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Agent skill

Cognee Integrations Setup

by topoteretes in topoteretes/cognee

Switches cognee's LLM, embedding, relational, vector and graph backends through environment variables, with the extras to install and the traps to avoid.

Apache-2.0Auto-check: notesAI & LLM Engineering

Install Cognee Integrations Setup

skills CLI
$ npx skills add topoteretes/cognee --skill cognee-integrations -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install topoteretes/cognee cognee-integrations --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/topoteretes/cognee.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/cognee-integrations .claude/skills/cognee-integrations && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
cognee-integrations
GitHub stars
32k
Token cost
~1k tokens
SKILL.md length
380 words
Files
1
Skills in repo
19
Repo updated
First seen
Licence
Apache-2.0

At a glance

Switches cognee's LLM, embedding, relational, vector and graph backends through environment variables, with the extras to install and the traps to avoid.

  • Switching cognee to a different LLM or embedding provider
  • SKILL.md covers LLM providers, Databases, Storage, cache, and the rest and MCP server (IDE integration), plus 1 more section
  • Calls pip and docker; needs LLM_API_KEY and EMBEDDING_API_KEY
  • Moving cognee's relational, vector or graph storage to another database

What it does

All cognee integration settings are environment variables in a .env file. The skill says to check the .env.template at the repo root before inventing variable names and to install the matching extra, such as cognee[postgres], before switching a backend. The LLM defaults to OpenAI, where only LLM_API_KEY is needed, and switching sets LLM_PROVIDER, LLM_MODEL, LLM_API_KEY and, where relevant, an endpoint and API version. Azure OpenAI, Gemini, Anthropic, local Ollama, custom OpenAI-compatible endpoints such as OpenRouter or vLLM, and AWS Bedrock are covered.

A classic trap is that the LLM and embeddings are configured independently, so setting only one leaves the other on OpenAI. Database choices are split by role. Relational is sqlite by default or postgres, vector is lancedb by default or pgvector, neptune_analytics or turso, with others such as ChromaDB, Qdrant, Weaviate and Milvus coming from community adapters that must be registered, and graph is ladybug by default or neo4j, neptune, ladybug-remote or postgres. The repo's docker-compose file ships postgres and neo4j profiles, and S3 storage and the MCP server for IDE use are also in scope.

When your agent uses it

  • Switching cognee to a different LLM or embedding provider
  • Moving cognee's relational, vector or graph storage to another database
  • Running cognee against local Ollama models
  • Connecting cognee's MCP server to an IDE

Example prompts

  • “Switch cognee to Azure OpenAI and configure the embeddings to match.”
  • “Move the vector store to pgvector and the graph store to Neo4j using the docker-compose profiles.”
  • “Run cognee fully locally with Ollama for both the LLM and the embeddings.”

Requirements

  • A cognee installation with a .env file
  • The matching pip extra for the backend being switched, such as cognee[postgres]

What it can do on your machine

Read from SKILL.md and the folder at commit 0ec7a9f. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • pip
    • docker

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Links to these hosts (documentation or services it may open):

    • github.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • LLM_API_KEY
    • EMBEDDING_API_KEY

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Cognee Integrations Setup loads about 1k tokens when it runs. Until then it costs about 73 tokens; SKILL.md has 380 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~73
When it runs · the whole SKILL.md, loaded when a task matches
~1k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NoteMentions a .env fileSKILL.md:8
    ration config is environment variables (`.env`). The authoritative,

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from topoteretes/cognee at commit 0ec7a9f, republished under its Apache-2.0 licence (© topoteretes). 380 words, ~1,003 tokens.

Download SKILL.mdSave it as .claude/skills/cognee-integrations/SKILL.md (or your agent's skills folder).
name
cognee-integrations
description
Use when the user wants to connect cognee to external services — switching LLM or embedding providers (OpenAI, Azure, Gemini, Anthropic, Ollama, OpenRouter), changing databases (Postgres, PGVector, Neo4j, Neptune, Turso), S3 storage, or the MCP server for IDE integration.

Set up cognee integrations

All integration config is environment variables (.env). The authoritative, always-current list with commented examples is .env.template at the repo root — check it before inventing variable names. Install the matching extra before switching a backend (e.g. pip install cognee[postgres]).

LLM providers

Default is OpenAI (LLM_API_KEY is all you need). To switch, set LLM_PROVIDER, LLM_MODEL, LLM_API_KEY, and (where relevant) LLM_ENDPOINT / LLM_API_VERSION:

  • Azure OpenAI: LLM_PROVIDER=azure, LLM_MODEL=azure/gpt-4o-mini, endpoint + api version required.
  • Gemini (no extra needed): LLM_PROVIDER=gemini, LLM_MODEL=gemini/gemini-2.0-flash-exp.
  • Anthropic (cognee[anthropic]): LLM_PROVIDER=anthropic, model e.g. claude-3-5-sonnet-20241022.
  • Ollama, local (cognee[ollama]): LLM_PROVIDER=ollama, LLM_ENDPOINT=http://localhost:11434/v1, and set the embedding block + HUGGINGFACE_TOKENIZER too.
  • Custom / OpenRouter / vLLM: LLM_PROVIDER=custom with the provider's OpenAI-compatible endpoint.
  • AWS Bedrock (cognee[aws]): LLM_PROVIDER=bedrock + AWS credentials/region.

