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v2.7.0-alpha.10 - ReasoningBank Memory & Advanced AI Capabilities 🧠

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@ruvnet ruvnet released this 13 Oct 22:22
v2.7.0-alpha.10
3cd2844

🧠 Claude-Flow v2.7.0-alpha.10: ReasoningBank & Advanced Memory

🌟 Major Features

✨ Agentic-Flow Integration (v1.5.13)

Claude-Flow now integrates agentic-flow@1.5.13 with a powerful Node.js backend that brings enterprise-grade reasoning and memory capabilities:

  • 🔄 Persistent Memory: All agent memories survive restarts via SQLite (.swarm/memory.db)
  • 🧠 ReasoningBank: Pattern-based reasoning system with semantic understanding
  • ⚡ Lightning Fast: 2-3ms query latency for semantic searches
  • 🔓 No API Keys Required: Hash-based embeddings work out-of-the-box

🔍 Semantic Search with MMR Ranking

Advanced search powered by Maximal Marginal Relevance with 4-factor scoring:

  • 40% Semantic Similarity - Find conceptually related memories
  • 20% Recency - Prioritize recent learnings
  • 30% Reliability - Trust proven patterns
  • 10% Diversity - Discover novel approaches
# Store and retrieve memories with semantic understanding
npx claude-flow@alpha memory store api_key "REST API configuration" \
  --namespace backend --reasoningbank

npx claude-flow@alpha memory query "API config" \
  --namespace backend --reasoningbank
# ✅ Found 3 results (semantic search) in 2ms

💾 Persistent Memory Architecture

Four specialized database tables power intelligent memory:

Table Purpose Size
patterns Core reasoning patterns ~50KB each
pattern_embeddings 1024-dim semantic vectors ~350KB each
task_trajectories Sequential reasoning steps Variable
pattern_links Causal relationships Minimal

🎯 Advanced Reasoning Capabilities

1. Task Trajectory Tracking

Records sequential reasoning steps for learning:

// Automatically captures agent reasoning flow
[Step 1] → Analyze requirements
[Step 2] → Design architecture  
[Step 3] → Implement solution
[Step 4] → Validate results

2. Pattern Linking & Causal Reasoning

Five relationship types for knowledge graphs:

  • causes - X leads to Y
  • requires - X needs Y first
  • conflicts - X incompatible with Y
  • enhances - X improves Y
  • alternative - X substitutes for Y

3. Cognitive Diversity Patterns

Six reasoning strategies for complex problems:

  • Convergent - Focus on single best solution
  • Divergent - Explore multiple possibilities
  • Lateral - Creative indirect approaches
  • Systems - Holistic interconnected thinking
  • Critical - Evaluate and challenge assumptions
  • Adaptive - Learn and evolve strategies

4. Bayesian Confidence Learning

Patterns improve over time based on outcomes:

  • Initial confidence: 0.5
  • Success: +10-20% confidence
  • Failure: -10-15% confidence
  • Automatic reliability scoring

⚡ Performance Characteristics

Metric Value Notes
Query Latency 2-3ms Semantic search with hash embeddings
Hash Embedding 1ms Deterministic 1024-dim vectors
OpenAI Embedding 50-100ms Optional enhanced accuracy
Pattern Storage 5-10ms Includes embedding generation
Storage Size ~400KB Per pattern with embedding

🚀 Quick Start

Install Latest Alpha

npx claude-flow@alpha init --force
npx claude-flow@alpha --version
# v2.7.0-alpha.10

Try Semantic Memory

# Store knowledge
npx claude-flow@alpha memory store api_design \
  "Use RESTful patterns with JWT auth" \
  --namespace backend --reasoningbank

# Query semantically
npx claude-flow@alpha memory query "authentication patterns" \
  --namespace backend --reasoningbank
# ✅ Found 1 result: api_design (score: 0.87)

# Check system status
npx claude-flow@alpha memory status --reasoningbank

Works Without API Keys!

Hash-based embeddings provide semantic search with zero configuration:

# No OPENAI_API_KEY needed!
npx claude-flow@alpha memory query "config" --reasoningbank
# ✅ Uses deterministic 1024-dim hash embeddings

Optional: Enhanced Embeddings

For even better semantic accuracy, add OpenAI API key:

export OPENAI_API_KEY=$YOUR_API_KEY
# Automatically uses text-embedding-3-small (1536 dims)

🔧 Technical Improvements

Node.js Backend Replaces WASM

  • Better Performance: Native SQLite with better-sqlite3
  • Simplified Deployment: No WASM module loading complexity
  • Enhanced Debugging: Standard Node.js stack traces
  • Broader Compatibility: Works in all Node.js environments

Database Schema

-- Core pattern storage
CREATE TABLE patterns (
  id TEXT PRIMARY KEY,
  title TEXT,
  content TEXT,
  namespace TEXT,
  components JSON,
  created_at DATETIME
);

-- Semantic embeddings
CREATE TABLE pattern_embeddings (
  pattern_id TEXT PRIMARY KEY,
  embedding BLOB,  -- 1024-dim float32 array
  embedding_type TEXT
);

-- Sequential reasoning
CREATE TABLE task_trajectories (
  id TEXT PRIMARY KEY,
  pattern_id TEXT,
  step_number INTEGER,
  action TEXT,
  result TEXT
);

-- Knowledge graph links
CREATE TABLE pattern_links (
  source_pattern_id TEXT,
  target_pattern_id TEXT,
  link_type TEXT,
  strength REAL
);

📊 Integration with Claude-Flow Agents

All 64+ agents now benefit from persistent memory:

# Agents automatically coordinate via ReasoningBank
npx claude-flow@alpha swarm init --topology mesh
npx claude-flow@alpha swarm spawn researcher "analyze API patterns"
# Researcher stores findings in ReasoningBank

npx claude-flow@alpha swarm spawn coder "implement REST API"
# Coder retrieves researcher's findings automatically

npx claude-flow@alpha swarm status
# All agent learnings persist across sessions

🐛 Bug Fixes

While this release focuses on new capabilities, it also includes critical fixes:

  • Semantic Search Results: Fixed parameter mapping and result structure
  • Namespace Filtering: Corrected domain vs namespace parameter handling
  • Compiled Code Sync: Updated dist-cjs/ with latest Node.js backend

📚 Documentation

🆙 Upgrade

# NPX (recommended)
npx claude-flow@alpha init --force

# Or global install
npm install -g claude-flow@alpha
claude-flow --version

🤝 Community

Built with ❤️ by rUv | Powered by ReasoningBank AI

v2.7.0-alpha.10 - Persistent Memory & Advanced Reasoning

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