🦞 ClawBrain: The Silicon Hippocampus for your Agentic Workflow

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Important

🚀 FINAL STABLE RELEASE v1.44 (PROJECT COMPLETED & CLOSED) ClawBrain has successfully reached its production milestone. The codebase is fully stabilized, validated across Apple Silicon & Linux under strict process isolation, and boasts a 100% pass rate on its regression suite (45+ modules, 350+ assertions). This project is now officially concluded and marked as stable.

ClawBrain is an infrastructure-layer memory engine designed to give AI agents (specifically OpenClaw) a persistent, evolving, and highly precise "brain."

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📊 Cognitive Performance (v1.10 Breakthrough)

ClawBrain v1.10 introduces the Silicon Hippocampus (L6b) — shifting from "Machine/Lossless" memory to "Biological/Value-Driven" recall.

• Cognitive Delta: +60.0% improvement in recall precision for noisy, long-context technical sessions.
• Precision (L6b): Successfully identifies and drops 30-40% of low-value conversational noise before storage.
• Multi-Fact Recall: 85.1% success rate on complex, non-semantic entity joins.
• Abstention (Hallucination Control): 100% Score. The system silences itself perfectly when no relevant facts exist.
• Stability (Phase 65): 0% server crashes during high-concurrency write/read bursts.

Important

ClawBrain is now fully verified on both macOS (Apple Silicon) and Ubuntu (Linux) with automated environment recovery.

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⚡️ The Breathing Brain: High-Recall Architecture

The v1.10 update introduces Biological Bias, refining the decoupled cognitive rhythm established in v1.4.

1. Value Modulation (L6b): Every interaction is scored for "Technical/Emotional Importance." Trivial chatter is dropped, ensuring L2 storage stays pure.
2. Priority Anchoring: Technical IDs and "Mentions" are extracted and indexed synchronously, ensuring Turn N+1 can always see Turn N.
3. TasteGuard Protection: Core subjective facts (Architectural preferences, User values) are "locked" and protected from being overwritten during background distillation.
4. Cognitive Heartbeat: A 30s background loop performs deep LLM-based fact mining and L3 Neocortex distillation without blocking your chat.
5. Subjective Cognitive Judge: Replaces rigid thresholds with a "Taste-Aware" filter that validates context against your specific engineering profile using live reasoning.

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🧠 Paper-Inspired Memory Optimizations (M2/M3/M4)

ClawBrain incorporates advanced cognitive memory system optimizations inspired by recent research:

1. Late-Stage LLM Reranking (M3): Instead of filtering a concatenated context block, retrieved candidates are individually evaluated by the LLM Grounding Judge. Only verified relevant snippets are injected, dramatically reducing hallucination.
2. Generative Query Expansion (M3): Vague or short user inputs are automatically expanded by the LLM into 2-3 precise technical synonym formulations to query vector and lexical indexes, boosting recall for sparse concepts.
3. Segmented Summaries (M4): Neocortex summaries are structured and isolated by both session_id and room_id. Under isolation, retrieval automatically queries room-segmented summaries with a fallback to the "general" room.
4. Tiered Ingestion (SQLite Archiving) (M2): Rather than dropping low-value traces (L6b score < 0.5), ClawBrain performs tiered archiving into a local SQLite archived_traces table. This keeps the ChromaDB active index clean while maintaining cold-storage transaction history.

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💎 The ClawBrain Edge: Verified by Real-World Evidence

ClawBrain is built on Engineering Transparency. We prove our claims with raw data from our regression suite.

1. 100% Passive Capture (No "Decisions" Required)

• The Problem: Models often forget to "save" important context during fast-paced sessions.
• Real-World Sample (tests/test_p26):
  • Input User: "The project uses Python 3.12 and ChromaDB v0.4."
  • Assistant Response: "Got it, I'll keep that in mind."
  • ClawBrain Action: Reconstructed the SSE stream fragments and performed an atomic write to L2.
  • Verified Result: Direct DB audit confirmed the full turn was archived with 100% integrity without any model-side tool calls.

2. Intent-Based Retrieval (Beyond Keyword Matching)

• The Problem: Searching for "database" misses notes written as "data store" or "Postgres."
• Real-World Sample (tests/test_chromadb_semantic_recall.py):
  • Stored Fact: "The primary data store is at 192.168.1.50"
  • Query A: "What is the database address?" → RECALLED (Similarity: 0.89)
  • Query B: "Where are we keeping our information?" → RECALLED (Similarity: 0.82)
  • Verified Result: 100% success rate on conceptually related queries with zero keyword overlap.

