LLM Agent Reasoning and Planning

gpt-oss is an open-weight large language model and reasoning engine designed for complex reasoning and agentic workflows. It functions as an AI agent framework and model serving API, allowing for local deployment and the hosting of standardized interfaces to expose model completions and internal reasoning processes. The project distinguishes itself as a quantized inference engine, utilizing tensor parallelism and weight quantization to run high-parameter models on limited hardware. It features a reasoning model that employs chain-of-thought processing to solve multi-step logical tasks. The s

MetaGPT is an agentic workflow engine and multi-agent orchestration framework designed to automate complex software engineering and data analysis tasks. It functions as an automated software factory that transforms high-level natural language requirements into functional web applications, technical documentation, and production-ready code. By utilizing a runtime environment that manages the lifecycle of specialized agents, the platform bridges the gap between user intent and finished software components. The system distinguishes itself through role-based agent orchestration and dynamic task d

This project is a collection of architectural templates and design patterns for building autonomous AI agents. It provides a framework for transitioning from simple prompt-response loops to goal-oriented systems that utilize structural patterns to increase autonomy and improve the reliability of complex task completion. The framework focuses on reasoning orchestration, specifically through the implementation of reflection and self-correction cycles. It enables the coordination of specialized agents via task delegation and state sharing to solve complex problems. The architectural surface cov

Kotaemon is an orchestration framework designed for building modular, agentic workflows that integrate document processing, retrieval-augmented generation, and multi-step reasoning. It provides a comprehensive platform for developing document-based question answering systems, allowing users to chain language models, prompt templates, and external tools into complex, automated pipelines. The system distinguishes itself through a highly modular architecture that emphasizes component-based composition and schema-driven data exchange. It supports autonomous agents capable of decomposing complex q

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

This project is an autonomous agent framework designed to integrate large language models with popular messaging platforms. It functions as a middleware platform that enables automated, multimodal interactions by decomposing complex user goals into sequential plans, executing them through external tools, and maintaining persistent context across sessions. The framework distinguishes itself through a modular skill architecture and a hybrid memory system. Users can extend system capabilities by installing custom logic modules from community hubs or generating them through natural language. The

Agentscope is a comprehensive toolkit for developing and orchestrating autonomous multi-agent systems. It provides a unified framework for building agents that can reason, execute tools, and manage memory, enabling the creation of complex, collaborative workflows where multiple specialized agents interact to solve multi-step objectives. The platform distinguishes itself through a robust orchestration engine that supports both sequential and concurrent agent pipelines. It utilizes a centralized event bus for real-time telemetry, allowing developers to track agent reasoning, tool usage, and sys

CrewAI is a multi-agent orchestration framework designed for building autonomous systems that execute complex, multi-step workflows. It provides a development platform where specialized agents are defined with specific roles, goals, and tool sets to perform tasks collaboratively. By leveraging a declarative workflow engine, the system manages task dependencies, state transitions, and execution logic, allowing for the creation of structured, stateful sequences of operations. The framework distinguishes itself through its hierarchical management capabilities, which utilize manager agents to coo

This is a framework for building and deploying customizable AI agent services using a standardized reference architecture. It provides the core infrastructure necessary to host multiple agents within a single service, supporting interactive chat interfaces and real-time response streaming. The project distinguishes itself with a human-in-the-loop mechanism that allows agent execution to be paused for manual approval or intervention. It also features path-based routing to direct requests to specific agents and a multi-stage content moderation system to filter outputs through safety guardrails.

This framework provides a development toolkit for building autonomous agents that utilize language models to solve complex, non-deterministic tasks. Its core design centers on a code-executing architecture where agents generate and run Python code snippets to perform logic, data manipulation, and tool interactions. By moving beyond structured data formats, the system enables agents to manage program flow and object state through iterative reasoning cycles. The project distinguishes itself through its focus on code-based agent implementation and secure execution environments. Developers can ch

LobeHub is a comprehensive multi-agent orchestration platform designed for building, configuring, and deploying specialized AI agents. It provides a unified chat-based gateway that allows users to manage autonomous agent teams across web, desktop, and mobile environments. By utilizing a framework that supports persistent memory and granular tool integration, the platform enables the execution of complex, multi-step workflows and domain-specific tasks. The platform distinguishes itself through an interactive artifact renderer that injects dynamic, visual UI elements directly into the chat stre

