agentforge-mini 0.2.0

A tiny, provider-agnostic framework for building tool-using AI agents.

AgentForge

A tiny, provider-agnostic framework for building tool-using AI agents in Python.

No SDK lock-in, no heavy dependencies (just requests), no magic. One small Agent class, a @tool decorator, and a clean reason-act loop you can actually read in one sitting. Works with any OpenAI-compatible endpoint: OpenAI, OpenRouter, Groq, Together, Mistral, or a local llama.cpp / Ollama server.

from agentforge import Agent, tool

@tool
def add(a: int, b: int) -> int:
    "Add two numbers."
    return a + b

agent = Agent(model="gpt-4o-mini", tools=[add])
print(agent.run("What is 21 + 21?"))
# -> "21 + 21 equals 42."

Why

Most agent frameworks are huge. If all you want is "call my Python functions from an LLM and loop until done," you shouldn't need 40 dependencies and a graph DSL. AgentForge is ~200 lines of readable code that does exactly that.

• Provider-agnostic — point base_url at any OpenAI-compatible API.
• Zero ceremony — a function + a docstring becomes a tool. Types are inferred from annotations into JSON schema automatically.
• Transparent loop — the whole reason-act cycle lives in one method you can read and patch.
• Resilient — automatic retry with exponential backoff on 429 / 5xx / network errors.
• Streaming + async — stream the final answer token-by-token, or await the agent from async code.
• Tested offline — the test suite mocks the provider, so it runs with no API key and no network.

Install

pip install agentforge-mini

Or from source:

git clone https://github.com/kangbaxso/agent-forge
cd agent-forge
pip install -e .

Usage

1. Define tools

Any function with a docstring becomes a tool. Parameter types come from annotations:

from agentforge import tool

@tool
def get_weather(city: str) -> dict:
    "Get the current weather for a city."
    ...

2. Build an agent

from agentforge import Agent

agent = Agent(
    model="gpt-4o-mini",
    tools=[get_weather],
    system="You are a concise assistant.",
    verbose=True,        # log each tool call
    max_steps=8,         # safety cap on the loop
)

3. Run it

answer = agent.run("What's the weather in Tokyo?")

Stream the answer

for token in agent.stream("Explain async IO in one paragraph."):
    print(token, end="", flush=True)

Tool-calling rounds run first (non-streamed); once the model is ready to answer in plain text, that answer streams token-by-token.

Use it from async code

answer = await agent.arun("What's 21 + 21?")

Resilience

The provider retries automatically on 429 and 5xx responses and on network errors, with exponential backoff. Tune it:

agent = Agent(model="gpt-4o-mini", max_retries=5)

After exhausting retries it raises agentforge.ProviderError.

Point it at any provider

Set two environment variables — that's the whole config:

# OpenAI (default)
export OPENAI_API_KEY=sk-...

# OpenRouter
export OPENAI_BASE_URL=https://openrouter.ai/api/v1
export OPENAI_API_KEY=sk-or-...

# Groq
export OPENAI_BASE_URL=https://api.groq.com/openai/v1
export OPENAI_API_KEY=gsk_...

# Local llama.cpp / Ollama
export OPENAI_BASE_URL=http://localhost:8080/v1
export OPENAI_API_KEY=not-needed

…or pass base_url= / api_key= directly to Agent(...).

Run the example

export OPENAI_API_KEY=sk-...
python examples/quickstart.py

Run the tests

No API key needed — the provider is mocked:

python tests/test_agent.py
# or
python -m pytest tests/ -q

How the loop works

1. Send the conversation + your tool schemas to the model.
2. If the model asks to call tools, AgentForge runs them locally and feeds the results back as role: tool messages.
3. Repeat until the model returns a plain answer or max_steps is reached.

That's it. The entire loop is in agentforge/agent.py.

License

MIT — see LICENSE.