The classic trap: LLM and embeddings are configured independently (EMBEDDING_PROVIDER, EMBEDDING_MODEL, EMBEDDING_ENDPOINT, EMBEDDING_API_KEY). Configuring only one leaves the other on OpenAI — either keep a valid OpenAI key or configure both.

Databases

  • Relational (DB_PROVIDER): sqlite (default) or postgres (cognee[postgres]; host/port/user/password/name via DB_* vars).
  • Vector (VECTOR_DB_PROVIDER): lancedb (default), pgvector (cognee[postgres], needs VECTOR_DB_URL), neptune_analytics (cognee[neptune]), turso (cognee[turso]). Anything else (ChromaDB, Qdrant, Weaviate, Milvus, …) lives in community adapters — install from https://github.com/topoteretes/cognee-community and register with use_vector_adapter before use; setting VECTOR_DB_PROVIDER alone raises "Unsupported vector database provider".
  • Graph (GRAPH_DATABASE_PROVIDER): ladybug (default), neo4j (cognee[neo4j], bolt URL + credentials), neptune (cognee[neptune]), ladybug-remote, postgres (no raw Cypher / natural-language search).

The repo docker-compose.yml ships ready-to-use postgres (pgvector) and neo4j profiles with matching default credentials. From a container, reach host services with DB_HOST=host.docker.internal.

Show full SKILL.md (149 more words)Show less

Storage, cache, and the rest

  • S3 storage (cognee[aws]): STORAGE_BACKEND=s3 + bucket/credentials, and point DATA_ROOT_DIRECTORY/SYSTEM_ROOT_DIRECTORY at s3:// paths.
  • Session cache: CACHE_BACKEND = sqlite (default) | postgres | redis | fs | tapes.
  • Ontologies: ONTOLOGY_FILE_PATH to an OWL file, resolver/matching via ONTOLOGY_RESOLVER / MATCHING_STRATEGY.

MCP server (IDE integration)

docker compose --profile mcp up starts the MCP server on port 8001 (Streamable HTTP at http://localhost:8001/mcp), built from cognee-mcp/. Point Cursor / Claude Desktop / Claude Code at it to use cognee memory from the IDE. Configure its DB_* env to match the main service so both see the same data.

After changing providers mid-project

Embeddings from different models are not comparable — after switching the embedding provider or model, reset local state (cognee-cli forget --all or await cognee.forget(everything=True)) and re-ingest with remember().

To drop just the graph and vectors while keeping the ingested files, use await cognee.forget(dataset="my_project", memory_only=True) — the dataset can then be rebuilt under the new embedding model without re-uploading anything.

© topoteretes, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in .agents/skills/cognee-integrations of topoteretes/cognee.

Open the folder on GitHubat commit 0ec7a9f

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Works with

Categories

Questions about Cognee Integrations Setup

What does Cognee Integrations Setup do?

Switches cognee's LLM, embedding, relational, vector and graph backends through environment variables, with the extras to install and the traps to avoid. env file.template at the repo root before inventing variable names and to install the matching extra, such as cognee[postgres], before switching a backend.

When should I use Cognee Integrations Setup?

Cognee Integrations Setup fits situations like: switching cognee to a different LLM or embedding provider; moving cognee's relational, vector or graph storage to another database; running cognee against local Ollama models; connecting cognee's MCP server to an IDE.

How do I install Cognee Integrations Setup in Claude Code?

Run `npx skills add topoteretes/cognee --skill cognee-integrations -a claude-code`. Or copy the skill folder (.agents/skills/cognee-integrations in topoteretes/cognee) into .claude/skills/cognee-integrations in your project. Claude Code loads it when a task matches its description.

How do I install Cognee Integrations Setup in Codex?

Run `npx skills add topoteretes/cognee --skill cognee-integrations -a codex`. Or copy the skill folder (.agents/skills/cognee-integrations in topoteretes/cognee) into .agents/skills/cognee-integrations in your project. Codex loads it when a task matches its description.

Can I use Cognee Integrations Setup in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add topoteretes/cognee --skill cognee-integrations -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/cognee-integrations, .gemini/skills/cognee-integrations, .github/skills/cognee-integrations and .opencode/skills/cognee-integrations in your project.

What does Cognee Integrations Setup need to run?

Going by SKILL.md and its folder, Cognee Integrations Setup needs the command-line tools its instructions call (pip and docker) and credentials named LLM_API_KEY and EMBEDDING_API_KEY. Our summary lists: A cognee installation with a .env file; The matching pip extra for the backend being switched, such as cognee[postgres].

Does Cognee Integrations Setup access the network?

SKILL.md names 1 domain. As links in the text: github.com. This is read from the text; nothing was executed.

Is Cognee Integrations Setup safe to install?

Our automated static check of SKILL.md found notes only (mentions a .env file), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.

What licence does Cognee Integrations Setup use?

Cognee Integrations Setup is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Cognee Integrations Setup use?

About 1k tokens (SKILL.md is roughly 4k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Cognee Integrations Setup?

Skills that share tags, products or a category with Cognee Integrations Setup: Codebase Management (giancarloerra/SocratiCode, 3.3k stars), Pgvector Semantic Search (timescale/pg-aiguide, 1.9k stars), Codebase Exploration (giancarloerra/SocratiCode, 3.3k stars) and Using Ccproxy Inspector (starbaser/ccproxy, 350 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Cognee Integrations Setup?

topoteretes (a GitHub organization) maintains it in topoteretes/cognee, which has 31,919 GitHub stars. The repository holds 19 skills in this directory. The repository was last updated on October 9, 2026.

Source: topoteretes/cognee on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.