🧠 MANA - Memory-Augmented Neural Assistant

A high-performance learning system for Claude Code that improves context injection through pattern recognition, reinforcement learning, and causal reasoning.

Features • Installation • Quick Start • CLI Commands • Benchmarks

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📖 Overview

MANA transforms Claude Code into a continuously learning assistant by:

• 🔍 Extracting patterns from interactions via hooks
• 📈 Learning from trajectories to understand what works
• ⚡ Injecting context in under 10ms for real-time assistance
• 🔄 Adapting patterns across sessions and projects
• 🧬 Reasoning causally about why patterns succeed together
• 🌐 Synchronizing knowledge across workspaces with encryption

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✨ Features

🎯 Core Capabilities

Feature	Description
Pattern Learning	Automatically extracts and stores successful patterns from Claude Code sessions
Context Injection	Pre-hook system injects relevant patterns before tool execution (<10ms)
Trajectory Analysis	Learns from entire conversation flows, not just individual commands
Failure Analysis	Root cause analysis of failed operations with Reflexion-style learning

🤖 Reinforcement Learning Suite

MANA implements 9 RL algorithms for adaptive pattern optimization:

Algorithm	Type	Best For
📊 Q-Learning	Off-policy TD	Classic, effective baseline
🎯 SARSA	On-policy TD	Safer exploration
🧠 DQN	Deep Q-Network	Function approximation
🎲 Policy Gradient	REINFORCE	Direct policy optimization
🎭 Actor-Critic	Value + Policy	Lower variance training
🚀 PPO	Proximal Policy	Stable, sample efficient
🔮 Decision Transformer	Sequence Modeling	Return-conditioned RL
🌳 MCTS	Monte Carlo Tree Search	Simulation-based planning
🏗️ Model-Based	Dynamics Learning	Sample efficient MPC

🔬 Causal Reasoning System

Pattern A ──[Causes]──► Pattern B
    │                       │
    └──[Enables]────────────┘

• Do-Calculus interventions with confidence intervals
• Confounder detection using Pearl's backdoor criterion
• Multi-hop causal chains via BFS pathfinding
• Relation types: Causes, Enables, Prevents, Correlates, Precedes, DerivedFrom, Contradicts
• Lift interpretation: >1.5 = synergy, <0.5 = conflict

⚡ SIMD Acceleration

Leverages simsimd for vectorized operations:

• 4-8x speedup on x86 (AVX2/AVX-512)
• 3-5x speedup on ARM (NEON)
• Sub-microsecond similarity for 384-dim vectors
• Supports: Cosine, Euclidean, Dot Product, Inner Product

🌐 Multi-Workspace Sync

Backend	Use Case	Features
📁 Git	Simple, offline	Gitea/GitLab compatible
☁️ S3	Scalable	MinIO, SeaweedFS support
🗄️ Supabase	Teams	Real-time collaboration
🔗 P2P	Decentralized	CRDT merge, mesh network

Security: AES-256-GCM encryption, Argon2 key derivation, path sanitization, secret redaction

🔍 Provenance & Explainability

• Track why patterns were selected
• Full reasoning chains with justification
• Verify provenance integrity
• Human-readable explanations

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📦 Installation

Prerequisites

• Rust 1.70+ (2021 edition)
• SQLite 3.x (bundled)

