memory-memx

MemX is a local-first long-term memory plugin for OpenClaw. It helps agents keep working with you across days, projects, decisions, corrections, and evolving preferences.

What it adds: stable work memory, task state, relationship recall, learned habits, automatic cleanup, and compact evidence injection.

What it can do

Remember work over time

MemX keeps the useful parts of long conversations: project decisions, user preferences, task status, important events, and raw evidence. Long inputs and long agent replies are split into linked segments so precise slices can be recalled without losing the original turn.

Connect related things

MemX includes relationship-aware memory. It can keep track of how projects, repos, tools, people, resources, blockers, and outcomes relate to each other. When the same object is mentioned with different names, MemX can use aliases and identity evidence to keep the memory connected.

Example: if a project is later called "Raven API", "the auth repo", or just "Raven", MemX can keep those references tied together when the evidence supports it.

Learn from repeated collaboration

MemX can notice stable patterns across repeated work. For example, it can learn that you prefer small reversible patches, that a certain project needs API checks before UI work, or that a recurring task usually follows the same review flow.

These learned patterns remain tied to supporting evidence. They are not loose summaries with no source.

Maintain itself

MemX continuously keeps memory usable:

• repeated evidence can become a stable memory;
• corrected information can replace older information;
• old task state can stop competing with current state;
• high-level summaries can point back to raw evidence;
• noisy control turns such as OpenClaw heartbeat checks are ignored.

The result is a memory store that evolves with the work instead of becoming a stale transcript.

Recall useful evidence

When the agent needs memory, MemX does not dump everything into the prompt. It searches across facts, events, state, chunks, relationships, resources, and learned patterns, then builds compact evidence lines for the current question.

The agent sees what matters now, with enough source context to answer reliably.

Evaluation signal

In the current internal long-running engineering-memory replay suite, MemX reached 100% recall of the expected memory evidence. That means the expected evidence was written, retrievable, and available to prompt injection in the tested scenarios.

Quick install

Requirements: OpenClaw with Node.js 22+, git, and Python 3 if you use local embeddings.

Install the plugin:

git clone https://github.com/NeoLi00/openclaw-memx.git
cd openclaw-memx

openclaw plugins install .
openclaw plugins enable memory-memx

Enable memory:

openclaw config set plugins.entries.memory-memx.config.enabled true
openclaw config set plugins.entries.memory-memx.config.autoCapture true
openclaw config set plugins.entries.memory-memx.config.autoRecall true
openclaw config set plugins.entries.memory-memx.config.reflectionEnabled true

Restart and verify:

openclaw gateway run --bind loopback --force

openclaw plugins list
openclaw plugins info memory-memx
openclaw plugins doctor

Model and embedding setup

MemX can reuse your existing OpenClaw provider. If OpenClaw already has a non-subscription API provider configured, you can simply point MemX at that provider/model:

openclaw config set plugins.entries.memory-memx.config.advanced.llmClassifierModel provider/model

For embeddings, MemX defaults to local sentence-transformers-local. To use the recommended local embedding setup:

python3 -m pip install --user sentence-transformers torch
openclaw config set plugins.entries.memory-memx.config.embedding.provider sentence-transformers-local
openclaw config set plugins.entries.memory-memx.config.embedding.model intfloat/multilingual-e5-small
openclaw config set plugins.entries.memory-memx.config.embedding.localDevice auto

Recommended cost-quality setup

The following combination is recommended for a practical balance of cost, quality, multilingual retrieval, and local-first operation:

DeepSeek example:

export DEEPSEEK_API_KEY="sk-your-deepseek-key"

openclaw config set models.providers.deepseek.api openai-completions
openclaw config set models.providers.deepseek.baseUrl https://api.deepseek.com
openclaw config set models.providers.deepseek.apiKey '${DEEPSEEK_API_KEY}'

openclaw config set agents.defaults.model.primary deepseek/deepseek-v4-flash
openclaw config set plugins.entries.memory-memx.config.advanced.llmClassifierModel deepseek/deepseek-v4-flash