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

Architecture

Agent support

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 variable instead of storing plaintext. For local Ollama, omit the key.

The default embedding setup is local sentence-transformers-local with intfloat/multilingual-e5-small. Add --embedding-provider and --embedding-model only when you want to override that default. Use --dry-run to preview the files and exec-form commands before writing anything.

Claude Code

npx -y -p github:NeoLi00/memX memx quickstart claude-code \
  --llm-provider openai-compatible \
  --llm-base-url https://llm.example.com/v1 \
  --llm-model fast-memory-model \
  --llm-api-key sk-your-provider-key

Codex

npx -y -p github:NeoLi00/memX memx quickstart codex \
  --llm-provider openai-compatible \
  --llm-base-url https://llm.example.com/v1 \
  --llm-model fast-memory-model \
  --llm-api-key sk-your-provider-key

OpenClaw

npx -y -p github:NeoLi00/memX memx quickstart openclaw \
  --llm-provider openai-compatible \
  --llm-base-url https://llm.example.com/v1 \
  --llm-model fast-memory-model \
  --llm-api-key sk-your-provider-key

Generic MCP

npx -y -p github:NeoLi00/memX memx quickstart mcp \
  --llm-provider openai-compatible \
  --llm-base-url https://llm.example.com/v1 \
  --llm-model fast-memory-model \
  --llm-api-key sk-your-provider-key

For Claude Code, Codex, and generic MCP clients, start the shared local service after configuration:

npx -y -p github:NeoLi00/memX memx-server

What memX can do

• Remember work over time: project decisions, user preferences, task status, long source segments, and raw evidence stay linked to the original turn.
• Connect related things: projects, repos, tools, files, resources, blockers, and outcomes can be represented as entities and graph edges.
• Learn collaboration patterns: repeated evidence can become reusable guidance without losing its supporting sources.
• Maintain itself: corrections can supersede older facts, stable evidence can be promoted, and stale task state stops competing with current state.
• Recall compact evidence: facts, events, state, chunks, relationships, resources, and learned patterns are searched together, then injected as small evidence lines.