# cognee-llm (:8011)

OpenAI-compatible wrapper around the Kimi Code CLI (`@moonshot-ai/kimi-code`, home
`/root/.kimi-code`), built for Cognee's batch/structured LLM calls. **Opposite policy to
`kimi-agent`**:

- **Stateless one-shot** — fresh temp dir under `/workspace/<uuid>` per request, `kimi -p
  <prompt> --output-format stream-json`, **no `-r`/`-S` resume**, dir removed after every call
  (success or failure).
- **Non-streaming** — always returns a full `chat.completion` body, even if the caller sets
  `stream: true`.
- **No media, no MCP** — text-only prompt built from `messages`; no image persistence, no
  `.mcp.json`.
- **Structured/low-temperature intent via prompt, not a sampling param** — the CLI has no raw
  temperature knob (it's an agent loop, not a completions API), so determinism/JSON-only output
  is enforced with an instruction preamble prepended to the caller's system prompt.
- **Bounded concurrency** — `MAX_CONCURRENCY = 3` in `server.js`, queued beyond that.

Endpoints: `GET /v1/models` (model id `cognee-llm`), `POST /v1/chat/completions`.

Own disposable in-container `/workspace` (no host bind mount — nothing here is meant to
survive a request, let alone a container restart) + own `cognee-llm-home` volume
(`/root/.kimi-code`), same Kimi subscription as `kimi-agent`/`adolf-llm`, separate volume so
each wrapper's CLI state stays isolated.

## Important: this should NOT be Cognee's default LLM backend

Per `docs/SPIKE-FINDINGS.md` gate 5 (P0 spike, empirically measured against a throwaway authed
container):

- JSON output from the CLI is clean and schema-conformant when instructed — that part works.
- **Latency is the blocker**: ~5s fixed per-invocation floor (process spawn, config/credential
  load) even for a trivial call, ~22-24s for a realistic structured extraction call. Cognify
  issues one such call per chunk/entity-extraction step, so a batch of even a few dozen chunks
  reaches many minutes of wall time serialized.
- Every call is agentic (tool-call round trips are possible even for "just extract JSON"
  prompts), and hammering the single-seat Kimi subscription with concurrent batch CLI spawns
  risks rate-limiting/throttling that hasn't been (and shouldn't be) tested at scale.

**Recommendation: default Cognee's `LLM_API_BASE` to a LiteLLM-routed model (`judge`/local
qwen, per `ARCHITECTURE.md` §3.3's own stated fallback), not this wrapper.** This service stays
buildable/available as the optional, low-volume path (`http://cognee-llm:8011/v1`) — e.g. for
experimentation or if a future need specifically wants Kimi-subscription-backed structured
calls — but P4 should wire Cognee's default LLM to LiteLLM, not here.

## Smoke test

```bash
cd /home/alvis/agap_git/openai
docker build -t cognee-llm:local ./cognee-llm
docker run --rm -d --name cognee-llm-smoke -p 18011:8011 cognee-llm:local
curl -s http://localhost:18011/v1/models
docker rm -f cognee-llm-smoke
```

A full `/v1/chat/completions` round-trip needs a `kimi login`-authed
`/root/.kimi-code` volume (shared Kimi subscription) — not present in a bare smoke container,
so that step is deferred to integration/P4 wiring.