# Multi-agent tool comparisons

- [Agent2Agent (A2A) Protocol vs Model Context Protocol (MCP)](https://multiagentguide.top/compare/a2a-vs-mcp.md): A2A and MCP solve different problems and are usually used together. MCP connects an AI application to servers that offer tools, resources and prompts. A2A lets one agent discover another agent, send it messages and track tasks without either side exposing its internals. Both are open protocols under Linux Foundation governance with official SDKs in several languages.
- [AG2 vs AutoGen](https://multiagentguide.top/compare/ag2-vs-autogen.md): AG2 and AutoGen share history but are now different codebases. AG2 diverged from AutoGen in November 2024; its v1.0 package exposes a new async Agent API and moved the classic autogen import to a separate ag2-classic package. Microsoft's AutoGen (autogen-agentchat 0.7.x) is in maintenance mode. Neither is a drop-in replacement for the other.
- [Claude Code agent teams vs Claude Code subagents](https://multiagentguide.top/compare/agent-teams-vs-subagents.md): Subagents are helpers inside one Claude Code session: each works in its own context and returns a result to the caller. Agent teams are several full Claude Code sessions with a lead, a shared task list and direct messaging between teammates. Teams are experimental, off by default and cost more tokens; subagents are the standard, cheaper option.
- [AgentScope vs AutoGen](https://multiagentguide.top/compare/agentscope-vs-autogen.md): AgentScope 2.x is an actively released Apache-2.0 framework: a ReAct agent with a permission system, sandboxed workspaces, experimental team pipelines and an agent service, with MCP, A2A and AG-UI documented. AutoGen offers group-chat teams on an actor-style core but is in maintenance mode with no new features. For new work, AgentScope is the maintained option.
- [CrewAI vs Agno](https://multiagentguide.top/compare/crewai-vs-agno.md): CrewAI models work as crews of role-based agents running tasks sequentially or under a manager, plus Flows for deterministic steps. Agno offers agents, teams with a leader in coordinate, route, broadcast or tasks mode, and workflows, served by its AgentOS runtime with your own database. Both are open-source Python with paid vendor platforms.
- [Cursor vs Devin](https://multiagentguide.top/compare/cursor-vs-devin.md): This compares the multi-agent features of two closed-source products. Cursor runs agents in parallel from its Agents Window, in local git worktrees or cloud VMs, with subagents and a Projects coordinator. Devin is a hosted agent whose sessions can launch managed child sessions in separate VMs, or run Dynamic Workflows written as Python scripts. Both are subscription products.
- [DeerFlow vs OpenHands](https://multiagentguide.top/compare/deer-flow-vs-openhands.md): DeerFlow 2.0 is ByteDance's self-hosted agent harness built on LangGraph: a lead agent with sandboxes, skills, memory and sub-agents, aimed at research, reports and coding. OpenHands centers on coding: Agent Canvas (beta) manages the OpenHands agent or Claude Code, Codex and Gemini CLI across local, Docker, VM or cloud backends. Both are MIT-licensed.
- [Google ADK (Python) vs LangGraph](https://multiagentguide.top/compare/google-adk-vs-langgraph.md): Both are graph runtimes for agents. Google ADK 2.0 adds a Workflow graph next to coordinator agents with subagents, and ships a CLI, dev web UI and eval runner; it is optimized for Gemini and Google Cloud deployment. LangGraph is a lower-level state graph with checkpoints, interrupts and time travel, deployed through LangSmith or self-hosted.
- [Haystack vs LlamaIndex](https://multiagentguide.top/compare/haystack-vs-llamaindex-agents.md): Both are Python frameworks that grew from retrieval into agents. Haystack builds everything from typed components wired into pipelines, with an Agent component and coordinator-plus-specialist multi-agent setups. LlamaIndex builds agents on event-driven Workflows, with AgentWorkflow handoffs between agents. Haystack favors explicit data flow; LlamaIndex favors handoff-style agent teams and a large integration catalog.
- [Herdr vs cmux](https://multiagentguide.top/compare/herdr-vs-cmux.md): Herdr and cmux are terminal-level tools for watching many coding agents at once; neither manages merges for you. Herdr is an Apache-2.0 Rust binary with a background server, so agents survive detach and SSH drops, on macOS, Linux and Windows. cmux is a native macOS terminal app with notification rings, an approvals feed and a scriptable browser pane.
- [LangGraph vs CrewAI](https://multiagentguide.top/compare/langgraph-vs-crewai.md): LangGraph is a low-level graph runtime: you define typed state, nodes and edges, and get checkpoints, interrupts and time travel. CrewAI supplies the agent abstractions for you: roles, goals, tasks, sequential or hierarchical crews, plus Flows for deterministic steps. Both are MIT-licensed Python libraries with a paid hosted platform from the vendor.
