memX

English · 中文 ·
  Architecture

memX turns completed work into structured, searchable, self-maintained memory, then injects only the evidence an agent needs for the current query.
It connects natively to Codex, Claude Code, and OpenClaw, and reaches any MCP-compatible client through the same local memory layer.

Benchmarks

      Suite
      Scope
      R@3 success rate

      LongMemEval-S
      Long-context memory retrieval
      94.2%

      Real engineering cases
      30 cases, each with 20+ turns
      100%

Architecture

Agent support

    Codex
    native hooks, MCP hidden by default

    Claude Code
    native hooks, MCP hidden by default

    OpenClaw
    native + hooks

    MCP
    MCP clients
    any MCP-compatible client

Quick start

Requirements: Node.js 22.14+ or Node 24. OpenClaw installs require OpenClaw 2026.3.25+. Python 3 is
needed only for the default local embedding runtime.

The README commands use the GitHub package spec. A fresh run pulls current GitHub code, so installs
do not wait for an npm publish. To use the npm release channel later, replace
github:NeoLi00/memX with @neoli00/memx.

Fill in these values before running a command:

- --llm-provider: the provider adapter memX should call. Choose one of openai-compatible,
  anthropic, google, or ollama.
- --llm-base-url: the base URL for that provider. Examples: https://api.openai.com/v1,
  https://api.anthropic.com/v1, https://generativelanguage.googleapis.com/v1beta, or
  http://127.0.0.1:11434 for Ollama.
- --llm-model: the model memX uses for memory compilation, recall planning, and maintenance.
  Pick a fast, low-cost model with reliable JSON output.
- --llm-api-key: the API key for the provider. Use --llm-api-key-env PROVIDER_API_KEY if you
  want the config to reference an environment…