, currently holding ornith:35b + qwen2.5:3b. A separate full memory model evicts something, and eviction here is a weights reload measured in tens of seconds — we already watched ornith cold-start blow a 60s gateway timeout. A LoRA rides on the resident base for near-zero extra VRAM.
2. Iteration. Post-training emits a ~50 MB adapter, not a 6 GB model. Swap without redeploying.
3. Reversibility. A gate-behaviour regression reverts by pointing at the previous adapter.

Infra consequence to accept up front: Ollama cannot hot-swap LoRA adapters. Serving one means either moving the memory model to a vLLM InferenceService with --enable-lora (the reasoning predictor is already vLLM v0.11.0, so the pattern exists), or merging into a GGUF for Ollama and losing swappability. P1–P4 run prompted-only on the stock model, so this is deferred, not dodged.

Repository layout

Rust workspace at Poimen/memory:

Cargo.toml                    workspace
crates/
  mem-core/     domain types; Level enum; gate-response parser; the gated loop
  mem-chunk/    RecordSource trait; chunking policy; flush triggers  (see below)
  mem-llm/      gateway client — chat completions, embeddings, rerank
  mem-ingest/   source adapters: pi sessions, claude transcripts, loop.sh artifacts
  mem-store/    JSONL log writer/reader; pgvector repo; Obsidian projector
  mem-cli/      binary `mem`: ingest | synthesize | rebuild | query | verify | skill | label
memory-tasks/   the task board — INDEX.md + one file per task, tracked
queries/        standing query YAML, one file per project
vault/          Obsidian output (own git repo or gitignored)
log/            JSONL event log — authoritative, tracked

Crates: sqlx (postgres, runtime-tokio-rustls) + pgvector (sqlx feature), tokenizers for chunk sizing against the real Qwen2 tokenizer, futures, serde/serde_json, clap, reqwest.

Reuse rather than reinvent: mem-llm's request shape and the apikey header convention are proven in agent-rust/loop.sh; response parsing mirrors its extract_text().

mem-chunk — its own crate, stream-shaped from day one

Chunking is split out because it is the seam where new input kinds arrive. Sources today are files with an EOF; telemetry, a live session tail, or a broker will not have one. Designing the boundary as a stream now means a future source implements a trait rather than forcing the loop to be rewritten — and it costs nothing, since the rest of the stack is already tokio (sqlx runtime-tokio, reqwest).

// mem-chunk
pub trait RecordSource {
    /// Normalised records: role, text, timestamp, provenance. Sources decide
    /// how to produce them; the chunker never learns about pi vs claude vs a socket.
    fn records(self) -> impl Stream<Item = Result<Record>>;
}

pub struct ChunkPolicy {
    pub max_tokens: usize,        // 5000, paper default
    pub split_on: Boundary,       // never mid-message
    pub flush: FlushTrigger,      // see below
}

pub fn chunks<S: RecordSource>(src: S, p: ChunkPolicy) -> impl Stream<Item = Chunk>;

Batch sources become streams for free via futures::stream::iter, so mem-ingest gets no more complex today.

The one thing that genuinely differs for streams is the flush trigger. A file chunker emits a partial chunk at EOF; a stream has no EOF, so a partially-filled chunk would sit forever. FlushTrigger is therefore Tokens(n) today and gains OrIdle(Duration) when a stream source lands — carrying it in the policy now means the later change is one enum variant, not a signature change through the loop.

Worth noting for whenever that happens: the exit gate changes meaning on an unbounded source. At L1 over a finite transcript it is switched off because "read everything" is well defined (paper §3.3). Over a live stream there is no everything, so the gate stops being an optimisation and becomes the only termination condition — which is an argument for keeping it trained even while it is switched off.

Storage schemas

JSONL event log — authoritative

log/<project>/<query-id>/<run-id>.jsonl. Every record carries level.

