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MAP Framework installer - Modular Agentic Planner for Claude Code

Project description

MAP Framework for Claude Code

Implementation of Modular Agentic Planner (MAP) — a cognitive architecture for AI agents inspired by prefrontal cortex functions. Orchestrates 8 specialized agents for development with automatic quality validation.

Based on: Nature Communications research (2025) — 74% improvement in planning tasks Enhanced with: ACE (Agentic Context Engineering) — continuous learning from experience

📖 Documentation Structure

  • README (this file) - Quick start and overview
  • INSTALL.md - Complete installation guide with PATH setup and troubleshooting
  • ARCHITECTURE.md - Technical deep dive, customization, and MCP integration
  • USAGE.md - Practical examples, best practices, and cost optimization

🚀 Quick Start

Inside Claude Code (Recommended)

# Feature development
/map-feature implement user authentication with JWT tokens

# Debugging
/map-debug fix the API 500 error on login endpoint

# Refactoring
/map-refactor refactor UserService class with dependency injection

# Code review
/map-review review the recent changes in auth.py

Command Line Usage

MAP Framework works exclusively through slash commands in Claude Code:

# Start Claude Code in your project directory
cd your-project
claude

# Use slash commands inside Claude Code
/map-feature implement user authentication with JWT tokens

Note: Direct claude --agents syntax is not applicable to MAP Framework, as the orchestration logic is implemented in slash command prompts (.claude/commands/map-*.md), not as a separate agent file.

📦 Installation

Stable Release (Recommended)

# Using pip
pip install mapify-cli

# OR using UV (recommended for isolated tools)
uv tool install mapify-cli

# Verify installation
mapify --version

# Initialize in your project
cd your-project
mapify init

Version Pinning:

# Install specific version
pip install mapify-cli==1.0.0

# Install with version constraints (semantic versioning: MAJOR.MINOR.PATCH)
pip install "mapify-cli>=1.0.0,<2.0.0"  # Allow 1.x versions, exclude 2.0.0+

Version Information:

  • Check installed version: mapify --version
  • PyPI releases - Available versions and package details
  • GitHub releases - Changelog and release notes

Development Installation

For contributors or testing bleeding-edge features:

# Install from git repository
uv tool install --from git+https://github.com/azalio/map-framework.git mapify-cli

# OR clone and install locally
git clone https://github.com/azalio/map-framework.git
cd map-framework
pip install -e .

Other installation methods (manual copy, troubleshooting): See INSTALL.md

For maintainers: Release process documented in RELEASING.md

Requirements

  • Claude Code CLI — installed and configured
  • Python 3.11+ — for mapify CLI (optional)
  • Git — for cloning repository

🏗️ Architecture

MAP Framework orchestrates 8 specialized agents through slash commands:

  • TaskDecomposer breaks goals into subtasks
  • Actor generates code, Monitor validates quality
  • Predictor analyzes impact, Evaluator scores solutions
  • Reflector/Curator enable continuous learning via ACE playbook

The orchestration logic lives in .claude/commands/map-*.md prompts, coordinating agents via the Task tool.

See ARCHITECTURE.md for:

  • Detailed agent specifications and responsibilities
  • MCP integration architecture and tool usage patterns
  • Agent coordination protocol and workflow stages
  • Template customization guide with examples
  • Hooks integration (automated validation, knowledge storage, context enrichment)
  • Context engineering principles and optimizations

🔌 MCP Integration

MAP uses MCP (Model Context Protocol) servers for enhanced capabilities:

  • cipher - Knowledge base for storing and retrieving successful patterns
  • claude-reviewer - Professional code review with security analysis
  • context7 - Up-to-date library documentation
  • sequential-thinking - Chain-of-thought reasoning for complex problems
  • codex-bridge - AI code generation (requires extended timeout)
  • deepwiki - GitHub repository intelligence

