PYFORGEAI

AI AGENT FRAMEWORK

pyforgeai is the PyPI package for the forgeai Python framework, a lightweight production-first toolkit for building autonomous AI agents with:

• async execution
• pluggable tools
• memory abstraction
• multi-provider LLM support
• structured observability
• simple orchestration

It is designed for clean architecture and easy extension, without unnecessary abstractions.

Repository: https://github.com/Pulkit-Py/pyforgeai

Table of Contents

• Why pyforgeai
• Author and Profiles
• Core Concepts
• Project Structure
• Installation
• Quick Start
• Configuration
• Providers
• FastAPI Integration
• Observability
• Testing and Quality
• How to Extend
• Current Limitations
• Troubleshooting

Why pyforgeai

• Async-first runtime (asyncio) for modern Python services.
• Strong typing and Pydantic schemas for reliable interfaces.
• Minimal, modular architecture that is easy to reason about.
• Provider-agnostic model layer (BaseProvider).
• Developer-friendly defaults and fallbacks for local/offline development.

Author and Profiles

• GitHub: https://github.com/Pulkit-Py
• Instagram: https://www.instagram.com/pulkit_py/
• LinkedIn: https://www.linkedin.com/in/pulkit-py/

Core Concepts

• Agent: reasons over goal + role + memory + user input, then optionally calls tools.
• Engine: controls retries, iteration limits, early stop behavior, and metrics.
• BaseTool: async tool interface (run(input: str) -> str).
• BaseMemory: async memory interface (add, get_context).
• BaseProvider: async LLM interface (generate(prompt: str) -> str).
• AgentTeam: sequential multi-agent orchestration (output of agent A -> input of agent B).

Project Structure

forgeai/
├── agent/
│   └── base.py
├── config.py
├── engine/
│   └── engine.py
├── memory/
│   ├── base.py
│   └── short_term.py
├── observability/
│   ├── logger.py
│   └── metrics.py
├── orchestration/
│   └── team.py
├── providers/
│   ├── base.py
│   ├── factory.py
│   ├── openai_provider.py
│   ├── ollama_provider.py
│   ├── anthropic_provider.py
│   ├── gemini_provider.py
│   ├── deepseek_provider.py
│   └── grok_provider.py
├── schemas/
│   └── agent_schema.py
└── tools/
    ├── base.py
    └── python_tool.py

Installation

1) Python version

• Python 3.11+ is required.

2) Install package

pip install -e .

Install from PyPI:

pip install pyforgeai

3) Install provider extras (optional)

pip install -e .[ollama]
pip install -e .[openai]
pip install -e .[anthropic]
pip install -e .[gemini]
pip install -e .[api]

4) Full development install

pip install -e .[dev,all]

Quick Start

Run local example

example_usage.py uses provider factory + environment config.

python example_usage.py

By default, this project is configured for Ollama local usage in forgeai/config.py.

Configuration

Configuration is loaded via ForgeAIConfig.from_env() from forgeai/config.py.

Supported env vars:

• FORGEAI_DEFAULT_PROVIDER (default: ollama)
• FORGEAI_DEFAULT_MODEL (default: qwen3:4b)
• FORGEAI_PROVIDER_TIMEOUT_S (default: 30.0)
• FORGEAI_PROVIDER_RETRIES (default: 1)
• FORGEAI_MAX_ITERATIONS (default: 5)
• FORGEAI_MAX_RETRIES (default: 2)
• OPENAI_API_KEY
• OPENAI_MODEL
• ANTHROPIC_API_KEY
• GEMINI_API_KEY or GOOGLE_API_KEY
• DEEPSEEK_API_KEY
• XAI_API_KEY

Example:

set FORGEAI_DEFAULT_PROVIDER=ollama
set FORGEAI_DEFAULT_MODEL=qwen3:4b
set FORGEAI_MAX_ITERATIONS=2
python example_usage.py

Providers

Use create_provider(...) from forgeai.providers.factory:

from forgeai.providers.factory import create_provider

provider = create_provider("ollama", model="qwen3:4b", host="http://localhost:11434")

Supported names:

• openai
• ollama
• anthropic
• gemini
• deepseek
• grok (or xai)

All providers implement:

class BaseProvider:
    async def generate(self, prompt: str) -> str: ...

FastAPI Integration

A ready example exists at examples/fastapi_app.py.

Run:

uvicorn examples.fastapi_app:app --reload

Endpoints:

• GET /health
• POST /run

Request body example:

{
  "prompt": "Write a hello world FastAPI app",
  "provider": "ollama",
  "model": "qwen3:4b"
}

Observability

forgeai includes JSON structured logging and basic metrics:

• per-step latency
• token usage placeholder
• provider/tool call counters
• run correlation id in engine logs

Use logger:

from forgeai.observability.logger import get_logger

logger = get_logger("forgeai-service")

Testing and Quality

Run checks:

ruff check .
mypy forgeai
pytest -q

Current test coverage includes:

• memory behavior
• agent tool-flow behavior
• engine early-stop behavior
• provider factory and fallback behavior

How to Extend

Add a custom tool

from forgeai.tools.base import BaseTool

class MyTool(BaseTool):
    def __init__(self) -> None:
        super().__init__(name="my_tool", description="Does something useful")

    async def run(self, input: str) -> str:
        return f"processed: {input}"

Add a custom memory backend

Implement BaseMemory:

• async add(entry: str) -> None
• async get_context(query: str) -> str

Add a new provider

Implement BaseProvider.generate(prompt: str) -> str, then register it in:

• forgeai/providers/factory.py
• forgeai/providers/__init__.py

Current Limitations

• PythonTool uses exec and is not sandboxed. For untrusted input, run in an isolated runtime.
• Metrics are intentionally lightweight and not yet integrated with Prometheus/OpenTelemetry.
• Memory is short-term in-process only (no persistent/vector memory by design right now).

Troubleshooting

• No module named pytest

  • Install dev deps: pip install -e .[dev]

• Provider returns fallback response

  • Check API key env vars.
  • Ensure relevant SDK is installed (pip install -e .[provider]).

• Ollama connection issues

  • Ensure Ollama is running locally and model is pulled.
  • Verify host URL (http://localhost:11434 by default).

License

MIT

Support

If you found this project helpful, consider:

• Giving it a ⭐ on GitHub
• Following me on social media
• Sharing it with others who might find it useful

GitHub Repository: https://github.com/Pulkit-Py/pyforgeai

For support, please open an issue on the GitHub repository.

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