Open-source alternatives to ReAct

This project is a collection of educational resources and technical guides focused on the development and implementation of large language models. It provides a comprehensive curriculum covering transformer architectures, training methods, and deployment strategies. The materials provide detailed instructions for building autonomous agents using reasoning loops and tool integration, as well as guides for fine-tuning models through supervised learning and preference optimization. It also includes tutorials for constructing retrieval augmented generation pipelines and implementing transformer m

Qwen-Agent is a development framework for building autonomous software applications that leverage large language models to plan, reason, and execute complex tasks. It functions as an orchestration engine that enables models to interact with external APIs, manage persistent memory, and maintain context across multi-step workflows. The framework distinguishes itself through a multi-agent collaboration platform that allows independent agent instances to exchange structured messages and delegate sub-tasks to one another. By utilizing iterative reasoning loops and dynamic prompt injection, the sys

Langroid is a multi-agent orchestration framework and tool integration suite designed for building complex AI applications. It serves as a multi-modal integration layer that connects diverse local and remote language models with an agentic retrieval-augmented generation system. The project distinguishes itself through a collaborative message-exchange paradigm, allowing specialized agents to delegate tasks hierarchically and coordinate via structured communication. It features an advanced state management system for conversational AI, including the ability to rewind and prune conversation hist

This project is a comprehensive guide and framework for large language model prompt engineering. It provides a collection of techniques and patterns for optimizing model responses through structured system prompts, context management, and a variety of implementation patterns. The project focuses on several specialized domains, including the creation of autonomous agents through reasoning loops and the implementation of retrieval augmented generation to inject semantic context into prompts. It also provides methods for enforcing structured outputs in serialization formats like JSON or YAML for

This repository is a collection of guides, notebooks, and recipes for implementing advanced prompting techniques and workflow patterns with large language models. It serves as a prompt engineering guide, an evaluation suite for scoring prompt quality, and a framework for orchestrating agents and integrating external tools. The project provides implementation patterns for building applications with Claude, specifically focusing on coordinating multiple models to split complex tasks between high-reasoning and high-efficiency agents. It includes technical demonstrations for multimodal data proce

The BeeAI Framework is an LLM agent framework and multi-agent orchestration engine used to build autonomous agents that coordinate reasoning, tool execution, and complex workflows. It functions as a structured AI output controller and RAG integration library, providing a unified interface to manage multiple language model providers. The framework is distinguished by its implementation of the Model Context Protocol, allowing agents, tools, and models to be shared between different AI platforms and hosted as agentic tooling servers. It enables the design of collaborative agent teams through dec

Phidata is an LLM agent framework and agentic workflow orchestrator used to build autonomous agents that integrate custom data, tools, and memory. It provides a production environment for serving these agents as services via APIs, utilizing server-sent events and websockets for real-time communication. The system distinguishes itself through a human-in-the-loop control layer that requires manual approval and administrative sign-off for specific tool executions. It also implements a multi-tenant AI infrastructure that uses token-based roles to ensure data isolation between different tenants.

Auto-GPT is an autonomous agent framework designed for creating and deploying AI agents that use large language models to plan and execute complex goals independently. The system provides a comprehensive environment for managing the entire agent lifecycle, from initial design and testing to live production deployment. The project features a low-code workflow designer that allows users to define agent behaviors by connecting functional blocks in a visual interface. It includes an agent marketplace for discovering and deploying pre-configured agent templates and a standardized evaluation tool t

OpenHarness is a framework for building and orchestrating AI agents that utilize tools and plugins to execute complex tasks. It provides an orchestration system for managing language model lifecycles and a multi-agent coordination system for delegating workloads across teams of specialized subagents. The project features an agent gateway that bridges language model agents to external chat platforms and communication channels. It includes a tool integration engine for executing shell, file, and web operations, supported by a memory and skill manager that handles persistent user preferences and

GenericAgent is an LLM agent framework and autonomous system controller designed to manage local systems, web browsers, and hardware interfaces through action and observation loops. It functions as a tool orchestrator that routes model calls to local executors, enabling the automation of complex tasks on a host machine. The project is distinguished by its self-evolving AI agent capabilities, which convert successful execution paths into reusable procedural scripts and skill trees to reduce future reasoning overhead. It employs a context optimization engine that utilizes layered memory hierarc

This project is a comprehensive framework for building, evaluating, and connecting autonomous agent systems. It provides a library of standardized architectural patterns for implementing complex agent workflows, including multi-agent orchestration, iterative reasoning, and memory management. By offering a unified interface for model providers, the framework allows for consistent agent execution across different artificial intelligence services. The framework distinguishes itself through a focus on rigorous benchmarking and deterministic control. It includes a suite of tools for evaluating age

GLM-4.5 is a multimodal large language model and advanced reasoning system. It functions as an AI coding assistant, an autonomous AI agent, and a multimodal content generator capable of processing and generating text, images, audio, and video within a single unified system. The project is distinguished by its deep reasoning capabilities, utilizing chain-of-thought processing to solve complex mathematical, logical, and technical problems. It features an agentic architecture that allows for autonomous task execution, long-horizon goal planning, and the ability to interact with external tools an

This project is a Llama Stack agentic framework and orchestrator used to build autonomous AI applications. It coordinates model inference and tool execution to decompose complex goals into multi-step reasoning chains and continuous inference loops. The framework incorporates a dedicated safety guardrail system that filters model inputs and outputs through safety models to enforce system-level content restrictions. It also includes a tool integration layer that maps model-generated function requests to external runtime definitions to execute actions beyond text generation. The system provides

