agent-llm-service 0.0.1

A GenAI project built with LangChain and modern Python tools

Agent LLM Service

Agent LLM Service is a robust, clean, and extensible Python library designed for executing LLM requests across multiple language models (e.g., OpenAI, Groq, Gemini). It offers reliable fallback mechanics, model round-robins, API rate-limit management, built-in tool/function-calling structures, and exponential backoff retry logic.

It is built as the core execution engine for any multi-agent system, strictly adhering to modern Python typing (Pydantic v2) and asynchronous request handling (httpx, asyncio).

Key Features

• Execution Pool & Failover Strategy: Automatically cycle through fallback models when encountering rate limits or temporary provider issues (LlmExecutionPool).
• Resilient Polling & Backoffs: Built-in retry with exponential backoff (LlmRunner).
• Modular Provider Interface: Easily write adapters and unified RawLlmProvider handlers that work with OpenAI-spec endpoints.
• Native Tool Calling Definitions: Extensively typed schemas to define custom tools (BaseTool) and dispatch LLM decisions automatically mappings to real functions (ToolRegistry).
• Asynchronous by Default: Built utilizing async (httpx.AsyncClient) for scaling safely across multi-agent fleets.

Getting Started

Installation

Ensure you are using Python >= 3.12. Install dependencies using uv or pip:

uv sync  # or `pip install -e .`

Quick Usage

from agent_llm_service import (
    LlmProviderConfig, 
    RawLlmProvider, 
    LlmRunner
)

# Configure the provider (e.g., Groq via OpenAI schema)
config = LlmProviderConfig(
    name="Groq",
    slug="groq",
    api_key_env_var="GROQ_API_KEY",
    base_url="https://api.groq.com/openai/v1",
    enabled=True
)

provider = RawLlmProvider(config=[config])
runner = LlmRunner(provider=provider)

# Async LLM Run
response = await runner.acall(
    model="groq/llama3-8b-8192",
    messages=[{"role": "user", "content": "What is the capital of France?"}],
)
print(response.content)

Documentation

• Architecture Review: Deep dive into the modular structure, object domains, and the pool failover strategies.
• How to Use (Examples & Tool Calling): Extensive recipes on handling single LLM runs, pools, and tool registry integrations.

Contributing

1. Fork the repository and create an issue.
2. Ensure you run the linter (uv run ruff check src --fix).
3. Submit a PR.

License

MIT License.