Mastra is an orchestration framework designed for building, deploying, and managing autonomous AI agents and multi-agent systems. It provides a comprehensive suite of primitives for creating resilient AI applications, including durable workflow orchestration, event-driven agent loops, and semantic memory management. By integrating these core components, the platform enables developers to build complex, multi-step processes that can reason about goals and execute tasks without manual intervention. The framework distinguishes itself through its focus on observability and secure, isolated execut

LangChain is an orchestration framework designed for building, managing, and deploying applications powered by large language models. It provides a unified integration layer that normalizes disparate model provider APIs into a consistent set of primitives, enabling developers to build complex, multi-step AI workflows that manage state, memory, and tool execution. The project distinguishes itself through a durable execution runtime that maintains persistent state across long-running processes by checkpointing progress to external storage. It models agent workflows as directed graphs, allowing

LangChain is a framework for building applications that chain large language models with external data sources and third-party tools. It serves as an orchestrator for autonomous agents that use language models to plan and execute multi-step tasks, while providing a toolkit for linking interoperable AI components into sequences to prototype complex model behaviors. The project provides a model agnostic integration layer, allowing users to switch between different language model providers using a standardized interface. It also includes tools for observability and evaluation to track the perfor

Eino is an AI agent development kit and LLM application framework designed for building autonomous agents and orchestrating complex language model workflows. It serves as a multi-agent orchestration engine and workflow orchestrator, providing a graph-based execution model to route data between models, tools, and retrievers. The framework distinguishes itself through a robust set of multi-agent coordination patterns, including supervisor-led management, sequential flows, and autonomous reasoning loops like ReAct. It features advanced agent execution controls such as active turn preemption, che

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

OpenManus is an autonomous agent framework designed to build intelligent software entities capable of executing complex, multi-step tasks through independent decision-making. It functions as a workflow orchestration engine that uses a central language model to interpret user goals, break them down into actionable steps, and manage the execution flow of agents. The system maintains coherence across tasks through a stateful execution context that tracks progress and intermediate data. The platform distinguishes itself through a dynamic capability discovery mechanism that inspects tool definitio

This framework provides a development environment for building collaborative systems where autonomous agents interact to solve complex tasks through conversational workflows. It functions as a conversational workflow engine and event-driven runtime, coordinating multi-step processes by translating high-level goals into structured dialogue sequences between specialized agents. The system distinguishes itself through its message-passing orchestration, which manages state transitions and task delegation between independent participants. It supports dynamic conversation state management to provid

This project provides a comprehensive framework for building, training, and managing autonomous agents. It enables the construction of systems that utilize language models to plan, manage memory, and execute multi-step tasks through iterative reasoning loops and tool-based actions. The framework distinguishes itself by offering specialized capabilities for interacting with graphical user interfaces and legacy software, allowing agents to perceive visual elements and perform actions like a human user. It supports complex, cross-application workflows through graph-based orchestration and provid

LlamaIndex is a comprehensive development framework designed to connect private or external data sources to large language models. It functions as a data-centric toolkit that enables the construction of retrieval-augmented generation systems, allowing developers to build applications that provide context-aware answers based on specific organizational information. The project distinguishes itself through a robust agentic orchestration engine that supports the creation of autonomous agents capable of multi-step reasoning, memory management, and complex tool execution. Beyond simple retrieval, i

LangGraph is a framework for building stateful, multi-step agentic workflows by modeling application logic as a directed graph. It provides a runtime environment where complex tasks are orchestrated through interconnected nodes and edges, allowing developers to manage state transitions, persistent memory, and control flow across long-running automated processes. The platform distinguishes itself through its native support for human-in-the-loop automation, enabling developers to define breakpoints that pause execution for manual review, modification, or approval. It also features checkpoint-ba

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

This project provides a modular framework for building and orchestrating autonomous AI agents. It functions as an agentic workflow engine that manages the full lifecycle of task execution, including model reasoning, tool invocation, and the integration of results. By utilizing a centralized orchestration platform, the system enables the creation of multi-agent teams that collaborate on complex objectives through structured communication and shared task graphs. The framework distinguishes itself through its focus on persistent, stateful operations and multi-agent coordination. It employs file-

Letta is a framework for building, deploying, and managing autonomous AI agents that maintain persistent state across long-term interactions. It provides a comprehensive suite of primitives for defining agents with configurable personas, modular memory blocks, and tool-use capabilities, enabling them to retain user preferences and conversation history over extended sessions. The platform distinguishes itself through its advanced memory management and orchestration capabilities. It allows agents to autonomously update their own memory, perform retrieval-augmented generation, and coordinate com

