attune-ai 2.0.1

AI collaboration framework with real LLM agent execution, AskUserQuestion tool integration, Socratic agent generation, progressive tier escalation (70-85% cost savings), meta-orchestration, dynamic agent composition (10 patterns including Anthropic-inspired), intelligent caching (85% hit rate), semantic workflow discovery, visual workflow editor, MCP integration for Claude Code, and multi-agent orchestration.

Attune AI

AI-powered developer workflows with cost optimization and pattern learning.

Run code review, debugging, testing, and release workflows from your terminal or Claude Code. Smart tier routing saves 34-86% on LLM costs.

pip install attune-ai[developer]

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🎯 Transitioning to Claude-Native Architecture

Attune AI is evolving to focus exclusively on Anthropic/Claude to unlock features impossible with multi-provider abstraction:

• 📦 Prompt Caching: 90% cost reduction on repeated prompts
• 📖 Flexible Context: 200K via subscription for most tasks, up to 1M via API for large codebases
• 🧠 Extended Thinking: See Claude's internal reasoning process
• 🔧 Advanced Tool Use: Optimized for agentic workflows

Timeline:

• ✅ v4.8.0 (Jan 2026): Deprecation warnings for OpenAI/Google/Ollama providers
• ✅ v5.0.0 (Jan 26, 2026): Non-Anthropic providers removed (BREAKING - COMPLETE)
• ✅ v5.0.2 (Jan 28, 2026): Cost optimization suite with batch processing and caching monitoring

Migration Guide: docs/CLAUDE_NATIVE.md

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What's New in v2.0.0

🎉 Introducing Attune AI - A fresh start with a new name and renewed focus:

Package Rebrand: empathy-framework is now attune-ai

• New package name, same powerful features
• All capabilities from v5.x preserved
• Starting fresh at v2.0.0 to mark the new brand

🤖 Multi-Agent Orchestration - Build and coordinate specialized AI agents:

• Agent Coordination Dashboard - Real-time monitoring with 6 coordination patterns
• Custom Agents - Create specialized agents for your workflow needs
• LLM Agents - Leverage Claude's advanced capabilities
• Dashboard at http://localhost:8000 with python examples/dashboard_demo.py (requires Redis)

💰 Intelligent Cost Optimization:

• Authentication Strategy - Smart routing between Claude subscription (free) and Anthropic API
• Batch API - 50% cost savings for non-urgent tasks
• Smart Tier Routing - Automatic model selection saves 34-86% on costs
• Precise Token Counting - >98% accurate cost tracking

⚡ Performance & Scale:

• 18x Faster - Redis caching, parallel scanning, incremental updates
• 99.9% Memory Reduction - Generator expressions across 27 optimizations
• Natural Language - Use plain English for workflow commands
• 13,489 Tests - Comprehensive test coverage (99.9% passing)

🔐 Security & Quality:

• Automated Security Scanning - 82% accuracy, blocks critical issues
• Path Traversal Protection - All file operations validated
• HIPAA/GDPR Compliance - Enterprise-ready security options

🧭 Developer Experience:

• MCP Integration - 10 tools auto-discovered by Claude Code
• Hub-Based Commands - Organized workflows (/dev, /testing, /release, etc.)
• Socratic Workflows - Interactive discovery through guided questions
• $0 Workflows - Run via Claude Code with no API costs, unless a 1 million API context is required.

Migration from Empathy Framework:

# Old package
pip uninstall empathy-framework

# New package
pip install attune-ai[developer]

# Update imports in your code
# from empathy_os → from attune

See Full Changelog | Migration Guide

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Quick Start

1. Install

pip install attune-ai[developer]

2. Configure

# Auto-detect API keys
python -m attune.models.cli provider

# Or set explicitly
python -m attune.models.cli provider --set anthropic

3. Use

In Claude Code:

/dev           # Developer tools (debug, commit, PR, review)
/testing       # Run tests, coverage, benchmarks
/workflows     # Automated analysis (security, bugs, perf)
/plan          # Planning, TDD, code review
/docs          # Documentation generation
/release       # Release preparation

