AgentToolDrop

45 of 71

Cognee

Turns documents, code and conversations into a knowledge graph that agents store to and recall from

Install with uv

uv pip install "cognee[gliner]"

Copied from the project's docs at github.com/topoteretes/cognee

Why you'd want it

Cognee ingests text, code and session data and builds connected memory: entities, relationships and searchable chunks for text, and a graph of symbols and dependencies for code. Its API centers on four operations (remember, recall, improve and forget), and with the gliner extra it builds and queries memory with small local CPU models and no API key, while adding an LLM key enables generated answers. It ships plugins for Claude Code and Codex, an MCP server for other clients, a REST API and Docker images for self-hosting.

Best for

  • Agent memory across runs on your own infrastructure
  • Shared memory over docs, tickets and code
  • Local experiments without an LLM API key

Use it when

You want agents to keep and query project knowledge, past decisions and fixes across sessions, on infrastructure you control.

Skip it if

You are not on Python 3.10 to 3.14 for the library. Running the whole memory layer on a single Postgres instance is a demo feature; the production version is a licensed product.

Related tools

Same category or shared tags, by drop score.

  1. Rank 38

    Graphiti

    Memory

    Builds temporal knowledge graphs from agent conversations and data, tracking how facts change over time

    • score 58
    • 31.8k stars
    • pushed
    • v0.31.0 on
    • Apache-2.0

    Install with pip

    pip install graphiti-core
    Use it when, skip it if, stats for Graphiti

    Why you'd want it

    Graphiti turns episodes (raw text or JSON) into entities and relationships and gives each fact a validity window, so when information changes the old fact is invalidated rather than deleted and you can query what was true at any point. New data is added incrementally without recomputing the graph, and retrieval combines semantic embeddings, BM25 keyword search and graph traversal. Every fact traces back to the episode that produced it, custom entity and edge types are defined with Pydantic models, and a bundled MCP server lets assistants add and search memory.

    Best for

    • Agents working on data that changes over time
    • Memory that needs provenance back to sources
    • Teams comfortable running a graph database

    Use it when

    You need agent memory that answers questions about relationships and history, not just similar text chunks.

    Skip it if

    You do not want to run a graph database: Graphiti needs Neo4j, FalkorDB or Amazon Neptune plus an LLM with structured output, OpenAI by default. Zep is the managed option from the same team, and anonymous telemetry is on by default (opt out with GRAPHITI_TELEMETRY_ENABLED=false).

    Stats

    Stars
    31,752
    Last push
    Release
    v0.31.0,
    License
    Apache-2.0
    Pricing
    Open source
    Works with
    Claude Desktop, Cursor, Any MCP client
    Drop score
    58 of 100

    Full stat block for Graphiti

  2. Rank 19

    Mem0

    Memory

    Adds long-term user, session and agent memory to AI apps through a Python or TypeScript SDK

    • score 66
    • 67.2k stars
    • pushed
    • ts-v3.3.1 on
    • Apache-2.0

    Install with pip

    pip install mem0ai
    Use it when, skip it if, stats for Mem0

    Why you'd want it

    Mem0 extracts facts from conversations with an LLM call and stores them so later turns can search them by user, session or agent. Retrieval combines semantic search, BM25 keyword matching and entity linking, with time-aware ranking for questions about current state versus past events. You can run it as a library from pip or npm, as a self-hosted server with a dashboard and API keys through Docker Compose, or on the managed Mem0 Platform.

    Best for

    • Personalized assistants and support bots
    • Remembering user preferences across sessions
    • Starting with a library and moving to a managed service later

    Use it when

    Your agent should remember facts about users or its own past actions between conversations without you designing the storage and retrieval.

    Skip it if

    You want memory with no LLM or embedding calls: Mem0 requires an LLM and defaults to OpenAI models. The README notes its benchmark scores come from the managed platform, which includes optimizations not in the open-source SDK.

    Stats

    Stars
    67,205
    Last push
    Release
    ts-v3.3.1,
    License
    Apache-2.0
    Pricing
    Open source
    Drop score
    66 of 100

    Full stat block for Mem0

  3. Rank 6

    Ollama

    Model/API

    Runs open models locally behind a CLI and a REST API, with an OpenAI-compatible endpoint

    • score 83
    • 183k stars
    • pushed
    • v0.40.4 on
    • MIT

    Install on macOS or Linux

    curl -fsSL https://ollama.com/install.sh | sh
    Use it when, skip it if, stats for Ollama

    Why you'd want it

    Ollama downloads and runs open models such as Gemma with one command (ollama run gemma4) and serves them on a local REST API at localhost:11434. It supports a subset of the OpenAI API, so OpenAI clients work by changing the base URL, and it ships official Python and JavaScript libraries. ollama launch connects local models to coding agents including Claude Code, Codex, Copilot CLI and OpenCode, and the same API can reach Ollama's cloud models with an API key.

    Best for

    • Running open models on your own machine
    • Offline or private agent development
    • Pointing coding agents at local models

    Use it when

    You want a local model server that existing OpenAI-style clients and coding agents can talk to with minimal setup.

    Skip it if

    You only use hosted models from providers like Anthropic or OpenAI. Its OpenAI compatibility covers a subset of the OpenAI API, so check the docs if you depend on less common endpoints.

    Stats

    Stars
    183,183
    Last push
    Release
    v0.40.4,
    License
    MIT
    Pricing
    Open source
    Works with
    macOS, Linux, Windows, Docker, Claude Code, Codex
    Drop score
    83 of 100

    Full stat block for Ollama

For agents

The same entry, as files an agent can read without scraping this page. 

# read one entry
curl -s https://agenttooldrop.com/tools/cognee.md

# list the MCP servers in the catalog
curl -s https://agenttooldrop.com/tools.json \
  | jq -r '.tools[] | select(.category=="mcp-server") | .slug'