{"type":"run","level":"L1","project":"poimen","query_id":"infra-root-causes","input_level":"chunk","model":"qwen2.5:3b-instruct","chunk_tokens":5000,"memory_budget":1024,"exit_gate":false,"ts":"..."}
{"type":"chunk","level":"L0","t":1,"source":"pi:2026-07-21T16-23-59_019f857d","span":[0,42],"sha256":"..."}
{"type":"gate","level":"L1","t":1,"update":false,"exit":false,"think":"...","latency_ms":812}
{"type":"evidence","level":"L0","t":7,"source":"pi:...","text":"...","sha256":"..."}
{"type":"memory","level":"L1","t":7,"text":"...","tokens":142,"parents":["<L0 sha>"],"sha256":"..."}
{"type":"run_end","level":"L1","chunks_seen":412,"chunks_used":17,"final_memory_sha":"..."}

The L2 pass writes the same record types with "level":"L2", "input_level":"L1", and parents holding L1 shas. evidence records appear only when the update gate opened, so update-rate is directly measurable and memory at any t is replayable.

pgvector — projection

One table across all levels, because retrieval wants to search them together and filter:

CREATE TABLE memory_node (
  id        BIGSERIAL PRIMARY KEY,
  level     TEXT NOT NULL CHECK (level IN ('L0','L1','L2')),
  project   TEXT NOT NULL,
  query_id  TEXT,                        -- null at L2
  run_id    TEXT NOT NULL,
  t         INT  NOT NULL,
  source    TEXT,                        -- set at L0
  text      TEXT NOT NULL,
  sha256    TEXT NOT NULL UNIQUE,
  embedding vector(768) NOT NULL,
  created_at TIMESTAMPTZ NOT NULL DEFAULT now()
);
CREATE TABLE memory_edge (               -- provenance: child <- parent
  child_sha  TEXT NOT NULL REFERENCES memory_node(sha256),
  parent_sha TEXT NOT NULL REFERENCES memory_node(sha256),
  PRIMARY KEY (child_sha, parent_sha)
);
CREATE INDEX ON memory_node USING hnsw (embedding vector_cosine_ops);
CREATE INDEX ON memory_node (project, level);

erDiagram
    MEMORY_NODE ||--o{ MEMORY_EDGE : "child_sha -> sha256"
    MEMORY_NODE ||--o{ MEMORY_EDGE : "parent_sha -> sha256"

    MEMORY_NODE {
        bigserial id PK
        text level "L0 | L1 | L2"
        text project
        text query_id "NULL at L2"
        text run_id
        int t
        text source "set at L0"
        text text
        text sha256 UK "content identity"
        vector_768 embedding
        timestamptz created_at
    }

    MEMORY_EDGE {
        text child_sha PK,FK
        text parent_sha PK,FK
    }

One table across L0/L1/L2 (not three) — retrieval searches all levels together and filters by level. memory_edge is the provenance graph: L1 rows point back at the L0 chunks that produced them, L2 rows point back at L1 parents. sha256 UNIQUE is content identity (dedup key, hash excludes run id/timestamp — M0.2) and is what memory_edge actually references, not the surrogate id.

Retrieval: HNSW recall filtered by level, then bge-reranker-base via /v1/rerank for precision — that endpoint scored 0.98 vs 0.00009 on a discrimination probe, so it earns its place. Default query searches L1+L2 and walks memory_edge down to L0 for citations.

Infra — new CNPG cluster with the extension managed declaratively (CNPG 1.30 supports this, so no manual psql, consistent with the GitOps hard rule). Follows k8s/infra/databases/temporal-db.yaml exactly:

# k8s/infra/databases/memory-db.yaml
apiVersion: postgresql.cnpg.io/v1
kind: Cluster
metadata: { name: memory-db, namespace: memory }
spec:
  instances: 3
  imageName: ghcr.io/cloudnative-pg/postgresql:16.2
  bootstrap: { initdb: { database: memory, owner: app, encoding: UTF8, localeCollate: C, localeCType: C } }
  enableSuperuserAccess: false
  storage: { size: 10Gi, storageClass: longhorn-cnpg }
  monitoring: { enablePodMonitor: true }
  affinity:
    podAntiAffinityType: preferred
    tolerations: [{ key: node-role.kubernetes.io/control-plane, operator: Exists, effect: NoSchedule }]
---
apiVersion: postgresql.cnpg.io/v1
kind: Database
metadata: { name: memory-db-vector, namespace: memory }
spec:
  name: memory
  owner: app
  cluster: { name: memory-db }
  extensions: [{ name: vector, ensure: present }]

