Report Abuse

Basic Information

Council is an open-source Python platform for building applications that use large language models (LLMs). It provides a unified interface to interact with multiple LLM providers and local models, and is designed to give developers control flow and scalable oversight for LLM-driven applications. The project focuses on enterprise-grade quality control, monitoring and production readiness by standardizing message formatting, managing model parameters and API credentials, and offering built-in error handling and retry logic. The repository includes installation options via pip, configuration via environment files, and documentation and support channels for developers. It targets teams who need consistent LLM behavior across providers and operational visibility into usage and costs.

Links

Categorization

App Details

Features
Unified LLM interface with consistent API and message formatting across providers and built-in retry and error handling. Provider flexibility that supports OpenAI, Anthropic, Google Gemini and local models via Groq and Ollama, allowing easy switching between backends. Configuration management for model parameters such as temperature and max tokens and for provider-specific settings. Built-in monitoring and usage tracking that captures token usage, number of API calls and response times. Production-oriented utilities including robust retry mechanisms, consumption tracking and a configuration system for managing API credentials and retry behavior. Developer tooling includes linting and formatting instructions and multiple installation methods.
Use Cases
Council helps developers integrate and operate LLMs more reliably by presenting a single, consistent interface to multiple providers and local runtimes. Teams can switch providers without changing application logic and can centrally manage model parameters, retries and API credentials. The built-in monitoring and usage tracking give visibility into token consumption, API call counts and latency, which aids cost control and performance tuning. Error handling and retry logic improve robustness in production deployments. The project also provides documentation, examples and community support to accelerate development and maintenance of enterprise LLM applications. Installation is supported via pip and configuration via environment files to streamline setup.

Please fill the required fields*