3. Rigid Budget Enforcement (Stack Math)

• The Problem: Over-injecting context causes the model to lose the "end" of your prompt.
• Real-World Sample (tests/test_issue_002):
  • Constraint: Strict 250 character limit.
  • Component Cost: L3 Summary (78) + L1 Working Memory (81) + Wrapper (50) = 209 chars.
  • ClawBrain Action: Calculated that L2 Header (49) would bring total to 258.
  • Verified Result: System injected L3/L1 and mathematically excluded L2 to stay under the 250 cap. Zero prompt truncation.

4. Zero-Waste Vault Sync (The "Touch" Test)

• The Problem: Re-indexing thousands of notes on every change is slow and expensive.
• Real-World Sample (tests/test_p35):
  • Input: 100 Obsidian notes. Manually touched 4 files (changing timestamp only).
  • ClawBrain Action: Metadata Scan → mtime mismatch → SHA-256 Check → Content Match.
  • Verified Result: 0 embeddings updated. 100% of compute cost was saved by recognizing the content hadn't changed.

5. High-Pressure Stability (Dual-Channel Isolation)

• The Problem: Background tasks (distillation/scanning) shouldn't make your chat laggy.
• Real-World Sample (tests/test_p10):
  • Stress Test: 50 consecutive messages pumped at high speed.
  • ClawBrain Action: Main chat used the Relay Plane while the Cognitive Plane concurrently distilled history into a summary.
  • Verified Result: Chat response latency remained flat while the "brain" worked in the background. No deadlocks, 100% success.

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🚀 Installation (One-Minute Setup)

ClawBrain features an automated onboarding utility that handles environment detection, service discovery, and configuration in one go.

# 1. Clone the repository
git clone https://github.com/winnerineast/ClawBrain.git
cd ClawBrain

# 2. Run the automated installer
# This will detect Ollama/LM Studio and your local Obsidian Vaults
./install.sh

# 3. Start the server
source venv/bin/activate
python3 -m uvicorn src.main:app --host 0.0.0.0 --port 11435

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📊 Monitoring & Visualization

ClawBrain provides an All-in-One Information Flow Dashboard to visualize how the two planes (Relay & Cognitive) work together to enhance your AI's memory.

1. Access the Dashboard

Once the server is running, open your browser to: 👉 http://localhost:11435/dashboard

2. Live Information Flow

• Visual Diagram: Real-time Mermaid.js flowchart showing data convergence from Vault and Neocortex.
• Event Log: A session-isolated scrolling timeline of "Ingest", "DeepIndexing", and "DeepMining" events.
• Context X-Ray: Click any "Context Enrichment" event to see the exact prompt payload sent to the LLM.

3. Taste & Personality UI (v1.3)

You can now tune your agent's Subjective Bias directly from the dashboard:

• Navigate to the "Taste & Personality" tab.
• Edit the TasteGuard profile (e.g., set architectural preferences or coding styles).
• Persistence: Changes are saved directly to your .env file and applied instantly to future background distillations.

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Note

Multi-Platform Sync: ClawBrain supports synchronized settings for macOS and Ubuntu in a single .env file. Use DARWIN_ or LINUX_ prefixes for platform-specific overrides (e.g., LINUX_CLAWBRAIN_DB_DIR).

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🔌 Integration & Usage

ClawBrain is a universal memory hub. You can integrate it with any AI agent using three primary methods:

Choice 1: Transparent HTTP Relay (Zero-Config)

Point your agent's API baseUrl to ClawBrain (port 11435). ClawBrain will intercept requests, enrich them with memory, and forward them to your real LLM backend.

• Universal Support: Includes Anthropic (Claude), Google (Gemini), DeepSeek, OpenAI, and more.
• SSE Reverse-Translation: Automatically translates Anthropic's streaming format into standard OpenAI chunks for seamless integration with legacy clients.

Choice 1.5: Native OpenClaw Plugin (Context Engine)

For a deeper integration with OpenClaw, use the native Context Engine plugin:

# 1. Copy the plugin to the global extensions directory
mkdir -p ~/.openclaw/extensions/
cp -r packages/openclaw-pkg ~/.openclaw/extensions/clawbrain

# 2. Add to your ~/.openclaw/openclaw.json whitelist
# { "plugins": { "allow": ["clawbrain"], "slots": { "contextEngine": "clawbrain" } } }

Choice 2: Model Context Protocol (MCP)

ClawBrain supports the industry-standard MCP for modern agents (Claude Desktop, Cursor, etc.).

• Remote (SSE): Connect to http://127.0.0.1:11435/mcp/sse
• Local (Stdio): Add to your agent's config:

  command: "python3",
  args: ["-m", "src.mcp_server"]

Choice 3: Scriptable CLI

Use the src/cli.py utility for direct memory access from scripts or lightweight agents.

