// MemClaw Context Engine — automatic long-term memory for OpenClaw. Once installed, automatically remembers important facts from conversations and recalls relevant context before responding. No manual tool calls needed for daily use.
Generates .ai-context knowledge base for coding agents. Activate when: (1) setting up a new project for AI-assisted development, (2) user asks to "create project knowledge" or "setup ai-context", (3) existing .ai-context needs regeneration. Creates tiered documentation structure optimized for agent comprehension and token efficiency.
Project knowledge base for coding agents. Activate when: (1) starting a new session in this project, (2) encountering unfamiliar code patterns or architecture decisions, (3) user asks about project design or rationale, (4) before making significant structural changes. Contains tiered knowledge from stable design principles to dynamic issues.
MemClaw — High-performance memory plugin for OpenClaw. Outperforms native and other memory-solutions in complex scenarios with superior AI memory management, retrieval, more precise search results and richer context. Use memclaw for all memory operations, replacing built-in memory.
MemClaw Maintenance Guide — Installation, configuration, and maintenance guidance. For daily usage and tool operations, use the [`memclaw` skill](https://clawhub.ai/sopaco/memclaw) instead.
| name | memclaw-context-engine |
| description | MemClaw Context Engine — automatic long-term memory for OpenClaw. Once installed, automatically remembers important facts from conversations and recalls relevant context before responding. No manual tool calls needed for daily use. |
Automatic Memory: MemClaw Context Engine is a native context management plugin for OpenClaw. It automatically captures conversation memories and recalls relevant context — no manual tool calls required. Compared to built-in memory, it provides L0/L1/L2 tiered retrieval with up to 95% token savings.
MemClaw is an open-source memory enhancement suite based on Cortex Memory. Both MemClaw and this Skill are open-sourced on GitHub.
afterTurn (end of each conversation turn), automatically extracts and batches user/assistant messages to Cortex Memory. Sessions auto-commit when token count (50k), message count (20), or time interval (30min) thresholds are met.assemble (before each model run), automatically searches for relevant memories using recent user messages and injects them as simulated cortex_search tool results. Includes 60s cooldown and 70% query overlap deduplication.openclaw plugins install @memclaw/memclaw-context-engine if not presentplugins.slots.contextEngine: "memclaw-context-engine" in openclaw.json to activateopenclaw.json)agents.defaults.memorySearch.enabled: false in openclaw.json to avoid conflicts with built-in memoryWhile the Context Engine handles memory automatically, these tools are available for explicit agent use:
Layered semantic search across ALL memories with L0/L1/L2 control.
| Parameter | Required | Description |
|---|---|---|
query | Yes | Search query (natural language) |
scope | No | Search scope URI (omit to search ALL) |
limit | No | Max results (default: 10) |
min_score | No | Min relevance 0-1 (default: 0.65) |
return_layers | No | ["L0"], ["L0","L1"], or ["L0","L1","L2"] |
Example: User asks "What database did I prefer?"
cortex_search(query="database preference", return_layers=["L0"])
Returns L0 snippet + L2 full content. Equivalent to cortex_search(return_layers=["L0","L2"]).
| Parameter | Required | Description |
|---|---|---|
query | Yes | Search query |
scope | No | Search scope URI |
limit | No | Max results (default: 10) |
Explicitly store a message with optional metadata.
| Parameter | Required | Description |
|---|---|---|
content | Yes | Content to store |
role | No | "user", "assistant", "system" (default: user) |
session_id | No | Session ID (uses default if not specified) |
metadata | No | Optional tags, importance, etc. |
Example: User says "Remember my email is xxx@example.com"
cortex_add_memory(content="User email: xxx@example.com", role="user", metadata={"tags": ["contact"], "importance": "high"})
Close session and trigger memory extraction pipeline.
| Parameter | Required | Description |
|---|---|---|
session_id | No | Session ID to close |
Delete a memory by exact URI.
