For a framework for building autonomous AI agents, the strongest matches are modelscope/ms-agent (ms-agent is an LLM agent framework and multi-agent orchestration), significant-gravitas/auto-gpt (Auto-GPT is a flagship autonomous agent framework that decomposes) and opendevin/opendevin (OpenDevin is an autonomous software engineering agent and orchestrator). microsoft/agent-framework and nirdiamant/genai_agents round out the shortlist. Each is ranked by relevance to your query, popularity and recent activity.
We curate open-source GitHub repositories matching “advanced artificial intelligence agents”. Results are ranked by relevance to your query — pick filters below to narrow, or refine with AI.
ms-agent is an LLM agent framework and multi-agent orchestration system designed to build autonomous entities that combine large language models with tool calling and structured workflows. It serves as a tool integration platform and workflow engine for executing complex tasks through the coordination of specialized agents. The project distinguishes itself through a multimodal agent workflow engine capable of automating the production of text, images, and video. It features a sandboxed code execution environment for running generated code and quantitative data analysis in isolated containers,
ms-agent is an LLM agent framework and multi-agent orchestration system that directly provides tool calling, memory management, sandboxed code execution, and extensible integration, covering the core capabilities needed to build advanced autonomous AI agents that reason, plan, and perform multi-step tasks.
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
Auto-GPT is a flagship autonomous agent framework that decomposes complex goals into multi-step tasks with memory, tool integration, and a visual designer, making it a strong fit for building advanced AI agents that reason and act autonomously.
OpenDevin is an autonomous software engineering agent and orchestrator designed to execute coding tasks and manage development workflows using large language models. It functions as a centralized control center for managing and switching between various local and cloud artificial intelligence backends. The system utilizes a Docker sandbox environment to isolate autonomous agents in containers, protecting the host filesystem during code execution. It includes an automated engineering workflow tool that integrates with version control and chat services to trigger tasks via webhooks or scheduled
OpenDevin is an autonomous software engineering agent and orchestrator that provides a sandboxed code execution environment, multi-agent orchestration, and task decomposition, making it a relevant platform for building and managing task-driven agents, though it is primarily focused on coding workflows.
The agent-framework is an LLM agent orchestration framework and multi-agent workflow engine designed for building autonomous AI agents. It provides a tool integration layer for binding external functions, APIs, and sandboxed code as executable tools for language models. The framework distinguishes itself through a graph-based system for designing sequential and parallel task flows, featuring state management and checkpointing for long-running processes. It implements comprehensive conversational state management and an observability suite that uses telemetry to trace execution flows and monit
This is a comprehensive open-source framework for building autonomous AI agents with tool integration, multi-agent orchestration, graph-based planning, and state management—directly addressing the need for reasoning, tool use, and multi-step tasks.
GenAI_Agents is a development framework and orchestration engine designed for building autonomous, multi-agent systems. It provides the infrastructure to construct complex, state-managed workflows where specialized agents collaborate to execute multi-step tasks, manage long-term memory, and perform iterative reasoning. The platform distinguishes itself through its graph-based orchestration model, which allows developers to define intricate agentic processes with explicit state transitions. It supports advanced control mechanisms such as human-in-the-loop intervention for manual oversight and
GenAI_Agents is a development framework and orchestration engine for building autonomous multi-agent systems, directly supporting graph-based workflows, long-term memory, tool integration, and human-in-the-loop control — covering the core requirements of this search.
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
Cline is a dedicated agent runtime and multi-agent orchestration engine that lets you build autonomous AI agents with task planning, tool execution, shared state, and code execution—directly fitting the search for an open-source framework for advanced autonomous agents.
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
MetaGPT is a multi-agent orchestration framework designed for autonomous agents to reason, plan, and execute complex software engineering tasks, directly matching the need for an advanced AI agent platform with tool use, memory, and multi-step capabilities.
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
camel-ai/camel is a comprehensive open-source framework for building autonomous multi-agent systems with roleplay-based task decomposition, tool-calling integration, and iterative reasoning, directly matching the search for an advanced AI agent platform with multi-agent orchestration and tool use.
