tools-update-cron: sync 2026-08-30 — 9 skill(s) updated
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name: cognee-brain
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description: "Use when user says brain or cognee. Cognee memory brain (Kuzu+LanceDB)."
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version: 1.0.0
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author: Hermes Agent
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license: MIT
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platforms: [linux]
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metadata:
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hermes:
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tags: [cognee, brain, memory, knowledge-graph, kuzu, lancedb]
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related_skills: [ai-vault-kb, save, memory-ingest]
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---
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# Cognee Brain — homelab memory layer
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The homelab memory layer ("the brain") is **Cognee** on BRAIN-MAIN at `10.0.0.23`.
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It replaced the previous memory layer on 2026-08-29. Cognee ingests text, builds a
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knowledge graph (entities + relationships) and a vector index side-by-side, and
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exposes them through Python, CLI, MCP, and REST.
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## When to Use
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- User says "brain", "cognee", "what do we know about X", "remember this"
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- Before research/infra tasks — query the brain first
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- After research with concrete conclusions — write durable facts back
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## Endpoints
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- API: `http://10.0.0.23:8080` (cognee-backend, cognee/cognee:main)
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- MCP: `http://10.0.0.23:8001/mcp` (cognee-mcp, cognee/cognee-mcp:main)
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- Dataset: `homelab-stack` (the single shared dataset — use it everywhere)
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- LLM: Ollama cloud-passthrough `http://10.0.0.23:11434/v1` (minimax-m3:cloud)
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- Embed: mini `http://10.0.0.30:11434/api/embed` (qwen3-embedding:0.6b, 1024 dims)
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- Graph: Kuzu (Ladybug) files under `/var/lib/cognee/system/databases`
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- Vectors: LanceDB files under `/var/lib/cognee/system/databases/cognee.lancedb`
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## MCP tools (auto-discovered in Hermes as `mcp__cognee__*`)
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- `remember` — store data. Without `session_id` = permanent memory (add + cognify
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pipeline, builds the graph). With `session_id` = session cache only (fast, no
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extraction). Pass `data` (text) OR `filename`+`content_base64` (file upload).
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`dataset_name` defaults to the client's agent-scoped dataset — ALWAYS pass
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`dataset_name="homelab-stack"` explicitly.
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- `recall` — search memory. `query` (required), optional `search_type`
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(HYBRID_COMPLETION default; CHUNKS/GRAPH_COMPLETION/RAG_COMPLETION/etc.),
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`datasets` (comma-separated), `top_k`.
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- `forget` — delete. `dataset` (name), `dataset_id`, `data_id`+`dataset`, or
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`everything`.
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## REST API (for curl / scripts)
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- `POST /api/v1/remember` (multipart: `data=@file` + `datasetName=homelab-stack`)
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- `POST /api/v1/search` (JSON: `query`, `datasets`, `searchType`)
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- `DELETE /api/v1/datasets?dataset_name=...`
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- `GET /health` → `{"status":"ready","health":"healthy","version":"1.5.3-local"}`
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**Field-name gotcha:** the search API uses `query` + `searchType` (NOT
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`query_text`/`query_type`). Wrong names silently fall back to the default query
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"What is in the document?" and return a generic summary.
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## Write rules
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- **One dataset:** `homelab-stack`. Never create a second dataset for the same
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domain — the whole point is a single combined view.
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- **`remember` is add+cognify in one call** — no separate cognify step.
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- **No group_id, no triplet, no temporal supersession.** Cognee uses datasets. To correct a fact, `remember` the corrected statement;
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Cognee's graph merges entities by name.
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- **Don't re-ingest the same content twice** — `remember` is not idempotent.
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- **Embed dim is 1024** (`EMBEDDING_DIMENSIONS=1024`, qwen3-embedding:0.6b). Never let it
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default to 3072.
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## Verify a write
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- `recall` with a distinctive phrase from the content; a correct answer proves
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the graph has it.
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- Check `docker logs cognee-backend` for `api.openai.com` / `ProviderConfigMismatch`
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— zero hits means the LAN-only provider pairing held.
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## Pitfalls
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- **`remember` returns `status: completed` synchronously** (unless `background=true`).
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A completed status means the graph is built — no async polling needed.
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- **Search field names** are `query`/`searchType` (see above).
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- **No auth** on API/MCP (`REQUIRE_AUTHENTICATION=false`) — LAN-trust only.
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- **Container runs as uid 1000** — `/var/lib/cognee/{system,data}` must stay 1000:1000.
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- **MCP allowed_hosts** = 10.0.0.23/42/28/15 (edit `MCP_ALLOWED_HOSTS` in compose to add hosts).
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- **Start/stop:** `sudo systemctl start|stop cognee.service` (docker compose up/down).
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