memX
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- License — License: MIT
- Description — Repository has a description
- Active repo — Last push 0 days ago
- Community trust — 76 GitHub stars
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- process.env — Environment variable access in dist/.runtime/src/bin/memx-hook.mjs
- exec() — Shell command execution in dist/.runtime/src/cli/registerCli.mjs
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memX: self-learning, self-maintaining memory for AI agents; native support for claude code, codex, and openclaw
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
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Codex | native hooks, MCP hidden by default |
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Claude Code | native hooks, MCP hidden by default |
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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, replacegithub:NeoLi00/memX with @neoli00/memx.
Fill in these values before running a command:
--llm-provider: the provider adapter memX should call. Choose one ofopenai-compatible,anthropic,google, orollama.--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, orhttp://127.0.0.1:11434for 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_KEYif 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 withintfloat/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.
For Codex and Claude Code, native hooks are the default lifecycle path for automatic recall and
turn capture. Their MCP server uses --mcp-tools none by default, so no memX tools are exposed to
the agent; this prevents duplicate recall/write and prevents the agent from reading audit data as a
side channel. Use --mcp-tools full only when you intentionally want the agent to see the complete
MCP tool set. Generic MCP quickstart stays full by default because it has no native lifecycle
hooks. Default native memories are also host-scoped, so Codex and Claude Code do not share the same
local database unless you deliberately override the database path and actor settings.
Claude Code
This installs the shared memX config, a local Claude Code plugin marketplace, native lifecycle
hooks, and the plugin-provided MCP server in one run.
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
This installs the shared memX config, Codex MCP config, and native lifecycle hooks in one run.
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
Clean uninstall
Each uninstall command backs up the target config first, then removes only memX-owned entries.
Claude Code and Codex cleanup also uninstall the native plugin, remove the local marketplace, and
delete the generated marketplace snapshot.
OpenClaw cleanup also removes stale memx / memory-memx slot, allow, and entry references, then
best-effort uninstalls both current and legacy plugin files if OpenClaw can still see them.
npx -y -p github:NeoLi00/memX memx uninstall openclaw
npx -y -p github:NeoLi00/memX memx uninstall codex
npx -y -p github:NeoLi00/memX memx uninstall claude-code
Add --dry-run to preview, or --config /path/to/config when using a non-default config path.
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.
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