Flowise is a low-code platform designed for building and deploying complex language model workflows through a visual, node-based interface. It functions as an orchestrator for autonomous multi-agent systems, allowing users to construct conversational pipelines by connecting language models, memory stores, and external tools on a drag-and-drop canvas. The platform distinguishes itself through its support for sophisticated agentic patterns, including supervisor-worker delegation and iterative reasoning strategies. Users can design directed acyclic graphs to manage conditional branching, state p
Flowise is a visual low-code platform for building autonomous multi-agent workflows, supporting tool use, memory, iterative planning, supervisor-worker delegation, and external tool integration — directly matching the search for an AI agent platform that can orchestrate advanced multi-step tasks.
ROMA is an agentic workflow engine and recursive task orchestrator designed to coordinate autonomous agents in the execution of complex workflows. It functions as a multi-agent framework that decomposes high-level goals into atomic subtasks and manages their execution through a dependency graph. The system distinguishes itself through a hierarchical plan-execute loop that recursively decomposes objectives and synthesizes results from leaf-node tasks upward. It ensures execution purity via atomic task isolation, assigning dedicated storage directories to individual tasks to prevent data interf
ROMA is a multi-agent orchestration framework that uses recursive task decomposition and plan-execute loops to coordinate autonomous agents, directly supporting goal decomposition, multi-agent collaboration, tool-augmented models, and external system integrations — the core of an AI agent platform, though it does not explicitly cover every feature like code execution or web browsing.
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
AgentScope is a comprehensive toolkit for building autonomous multi-agent systems with reasoning, tool use, memory, and orchestration, directly matching your need for an advanced AI agent framework that supports multi-step tasks and collaboration.
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
huggingface/smolagents is a full-featured Python framework for building autonomous agents that generate and execute code, manage complex tasks through iterative reasoning, and integrate tools, memory, and planning—exactly the kind of platform this search is after.
ChatDev is an automated software engineering platform that orchestrates the end-to-end development lifecycle through a multi-agent framework. It functions as a programmable engine that coordinates specialized autonomous agents to handle design, coding, testing, and documentation tasks by transitioning through predefined phases of a software project. The system distinguishes itself by using role-based agent specialization to simulate a professional engineering team, assigning distinct personas and knowledge bases to individual agents. It employs prompt-driven task decomposition to break high-l
ChatDev is a multi-agent orchestration platform for automated software engineering that implements planning, role-based specialization, code execution, and memory — directly addressing your need to build autonomous agents with reasoning, tool use, and multi-step task execution.
Claude-flow is an autonomous agent coordination platform and orchestration framework designed for building complex, multi-step workflows powered by large language models. It functions as a TypeScript-based engine that decomposes high-level objectives into executable action sequences, enabling the creation of collaborative agent teams that operate with minimal manual oversight. The platform distinguishes itself through its ability to federate autonomous agents across network boundaries using secure communication channels and identity verification. It integrates a goal-oriented planning engine
Claude-flow is a TypeScript orchestration framework for building autonomous AI agent teams with goal-oriented planning, multi-agent coordination, memory persistence, and tool integration via the Model Context Protocol, directly matching the search for an advanced agent platform with reasoning and multi-step task capabilities.
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 leading orchestration framework for building autonomous AI agents, directly supporting tool use, memory, multi-step planning, and API integration through its unified model-agnostic layer and graph-based agent workflows.
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
Eino is an AI agent development kit and multi-agent orchestration engine with graph-based workflows, tool routing, and ReAct-like reasoning loops, making it a strong fit for building advanced autonomous agents that use tools and coordinate multiple agents.
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
Koog is an LLM agent framework with a graph-based workflow engine, dynamic model switching, MCP for tool integration, and RAG memory, covering tool use, planning, memory, multi-agent orchestration, and API integration — a solid match for building advanced autonomous agents.
Goose is an extensible agentic AI platform designed for autonomous task orchestration and developer-centric assistance. It provides a workflow engine that manages complex, multi-step objectives by delegating tasks to specialized subagents, all while maintaining stateful session continuity. The system is built to integrate directly into terminal and coding environments, allowing for automated file manipulation and context-aware interaction. The platform distinguishes itself through a secure, sandboxed runtime environment that enforces granular permission controls and policy-driven guardrails.
