MCP100 verdict
Not currently recommended. Cognee MCP server for persistent knowledge-graph memory and retrieval across AI sessions. Capabilities: Add/manage supported persistent memory; Build/query graph-based knowledge; Retrieve context across sessions/datasets; Operate over local/self-hosted or cloud-backed Cognee deployments
MCP100 Assessment
Editorial verdict
Reviewed September 13, 2026
Task Fit
Mapped
Local files and knowledge
Trust
Status warning
maintenance_source_health; install_supply_chain; advisory_freshness; adoption_evidence_presence; resource_type_requirements
Maintenance
Recheck needed
The source snapshot needs another refresh before maintenance can be interpreted.
Popularity
Signals unavailable
No independently comparable adoption metric is currently stored.
Evidence scope
No MCP-specific repository metric is stored
No MCP-specific package metric is stored
Stars observation date unavailable · Downloads observation date unavailable
Exact implementation repository metric.
Exact package is documented; download evidence is unavailable.
Publisher activity observed September 13, 2026
Stale source snapshot - recheck needed
Source relationship: First-party MCP
Reviewed September 13, 2026
Evidence-backed setup
This profile has type-appropriate, source-backed setup rather than an MCP-server client connector configuration.
Capability
Cognee MCP server for persistent knowledge-graph memory and retrieval across AI sessions. Capabilities: Add/manage supported persistent memory; Build/query graph-based knowledge; Retrieve context across sessions/datasets; Operate over local/self-hosted or cloud-backed Cognee deployments
Not measured yet
Estimated token footprint of the MCP tool schemas exposed to the agent.
Reviewed access
Review the exact access boundary before connecting this MCP to accounts, credentials, or sensitive data.
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github:topoteretes is listed as First-party MCP in MCP100’s current evidence.
Current official resources:
Original product, capability, tool, and usage documentation from the MCP publisher. Installation sections appear under Install & Agents.
📄 Read the research paper: Optimizing the Interface Between Knowledge Graphs and LLMs for Complex Reasoning — Markovic et al., 2025
| I want to… | Start here |
|---|---|
| Build memory without an LLM | Local Python quickstart |
| Explore a prebuilt graph without downloading models | Bundled demo |
| Generate answers with a local or hosted LLM | Optional LLM setup |
| Give an existing agent memory | Plugins and MCP |
| Run Cognee on my infrastructure | Deployment options |
| Use a managed service | Cognee Cloud |
Cognee builds connected memory from different sources. Text becomes entities, relationships, and searchable chunks; code becomes a graph of symbols and dependencies. Session distillation curates accepted lessons into permanent memory.
At query time, retrieval selects relevant graph, vector, or code context. Your application can inspect the retrieved evidence and use it to answer a question or continue an agent task.
| Operation | What it does | Learn more |
|---|---|---|
remember | Store content or code in permanent memory, or in a session when a session ID is supplied. | Store memory |
recall | Retrieve context and answers, using automatic routing or a chosen search strategy. | Query memory |
improve | Enrich memory, apply feedback, and bridge session knowledge into the graph. | Improve memory |
forget | Remove a specific item or dataset. | Delete memory |
Explore the architecture and session lifecycle.
Install the Claude Code plugin:
claude plugin marketplace add topoteretes/cognee-integrations
claude plugin install cognee-memory@cognee
or Codex plugin
Make sure to enable hooks:
# ~/.codex/config.toml
[features]
hooks = true
codex plugin marketplace add topoteretes/cognee-integrations --ref main
codex plugin add cognee@cognee
Follow the plugin setup guide to configure local or remote memory.
| Interface | Start here |
|---|---|
| Claude Code memory plugin | Install and configure the plugin |
| OpenClaw memory plugin | Install @cognee/cognee-openclaw |
| Cursor, Cline, and other MCP clients | Cognee MCP guide and server README |
| Python applications | Python API reference |
| TypeScript applications | TypeScript SDK |
| Rust applications | Cognee-RS |
| Applications using HTTP |
Browse the integrations repository for agent frameworks, plugins, and source connectors. Each guide describes its setup and memory capture behavior.
To inspect a local installation in the UI:
cognee-cli -ui
The UI launcher requires Node.js/npm; Docker is needed for its MCP service. See local UI setup.
For a local API demo using a prebuilt image, follow the minimal Docker Compose guide. It includes a persistent-volume configuration and explains the single-user demo settings.
To run the API, UI, and MCP server from a source checkout, clone this repository, enter its directory, copy .env.template to .env, and configure your providers. Then run:
docker compose --profile ui --profile mcp up
The default ports are API 8000, UI 3000, and MCP 8001. For deployment beyond a local demo, configure authentication, persistent storage, and compatible backends using the permissions guide and deployment templates. Cognee Cloud provides the managed option.
The default Docker image does not include GLiNER. To ingest text without an LLM in Docker, add the gliner extra to your image; the local quickstart installs it explicitly.
Graph memory traditionally means operating a stack — a graph database for relationships, a vector database for embeddings, Redis for sessions, and a relational database for metadata — all deployed, secured, and paid for before an agent remembers anything. In cognee 1.0 you can run the entire memory layer on a single Postgres instance.
⚠️ Warning: Using Postgres as a graph store is currently a released as a demo feature. The production ready feature is available as a licenced product. Use it to demo keeping relational metadata, PGVector, and graph working together
The BEAM evaluation measures conversational memory using synthetic long-context conversations and an LLM judge. The reported runs use Cognee's memory components with benchmark-specific data formatting, prompts, and retrieval configuration.
| BEAM context | Reported score (0–1) | Scope |
|---|---|---|
| 100K tokens | 0.79 | Fixed hybrid retrieval; four evaluation rounds over 20 questions from one held-out conversation. |
| 10M tokens | 0.67 | Exploratory result; question-type routing selected and scored on the same question set, averaged over five rounds. |
The two settings use different conversations, ingestion models, and retrieval-selection procedures. Read the methodology, models, limitations, and reproduction instructions before comparing these scores with other systems. The report also documents the remaining reproduction gap for the distributed 10M ingestion.
For the research behind Cognee's graph/LLM interface, see Optimizing the Interface Between Knowledge Graphs and LLMs for Complex Reasoning (Markovic et al., 2025).
We welcome contributions from the community! Your input helps make Cognee better for everyone. See CONTRIBUTING.md to get started.
We're committed to fostering an inclusive and respectful community. Read our Code of Conduct for guidelines.
We recently published a research paper on optimizing knowledge graphs for LLM reasoning:
@misc{markovic2025optimizinginterfaceknowledgegraphs,
title={Optimizing the Interface Between Knowledge Graphs and LLMs for Complex Reasoning},
author={Vasilije Markovic and Lazar Obradovic and Laszlo Hajdu and Jovan Pavlovic},
year={2025},
eprint={2505.24478},
archivePrefix={arXiv},
primaryClass={cs.AI},
url={https://arxiv.org/abs/2505.24478},
}
| REST API reference |