| Parameter | Required | Description |
|---|---|---|
uri | Yes | Exact memory URI to delete |
Example: User says "Forget my phone number"
cortex_forget(uri="cortex://user/default/preferences/phone-number.md")
Perform maintenance on MemClaw data. Runs automatically every 3 hours but can be called manually.
| Parameter | Required | Description |
|---|---|---|
dryRun | No | Preview changes without executing (default: false) |
commands | No | Commands to run: ["prune", "reindex", "ensure-all"] (default: all) |
What it does:
prune — Remove vectors whose source files no longer existreindex — Rebuild vector index and remove stale entriesensure-all — Generate missing L0/L1 layer filesWhen to call manually:
cortex_maintenance() # Run all commands
cortex_maintenance(dryRun=true) # Preview only
cortex_maintenance(commands=["prune"]) # Run specific command
| Tool | Purpose |
|---|---|
cortex_ls | Browse memory virtual filesystem |
cortex_get_abstract | Get L0 abstract (~100 tokens) |
cortex_get_overview | Get L1 overview (~2000 tokens) |
cortex_get_content | Get L2 full content |
| Field | Default | Description |
|---|---|---|
tenantId | tenant_claw | Tenant ID for data isolation |
autoStartServices | true | Auto-start Qdrant + cortex-mem-service |
autoRecall | true | Automatic memory recall during assembly |
autoCapture | true | Automatic message capture after each turn |
recallWindow | 5 | Recent user turns for recall query |
recallLimit | 10 | Max memories recalled per assembly |
recallMinScore | 0.65 | Min relevance score for recall |
commitTokenThreshold | 50000 | Token threshold for auto-commit |
commitTurnThreshold | 20 | Turn count threshold for auto-commit |
llmApiBaseUrl | https://api.openai.com/v1 | LLM API endpoint |
llmApiKey | — | LLM API key (required) |
llmModel | gpt-5-mini | LLM model name |
embeddingApiBaseUrl | https://api.openai.com/v1 | Embedding API endpoint |
embeddingApiKey | — | Embedding API key (required) |
embeddingModel | text-embedding-3-small | Embedding model name |
# Check plugin status
openclaw skills
# Check context engine slot
openclaw config get plugins.slots.contextEngine
# Disable memory (switch to legacy)
openclaw config set plugins.slots.contextEngine legacy
# Enable memory
openclaw config set plugins.slots.contextEngine memclaw-context-engine
# Restart gateway after changing slot
{
"plugins": {
"entries": {
"memclaw-context-engine": {
"enabled": true,
"config": {
"tenantId": "tenant_claw",
"autoStartServices": true,
"autoRecall": true,
"autoCapture": true,
"llmApiKey": "your-llm-api-key",
"llmModel": "gpt-5-mini",
"embeddingApiKey": "your-embedding-api-key",
"embeddingModel": "text-embedding-3-small"
}
}
},
"slots": {
"contextEngine": "memclaw-context-engine"
}
},
"agents": {
"defaults": {
"memorySearch": { "enabled": false }
}
}
}
| Symptom | Cause | Fix |
|---|---|---|
| Plugin not loaded | Slot not configured | Set plugins.slots.contextEngine: "memclaw-context-engine" |
| No memories recalled | No existing memory data | Use cortex_add_memory or wait for auto-capture + commit |
| Service errors | Qdrant/cortex-mem-service not running | Check ports 6333/6334/8085; verify autoStartServices: true |
extracted 0 memories | Wrong LLM API key or model | Check llmApiKey and llmModel in config |
| Port occupied | Port used by another process | Change port: set servicePort / qdrantPort in config |
| Inaccurate recall | recallMinScore too low | Increase threshold or adjust recallLimit |
Config file location:
~/Library/Application Support/memclaw/config.toml~/.local/share/memclaw/config.toml%LOCALAPPDATA%\memclaw\config.tomlNo Docker required — all dependencies bundled with plugin.