attune-ai 2.1.1

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 Socratic discovery.

Run code review, debugging, testing, and release workflows from Claude Code. One command guides you to the right workflow.

pip install attune-ai[developer]

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What's New in v2.1.0

🎯 Unified /attune Command - One entry point for all workflows:

/attune                    # Start Socratic discovery
/attune "fix a bug"        # Natural language
/attune debug              # Direct shortcut

Socratic Discovery - Ask what you're trying to accomplish, not which tool to use:

• 🔧 Fix or improve → Debug, review, refactor
• ✅ Validate work → Tests, coverage, security audit
• 🚀 Ship changes → Commit, PR, release
• 📚 Understand → Explain code, generate docs

Performance Audit Improvements - Removed false positive detection for intentional patterns like dirs[:] and defensive list copies.

See Full Changelog

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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 Empathy 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+):

Empathy Framework 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:

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

Python:

from attune import EmpathyOS

async with EmpathyOS() as empathy:
    result = await empathy.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
empathy telemetry savings --days 30

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Key Features

Multi-Agent Workflows

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

# Sequential coverage improvement
empathy 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
empathy workflow list
empathy workflow run <workflow-name>

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

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

# Meta-workflows
empathy meta-workflow list
empathy 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 Empathy 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
empathy workflow run security-audit

# Scan specific directory
empathy 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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