Goose is an open-source agentic AI platform with a workflow engine for multi-step task orchestration, hierarchical subagents, sandboxed execution, and a rich extension system — exactly what you need to build advanced autonomous agents that reason, use tools, and integrate with external services and the web.
PraisonAI is an autonomous AI agent platform that coordinates multiple LLM-powered agents for research, planning, and execution of complex workflows. It functions as a multi-agent orchestration framework, a workflow builder, and a Model Context Protocol server, while also providing retrieval-augmented generation through vector knowledge bases. Agents can interact via CLI, web, or standardized protocols with sandboxed code execution. The platform distinguishes itself with a rich set of agent communication protocols, including A2A, REST, WebSocket, voice and telephony integration, and MCP, allo
PraisonAI is an autonomous multi-agent orchestration platform that directly matches the search for building advanced AI agents — it supports planning, tool execution via protocols, sandboxed code execution, and extensible integrations through MCP and A2A, covering nearly all the required features like multi-agent orchestration, tool use, and workflow automation.
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
Langroid is a multi-agent orchestration framework with collaborative message exchange, hierarchical task delegation, and advanced state management, making it a strong fit for building autonomous AI agents that reason, use tools, and coordinate multiple agents.
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
Letta is a framework for building autonomous AI agents with tool-use capabilities and persistent memory, directly matching the need for advanced agent platforms that reason and perform multi-step tasks.
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
LobeHub is a multi-agent orchestration platform that provides persistent memory, granular tool integration, multi-step workflow execution, and multi-agent team management — directly meeting the need for building autonomous agents with reasoning, tool use, and task decomposition, while covering most of the required features.
AIOS is an LLM agent operating system and orchestration kernel designed to manage memory, resource scheduling, and tool execution for multiple autonomous AI agents. It serves as a comprehensive framework for developing and deploying agents, featuring a dedicated resource manager that coordinates model backends, GPU memory, and isolated kernel instances. The system distinguishes itself through a semantic memory engine that uses vector search and autonomous clustering for long-term knowledge management, and a semantic file system that allows users to control computer files and system operations
AIOS is an LLM agent operating system and orchestration framework that manages memory, resource scheduling, and tool execution for multiple autonomous agents, making it a solid match for building advanced AI agents — it supports multi-agent orchestration, semantic memory management, and extensible tool/execution environments, though planning, web browsing, and API integration are not highlighted as explicit features.
TradingAgents is an autonomous financial research and simulation framework that coordinates specialized agents to analyze market data and execute investment strategies. The system functions as a multi-agent debate environment where independent units critique financial insights through structured, adversarial reasoning to improve decision accuracy and mitigate investment risks. The platform distinguishes itself through a risk-gated transaction pipeline that validates all proposed financial actions against market volatility and liquidity constraints before execution on a simulated exchange. To
TradingAgents is a multi-agent AI framework for autonomous financial research and trading, coordinating specialized agents with structured reasoning and tool orchestration—so it fits the category of AI agent frameworks even though its financial focus means it lacks general features like memory, web browsing, code execution, or a plugin system.
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
LlamaIndex is a full-fledged agent framework that provides tool use, memory, multi-agent orchestration, and multi-step reasoning capabilities, making it a strong platform for building advanced autonomous AI agents.
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
OpenHands is an autonomous agent framework that orchestrates AI agents with reasoning, planning, and tool execution in containerized environments, aligning well with building advanced multi-step agents.
InternLM is a large language model and a comprehensive suite of weights designed for text generation and complex reasoning. It functions as an inference engine for serving responses, a fine-tuning framework for adjusting model weights, and a platform for building autonomous AI agents. The system is capable of processing long-context input sequences up to one million tokens for document analysis. It employs chain-of-thought reasoning to solve knowledge-intensive tasks by generating intermediate logic steps before producing a final answer. The project covers model weight optimization through s
InternLM is a large language model platform that supports building autonomous agents with tool calling, long-context memory, and chain-of-thought reasoning, fitting the search for agent frameworks, though it lacks multi-agent orchestration, code execution, and plugin extensibility.