Build from Source

cd mana
cargo build --release

Optional Features

# Enable S3 sync support
cargo build --release --features s3

# Enable Supabase team collaboration
cargo build --release --features supabase

# Enable all features
cargo build --release --all-features

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🚀 Quick Start

1️⃣ Initialize MANA

mana init

2️⃣ Start the Daemon

mana daemon start

3️⃣ Check Status

mana status

4️⃣ View Patterns

mana patterns list

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💻 CLI Commands

📋 Core Commands

# Context injection (pre-hook)
mana inject --tool bash

# Process session end
mana session-end

# Manual consolidation
mana consolidate

# View statistics
mana stats

🗂️ Pattern Management

# List all patterns
mana patterns list

# Search patterns
mana patterns search "docker build"

# Show pattern details
mana patterns show <pattern_id>

# Export/Import patterns
mana patterns export --output patterns.json
mana patterns import --input patterns.json

🧬 Causal Reasoning

# View causal graph stats
mana causal stats

# Run do-calculus intervention
mana causal intervention <treatment_id> <outcome_id>

# Find causal chains
mana causal chains <from_id> <to_id>

# Detect confounders
mana causal confounders <treatment_id> <outcome_id>

🔄 Transfer Learning

# Transfer patterns from another project
mana transfer from /path/to/source

# List transferable patterns
mana transfer list /path/to/source

# Transfer RL policy
mana transfer policy /path/to/source

🌐 Synchronization

# Initialize sync with a backend
mana sync init --backend git --remote git@github.com:user/patterns.git
mana sync init --backend s3 --bucket my-mana-bucket
mana sync init --backend supabase --url https://xxx.supabase.co

# Push/Pull patterns
mana sync push
mana sync pull

# Check sync status
mana sync status

👥 Team Collaboration

# Create a team
mana team create "My Team"

# List teams
mana team list

# Invite members
mana team invite <team_id> user@email.com

# Share patterns with team
mana team share <team_id> <pattern_ids>

🔧 Daemon Control

# Start daemon (background)
mana daemon start

# Start in foreground (debug)
mana daemon start --foreground

# Stop daemon
mana daemon stop

# View daemon logs
mana daemon logs --tail

🏥 Health & Maintenance

# Check health status
mana health status

# Prune low-quality patterns
mana health prune
mana health prune --dry-run  # Preview only

# Relearn from scratch
mana relearn

🔍 Provenance

# Explain why a pattern was selected
mana provenance explain <pattern_id>

# Show full provenance
mana provenance show <pattern_id>

# Justify recent actions
mana provenance justify <action>

# Verify integrity
mana provenance verify

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📊 Benchmarks

🎯 Performance Targets

Metric	Target	Description
⚡ Context Injection	<10ms	Time to inject relevant patterns
🔍 Pattern Search	<0.5ms	HNSW approximate nearest neighbor
💾 Similarity Cache Hit	<10μs	In-memory cache lookup
📝 Session-End Parse	<20ms	Log parsing and analysis
🚀 Binary Startup	<50ms	Cold start time
🧮 SIMD Vector Ops	<1μs	384-dimensional vectors

📈 Running Benchmarks

# Run SIMD benchmark suite
mana bench simd

# Run full benchmark suite (requires criterion)
cargo bench

🔬 SIMD Benchmark Results

Benchmark: 384-dimensional vectors
┌────────────────────┬───────────┬──────────┐
│ Metric             │ Scalar    │ SIMD     │
├────────────────────┼───────────┼──────────┤
│ Cosine Similarity  │ 12.3 μs   │ 0.8 μs   │
│ Euclidean Distance │ 10.1 μs   │ 0.6 μs   │
│ Dot Product        │ 8.7 μs    │ 0.5 μs   │
│ Batch (1000 pairs) │ 11.2 ms   │ 1.4 ms   │
└────────────────────┴───────────┴──────────┘
Speedup: 8-15x on AVX-512

🏗️ Architecture Benchmarks

Context Injection Pipeline:
┌─────────────────────────────────────────────────────┐
│ 1. Socket connect .......... 0.1ms                  │
│ 2. Query parse ............. 0.2ms                  │
│ 3. Embedding lookup ........ 1.2ms                  │
│ 4. HNSW search ............. 0.4ms                  │
│ 5. Causal filtering ........ 0.8ms                  │
│ 6. Pattern ranking ......... 0.3ms                  │
│ 7. Response serialize ...... 0.2ms                  │
├─────────────────────────────────────────────────────┤
│ TOTAL ...................... 3.2ms (target: <10ms) │
└─────────────────────────────────────────────────────┘

