Cognee Integrations

OrganizationPopular

topoteretes

cognee-integrations

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.

Overview

Publisher	topoteretes
Repository	`cognee`
Skill name	`cognee-integrations`
Stars	32K
Forks	3.3K
Bundled files	Instructions only
License	Apache-2.0
Links	Skill source Repository Homepage

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
Self-contained
Everything the model needs lives in the instructions — no extra files to sync.
Open source
Published by topoteretes on GitHub. Read the source before you install it.

Installation

Install the Cognee Integrations AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/topoteretes/cognee/tree/main/.agents/skills/cognee-integrations
Enable the skill in any chat where you want it available.

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/topoteretes/cognee.git /tmp/cognee
mkdir -p .claude/skills
cp -r /tmp/cognee/.agents/skills/cognee-integrations .claude/skills/cognee-integrations

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Cognee Integrations in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Cognee Integrations on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Cognee Integrations is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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.

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.

Frequently asked questions

What does the Cognee Integrations AI skill do?

Why use Cognee Integrations on TypingMind?

Because you install it once and use it with any model. Cognee Integrations is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Cognee Integrations in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/topoteretes/cognee/tree/main/.agents/skills/cognee-integrations. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Cognee Integrations?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Cognee Integrations?

As many as you like. As long as a model supports skills, you can use Cognee Integrations with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Cognee Integrations AI skill free?

Yes. It is published on GitHub by topoteretes under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Set up cognee integrations

LLM providers

Databases

Storage, cache, and the rest

MCP server (IDE integration)

After changing providers mid-project

Frequently asked questions

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