OpenClaw Persistent Memory
Overcoming Context Compaction & Silent Fact Loss.

TL;DR

Give OpenClaw autonomous agents persistent memory across every run. The Memwyre plugin auto-injects past project context when an agent session starts and auto-captures decisions when it ends — no manual context files needed. Includes save_memory and search_memwyre tools for on-demand access during agent runs.

The Problem: OpenClaw Autonomous Agents Forget Everything

Autonomous coding agents operate on fundamentally different paradigms than standard conversational chatbots. In OpenClaw, an agent doesn't simply respond to a prompt; it enters an autonomous loop: reading file trees, running tests in headless terminals, spinning up Playwright/Puppeteer browser instances, and analyzing visual DOM states.

However, every time an autonomous agent run terminates or the execution context window reaches its token limit, everything resets. Architectural contracts formulated on step 3 are wiped out by step 45, forcing the agent to repeat expensive exploration loops and introduce code regressions.

The Mechanics of Context Compaction: Why Autonomous Agents Suffer from Amnesia

To understand why autonomous agents make circular mistakes during multi-hour runs, developers must look at how agent runtimes manage the LLM token budget. OpenClaw tracks accumulated tokens in the message transcript. When context window consumption hits maxContextTokens (typically 80% of capacity, or ~100,000 tokens on a 128K model), the runtime initiates Context Compaction:

1. The Compaction Loop & Lossy Compression

The runtime slices the message history, retaining only the initial system prompt and the latest 2–3 messages. The intermediary 30 turns (containing shell outputs, diff attempts, and compiler errors) are passed to an LLM with the instruction: "Summarize the conversation progress so far."

While this reduces context tokens by ~85%, it destroys factual fidelity. High-level summaries like "Agent debugged Redis auth issue and updated server configuration" discard the actual port numbers, cryptographic salt algorithms, and configuration flags discovered during trial-and-error.

2. Browser Screenshot Buffer Leaks

Autonomous browser verification is one of OpenClaw's strongest features. However, capturing full-page PNG screenshots in base64 format places immense strain on the Node.js V8 heap. Holding multiple visual states in the in-memory transcript causes OpenClaw's memory footprint to surge from 150 MB to over 3.5 GB, causing garbage collection spikes and eventual OOM (Out of Memory) crashes during long refactors.

Mastering OpenClaw Tool Profiles & Agent Configuration

OpenClaw governs agent autonomy through explicit Tool Profiles. Understanding how tool permissions interact with memory persistence is vital to prevent agents from getting trapped in permission prompts:

Production OpenClaw Configuration Template

Below is an enterprise-hardened ~/.openclaw/config.json configuration template. It optimizes context thresholds, establishes memory hooks, and configures sandbox boundaries:

{
  "agent": {
    "defaultModel": "anthropic/claude-3-7-sonnet-20250219",
    "toolProfile": "coding",
    "maxContextTokens": 100000,
    "temperature": 0.2,
    "maxIterations": 50
  },
  "browser": {
    "headless": true,
    "screenshotQuality": 60,
    "maxScreenshotsPerSession": 5
  },
  "plugins": {
    "entries": {
      "@memwyre/openclaw-plugin": {
        "enabled": true,
        "config": {
          "apiKey": "bv_sk_your_api_key_here",
          "hostUrl": "https://api.memwyre.tech",
          "autoInjectContext": true,
          "autoCaptureSession": true
        }
      }
    }
  }
}

Token Economics & RAM Optimization in Long-Running Agents

Autonomous loops burn tokens and workstation memory at scale. Decoupling memory into an external entity graph delivers drastic efficiency dividends:

Four Approaches to OpenClaw Memory

These approaches are complementary, not exclusive. Use manual files for static rules and Memwyre for dynamic OpenClaw session memory — they work together.

Memwyre vs. Mem0 & Zep for OpenClaw

When building memory for autonomous agents, framework solutions like Mem0 (SDK-first memory) and Zep / Graphiti (temporal knowledge graphs) are popular choice for custom backend developers. However, integrating them into OpenClaw requires writing custom Python/TS wrappers, orchestrating database instances, and managing manual API calls inside your agent loops.

Where Memwyre differs for OpenClaw developers:

• Zero-code CLI integration: Memwyre installs directly into OpenClaw with one command (openclaw plugins install @memwyre/openclaw-plugin). It automatically hooks into OpenClaw's session lifecycle events to handle context injection and session capture without writing custom SDK code.
• Cross-tool ecosystem: Mem0 and Zep require custom sync pipelines to share memory between your CLI agents and your IDE. Memwyre's shared vault connects OpenClaw agent sessions directly to Cursor AI, VS Code (MCP), and Claude Code.
• Benchmark performance: On the LoCoMo-10 benchmark (Snap Research, ACL 2024), Memwyre's cross-encoder engine scores 70.5% accuracy vs. 43.7% for flat vector RAG baselines, using 81% fewer tokens. (Deep dives available on our Memwyre vs. Mem0 and Memwyre vs. Zep pages.)