DeepSeek-TUI is an AI coding agent orchestrator and framework designed to automate complex programming tasks. It functions as a harness for coordinating AI models that can read source code, edit files, and execute shell commands through automated agent workflows. The system is distinguished by its multi-agent coordination capabilities, which allow for the spawning of parallel sub-agents to handle concurrent investigations or implementation slices. It employs autonomous goal-seeking loops to pursue objectives across multiple turns and utilizes a tool integration gateway to connect models to ex

Kiln is an LLM development workbench and evaluation framework designed for designing, testing, and optimizing prompts and AI agents. It functions as a multi-agent orchestrator and a RAG optimization tool, providing a visual interface for the iterative development of AI systems. The project distinguishes itself through a comprehensive fine-tuning pipeline that supports zero-code model training and reasoning distillation. It enables the creation of hierarchical multi-agent systems where specialized actors coordinate via tool calling, and it implements a Model Context Protocol server to expose t

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

OptiLLM is an inference proxy and gateway router that directs prompts to specific language models based on cost, performance, and provider health. It functions as a middleware layer designed to optimize requests through intelligent routing, load balancing, and context management. The project provides specialized capabilities for data protection by anonymizing personally identifiable information before requests reach a model. It also acts as a reasoning orchestrator and tool integration layer, using inference-time loops and self-reflection to improve accuracy while connecting models to externa

gptel is an LLM Emacs client and multi-backend AI integration system that allows users to interact with large language models directly inside the Emacs text editor. It serves as an AI-powered text refactoring tool and a context-aware prompt manager, providing a unified interface to connect with various AI providers, including local Ollama instances, AWS Bedrock, and Gemini. The project distinguishes itself as a Model Context Protocol client, connecting to MCP servers to provide language models with external tools and data sources. It enables context-augmented prompting by aggregating text fro

This project is a comprehensive educational curriculum and structured learning path covering the full lifecycle of large language models. It provides a guided progression through the theory, architecture, training, and deployment of these models. The curriculum includes specialized guides on transformer architecture, model training tutorials, and frameworks for designing autonomous agents. It also provides dedicated resources for studying model safety and ethics. The material covers a wide range of technical capabilities, including distributed training strategies, parameter-efficient fine-tu

Auto-GPT is an autonomous agent framework that uses large language models to decompose complex goals and execute multi-step tasks without human intervention. It functions as a workflow automation tool that chains language model tasks and manages memory to achieve specific objectives. The project features a visual agent designer that allows users to define behaviors and goals by connecting functional blocks through a graphical interface. It employs a vector database memory system to recall information across different sessions and a sliding-window buffer for immediate short-term context. The

DeepSeek-R1 is an open-weights large language model focused on advanced reasoning. It uses chain-of-thought processing and internal monologues to solve complex mathematical and logical problems by breaking tasks into sequential, verifiable thought processes. The model is developed using reinforcement learning to optimize reasoning patterns and verify logical steps. It employs a distillation process to transfer these high-performance logic capabilities from a large teacher model into smaller, computationally efficient versions. The training framework incorporates group relative policy optimiz

Agent Zero is an LLM agent framework and multi-agent orchestrator that provides an AI-powered interface for operating system tasks. It functions as a containerized AI workspace, allowing large language models to interact with a filesystem and terminal within an isolated Linux environment. The system distinguishes itself through a hierarchical orchestration model that decomposes complex goals by spawning specialized sub-agents to collaborate and consolidate results. It features a plugin-based architecture for extending capabilities via a community plugin hub, a custom skills system, and extern

gh-aw is a GitHub automation platform and orchestration framework that uses an agentic workflow engine to automate repository management and code reviews. It translates natural language markdown and configuration files into secure, automated task sequences driven by large language models. The system integrates a Model Context Protocol gateway to route calls between AI agents and external tools. It distinguishes itself through a comprehensive security guardrail system that provides sandboxed execution for protocol servers, network egress controls via domain allowlists, and human-in-the-loop ap

Qwen2.5 is a suite of large language model foundation models designed for natural language generation, code production, and complex mathematical reasoning. The project encompasses a multilingual language model capable of processing dozens of languages and a specialized code generation model for technical problem solving and debugging. The framework is distinguished by its long context capabilities, enabling the analysis of massive inputs ranging from 256K up to 1 million tokens. It further functions as an agentic framework, utilizing standardized templates and parsers to execute autonomous wo

OpenGPTs is a platform for building, deploying, and managing customizable AI assistants. It serves as an orchestrator that allows for the configuration of large language models with specific personas, cognitive architectures, and tool integrations. The system provides a complete lifecycle manager for AI agents, enabling the drafting of configurations, testing within sandboxes, and publishing assistants for public or internal distribution. It integrates a knowledge base interface using retrieval-augmented generation to attach documents to bots for context-aware responses. The platform covers

This project is an AI-powered IDE extension and LLM coding assistant that provides a conversational interface for generating, refactoring, and debugging code. It functions as an AI agent framework and a Model Context Protocol client, connecting AI models to external data sources and tools to automate complex development tasks. The system is distinguished by its use of autonomous AI agents capable of multi-step task execution, including the ability to read files, modify code, and run terminal commands iteratively. It supports recursive agent orchestration through subagent delegation and employ

XLeRobot is an embodied AI robotics platform and hardware ecosystem designed for developing and deploying autonomous robots. It integrates a dual-arm mobile robot platform with an LLM-based robot controller, a physics-based simulation environment, and a teleoperation interface to translate natural language instructions into physical actions. The project emphasizes low-cost robot fabrication using 3D printing and affordable components to create a mobile base with interchangeable arms and grippers. It features a specialized teleoperation workflow that allows for remote hardware control via VR i