Obsidian vault — projection

The tier graph becomes the note graph:

vault/poimen/
  index.md                     <- L2 synthesis, links to every L1 note
  infra-root-causes.md         <- L1
  architecture-decisions.md    <- L1
  evidence/                    <- L0, optional (--emit-evidence-notes, default off)
vault/skills/
  _drafts/<name>/SKILL.md      <- machine-written, never auto-loaded
  <name>/SKILL.md              <- human-promoted, loadable

---
project: poimen
level: L1
query_id: infra-root-causes
updated: 2026-08-17
chunks_seen: 412
chunks_used: 17
---
# Infra root causes — poimen

<final memory text>

## Provenance
- [[pi-2026-07-21-019f857d]] chunk 66 — Kong body buffer

L0 defaults to inline citations rather than notes — 17 per query is manageable but grows unbounded across projects. The flag exists for when the graph view is worth the file count.

Skills — the procedural projection

A skill is a projection, not a level. L0/L1/L2 are all descriptive — what happened. A skill is procedural — what to do next time. That is a change of modality, not a further compression, so the gated loop does not produce it: the update gate's question ("does this chunk contain evidence for Q") has no meaning when the output is an instruction.

The format is free. SKILL.md is YAML frontmatter plus markdown, which is exactly an Obsidian note — verified against ~/.claude/skills/seo-geo-claude-skills/research/keyword-research/SKILL.md, whose frontmatter carries name, description, when_to_use, argument-hint. So vault/skills/<name>/SKILL.md is simultaneously a vault note and a loadable skill, with no conversion step:

pi --skill vault/skills/            # or set skillsPath in ~/.pi/config.json
ln -s .../vault/skills/<name> ~/.claude/skills/<name>

mem skill draft --from poimen/infra-root-causes reads an L1 or L2 note and writes a draft. Emission should follow the existing authoring rubric rather than inventing one — the installed grafana-core:skill-authoring skill encodes Anthropic's Agent Skills guidance and a four-dimension rubric (conciseness, actionability, workflow clarity, progressive disclosure). Description quality is the whole game: a skill whose description does not match how the user actually phrases the request never fires.

Drafts are never auto-loaded, and promotion is manual. This is the one place the system can close a loop on itself, and the failure is subtle:

a memory-derived skill is auto-loaded → it appears in future session transcripts → those transcripts are ingested as evidence → the memory that produced the skill is reinforced by its own output

No external verifier breaks that cycle. It is the same hazard poimen §16 names when it gates "automatic workflow mutation without human approval" by default, and it is why _drafts/ is a separate directory rather than a frontmatter flag — a directory cannot be accidentally globbed into --skill.

Two mechanical guards:

1. Promotion is a human move out of _drafts/, reviewable as a diff.
2. Provenance marks derived text. Every emitted skill carries generated_from: <L2 sha> in frontmatter, and mem-ingest tags chunks matching a known emitted artifact as derived: true and excludes them from evidence. Without this the corpus slowly becomes its own training data.

Phases

P1 — Read-only spine. mem-ingest implements RecordSource for pi sessions and Claude transcripts; mem-chunk chunks to 5000 tokens on message boundaries; mem ingest --dry-run prints the chunk plan with no model calls. Both sources are batch, but they go through the stream interface so the seam is exercised from the first commit rather than retrofitted.

P2 — Gated loop at L1, prompted only. mem-llm + gate-response parser (paper Figure 10a prompt, adapted for standing queries). Writes the JSONL log with L0 evidence and L1 memory records. Exit gate recorded, not acted on. This is the paper's "w/o RL" baseline, which Figure 9 shows already works.

P3 — Projections. Obsidian projector and pgvector repo, both rebuildable via mem rebuild --from-log. Infra commit for memory-db.

P4 — L2 synthesis and retrieval. mem synthesize runs the same loop over L1 memories with the exit gate on. mem query — embed, HNSW recall by level, rerank, return with provenance walked through memory_edge.

P5 — Skill drafting. mem skill draft --from <note> emits vault/skills/_drafts/<name>/SKILL.md with generated_from provenance; mem-ingest grows the derived: true exclusion filter. Promotion stays manual. Cheap to build and it is the phase that makes the memory do something rather than only be read.

P6 — Post-training (separate, Python). Boundary is the JSONL. mem label uses the 32B reasoning model as an offline evidence labeler to produce per-chunk U_t ground truth (the paper had synthetic NIAH labels; we do not, and this is the honest cheapest substitute). Then verl trains a LoRA with the paper's rewards: r_update ±1, r_exit {0, −0.5 late, −0.75 early}, strict r_format, α=0.9 mixing trajectory- and turn-level advantage. Requires the vLLM decision above.

