Skip to main content
Glama
Sign In
topoteretes

cognee-mcp

by topoteretes
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

Related Servers

Alternatives to cognee-mcp

No user-submitted related servers found.

    Related Servers

    • Mnemo MCP by expandingideas-ai

      Mnemo MCPofficial

      expandingideas-ai
      A
      license
      Not graded
      quality
      D
      maintenance
      Persistent AI memory server with hybrid search and embedded sync. Enables AI agents to store, retrieve, and manage information across sessions with temporal knowledge graph support.
      MIT

    • local-memory-mcp

      mason-yb-zhang
      F
      license
      Not graded
      quality
      C
      maintenance
      Provides AI agents with persistent, local cross-session shared memory by combining vector semantic retrieval with knowledge graph relationships, and supports short/long-term memory management and local backups.
      -

    • dejaview-mcp

      JakeC77
      A
      license
      Not graded
      quality
      D
      maintenance
      Provides persistent knowledge graph memory for AI agents, enabling them to store, recall, and query facts about people, projects, and relationships across sessions.
      MIT

    • simple-good-llm-memory-mcp

      esinecan
      A
      license
      Not graded
      quality
      F
      maintenance
      Enables LLMs to store, search, and manage memories with hybrid semantic and keyword search using ChromaDB and Neo4j for persistent memory and knowledge graph capabilities.
      MIT

    • remembrallmcp

      roboticforce
      A
      license
      Not graded
      quality
      B
      maintenance
      Persistent knowledge memory layer for AI agents. Hybrid semantic + full-text search with pgvector, code dependency graph with blast-radius impact analysis, and incremental indexing for 7 languages. In-process ONNX embeddings, no external API required.
      44 npm
      35
      MIT
    Deploy Server
    Context All Your Agents Can ShareAgents share what they learn, hand off work, and hold it when you switch models. Start with 5 GB free.fulcradynamics.comAd

    TDQS

    A3.9/5.0

    Scored across 6 tools

    Disambiguation4/5

    The core memory verbs (remember/forget/recall) are cleanly separated by write/delete/read semantics, and the meta-pair search_tools/call_tool is unambiguous. The only mild overlap is recall vs code_search, since both are retrieval tools, but code_search explicitly scopes itself to the code graph and excludes conversational memory, which resolves most confusion.

    Naming Consistency4/5

    All names are snake_case and fall into two coherent families: bare verbs for memory operations (remember, forget, recall) and verb_noun for infrastructure (call_tool, search_tools, code_search). This is a defensible grouping rather than chaos, though the bare-verb vs verb_noun split keeps it from being a fully uniform pattern.

    Tool Count5/5

    Six tools is well within the sweet spot and each one covers a distinct responsibility: store, retrieve, delete, code-graph query, plus dynamic tool discovery and invocation. Nothing feels padded or missing at the surface-count level.

    Completeness4/5

    The lifecycle is largely covered: remember (create), recall (read), forget (delete), and a specialized code_search, with file upload and session modes handled via parameters. Gaps exist around explicitly updating existing memory and enumerating datasets/items (forget accepts a dataset name/id but nothing lists them), which agents can work around by re-storing or semantic recall.

    Maintenance

    ActivityActive
    ResponsivenessSlow

    Resources