# 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.

## This IS Cognee's LLM backbone

By design, Cognee's LLM runs on the flat Kimi subscription through this wrapper — the whole
reason it exists — mirroring how `adolf-llm` backs the assistant. P4 wires cognee's
`LLM_ENDPOINT` → `http://cognee-llm:8011`, `LLM_MODEL` → `openai/cognee-llm`.

**Accepted tradeoff (SPIKE-FINDINGS gate 5).** The CLI's JSON output is clean/schema-conformant,
but it's slower than a raw API: ~5s fixed per-invocation floor + ~22-24s for a realistic
structured-extraction call, and every call is agentic. Cognify issues one call per
chunk/entity-extraction step, so large batches serialize into minutes. To protect the
single-seat subscription, `MAX_CONCURRENCY = 3` bounds concurrent spawns.

**Documented fallback (not the default):** if cognify throughput ever becomes a real problem,
route cognee's LLM to a LiteLLM model instead (`ARCHITECTURE.md` §3.3) — see the commented block
in `cognee/cognee.env`. Embeddings already run on LiteLLM's `nomic-embed` regardless (embeddings
can't go through the agentic CLI).

## 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.