# Natural language support:
/workflows "find security issues"
/plan "review my code"

# Direct tool access via MCP (v5.1.1+):
# Claude Code automatically discovers Attune AI tools through the MCP server
# Just describe what you need in natural language:
"Run a security audit on src/"          → Invokes security_audit tool
"Generate tests for config.py"          → Invokes test_generation tool
"Check my auth configuration"           → Invokes auth_status tool
"Analyze performance bottlenecks"       → Invokes performance_audit tool

MCP Server Integration (v5.1.1+):

Attune AI now includes a Model Context Protocol (MCP) server that exposes all workflows as native Claude Code tools:

• 10 Tools Available: security_audit, bug_predict, code_review, test_generation, performance_audit, release_prep, auth_status, auth_recommend, telemetry_stats, dashboard_status
• Automatic Discovery: No manual configuration needed - Claude Code finds tools via .claude/mcp.json
• Natural Language Access: Describe your need and Claude invokes the appropriate tool
• Verification Hooks: Automatic validation of Python/JSON files and workflow outputs

To verify MCP integration:

# Check server is running
echo '{"method":"tools/list","params":{}}' | PYTHONPATH=./src python -m attune.mcp.server

# Restart Claude Code to load the MCP server
# Tools will appear in Claude's tool list automatically

See .claude/MCP_TEST_RESULTS.md for full integration details.

CLI:

attune workflow run security-audit --path ./src
attune workflow run test-coverage --target 90
attune telemetry show  # View cost savings

Python:

from attune import EmpathyOS

async with EmpathyOS() as attune:
    result = await attune.level_2_guided(
        "Review this code for security issues"
    )
    print(result["response"])

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Command Hubs

Workflows are organized into hubs for easy discovery:

Hub	Command	Description
Developer	/dev	Debug, commit, PR, code review, quality
Testing	/testing	Run tests, coverage analysis, benchmarks
Documentation	/docs	Generate and manage documentation
Release	/release	Release prep, security scan, publishing
Workflows	/workflows	Automated analysis (security, bugs, perf)
Plan	/plan	Planning, TDD, code review, refactoring
Utilities	/utilities	Project init, dependencies, profiling
Learning	/learning	Pattern learning and session evaluation
Context	/context	State management and memory
Agent	/agent	Create and manage custom agents

Natural Language Support:

# Use plain English - intelligent routing matches your intent
/workflows "find security vulnerabilities"  # → security-audit
/workflows "check code performance"         # → perf-audit
/workflows "predict bugs"                   # → bug-predict
/plan "review my code"                      # → code-review
/plan "help me plan this feature"           # → planning

# Or use traditional workflow names
/workflows security-audit
/plan code-review

Interactive menus:

/dev                    # Show interactive menu
/dev "debug auth error" # Jump directly to debugging
/testing "run coverage" # Run coverage analysis
/release                # Start release preparation

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Socratic Method

Workflows guide you through discovery instead of requiring upfront configuration:

You: /dev

Claude: What development task do you need?
  1. Debug issue
  2. Create commit
  3. PR workflow
  4. Quality check

You: 1

Claude: What error or unexpected behavior are you seeing?

How it works:

1. Discovery - Workflow asks targeted questions to understand your needs
2. Context gathering - Collects relevant code, errors, and constraints
3. Dynamic agent creation - Assembles the right team based on your answers
4. Execution - Runs with appropriate tier selection

Create custom agents with Socratic guidance:

/agent create    # Guided agent creation
/agent team      # Build multi-agent teams interactively

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Cost Optimization

Skills = $0 (Claude Code)

When using Claude Code, workflows run as skills through the Task tool - no API costs:

/dev           # $0 - uses your Claude subscription
/testing       # $0
/release       # $0
/agent create  # $0

API Mode (CI/CD, Automation)

For programmatic use, smart tier routing saves 34-86%:

Tier	Model	Use Case	Cost
CHEAP	Haiku / GPT-4o-mini	Formatting, simple tasks	~$0.005
CAPABLE	Sonnet / GPT-4o	Bug fixes, code review	~$0.08
PREMIUM	Opus / o1	Architecture, complex design	~$0.45

# Track API usage and savings
attune telemetry savings --days 30

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Key Features

Multi-Agent Workflows

# 4 parallel agents check release readiness
attune orchestrate release-prep

# Sequential coverage improvement
attune orchestrate test-coverage --target 90

Response Caching

Up to 57% cache hit rate on similar prompts. Zero config needed.