Task breakdown

Board lives in memory-tasks/ at the repo root. Format follows agent-rust/tasks/: one file per task, self-contained, each with Acceptance / Verify (harness, numbered assertions, command) / False pass / Traps, a Status field as source of truth, and memory-tasks/INDEX.md mirroring it. Ids are M<phase>.<n> and frozen once written — phase order is declared in INDEX.md, never derived from the id.

memory-tasks/
  INDEX.md                          board + phase order + progress mirror
  M0.1-cargo-workspace.md
  M0.2-domain-types.md
  ...
  M5.6-m5-gate.md

Note agent-rust/.gitignore excludes tasks, which silently untracks the whole board there. Naming this memory-tasks/ sidesteps that pattern — and it should be tracked, since the task files carry the acceptance criteria.

M0 — Read-only spine (no model calls anywhere in this phase)

id	task	size	deps
M0.1	Cargo workspace + six crate skeletons, CI builds clean	S	—
M0.2	mem-core domain types: Level, Record, Chunk, MemoryNode, sha256 identity	S	M0.1
M0.3	mem-chunk: RecordSource trait, ChunkPolicy, FlushTrigger::Tokens	M	M0.2
M0.4	Tokenizer-backed sizing against the real Qwen2 tokenizer	M	M0.3
M0.5	mem-ingest: pi session adapter (~/.pi/agent/sessions/<encoded-cwd>/*.jsonl)	M	M0.3
M0.6	mem-ingest: claude transcript adapter (~/.claude/projects/**/<uuid>.jsonl)	S	M0.5
M0.7	mem ingest --dry-run — chunk plan, token histogram, source breakdown	S	M0.4, M0.6
M0.8	M0 gate — both adapters through one RecordSource, no source-specific code past the trait	M	gate

M1 — Gated loop at L1

id	task	size	deps
M1.1	mem-llm chat client — apikey header, retry, timeout	M	M0.1
M1.2	Standing-query YAML loader + schema validation, unresolved id fails at load	M	M0.2
M1.3	GRU-Mem prompt template (paper Fig 10a), memory + chunk + question assembly	M	M1.2
M1.4	Gate-response parser: <think>/<check>/<update>/<next>, strict, malformed = hard error	M	M1.3
M1.5	The gated loop — U_t mutate-or-retain, E_t recorded not acted on, 1024-token budget	L	M1.4
M1.6	JSONL event log writer, level on every record, parents on memory	M	M1.5
M1.7	mem ingest end to end + update-rate reported on stdout	M	M1.6
M1.8	M1 gate — full run on poimen, update-rate < 30%, memory tokens flat not climbing	M	gate

M2 — Projections

id	task	size	deps
M2.1	mem-llm embeddings client, 768-dim, batched	S	M1.1
M2.2	k8s/infra/databases/memory-db.yaml — CNPG Cluster + Database with extensions: [vector]	M	—
M2.3	memory_node / memory_edge schema + sqlx migrations, HNSW indexes	M	M2.2
M2.4	mem-store pgvector repo — upsert by sha, edge insert	M	M2.3, M2.1
M2.5	Obsidian projector — frontmatter, wikilinks, L0 citations, --emit-evidence-notes	M	M1.6
M2.6	mem rebuild --from-log — drop and rebuild both projections	M	M2.4, M2.5
M2.7	mem verify — every L1 has ≥1 L0 parent, every parent sha resolves	S	M2.6
M2.8	M2 gate — rebuild is byte-identical (git -C vault diff --exit-code empty)	M	gate

M3 — L2 synthesis and retrieval

id	task	size	deps
M3.1	L2 pass — same loop, input Stream<L1 memory>, exit gate on	M	M1.5
M3.2	mem-llm rerank client (bge-reranker-base)	S	M1.1
M3.3	mem query — embed, HNSW recall filtered by level, rerank, walk edges to L0	M	M3.2, M2.4
M3.4	M3 gate — known-answer query returns the right L1 node with a real L0 citation	M	gate

M4 — Skills

id	task	size	deps
M4.1	mem skill draft --from <note> → _drafts/, frontmatter incl. generated_from	M	M3.1
M4.2	derived: true ingest filter — emitted artifacts excluded from evidence	M	M4.1, M0.5
M4.3	M4 gate — draft absent from --list-skills; no L0 node matches an emitted artifact	M	gate

