@awareness.market/openclaw-memory

@awareness.market/openclaw-memory

OpenClaw memory plugin backed by Awareness Memory Cloud.

Online docs: https://awareness.market/docs?doc=openclaw

Benchmark: LongMemEval (ICLR 2025)

Awareness Memory achieves 95.6% Recall@5 on LongMemEval (ICLR 2025) — 500 human-curated questions, zero LLM calls, hybrid BM25+Vector retrieval. Full results →

╔══════════════════════════════════════════════════════════════╗
║   Awareness Memory — LongMemEval Benchmark Results           ║
║                                                              ║
║   Recall@1    77.6%       Recall@5    95.6%  ◀ PRIMARY       ║
║   Recall@3    91.8%       Recall@10   97.4%                  ║
║                                                              ║
║   Method:     Hybrid RRF (BM25 + Vector)                     ║
║   LLM Calls:  0       Hardware:  M1 8GB, 14 min             ║
╚══════════════════════════════════════════════════════════════╝

┌─────────────────────────────────────────────────────────────┐
│          Long-Term Memory Retrieval — R@5 Leaderboard       │
├─────────────────────────────────┬───────────┬───────────────┤
│  System                         │  R@5      │  Note         │
├─────────────────────────────────┼───────────┼───────────────┤
│  MemPalace (ChromaDB raw)       │  96.6%    │  R@5 only *   │
│  ★ Awareness Memory (Hybrid)    │  95.6%    │  Hybrid RRF   │
│  OMEGA                          │  95.4%    │  QA Accuracy  │
│  Supermemory                    │  81.6%    │  QA Accuracy  │
│  Zep / Graphiti                 │  71.2%    │  QA Accuracy  │
│  GPT-4o (full context)          │  60.6%    │  QA Accuracy  │
├─────────────────────────────────┴───────────┴───────────────┤
│  * MemPalace 96.6% is R@5 only, not QA Accuracy.           │
└─────────────────────────────────────────────────────────────┘

Installation

Plugin (full integration):

openclaw plugins install @awareness.market/openclaw-memory

Or Skill (via ClawHub):

npx clawhub@latest install awareness-memory

For local development:

openclaw plugins install -l ./openclaw

Configuration

Edit ~/.openclaw/openclaw.json:

{
  "plugins": {
    "slots": {
      "memory": "openclaw-memory"
    },
    "entries": {
      "openclaw-memory": {
        "enabled": true,
        "config": {
          "apiKey": "aw_your-api-key",
          "baseUrl": "https://awareness.market/api/v1",
          "memoryId": "your-memory-id",
          "agentRole": "builder_agent",
          "autoRecall": true,
          "autoCapture": true,
          "recallLimit": 8
        }
      }
    }
  }
}

For cloud or bot deployments, you can skip device-auth and config file edits by pre-setting environment variables on the server:

export AWARENESS_API_KEY="aw_your-api-key"
export AWARENESS_MEMORY_ID="your-memory-id"
export AWARENESS_BASE_URL="https://awareness.market/api/v1"

When these variables are present, the plugin will use them as the highest-priority cloud configuration.

Available Tools

Tool	Description
__awareness_workflow__	Workflow reference that stays visible in the tool list
awareness_init	Load cross-session project memory and context
awareness_get_agent_prompt	Fetch full activation prompt for a specific agent role (sub-agent spawning)
awareness_recall	Semantic + keyword hybrid recall from persistent memory
awareness_lookup	Structured data: tasks, knowledge, risks, timeline
awareness_record	Write events, batch save, ingest, update tasks

Auto Features

Auto Recall

When autoRecall is enabled, the plugin loads context and relevant recall results before the agent starts.

Auto Capture

When autoCapture is enabled, the plugin stores a concise run summary after the agent finishes.

Config Options

Option	Type	Default	Description
apiKey	string	required	Awareness API key
memoryId	string	required	Target memory UUID
baseUrl	string	https://awareness.market/api/v1	Awareness API base URL
agentRole	string	builder_agent	Agent role for scoped recall
autoRecall	boolean	true	Auto-load memory context before each run
autoCapture	boolean	true	Auto-store a conversation summary after each run
recallLimit	integer	8	Max results for auto-recall

Perception (Record-Time Signals)

When the skill's awareness_record tool writes to memory, the response may include a perception array -- automatic signals the system surfaces without you asking. These are computed from pure DB queries (no LLM calls), adding less than 50ms of latency.

Signal types:

Type	Description
contradiction	New content conflicts with an existing knowledge card
resonance	Similar past experience found in memory
pattern	Recurring theme detected (e.g., same category appearing often)
staleness	A related knowledge card hasn't been updated in a long time
related_decision	A past decision is relevant to what you just recorded

// Inside the skill's record.js script:
const result = await awareness_record({
  content: "Decided to use RS256 for JWT signing",
  insights: {
    knowledge_cards: [{ title: "JWT signing", category: "decision", summary: "Use RS256" }]
  }
});
if (result.perception) {
  result.perception.forEach(s => console.log(`[${s.type}] ${s.message}`));
  // [pattern] This is the 4th 'decision' card -- recurring theme
  // [resonance] Similar past experience: "JWT auth migration"
}

Verification

openclaw plugins list   # if installed as plugin
openclaw skills list    # if installed as skill

You should see openclaw-memory or awareness-memory loaded.

What makes Awareness different

Most memory systems pick one extraction strategy. Awareness combines them:

• Hybrid retrieval by default — BM25 full-text + vector cosine + knowledge-graph 1-hop expansion, fused with Reciprocal Rank Fusion. 95.6% R@5 on LongMemEval, zero LLM calls on the retrieval side.
• Salience-aware extraction (v0.6.12+) — the agent's LLM self-scores every card on novelty / durability / specificity; cards below 0.4 on novelty or durability are dropped server-side. Framework metadata (Sender (untrusted metadata), turn_brief, [Operational context ...]) is filtered before extraction runs, so raw log turns never leak into your knowledge base.
• Project isolation — X-Awareness-Project-Dir header scopes memory per project. Agents working on different projects from the same OpenClaw instance don't cross-contaminate.
• Learning over time — Ebbinghaus-style card decay, skill crystallization from repeated patterns (F-032 / F-034), workspace graph self-prune to keep index.db bounded.
• Zero-LLM backend — all extraction runs on the agent's LLM. The backend is a coordinator + storage layer; no inference costs pass through to you.
• One memory, many clients — same daemon reachable via Claude Code skills, OpenClaw plugin, npm / pip / ClawHub, and a plain MCP server. Install any one surface and the rest just work against the same memory.

See docs/analysis/MEMPALACE_COMPARISON_2026-04-17.md for the honest side-by-side against MemPalace (96.6% R@5 via raw verbatim storage) — what we'd adopt from their approach and what we keep from ours.

License

Apache-2.0