agentforgeX 0.1.0

AgentForge: LLM-driven agent and multi-agent system generator

AgentForge

Create single agents or robust multi-agent systems from plain English. One command plans, generates, quality‑checks, and packages a runnable agent; another runs a supervisor + workers loop.

Features

🤖 Multi-LLM Support

• OpenAI GPT Models (via API key)
• Grok (via xAI API)
• Groq
• Ollama (local models like Llama3)

🏗️ Agentic Pipeline

1. Planning Agent: Analyzes requirements and creates detailed architecture plans
2. Code Generation Agent: Generates production-ready Python code with best practices
3. Testing Agent: Creates comprehensive test suites and suggests improvements

💪 Robust Features

• Comprehensive Error Handling: Retry logic, timeout management, and graceful failure handling
• Configurable Settings: JSON-based configuration with environment variable overrides
• Detailed Logging: Full audit trail with configurable log levels
• Organized Output: Timestamped directories with generated code, tests, and documentation
• Input Validation: Thorough validation of inputs and API responses
• Type Safety: Full type hints and validation

🚀 Quick Start

git clone <repository-url>
cd AgentForge
python -m venv .venv && source .venv/bin/activate  # Windows: .venv\Scripts\activate
pip install -e '.[dev]'

# Single agent (plan + code + tests + packaging)
agentforge generate --provider openai --use-case "Summarize daily sales CSVs and flag anomalies"

# Multi-agent (offline deterministic)
agentforge multi --task "Explain caching layers" --provider echo --verbose

# Planning only
agentforge plan --provider ollama --use-case "Design an FAQ chatbot"

Set environment variables for real providers:

For OpenAI:

export OPENAI_API_KEY="your-openai-api-key"

For Grok (xAI):

export XAI_API_KEY="your-xai-api-key"

For Ollama:

Make sure Ollama is running locally:

ollama serve

🧩 Single-Agent Generation (Detailed)

The generate subcommand performs:

1. Planning (LLM architecture plan with retries)
2. Code generation + heuristic quality evaluation & refinement
3. Test suggestions
4. Deterministic fallback template if quality fails
5. Packaging (requirements, run scripts, Dockerfile, README)

Artifacts land in generated_agents/<timestamp>/.

Legacy direct call (still works):

python src/main.py <provider> "<use case>"

Run as an API Service

Start the FastAPI server (after installing new dependencies):

uvicorn src.api.app:app --reload

Then call endpoints:

curl -X POST http://127.0.0.1:8000/plan -H 'Content-Type: application/json' \
  -d '{"provider":"openai","use_case":"Build an agent that summarizes emails"}'

Full pipeline:

curl -X POST http://127.0.0.1:8000/pipeline -H 'Content-Type: application/json' \
  -d '{"provider":"ollama","use_case":"Create an agent that tags support tickets"}'

More Examples

agentforge generate --provider grok --use-case "Analyze CSV data and output anomaly report"
agentforge generate --provider ollama --use-case "Customer support chatbot that escalates complex issues"
agentforge generate --provider openai --use-case "News summarizer that emails an AM briefing"

Configuration

Configuration File

Create or modify config.json to customize behavior:

{
  "max_retries": 3,
  "min_plan_length": 50,
  "min_code_length": 100,
  "output_base_dir": "generated_agents",
  "create_timestamped_dirs": true,
  "save_logs": true,
  "log_level": "INFO",
  "default_timeout": 120,
  "default_temperature": 0.7,
  "default_max_tokens": 4000,
  "openai_model": "gpt-4o-mini",
  "grok_model": "grok-beta",
  "ollama_model": "llama3"
}

Environment Variables

Override configuration with environment variables:

export AGENTFORGE_MAX_RETRIES=5
export AGENTFORGE_LOG_LEVEL=DEBUG
export AGENTFORGE_OUTPUT_DIR=my_agents
export OPENAI_MODEL=gpt-4

Output Structure

Each run creates a timestamped directory with:

generated_agents/
└── 20240822_143022/
    ├── README.md           # Generation summary
    ├── agent_plan.txt      # Detailed architecture plan
    ├── custom_agent.py     # Generated agent code
    └── test_agent.py       # Test suite

Architecture

Core Components

• main.py: Main orchestration logic with robust error handling
• llm_providers.py: Multi-provider LLM interface with retry logic and proper response parsing
• config.py: Configuration management with file and environment variable support

Agentic Workflow

1. Input Validation: Validates provider and use case description
2. Planning Phase: Creates detailed agent architecture with retry logic
3. Code Generation: Produces clean, documented Python code
4. Testing Phase: Generates comprehensive test suites
5. Output Organization: Saves all artifacts with proper structure

Error Handling

• Retry Logic: Configurable retries for transient failures
• Timeout Management: Proper timeout handling for all API calls
• Graceful Degradation: Continues operation even if non-critical steps fail
• Detailed Logging: Comprehensive logging for debugging and monitoring

Advanced Features

Custom Models

Configure different models per provider:

{
  "openai_model": "gpt-4",
  "grok_model": "grok-2",
  "ollama_model": "llama3:8b"
}

Output Customization

Control output behavior:

{
  "create_timestamped_dirs": false,  # Use single output directory
  "output_base_dir": "my_custom_dir",
  "save_logs": false  # Disable log file creation
}

🤝 Multi-Agent Orchestration

Run a supervisor + worker loop:

agentforge multi --task "Draft phased migration plan" --provider openai
agentforge multi --task "Summarize caching strategy" --provider echo --verbose

Supervisor must output NEXT:<agent> or FINISH:<answer>. Workers output RESPOND:<answer> or TOOL:<name>:<arg>.

Add an extra worker (snippet):

from agents.base import BaseAgent
from agents.adapters import EchoLLM
from agents.orchestrator import Orchestrator
llm = EchoLLM()
sup = BaseAgent("supervisor", llm, "Supervisor: decide.")
worker = BaseAgent("worker", llm, "Worker: solve tasks.")
researcher = BaseAgent("researcher", llm, "Research facts.")
orch = Orchestrator({"worker": worker, "researcher": researcher}, sup, max_turns=8)

Offline deterministic path (no keys): --provider echo.

Dependencies

• Core: requests for HTTP API calls
• Development: pytest, black, flake8, mypy
• Documentation: mkdocs, mkdocs-material

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes with proper tests
4. Ensure code quality with black, flake8, and mypy
5. Submit a pull request

License

MIT License - see LICENSE file for details.

🛠️ Troubleshooting

Common Issues

API Key not found:

# Make sure environment variables are set
echo $OPENAI_API_KEY
echo $XAI_API_KEY

Ollama connection failed:

# Check if Ollama is running
curl http://localhost:11434/api/tags

Generation timeout:

# Increase timeout in config.json
export AGENTFORGE_TIMEOUT=300

Debug Mode

Enable detailed logging:

export AGENTFORGE_LOG_LEVEL=DEBUG
python src/main.py <provider> "<use_case>"

Check the log file for detailed error information:

📚 Extended Documentation

See docs/USAGE.md for advanced multi-agent usage, tool authoring, roadmap, and a troubleshooting matrix.

tail -f agent_forge.log