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
OpenManus is an autonomous agent framework with dynamic capability discovery, stateful execution context, goal decomposition, and agent orchestration — covering tool use, memory, planning, and multi-agent capabilities demanded by this search.
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
AutoGen is a conversational workflow engine and event-driven runtime for building multi-agent systems, directly supporting tool use, memory, task decomposition, and multi-agent orchestration as core capabilities—exactly the kind of advanced autonomous agent framework this search targets.
AgenticSeek is a multi-agent orchestration system designed to decompose complex user objectives into granular, actionable tasks. By coordinating a team of specialized autonomous workers, the platform manages end-to-end workflows, ensuring that each component of a project is assigned to the most capable agent for execution. The system operates as a local-first runtime, executing all artificial intelligence models directly on user hardware to maintain data sovereignty and privacy. It integrates a browser automation engine for autonomous web research and interaction, alongside a sandboxed enviro
AgenticSeek is a local-first multi-agent orchestration system that provides task decomposition, autonomous web browsing, sandboxed code execution, and coordinated specialized agents, making it a comprehensive platform for building advanced autonomous AI agents with tool use and multi-step reasoning.
AutoGPT is an orchestration platform designed for building, managing, and deploying autonomous agents. It provides a visual canvas-based environment where users can assemble agents by connecting modular blocks that represent actions, data flows, and conditional logic. The platform supports the entire agent lifecycle, including task scheduling, execution monitoring, and configuration management, while offering a marketplace for discovering and sharing community-built workflows. The project includes a legacy framework for command-line agent execution and an extensible component system for devel
AutoGPT is a platform for building and deploying autonomous agents, offering a visual canvas with modular blocks, task scheduling, and an extensible component system—exactly the kind of framework for advanced multi-step agent workflows you're looking for.
Claude Code is a command-line interface and multi-agent orchestration framework designed for autonomous software engineering. It enables AI agents to perform codebase modifications, debugging, and Git workflow management while coordinating multiple specialized agents to decompose and execute complex engineering tasks in parallel. The system distinguishes itself through a high degree of isolation and safety, utilizing Git worktrees to create independent working directories for concurrent agents and implementing a tiered permission system that combines user rules, project policies, and OS-level
Claude Code is an open-source multi-agent orchestration framework purpose-built for autonomous software engineering, handling task decomposition, tool use (code editing, debugging, Git), and concurrent agent coordination — squarely the kind of AI agent platform this search is after, though its focus on codebase tasks makes it narrower than general-purpose agent frameworks.
Eigent is a comprehensive platform for developing, configuring, and orchestrating autonomous AI agents. It functions as an agent development environment and workflow automation engine, enabling users to build modular agents equipped with custom toolsets, domain-specific skill packages, and external API connections to perform targeted operational tasks. The framework distinguishes itself through a robust multi-agent orchestration layer that coordinates teams of specialized agents to execute complex workflows. By utilizing hierarchical task decomposition, the system breaks high-level goals into
Eigent is a full agent-development platform that supports modular tool integration, external API connections, multi-agent orchestration, and hierarchical task decomposition — directly matching your need for building reasoning, tool-using agents, though it does not explicitly highlight memory management or code execution.
ChatGLM3 is a comprehensive framework for deploying, fine-tuning, and serving large language models. It functions as a high-performance inference engine designed to support conversational AI, enabling developers to build interactive agents capable of multi-turn dialogue, autonomous code execution, and structured tool invocation. The project distinguishes itself through its focus on hardware-agnostic deployment and resource optimization. It supports distributed model parallelism across multiple graphics cards, paged key-value caching for concurrent request processing, and weight quantization t
ChatGLM3 is a framework for deploying LLMs with built-in autonomous code execution and structured tool invocation, making it a solid foundation for building AI agents that reason and act, though it doesn't explicitly advertise multi-agent orchestration or planning features.
