Files

alvis 9094d71e2f ai: migrate LLM backbone from Kimi CLI to Codex CLI

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

2026-08-01 06:13:27 +00:00

Dockerfile

ai: migrate LLM backbone from Kimi CLI to Codex CLI

2026-08-01 06:13:27 +00:00

README.md

ai: migrate LLM backbone from Kimi CLI to Codex CLI

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server.js

ai: migrate LLM backbone from Kimi CLI to Codex CLI

2026-08-01 06:13:27 +00:00

service-block.yml

ai: migrate LLM backbone from Kimi CLI to Codex CLI

2026-08-01 06:13:27 +00:00

README.md

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/-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

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.