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This project is a comprehensive framework for building and managing autonomous agent systems. It provides a unified architecture for orchestrating multi-agent societies, where specialized agents collaborate through roleplay to decompose and solve complex tasks. The system integrates language models with external environments, enabling agents to perform real-world actions through a standardized tool-calling abstraction layer. The framework distinguishes itself through its focus on iterative reasoning and data reliability. It employs automated feedback loops to refine agent outputs and self-eva

Instructor is a framework designed for structured data extraction, validation, and language model integration. It functions as a library that transforms unstructured text into validated, type-safe objects by leveraging schema definitions and model-specific tool-calling capabilities. By acting as a validation middleware, the project ensures that language model outputs strictly conform to defined data structures. The library distinguishes itself through a robust validation-based retry loop that automatically re-submits failed responses with error feedback to iteratively correct schema complianc

This is an open-source Python SDK for building and orchestrating production-grade AI agents. It provides a unified framework for creating conversational agents that can use tools, maintain state, and coordinate across multiple language model providers including OpenAI, Anthropic, Google, Amazon Bedrock, and locally-hosted models. The SDK supports multi-agent orchestration through graphs, teams, and swarms, allowing several specialized agents to collaborate on complex tasks. Agents can be composed as callable tools that other agents invoke, and the framework includes policy handlers that inspe

AdalFlow 是一个自主 AI 代理框架和 LLM 应用库，旨在构建模块化工作流。它作为一个模型无关的接口和 RAG 流水线编排器，允许用户开发 ReAct 代理，利用迭代推理和外部工具执行来解决复杂任务。 该项目通过一个提示词优化系统脱颖而出，该系统使用文本梯度下降自动优化提示词模板和少样本示例。它将模型反馈视为可微分信号，实现了一种 LLM 反向传播形式，从而根据评估指标迭代提高输出质量。 该框架涵盖了广泛的功能面，包括带有语义向量搜索和重排序的检索增强生成、用于可观测性的基于跨度的执行追踪，以及模式驱动的结构化解析。它为众多专有和开源模型提供商提供了统一的通信层，并支持将 Python 函数转换为标准化的工具接口。 该系统使用 Python 实现，并与 MLflow 集成以进行工作流跟踪和分析。

Koog is an LLM agent framework used to build autonomous entities that execute tool-based workflows. It utilizes a graph-based workflow engine to define agent behaviors and decision paths as a directed graph of nodes and edges. The framework distinguishes itself through a model provider orchestrator that enables dynamic switching, load balancing, and automatic fallbacks between different AI backends. It implements the Model Context Protocol to connect agents to remote tool servers and features a RAG memory system using vector embeddings to maintain long-term conversation context. The project