agentapi-core 0.1.0

Lightweight framework for building agentic AI backends with FastAPI

AgentAPI

AgentAPI is an open-source Python framework for building agentic AI backends with a clean developer experience: minimal setup, provider abstraction, tool calling, memory, and streaming-first APIs.

It is designed for teams that want FastAPI-style simplicity for LLM agents, without heavy orchestration overhead.

Table of Contents

• Why AgentAPI
• Features
• Installation
• Quick Start
• Provider Configuration
• Tool Calling
• Streaming
• CLI
• Custom Providers
• Error Handling
• Project Structure
• Project Status
• Roadmap
• Contributing
• License

Why AgentAPI

• Keep agent backends simple and readable.
• Use one Agent interface across providers.
• Add tools as plain Python functions.
• Stream responses with minimal boilerplate.
• Start fast, then customize deeply when needed.

Features

• Agent class with memory and tool execution loop.
• Provider abstraction for openai, gemini, and openrouter.
• AgentAPI app integration with @app.chat.
• Automatic SSE when a chat handler returns an async iterator.
• Built-in project scaffolding and run helper via CLI.
• Environment-based configuration using .env.
• Extensible provider system (custom instance or registered factory).

Installation

Install from PyPI:

pip install agentapi-core

Install in editable mode while developing:

pip install -e .

Quick Start

Create main.py:

from agentapi import AgentApp, Agent

app = AgentApp()

agent = Agent(
    system_prompt="You are a helpful assistant",
    provider="openai",
)


@app.chat("/chat")
async def chat(message: str):
    return await agent.run(message)


@app.chat("/stream")
async def stream_chat(message: str):
    return agent.stream(message)

Run it:

uvicorn main:app --reload

Open docs:

• http://127.0.0.1:8000/docs
• http://127.0.0.1:8000/redoc

Provider Configuration

Create .env:

OPENAI_API_KEY=
GEMINI_API_KEY=
OPENROUTER_API_KEY=
DEFAULT_PROVIDER=openai

Supported provider names:

• openai
• gemini
• openrouter

Tool Calling

Define tools with plain Python:

from agentapi import tool


@tool
def get_weather(city: str) -> str:
    """Get weather for a city."""
    return f"Weather in {city}: sunny"

Attach tools to an agent:

agent = Agent(
    system_prompt="You are a weather assistant",
    provider="openai",
    tools=[get_weather],
)

Tool schemas are generated from function signatures and mapped to provider-specific tool formats internally.

Streaming

@app.chat auto-switches to SSE (text/event-stream) when your handler returns an async iterator.

Example:

@app.chat("/stream")
async def stream_chat(message: str):
    return agent.stream(message)

CLI

Create a new project scaffold:

agentapi new myproject

Interactive setup (asks for project name and provider):

agentapi new

Run app via helper:

agentapi run --app main:app --reload

Custom Providers

OpenAICompatibleProvider is an internal helper for OpenAI-compatible APIs. AgentAPI is not locked to it.

You can customize providers in two ways:

1. Pass a provider instance directly: provider=<BaseProvider instance>
2. Register a provider factory and reference by name.

from agentapi import Agent, BaseProvider
from agentapi.providers.base import ProviderResponse


class MyProvider(BaseProvider):
    async def chat(self, messages, *, tools=None, tool_calling=None):
        return ProviderResponse(content="hello", tool_calls=[], raw_message={})

    async def stream(self, messages, *, tools=None, tool_calling=None):
        yield "hello"


Agent.register_provider(
    "myprovider",
    lambda agent, settings, model: MyProvider(),
)

agent = Agent(system_prompt="You are helpful", provider="myprovider")

Error Handling

AgentAPI converts common runtime issues into clear API-level errors:

• Missing API keys -> configuration error message.
• Upstream provider failures -> provider error message with status context.
• Streaming endpoints emit SSE error events instead of hard crashes.

Project Structure

agentapi/
  agent/
    agent.py
    memory.py
    tools.py
  assets/
    agentapi-logo.png
    agentapi-favicon.png
  config/
    settings.py
  core/
    app.py
  providers/
    base.py
    gemini.py
    openai_compatible.py
    openai.py
    openrouter.py
examples/
  main.py

Project Status

Current phase: MVP

Implemented:

• Core agent runtime
• Provider abstraction (OpenAI, Gemini, OpenRouter)
• Tool calling and in-memory conversation memory
• Automatic SSE streaming on chat endpoints
• CLI scaffolding and run helper

Roadmap

• Add Anthropic provider.
• Expand memory backends (Redis/Postgres).
• Add richer observability and tracing hooks.
• Improve generated project templates.
• Add test suite and CI workflows.

Contributing

Contributions are welcome. See CONTRIBUTING.md for setup and PR workflow.

Publishing

This repository is configured to publish on GitHub Release.

1. Bump version in pyproject.toml.
2. Commit and tag a release version.
3. Create a GitHub Release.
4. GitHub Actions publishes to PyPI using trusted publishing.

Required one-time setup:

• In PyPI, create project agentapi-core.
• Configure trusted publisher for this GitHub repository.
• Keep release workflow enabled in .github/workflows/publish.yml.

Trusted Publisher Values

Use these exact values when adding publishers.

TestPyPI pending publisher:

• Project name: agentapi-core
• Owner: prajwalsuryawanshi
• Repository: agentapi
• Workflow filename: publish-testpypi.yml
• Environment name: testpypi

PyPI trusted publisher (after project exists):

• Project name: agentapi-core
• Owner: prajwalsuryawanshi
• Repository: agentapi
• Workflow filename: publish.yml
• Environment name: pypi

Note: package names on PyPI are normalized, so use lowercase agentapi-core.

License

MIT License. See LICENSE.