This project is a comprehensive framework for developing, orchestrating, and deploying autonomous agents. It provides a structured environment for building agents that utilize reasoning loops to perform multi-step tasks, manage state through graph-based workflows, and interact with external tools. By mapping unstructured model outputs into typed schemas, the framework ensures reliable integration with downstream application logic. The platform distinguishes itself through a focus on production-grade reliability and security. It incorporates hybrid memory systems that combine vector embeddings
This project is a framework for building autonomous agents with reasoning loops, tool integration, hybrid memory, and multi-agent support, making it a solid fit for the intent—though the Jupyter Notebook format and tutorial topics suggest it may lean toward an educational resource rather than a production-ready platform.
CopilotKit is an agentic framework designed to integrate large language models into application frontends, enabling natural language control over software features and data. It provides the infrastructure to build intelligent assistants that manage conversation history, track application state, and execute complex workflows through conversational prompts. The framework distinguishes itself by its ability to render dynamic, interactive user interface components in real time based on model outputs. By utilizing a standardized communication protocol, it maps natural language intents to executabl
CopilotKit is an agentic framework for building AI assistants that manage conversation state, orchestrate tasks, and integrate LLM tool calling, making it a solid fit for creating autonomous agents with reasoning and multi-step workflows, though its focus on frontend integration means it may not cover every advanced feature like code execution sandboxes or explicit planning out of the box.
This project is an orchestration framework designed to automate creative and research workflows by managing specialized artificial intelligence agents. It functions as a content generation system that delegates complex, multi-step tasks to model instances, ensuring that each agent operates within defined behavioral constraints and design methodologies. The framework distinguishes itself through its focus on structural integrity and brand consistency. It employs schema-driven validation to ensure that all generated content adheres to predefined templates and data formats. By utilizing custom s
This orchestration framework manages AI agents for multi-step creative and research workflows, with explicit support for tool execution and hierarchical multi-agent orchestration — it fits the core need of building autonomous agents.
This project is an AI software engineering tool and framework for building autonomous coding agents. It provides a system for automating program synthesis and bug fixing by integrating large language models with codebase analysis and iterative refinement loops. The framework features an agentic development server that exposes task execution interfaces to remote agents through a structured protocol. This allows for the remote execution of development tasks and the embedding of autonomous program synthesis capabilities into external software projects. The toolset covers AI-driven project scaff
smol-ai/developer is a framework for building autonomous coding agents that uses LLMs for task decomposition, code execution, and agent orchestration via a remote protocol—directly fitting the search for open-source autonomous agent platforms, though it is specialized for software engineering rather than a general-purpose tool.
The Open Agent Platform is an orchestration environment for building, deploying, and managing autonomous AI agents. It provides a framework for constructing both single-task performers and complex multi-agent systems, utilizing a central supervisor pattern to coordinate collaborative workflows and task delegation. The platform distinguishes itself through a graph-based execution model that defines the sequence of logic and tool calls, paired with a visual configuration interface that allows for the creation of agent workflows without manual coding. It incorporates enterprise-grade security by
LangChain's open-agent-platform is a no-code platform for building AI agents, squarely fitting the search for an agent-building framework/platform, though its description does not confirm coverage of the advanced reasoning, tool-use, or multi-step capabilities you are looking for.
| Repository | Stars | Language | License | Last push |
|---|---|---|---|---|
| modelscope/ms-agent | 4.3K | Python | Apache-2.0 | |
| significant-gravitas/auto-gpt | 185K | Python | NOASSERTION | |
| opendevin/opendevin | 77.5K | Python | NOASSERTION | |
| microsoft/agent-framework | 7.3K | Python | mit | |
| nirdiamant/genai_agents | 20K | Jupyter Notebook | other | |
| cline/cline | 63.8K | TypeScript | Apache-2.0 | |
| foundationagents/metagpt | 68.8K | Python | MIT | |
| camel-ai/camel | 17.3K | Python | Apache-2.0 | |
| flowiseai/flowise | 53.6K | TypeScript | NOASSERTION | |
| sentient-agi/roma | 5.1K | Python | — |