M3.5 — Distributed API Layer (Homelab Frontend integration)

id	task	size	deps
M3.5.1	HTTP server + router (actix-web or axum), Kong auth hook, request metrics	M	M0.1
M3.5.2	POST /ingest endpoint — ingest_id dedup, async queue (redis or in-mem), job polling	M	M1.7, M3.5.1
M3.5.3	GET /query endpoint — embed query, HNSW recall by level, rerank, walk edges to L0	M	M3.3, M3.5.1
M3.5.4	Federation: single query across projects, fan+merge results, deduplicate	M	M3.5.3
M3.5.5	GET /skills and /skills/{name} — loadable skills only, exclude _drafts, YAML frontmatter in JSON	M	M4.1, M3.5.1
M3.5.6	GET /projects and /projects/{id}/status — metadata, metrics, synthesis timestamps	S	M3.5.1
M3.5.7	Rate limiting (apikey-scoped per endpoint) + idempotency by sha256	M	M3.5.2
M3.5.8	M3.5 gate — end-to-end ingest→query via HTTP, load from cli and from agent simul	M	gate

M5 — Post-training (Python, separate from the Rust workspace; boundary is the JSONL)

id	task	size	deps
M5.1	mem label — 32B reasoning as offline evidence labeler, writes U_t ground truth	M	M1.6
M5.2	Labeler calibration — hand-label a holdout, measure agreement before trusting it	M	M5.1
M5.3	Training corpus export from the log to verl's expected format	M	M5.1
M5.4	vLLM InferenceService for Qwen2.5-3B with --enable-lora (GitOps, homelab)	L	—
M5.5	verl loop — r_update ±1, r_exit {0,−0.5,−0.75}, strict r_format, α=0.9	L	M5.3, M5.4
M5.6	M5 gate — adapter beats prompted baseline on held-out update accuracy	L	gate

Total 43 tasks, 7 gates. M0 and M2.2 have no model dependency and can start immediately; M5.4 is homelab work independent of everything else in M5 and can run in parallel. M3.5 depends on M2 (pgvector store exists) and M1 (ingest loop exists); can run in parallel with M4 and M5.

Verification

# P1 — corpus parses, chunk plan sane, zero model calls
cargo run -p mem-cli -- ingest --project poimen --dry-run

# P2 — one query end to end
cargo run -p mem-cli -- ingest --project poimen --query infra-root-causes
jq -r 'select(.type=="gate" and .level=="L1") | .update' log/poimen/infra-root-causes/*.jsonl | sort | uniq -c
#   expect: far more false than true. Update-rate > ~30% means the gate is not
#   discriminating — that is the paper's memory-explosion failure, Figure 6 is
#   the reference shape.
jq -r 'select(.type=="memory") | .tokens' log/.../*.jsonl | tail -1
#   expect: <= 1024 and roughly flat over t, not monotonically climbing

# levels are well-formed and edges close
jq -r '.level' log/poimen/**/*.jsonl | sort | uniq -c        # L0/L1 present
cargo run -p mem-cli -- verify --project poimen
#   asserts: every L1 memory has >=1 L0 parent; every parent sha exists

# P3 — projections truly derived
cargo run -p mem-cli -- rebuild --from-log --project poimen
git -C vault diff --exit-code        # empty: rebuild is byte-identical
psql -c "select level, count(*) from memory_node group by level;"

# P3.5 — API server online
cargo run -p mem-cli -- serve --port 8080 &
sleep 1
curl -H "apikey: test-key" http://localhost:8080/memory/projects
#   expect: ["poimen", ...]
curl -H "apikey: test-key" \
  "http://localhost:8080/memory/query?query=kong+body&level=L1,L2&project=poimen&limit=3"
#   expect: 200, array of memory nodes with score + parents
#
# ingest via HTTP (async):
jq -n '{project:"poimen", source:"test:local", records:[...]}' | \
  curl -X POST -H "apikey: test-key" \
    http://localhost:8080/memory/ingest -d @-
#   expect: 202, {"job_id": "ingest-<uuid>", "status_url": "/memory/ingest/ingest-<uuid>"}
#
# idempotency: same request twice with same ingest_id returns same job_id, no re-enqueue
# rate limit: 11th req in 1 second gets 429 Retry-After
# auth missing: 401 Unauthorized