This project serves as a dual-purpose platform that functions both as a comprehensive software engineering learning resource and an autonomous agent orchestration framework. It provides a structured curriculum focused on the Java ecosystem, offering technical roadmaps, interview preparation materials, and career mentorship. Simultaneously, it acts as a technical foundation for building intelligent systems, enabling developers to construct complex, multi-step agent pipelines. The framework distinguishes itself by integrating advanced automation capabilities directly into its educational missio

Tiny Universe is an educational monorepo that delivers multiple independent implementations of core AI subsystems as self-contained Jupyter notebooks. It provides from-scratch constructions of foundational architectures including a complete Transformer model built from the original paper specification, a denoising diffusion probabilistic model for image generation, and a ReAct-style autonomous agent framework that equips an LLM with tools for planning and multi-step task execution. The project distinguishes itself by covering the full lifecycle of modern AI systems through hands-on implementa

OpenHands is an autonomous agent framework designed for software engineering workflows. It provides a modular platform for orchestrating AI agents that reason, plan, and execute tasks within isolated, containerized development environments. By integrating with standard version control and development tools, the system enables agents to autonomously navigate codebases, implement features, and resolve issues through iterative reasoning and tool execution. The platform distinguishes itself through a model-agnostic orchestrator that connects diverse language models to a unified tool registry. It

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

AReaL is a system for agent orchestration, distributed model training, and parameter-efficient tuning. It provides a framework for developing multi-turn reasoning agents and training large models using reinforcement learning from human feedback. The project implements a toolkit for improving the visual reasoning and geometry problem solving capabilities of vision-language models. It utilizes a memory-efficient tuning system to optimize mathematical and reasoning models across different inference backends. The infrastructure supports large-scale training through tensor, pipeline, and expert p

DeepTutor is a framework for personalized AI tutoring and educational content generation. It functions as an agentic workflow system that executes reasoning loops to complete multi-step tasks, transforming raw sources into structured learning materials such as interactive books, quizzes, and concept graphs. The platform distinguishes itself through an extensible skill architecture that allows the installation and auditing of third-party capability packages from community registries. It utilizes persona-driven tool policies to deploy persistent AI companions with unique behavioral profiles and

This project is a LangChain-based framework for building retrieval-augmented generation systems, autonomous agents, and multimodal chatbots. It functions as an open-source orchestrator that connects local inference engines and online APIs to manage various large language model deployments. The system distinguishes itself by providing specialized interfaces for local knowledge bases, allowing the loading and vectorization of private documents to create context-aware assistants. It also supports multimodal capabilities, enabling the processing of both text and image inputs through vision-capabl

This project is an autonomous AI agent framework and workflow orchestrator designed to automate machine learning engineering. It functions as a reasoning engine that reads research papers and writes code to train and deploy machine learning models through iterative reasoning loops and tool execution. The system distinguishes itself by integrating a GPU-accelerated sandboxed execution environment, allowing it to run and verify machine learning scripts in isolated remote containers. It utilizes a model provider integration gateway to route inference requests across various hosted or local endpo

Cline is an extensible agent runtime and multi-agent orchestration engine designed to automate complex software engineering workflows. It functions as an integrated development environment extension that bridges strategic task planning with autonomous execution, allowing users to manage multi-step projects through human-in-the-loop oversight or independent agent operation. The platform distinguishes itself by enabling the creation of specialized agent teams that share a common state and coordinate through a centralized task manager. It enforces project-specific architectural guidelines and co

DB-GPT is an agentic data analysis platform and business intelligence AI that functions as a large language model data assistant. It provides a text-to-SQL interface and a sandboxed code execution environment to translate natural language into executable database queries and Python scripts. The platform utilizes iterative agentic reasoning to plan and execute multi-step data analysis workflows through tool calls. It features a modular skill-based extension system that allows domain knowledge and analysis workflows to be packaged into reusable functional components. The system integrates data

This project is a development framework for building autonomous agents that utilize language models to reason through multi-step tasks. It functions as an orchestrator that manages iterative loops of thought, action, and observation, allowing systems to process information and reach solutions without manual intervention. The framework distinguishes itself through a modular tool abstraction that connects language models to external data sources and code execution environments. By injecting tool-binding metadata into the prompt context, the system enables models to dynamically invoke custom fun