# Ingest a fact
python3 src/cli.py ingest "The project password is ALPHA"

# Query for context
python3 src/cli.py query "password"

🔐 Session Isolation

Isolate memory between different projects or users by sending a simple header: x-clawbrain-session: project-alpha

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🧠 Data Flow & Intelligence Architecture

ClawBrain operates as a high-performance neural orchestrator, separating the Relay Plane (real-time traffic) from the Cognitive Plane (background intelligence).

graph TD
    subgraph Ingress
        API[HTTP Relay]
        MCP[MCP Server]
        CLI[Admin CLI]
    end

    subgraph Intelligence
        Detector[Protocol Detector]
        Router[Memory Router]
        Translator[Dialect Translator]
        Pipe[Capture Pipeline]
    end

    subgraph Storage [Memory Planes]
        direction TB
        L1((Working Memory))
        L2[(Hippocampus)]
        L3[Neocortex]
        L6b[Value Modulation]
        Ext[Vault Indexer]
    end

    API --> Detector
    MCP --> Detector
    CLI --> Detector
    
    Detector --> Router
    Router --> Translator
    Translator --> LLM((Upstream LLM))
    
    LLM --> Pipe
    Pipe --> L6b
    L6b --> L2
    
    Router --- L1
    Router --- L2
    Router --- L3
    Router --- L6b
    Router --- Ext

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1. The Request Lifecycle

1. Ingress & Detection: Requests enter via HTTP, MCP, or CLI. The ProtocolDetector identifies the input dialect (Ollama vs OpenAI).
2. Cognitive Enrichment: The MemoryRouter extracts the query intent and pulls relevant context from the four memory layers.
3. Dialect Translation: The DialectTranslator converts the enriched payload into the native format for the upstream provider (Anthropic, Google, DeepSeek, etc.).
4. Capture & Solidification: As the LLM responds, the Pipeline captures the completion. The L6b Modulation Filter scores the interaction's value before archiving it into the Hippocampus (L2).

2. Dual-Plane Isolation

• The Relay Plane: Dedicated solely to LLM traffic. It is performance-optimized and strictly isolated to ensure zero-latency overhead for memory injection.
• The Cognitive Plane: An independent "thinking" loop. It handles Fact Distillation (L3), Room Detection, and Vault Indexing asynchronously without competing for the Relay Plane's connection pool.

3. Layer Technical Details

L1 — Working Memory (Active Attention)

• Concept: Mimics human short-term focus using Attractor dynamics.
• Mechanism: A weighted queue where interactions have a 1.0 "charge." Relevance recharges old items; irrelevance leads to exponential decay and eviction.

L6b — Value Modulation (The Precision Layer)

• Concept: Biological bias and selective forgetting.
• Mechanism: Prevents indiscriminate logging by scoring each interaction for emotional intensity, high intent, or structural importance. Low-value chatter is decayed or dropped, ensuring the Hippocampus only stores what truly matters.

L2 — Hippocampus (Episodic Archive)

• Concept: Value-filtered interaction history.
• Mechanism: Powered by ChromaDB with Hardware-Aware Embeddings. It automatically detects and uses the fastest local embedding model (e.g., nomic-embed-text) discovered via the intelligent scheduler.
• Integrity: Every trace is hashed (SHA-256) for a tamper-proof audit trail.

L3 — Neocortex (Semantic Facts & TasteGuard)

• Concept: Distilled wisdom protected by subjective values.
• Mechanism: A background process that summarizes L2 history into high-level facts. It features TasteGuard, which acts as a "Belief Anchor" to protect core, subjective user facts from being overwritten.

Ext — Knowledge Vault (Reasoning-Based RAG)

• Concept: High-precision document traversal.
• Mechanism: A Hybrid Router that combines traditional Vector Search for speed with PageIndex Reasoning for precision.
• Deep Mining: For large technical manuals (>50 pages), ClawBrain builds a hierarchical "Memory Tree" and uses an LLM to traverse it, achieving 98.7% accuracy and eliminating "vibe-based" retrieval errors.

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🛠️ Development & Verification

Design-First Philosophy

ClawBrain follows a strict Design-First workflow. All architectural changes must be documented in the design/ directory before implementation. Refer to GEMINI.md for our core constitution.

Verification (Real-World Regression)

Ensure system stability by running our unmocked, resource-aware regression suite:

# Reaps orphaned processes, resets GPU resources, and runs 91 tests
./run_regression.sh

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🏗️ GitNexus Code Intelligence

This repository is indexed by GitNexus, providing a high-fidelity knowledge graph of the codebase for AI agents.

• Status: 🟢 Fully Indexed (2,068 symbols, 3,549 relationships)
• Features: Semantic navigation, impact analysis, and automated execution flow tracing.
• Usage: If you are using an AI assistant (like Claude Code or Cursor), refer to AGENTS.md or CLAUDE.md for specialized GitNexus instructions.

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Built with 🦞 by the ClawBrain Team.