from attune.workflows import SecurityAuditWorkflow

workflow = SecurityAuditWorkflow(enable_cache=True)
result = await workflow.execute(target_path="./src")
print(f"Cache hit rate: {result.cost_report.cache_hit_rate:.1f}%")

Pattern Learning

Workflows learn from outcomes and improve over time:

from attune.orchestration.config_store import ConfigurationStore

store = ConfigurationStore()
best = store.get_best_for_task("release_prep")
print(f"Success rate: {best.success_rate:.1%}")

Multi-Provider Support

from attune_llm.providers import (
    AnthropicProvider,  # Claude
    OpenAIProvider,     # GPT-4
    GeminiProvider,     # Gemini
    LocalProvider,      # Ollama, LM Studio
)

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CLI Reference

# Provider configuration
python -m attune.models.cli provider
python -m attune.models.cli provider --set hybrid

# Workflows
attune workflow list
attune workflow run <workflow-name>

# Cost tracking
attune telemetry show
attune telemetry savings --days 30
attune telemetry export --format csv

# Orchestration
attune orchestrate release-prep
attune orchestrate test-coverage --target 90

# Meta-workflows
attune meta-workflow list
attune meta-workflow run release-prep --real

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Install Options

# Individual developers (recommended)
pip install attune-ai[developer]

# All LLM providers
pip install attune-ai[llm]

# With caching (semantic similarity)
pip install attune-ai[cache]

# Enterprise (auth, rate limiting)
pip install attune-ai[enterprise]

# Healthcare (HIPAA compliance)
pip install attune-ai[healthcare]

# Development
git clone https://github.com/Smart-AI-Memory/attune-ai.git
cd attune-ai && pip install -e .[dev]

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Environment Setup

# At least one provider required
export ANTHROPIC_API_KEY="sk-ant-..."
export OPENAI_API_KEY="sk-..."
export GOOGLE_API_KEY="..."

# Optional (but required for Agent Dashboard): Redis for memory
export REDIS_URL="redis://localhost:6379"

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VSCode Extension

Install the Attune AI VSCode extension for:

• Dashboard - Health score, costs, patterns
• One-Click Workflows - Run from command palette
• Memory Panel - Manage Redis and patterns
• Cost Tracking - Real-time savings display

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Documentation

• Quick Start Guide
• CLI Reference
• Testing Guide
• Keyboard Shortcuts
• Full Documentation

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Security

• Path traversal protection on all file operations
• JWT authentication with rate limiting
• PII scrubbing in telemetry
• HIPAA/GDPR compliance options
• Automated security scanning with 82% accuracy (Phase 3 AST-based detection)

See SECURITY.md for vulnerability reporting.

Security Scanning

Automated security scanning in CI/CD - 82% accuracy, blocks critical issues:

# Run security audit locally
attune workflow run security-audit

# Scan specific directory
attune workflow run security-audit --input '{"path":"./src"}'

Documentation:

• Developer Workflow Guide - Quick reference for handling security findings (all developers)
• CI/CD Integration Guide - Complete setup and troubleshooting (DevOps, developers)
• Scanner Architecture - Technical implementation details (engineers, architects)
• Remediation Process - 3-phase methodology for improving scanners (security teams, leadership)
• API Reference - Complete API documentation (developers extending scanner)

Key achievements:

• 82.3% reduction in false positives (350 → 62 findings)
• 16x improvement in scanner accuracy
• <15 minute average fix time for critical issues
• Zero critical vulnerabilities in production code

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Contributing

See CONTRIBUTING.md for guidelines.

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License

Apache License 2.0 - Free and open source for everyone. Use it, modify it, build commercial products with it. Details →

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Acknowledgements

This project stands on the shoulders of giants. We are deeply grateful to the open source community and all the amazing projects that make this framework possible.

View Full Acknowledgements →

Special thanks to:

• Anthropic - For Claude AI and the Model Context Protocol
• LangChain - Agent framework powering our meta-orchestration
• FastAPI - Modern Python web framework
• pytest - Testing framework making quality assurance effortless

And to all 50+ open source projects we depend on. See the complete list →

Want to contribute? See CONTRIBUTORS.md

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