Configuration files: .claude/mcp_config.json and mcp_config.json

See ARCHITECTURE.md for complete setup and usage patterns

📚 Usage Examples

# Feature development
/map-feature implement user profile page with avatar upload

# Bug fixing
/map-debug debug why payment processing fails for amounts over $1000

# Refactoring
/map-refactor refactor OrderService to use dependency injection

See USAGE.md for:

  • Comprehensive usage examples with detailed scenarios
  • Best practices for optimal results
  • Cost optimization strategies (40-60% savings)
  • Playbook management commands

🎓 ACE Playbook

Built-in learning system that improves with each task:

  • Reflector extracts patterns from successes and failures
  • Curator maintains structured knowledge base with quality tracking
  • Semantic search (optional) finds patterns by meaning, not keywords
  • Automatically grows high-quality pattern library

Playbook Commands

# View statistics
mapify playbook stats

# Search patterns
mapify playbook search "JWT authentication"

# View high-quality patterns
mapify playbook sync

Optional semantic search: pip install -r requirements-semantic.txt for meaning-based matching. Details in SEMANTIC_SEARCH_SETUP.md and ARCHITECTURE.md.

Playbook configuration: See ARCHITECTURE.md for top_k settings and optimization.

💰 Cost Optimization

MAP Framework uses intelligent model selection per agent:

  • Predictor & Evaluator use haiku (fast analysis) → ⬇️⬇️⬇️ cost
  • Actor, Monitor, Reflector, Curator use sonnet (quality-critical) → balanced cost

Result: 40-60% cost reduction vs all-sonnet while maintaining code quality.

See USAGE.md for detailed cost breakdown and model override strategies

🔗 Hooks Integration

MAP integrates with Claude Code hooks for automated validation, knowledge storage, and context enrichment. Active hooks protect template variables, auto-store successful patterns, enrich prompts with relevant knowledge, and track performance metrics.

See ARCHITECTURE.md and .claude/hooks/README.md for configuration

🛠️ Troubleshooting

Command Not Found

Error: Slash command not recognized

Solution:

  • Ensure you're in a directory with .claude/commands/ containing map-*.md files
  • Use /map-feature, /map-debug, /map-refactor, or /map-review
  • Run /help to see available commands

Agent Not Found

Error: Agent file not found

Solution: Ensure .claude/agents/ directory contains all 8 agent files (task-decomposer.md, actor.md, monitor.md, predictor.md, evaluator.md, reflector.md, curator.md, documentation-reviewer.md)

Semantic Search Warning

Warning: sentence-transformers not installed

Solution: pip install -r requirements-semantic.txt See SEMANTIC_SEARCH_SETUP.md for detailed troubleshooting

Infinite Loops

Actor-Monitor loop exceeding iterations

Solution: Orchestrator limits iterations to 3-5. Clarify requirements or add constraints.

More troubleshooting: See INSTALL.md for PATH issues, MCP configuration, and installation problems

🔧 Customization

Agent prompts in .claude/agents/*.md use Handlebars template syntax for dynamic context injection. You can safely modify instructions, examples, and validation criteria, but MUST NOT remove template variables like {{language}}, {{#if playbook_bullets}}, or {{feedback}} — these are critical for orchestration and ACE learning.

See ARCHITECTURE.md for:

  • Safe vs unsafe modifications with examples
  • Template variable reference
  • Model selection per agent
  • Adding custom agents
  • Template validation and git hooks

📊 Success Metrics

  • Monitor approval rate: >80% first try
  • Evaluator scores: average >7.0/10
  • Iteration count: <3 per subtask
  • Playbook growth: increasing high-quality patterns

🤝 Contributing

Improvements welcome:

  • Prompts for specific languages/frameworks
  • New specialized agents
  • CI/CD integrations
  • Success story examples
  • Plugin extensions for MAP Framework

📄 License

MIT License — see LICENSE file for details

🔗 References


MAP is not just automation — it's systematic quality improvement through structured validation and iterative refinement.

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