🧠 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

  :::bash

  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:

:::javascript
// 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

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

Try Semantic Memory

:::bash
# 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:

:::bash
# 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:

:::bash
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

:::sql
-- 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:

:::bash
# 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

• Memory System Guide - Complete ReasoningBank usage
• Release Notes - Detailed technical documentation
• Changelog - Full version history

🆙 Upgrade

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

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

🤝 Community

• GitHub: Report issues or contribute
• Discord: Join the Agentics Foundation

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