# P4 — synthesis and retrieval
cargo run -p mem-cli -- synthesize --project poimen     # expect exit gate to fire
cargo run -p mem-cli -- query "why did requests over 10KB fail?"
#   expect: infra-root-causes L1 node, Kong body-buffer passage, L0 citation
#
# Via API (same result):
curl -H "apikey: test-key" \
  "http://localhost:8080/memory/query?query=why+did+requests+over+10KB+fail"
#   expect: identical results

# P5 — skill drafts land unloadable, and the cycle stays open
cargo run -p mem-cli -- skill draft --from poimen/infra-root-causes
ls vault/skills/_drafts/                    # draft here, NOT in vault/skills/
pi --skill vault/skills/ --list-skills      # draft must not appear
curl -H "apikey: test-key" http://localhost:8080/memory/skills
#   expect: no drafts in list
cargo run -p mem-cli -- verify --derived-filter --project poimen
#   asserts: no L0 evidence node text matches an emitted skill artifact

The decisive P2 metric is update-rate, the one number distinguishing a working gate from an expensive summarizer. toolResults are 43% of records and mostly evidence-free, so a correct gate rejects the large majority of chunks.

P3.5 API gate: Ingest and query work over HTTP with correct idempotency, auth, and rate limiting. CLI and agents both submit to same endpoint; no duplication or ordering issues.

Distributed API Layer (Homelab Frontend)

Gateway: api.riotpiao.com routes agent and system memory requests through Kong.

Architecture assumption: Memory services run in CNPG cluster; API layer is HTTP facade exposing read/write workflows to distributed agents. Authority remains JSONL—API is a request demultiplexer, not a cache or alternative source of truth.

REST API Endpoints

POST   /memory/ingest                  <- async, idempotent by sha256
GET    /memory/query                   <- semantic search + rerank
GET    /memory/projects                <- list projects with L2 synthesis
GET    /memory/projects/{id}/status    <- ingest/synthesis status
GET    /memory/projects/{id}/notes     <- L1/L2 notes (Obsidian export)
GET    /memory/skills                  <- loadable skills (excludes _drafts)
GET    /memory/skills/{name}           <- one skill frontmatter + body

Request/Response contract:

# POST /memory/ingest (idempotent, async)
{"project": "poimen", "source": "agent:uuid", "records": [...], "ingest_id": "sha256-of-batch"}
→ 202 Accepted
  {"job_id": "ingest-<uuid>", "ingest_id": "...", "status_url": "/memory/ingest/ingest-<uuid>"}

# GET /memory/query (semantic search)
{"query": "why did requests over 10KB fail?", "level": ["L1", "L2"], "project": "poimen", "limit": 5}
→ 200 OK
  [
    {"level": "L1", "sha256": "...", "text": "...", "score": 0.92,
     "parents": [{"level": "L0", "source": "pi:...", "text": "..."}]},
    ...
  ]

# GET /memory/skills?loadable=true
→ 200 OK
  [
    {"name": "infra-root-causes", "description": "...", "when_to_use": "...",
     "generated_from": null, "promoted_at": "2026-08-20"}
  ]

Distributed Behavior

Ingestion: mem-ingest CLI submits batches to POST /memory/ingest via ingest_id (sha256 of batch text). Duplicate ingest_id returns same job_id without re-enqueuing — jobs are idempotent by content hash, not request. Server stores the mapping; HTTP 409 means already ingested (user caller resubmits without retry).

Query federation: Agents query single endpoint; server fans requests to appropriate project (selected by metadata or query text). Results walk memory_edge down to L0 server-side, so client gets complete citation graph in one round-trip.

Skills as cargo: GET /memory/skills returns YAML frontmatter in JSON so agent UIs can inspect description and when_to_use without fetching the file. Body is optional (fetch separately if needed to load).

