alvis
9094d71e2f
Retires the Moonshot/Kimi subscription in favour of the already-paid ChatGPT plan. Both CLI wrappers now run `codex exec`; the kimi-agent container is gone. adolf-llm + hindsight-llm: - runKimi -> runCodex (`codex exec --json --skip-git-repo-check`), resume via `codex exec resume <thread_id>`. - MCP moves from a per-session .mcp.json (a workaround for Kimi having no --mcp-config-file flag) to a $CODEX_HOME/config.toml generated once at startup from shared-mcp.json. Field translation is load-bearing: bearerTokenEnvVar -> bearer_token_env_var, enabledTools -> enabled_tools. - approval_policy="never" + sandbox_mode required, or unattended turns block on an approval prompt nobody can answer. kimi-agent removed. It was the ONLY large-tier deployment behind LiteLLM, so deleting it outright would have silently degraded every large-tier request to the local 4B model via the existing fallbacks. tier-large, the auto_router complex-reasoning route and their fallbacks now point at the codex-backed adolf-llm wrapper (model_name: codex-agent). Three environment blockers fixed along the way: - OpenAI geo-blocks this host (403 unsupported_country_region_territory). Both containers now egress via the host xray proxy, with NO_PROXY keeping MCP and *.alogins.net traffic off the tunnel. - node:22-slim ships no system CA store; the Rust codex binary validates TLS against it, so every HTTPS call failed with a generic transport error while Node's own fetch worked. ca-certificates added to both images. - `codex exec resume` rejects -C/--cd (plain `codex exec` accepts it), which broke follow-up turns while first turns succeeded. Known regression: Kimi's managed-usage API has no Codex equivalent, so the /usage route returns 501 and there is no quota probe for the codex model. The two quota plugins degrade quietly to no output. Also: stop tracking cognee.env (live LLM + JWT secrets) and gitignore it. The secrets remain in earlier history and should be rotated. Verified live: plain turn, SSE streaming, session resume, MCP tool call, bearer-token MCP call, and completions through both LiteLLM routes. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_014Y5QPagv4iun1ghpwM96Ff
3.3 KiB
cognee-llm (:8011)
⚠️ SUPERSEDED — Adolf's memory is migrating Cognee → Hindsight (2026-07-13). Hindsight runs its LLM on LiteLLM
:4000/ Ollama, so this bespoke Kimi-CLI wrapper is being retired, not ported (SPIKE gate 5 already concluded the extraction workload shouldn't sit on the Kimi seat). This service is decommissioned in migration task H4. Plan:agap_git/adolf/HINDSIGHT-MIGRATION.md. The doc below describes the outgoing Cognee stack, kept until H4 lands.
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/-Sresume, dir removed after every call (success or failure). - Non-streaming — always returns a full
chat.completionbody, even if the caller setsstream: 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 = 3inserver.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
cd /home/alvis/agap_git/ai
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.