How Cross-Tool Sync Actually Works

• OpenClaw (plugin): hooks into session lifecycle events. On start → retrieval API. On stop → POST transcript to capture endpoint.
• Claude Code / Cursor / VS Code (MCP) / Claude Desktop: connect via Memwyre MCP server, same vault.
• Same API key = same vault. Memory captured in an OpenClaw agent run is available in Cursor at the next prompt.

How the Memwyre OpenClaw Plugin Works

① SessionStart — Context Injection

When an OpenClaw agent session starts, the plugin fires before the first prompt. It reads your working directory, queries the Memwyre retrieval engine for past memories, and injects them into the agent's system prompt.

<memwyre-context>
## Past Memories for my-project
- Database uses PostgreSQL 15 with pgvector
- Auth flow: JWT + refresh tokens in httpOnly cookies
- Fixed: race condition in worker queue (use Redis lock)
</memwyre-context>

② Stop — Session Capture

When the agent run completes or goes idle, the plugin reads the session transcript (from event messages, session file, or ~/.openclaw/agents/main/sessions/ JSONL logs), sends it to Memwyre's background worker for extraction.

③ On-Demand Tools

Unlike the Claude Code plugin, OpenClaw's integration also provides two MCP tools:

• save_memory(text, tags): Manually save a note/decision during an agent run.
• search_memwyre(query, limit): Semantic search across your vault mid-session.

Troubleshooting & Edge Cases

• Misclassified memory: view/edit/delete via dashboard or API (DELETE /api/v1/memories/:id).
• Stale facts: Ebbinghaus decay auto-deprioritizes.
• Deduplication: extraction model handles near-duplicates.
• Tool profile requirement: OpenClaw's tool profile must be set to full or coding — the plugin is disabled under standard or minimal profiles.

Install in 60 Seconds

The Memwyre OpenClaw plugin installs directly via the OpenClaw CLI or JSON config:

1. 1. Install the plugin package:

   openclaw plugins install @memwyre/openclaw-plugin

2. 2. Configure your API key:

   Add the plugin entry to your ~/.openclaw/config.json settings file:

   {
     "plugins": {
       "entries": {
         "@memwyre/openclaw-plugin": {
           "enabled": true,
           "config": {
             "apiKey": "bv_sk_your_api_key_here",
             "hostUrl": "https://api.memwyre.tech"
           }
         }
       }
     }
   }

   Alternatively, export MEMWYRE_API_KEY="bv_sk_..." in your shell environment.

3. 3. Set OpenClaw Agent Tool Profile:

   Ensure OpenClaw is running with full or coding tool profile. Custom memory tools are bypassed under standard or minimal profiles.

4. 4. Run your agent:

   The plugin handles past context injection on agent start and saves session insights on agent exit.

What OpenClaw Remembers With Memwyre

• 🧠 Architecture Decisions: Database choices, API patterns, deployment configs, and framework decisions.
• 🐛 Debugging Solutions: Race conditions fixed, environment variable gotchas, and edge cases.
• 🔗 Entity Relationships: Connections enabling multi-hop reasoning.
• ✂️ Dynamic Pruning: Filters out noise to keep memory lean.

Benchmark: Why Retrieval Quality Matters

The improvement comes from architectural drivers like dynamic context pruning, two-stage cross-encoder re-ranking, and Ebbinghaus logarithmic recency decay. View the full LoCoMo-10 benchmark results →

Real-World Workflow: Multi-Agent Codebase Migration

Scenario: Using OpenClaw agents to handle a microservices migration. Agent #1 analyzes service boundaries on Monday. Agent #2 generates API contracts on Tuesday. Agent #3 writes integration tests on Wednesday. Without persistent memory, Agent #3 doesn't know the decisions Agent #1 made. With Memwyre, all three agents share the same vault.

Token Costs, Noise, & Security

Noise Filtration

Not every CLI error needs to be remembered. Memwyre's backend differentiates between ephemeral noise and structural knowledge.

Token Efficiency

By summarizing past sessions, the plugin injects a concise <memwyre-context> block (~1,500 token injections) vs megabytes of raw logs.

Enterprise Security

Zero-retention policies and self-hosting options ensure your code stays private.

License & Requirements

• License: Apache-2.0
• System requirements: Node.js 18+, any OS, no local DB needed
• Self-hosting: Docker deployment available
• Tool Profile: OpenClaw tool profile must be full or coding

Why Not Just Use Manual Context Files?

Manual context files are fine for static rules. But they face real limitations: manual maintenance, flat storage, and they are tool-locked.

FAQ