Status & observability:

• GET /memory/projects/{id}/status → {"last_ingest": "...", "chunks_total": N, "chunks_used": M, "synthesis_ran": "...", "next_synthesis_at": "..."}
• Metrics: ingest latency (p50/p99), query latency, update-rate per project, memory size trends

Scaling Constraints

Single points of failure:

• CNPG cluster (mitigated by ≥3 replicas + Longhorn)
• Ollama inference (separate from memory store; ingest is offline, query caches embeddings)

Throughput limits:

• Ingest: one gated loop per project sequentially (5000 tokens/chunk, gate latency 812ms); ~7 chunks/min = 35k tokens/min per project
• Query: HNSW recall is O(log n), rerank O(k log k), each << embedding roundtrip to Ollama (typically 200ms)

Caching strategy:

• Memory nodes are immutable (sha256 content hash) — safe to cache indefinitely post-write
• L2 synthesis is project-scoped and regenerated on mem synthesize — TTL 1h or explicit purge
• Embeddings cached per-query hash (same embedding twice = cache hit, save 200ms Ollama call)
• Client-side: ETag: <sha256> on all read endpoints, no conditional logic server-side (it's stateless)

Auth & rate limits:

• Kong apikey: header (existing pattern)
• Per-key limits: ingest 100 jobs/hour, query 1000 req/hour, skill fetch unlimited
• Burst allowance: 10 req/sec per key (ingest waits in queue; query returns 429 Retry-After if burst exceeded)

Integration with Existing Flows

From mem-cli (local or CI/CD):

mem ingest --project poimen --query infra-root-causes --gateway https://api.riotpiao.com

Client computes ingest_id locally (sha256 of all records), submits as batch, polls /memory/ingest/<job_id> until done.

From agents (in-session via Pi or Claude):

# Query within agent:
curl -H "apikey: $MEM_APIKEY" \
  "https://api.riotpiao.com/memory/query?query=why+did+X+fail&project=poimen&level=L1,L2"
  
# Ingest at session end:
{session_transcript_chunk} | curl -X POST -H "apikey: $MEM_APIKEY" \
  https://api.riotpiao.com/memory/ingest \
  -d @- -H "Content-Type: application/jsonl"

Skill loading in agent systems:

# Discovery:
curl -H "apikey: $MEM_APIKEY" https://api.riotpiao.com/memory/skills?loadable=true \
  | jq -r '.[] | .name' | xargs -I {} \
    curl https://api.riotpiao.com/memory/skills/{} > ~/.claude/skills/{}/SKILL.md

Error Taxonomy

200 OK — query succeeded, memory node found (or empty result)
202 Accepted — ingest accepted, job queued
204 No Content — query matched no nodes; not an error
400 Bad Request — malformed query or invalid project/level
401 Unauthorized — missing/invalid apikey
409 Conflict — ingest_id already processed (idempotent, safe retry)
429 Too Many Requests — rate limit exceeded, Retry-After header set
500 Internal Server Error — CNPG offline or embedding service down
503 Service Unavailable — gated loop busy (queue building), retry in 5s

Risks

• 3B gate quality unmeasured on this corpus. The paper evaluates on QA benchmarks with clean evidence labels; agent transcripts are messier. Mitigation: P2's update-rate is a cheap early read, and the 32B reasoning model can spot-audit a sample before committing to P5.
• L2 inherits L1's errors with no path back to source. Synthesis over memories cannot recover evidence the L1 gate wrongly discarded. memory_edge makes the omission visible (an L1 note with suspiciously few parents) but not recoverable without a re-run.
• Self-reinforcement through skills. The only cycle in the system: emitted skill → future session context → ingested as evidence → reinforces the memory that emitted it. Guarded by manual promotion plus the derived: true ingest filter, and both must hold. Audit it by checking that no L0 evidence node's text matches an emitted artifact.
• No ground-truth evidence labels. r_update needs them. Distant supervision from the 32B labeler inherits its bias; hold out a hand-labelled set to measure agreement before trusting it.
• Vault/log divergence. Hand edits are overwritten on rebuild. Either make the vault read-only or add an ## Notes region the projector preserves. Decide before anyone starts editing.
• Ollama has no LoRA path. P5 forces the vLLM decision. Do not discover this at P5.
• API latency at scale. Query federation fans requests to multiple projects; slowest project wins. Mitigation: query timeout 5s, client-side fallback to local JSONL search, async synthesis keeps L2 warm (cache hit 95%+).
• Ingest race on concurrent writes. Two agents submit overlapping session chunks to same project simultaneously. Mitigation: ingest_id based on content hash prevents duplicate evidence in log; gated loop is single-threaded per project, queues serialize. Allowed cost: cold-start ingest delay ~5m for backlog.