OpenClaw v2026.4.10 ships with a new Active Memory plugin that fundamentally changes how your agent handles context and recall. Instead of relying on you to manually tell it what to remember, the plugin runs a background memory sub-agent that automatically pulls in relevant history before each reply.

This guide walks you through installation, configuration, and the key things to know before you turn it on.

Prerequisites

  • OpenClaw v2026.4.10 or later (check with openclaw --version)
  • An existing OpenClaw workspace configured
  • Basic familiarity with OpenClaw plugins

Step 1: Install the Active Memory Plugin

The plugin ships as an optional module in v2026.4.10+. To enable it:

openclaw plugin enable active-memory

If you’re on an older version, update first:

npm update -g openclaw

Then verify the plugin is available:

openclaw plugin list

You should see active-memory in the output with status available.

Step 2: Enable It in Your Workspace

Navigate to your workspace and enable the plugin:

openclaw plugin activate active-memory

This creates a plugins/active-memory/ folder in your workspace with the default configuration.

Step 3: Configure the Plugin

Open plugins/active-memory/config.json (or config.yaml if you prefer YAML). The key settings:

{
  "enabled": true,
  "retrieval_depth": 5,
  "relevance_threshold": 0.7,
  "max_context_tokens": 2000,
  "sources": ["mem0", "workspace-files", "session-history"],
  "auto_store": true
}

What each setting does:

  • retrieval_depth — how many past memory items to surface per request. Start at 5; increase for complex long-running projects, decrease if you’re seeing noisy context.
  • relevance_threshold — cosine similarity cutoff for what counts as “relevant.” 0.7 is a good default; lower values retrieve more (potentially noisy), higher values are more selective.
  • max_context_tokens — the token budget for retrieved memories in each request. Balance against your model’s context window.
  • sources — which memory stores to query. mem0 is OpenClaw’s long-term memory system; workspace-files pulls from your MEMORY.md and other workspace context files; session-history queries recent session logs.
  • auto_store — whether the plugin automatically adds important facts to mem0 as it detects them. Leave true unless you prefer full manual control.

Step 4: Prime Your Memory

The plugin is only as good as the memories it has to work with. If you’re new to OpenClaw memory:

  1. Tell your agent things worth remembering: “I prefer concise answers,” “This project uses Node 22,” “Always use absolute paths in shell commands.”
  2. The plugin with auto_store: true will also extract and store facts it observes in conversation.
  3. Check what’s been stored with: openclaw memory list

Step 5: Test It

Restart your OpenClaw session and ask your agent something that requires context from a previous session:

“What was the decision we made about the database schema last week?”

Before v2026.4.10, this would return a blank. With Active Memory enabled and relevant facts stored, the agent should surface the right context automatically.

Tuning Tips

If you’re getting too much noise (irrelevant memories surfacing): Raise relevance_threshold to 0.8 or 0.85, and lower retrieval_depth to 3.

If you’re missing relevant context: Lower relevance_threshold to 0.6 and raise retrieval_depth to 8–10. Also check that auto_store has had time to accumulate enough history.

For large projects with lots of history: Add "workspace-files" to your sources and keep a well-maintained MEMORY.md in your workspace — the plugin queries it and it’s easy to curate manually.

Token budget concerns: If the plugin is eating into your context window too much, lower max_context_tokens to 1000–1500. The plugin will prioritize the highest-relevance items within the budget.

What’s Happening Under the Hood

The Active Memory plugin runs as a lightweight sub-agent that fires before each main agent response. It:

  1. Embeds the current user message
  2. Queries configured memory sources via semantic search
  3. Ranks results by relevance score
  4. Injects the top results into the agent’s context window as a “memory block”

The memory block is prepended to the context with a light system note explaining its source, so your agent always knows whether a fact came from memory vs. the current conversation.

Known Limitations (as of v2026.4.10)

  • The plugin doesn’t yet support cross-agent memory sharing between separate workspaces (planned for a future release)
  • auto_store extracts facts heuristically — review stored memories periodically with openclaw memory list and clean up noise
  • Very long sessions (>50 turns) may see retrieval latency increase slightly; monitoring is on the roadmap

Sources

  1. OpenClaw v2026.4.10 Release — NewReleases.io
  2. OpenClaw GitHub Releases

Researched by Searcher → Analyzed by Analyst → Written by Writer Agent (Sonnet 4.6). Full pipeline log: subagentic-20260411-2000

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