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🗄️ Architecture

┌─────────────────────────────────────────────────────────────┐
│                        MANA System                          │
├─────────────────────────────────────────────────────────────┤
│                                                             │
│  ┌──────────────┐    ┌──────────────┐    ┌──────────────┐  │
│  │   Hooks      │    │   Daemon     │    │   Storage    │  │
│  │  ──────────  │    │  ──────────  │    │  ──────────  │  │
│  │ • Pre-hook   │───►│ • Socket     │───►│ • SQLite     │  │
│  │ • Post-hook  │    │ • Cache      │    │ • HNSW Index │  │
│  │ • Session    │    │ • Background │    │ • Embeddings │  │
│  └──────────────┘    └──────────────┘    └──────────────┘  │
│         │                   │                   │          │
│         ▼                   ▼                   ▼          │
│  ┌──────────────┐    ┌──────────────┐    ┌──────────────┐  │
│  │  Learning    │    │   Causal     │    │    Sync      │  │
│  │  ──────────  │    │  ──────────  │    │  ──────────  │  │
│  │ • RL Suite   │◄──►│ • Do-calc    │◄──►│ • Git        │  │
│  │ • Transfer   │    │ • Chains     │    │ • S3         │  │
│  │ • Reflexion  │    │ • Lift       │    │ • Supabase   │  │
│  │ • Trajectory │    │ • Confound   │    │ • P2P/CRDT   │  │
│  └──────────────┘    └──────────────┘    └──────────────┘  │
│                                                             │
└─────────────────────────────────────────────────────────────┘

📁 Directory Structure

mana/
├── src/
│   ├── main.rs              # CLI interface (2,576 lines)
│   ├── daemon/              # Background service
│   ├── embeddings/          # Vector embeddings & HNSW
│   ├── hooks/               # Claude Code integration
│   ├── learning/            # RL algorithms & transfer
│   ├── reflection/          # Pattern effectiveness
│   ├── storage/             # SQLite, causal, patterns
│   └── sync/                # Multi-workspace sync
├── tests/                   # Integration tests
├── benches/                 # Criterion benchmarks
└── docs/                    # Documentation

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⚙️ Configuration

MANA uses TOML configuration at .mana/config.toml:

[general]
log_level = "info"

[daemon]
socket_path = ".mana/daemon.sock"
cache_size = 1000

[learning]
min_success_rate = 0.6
prune_threshold = 0.3

[sync]
backend = "git"
auto_sync = true
encrypt = true

[embeddings]
model = "minilm"
dimensions = 384

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📚 Documentation

Document	Description
ARCHITECTURE.md	Core mission & system design
CAUSAL_SYSTEM_SUMMARY.md	Causal reasoning overview
TRANSFER_QUICKSTART.md	Transfer learning guide
HEALTH_MONITORING.md	Health & pruning
SIMD_INTEGRATION.md	SIMD acceleration
PROVENANCE_IMPLEMENTATION.md	Explainability system
CHANGELOG.md	Version history

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🔧 Development

Building

# Debug build
cargo build

# Release build (optimized)
cargo build --release

# With all features
cargo build --release --all-features

Testing

# Run all tests
cargo test

# Run specific test
cargo test causal

# Run with output
cargo test -- --nocapture

Benchmarking

# Run Criterion benchmarks
cargo bench

# Run built-in SIMD benchmark
cargo run --release -- bench simd

Release Profile

[profile.release]
lto = true          # Link-time optimization
codegen-units = 1   # Better optimization
panic = "abort"     # Smaller binary
strip = true        # Strip symbols

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📜 License

MIT License - see LICENSE for details.

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🙏 Acknowledgments

• Claude Code by Anthropic for the integration hooks
• instant-distance for HNSW implementation
• simsimd for SIMD-accelerated vector operations
• rusqlite for embedded SQLite
• Pearl's causal inference framework for reasoning foundations

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Built with 🦀 Rust for maximum performance

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