- [Letta vs LangGraph](https://multiagentguide.top/compare/letta-vs-langgraph.md): Letta (formerly MemGPT) is a stateful-agent platform: each agent keeps long-term memory in a git-backed file store and delegates to subagents, driven through a TypeScript Agent SDK, CLI or App Server. LangGraph is a Python graph runtime where you design state, memory and control flow yourself. Letta is a ready harness; LangGraph is a building kit.
- [Mastra vs VoltAgent](https://multiagentguide.top/compare/mastra-vs-voltagent.md): Mastra and VoltAgent are TypeScript agent frameworks with the same core shape: supervisor agents delegating to subagents, plus typed workflows that can suspend and resume. Mastra adds A2A client and server and ACP for coding agents, with source-available ee/ directories. VoltAgent is MIT throughout, builds on the Vercel AI SDK and ships AG-UI as its own package.
- [MetaGPT vs ChatDev](https://multiagentguide.top/compare/metagpt-vs-chatdev.md): Both began as simulated software companies. MetaGPT still is one: product manager, architect, project manager and engineer roles follow an SOP, but the repository was last pushed in January 2026. ChatDev's main branch is now ChatDev 2.0, a YAML-defined workflow platform with a web console; the original system lives on the chatdev1.0 branch.
- [Microsoft Agent Framework vs AutoGen](https://multiagentguide.top/compare/microsoft-agent-framework-vs-autogen.md): Microsoft Agent Framework is the successor Microsoft recommends to AutoGen users: Python and .NET SDKs with graph workflows, checkpoints and first-party MCP, A2A and AG-UI packages. AutoGen is in maintenance mode with no new features; its latest Python release is python-v0.7.5 from September 2025. New projects should start on Agent Framework.
- [Multica vs Symphony](https://multiagentguide.top/compare/multica-vs-symphony.md): Both turn tickets into agent runs. Multica is its own task board: people assign issues to agents or squads, runs execute on connected machines, and work lands in review. Symphony, from OpenAI, is a spec plus an Elixir reference service that polls an existing tracker, creates a workspace per issue and runs Codex. Multica is source-available; Symphony is Apache-2.0.
- [OpenAI Agents API vs LangGraph](https://multiagentguide.top/compare/openai-agents-api-vs-langgraph.md): The OpenAI Agents API is a managed service: OpenAI runs the Codex harness, keeps session state and can provide a sandbox, while your app sends tasks and handles events. LangGraph is a library you run yourself, with full control over graph, state and model choice. The trade is managed convenience on OpenAI models versus control and portability.
- [OpenAI Agents SDK (Python) vs LangGraph](https://multiagentguide.top/compare/openai-agents-sdk-vs-langgraph.md): The OpenAI Agents SDK is a small set of primitives: agents, tools, handoffs, guardrails, sessions and tracing, run by a Runner loop. LangGraph is a graph runtime with typed state and checkpoints at every step. The SDK suits handoff-style assistants with little orchestration code; LangGraph suits workflows that need explicit branching and durable, resumable state.
- [OpenClaw vs Hermes Agent](https://multiagentguide.top/compare/openclaw-vs-hermes-agent.md): OpenClaw and Hermes Agent are both self-hosted, MIT-licensed personal agents that sit behind chat apps and can drive coding harnesses. OpenClaw is a TypeScript Gateway with isolated multi-agent routing, sub-agents and a JavaScript Swarm mode. Hermes is Nous Research's Python agent with delegated subagents, a Kanban board for multi-process work and self-written skills.
- [Orca vs Conductor](https://multiagentguide.top/compare/orca-vs-conductor.md): Orca and Conductor are desktop apps for running several coding agents in parallel, one git worktree per task, with diff review and pull requests. Orca is MIT-licensed, runs on macOS, Windows and Linux, and launches a long list of agent CLIs. Conductor is closed source and macOS only, bundles Claude Code, Codex and OpenCode, and sells cloud workspaces.
- [Orca vs Superset](https://multiagentguide.top/compare/orca-vs-superset.md): Orca and Superset are desktop environments that run CLI coding agents in parallel with one git worktree per task and built-in diff review. Orca is MIT-licensed and cross-platform. Superset is macOS-first under the Elastic License 2.0, adds per-workspace ports, a CLI, SDK and MCP server, and sells Pro features such as remote access, mobile and automations.
- [Paseo vs T3 Code](https://multiagentguide.top/compare/paseo-vs-t3-code.md): Paseo and T3 Code both put a desktop, web and mobile interface over coding agents that run on your own machine, with git worktrees for isolation. Paseo is a daemon with workspaces, an MCP server and tools that let agents spawn other agents. T3 Code is organized as threads, can fan one prompt out to several models, and is MIT-licensed.
- [Pydantic AI vs OpenAI Agents SDK (Python)](https://multiagentguide.top/compare/pydantic-ai-vs-openai-agents-sdk.md): Both are code-first Python agent libraries under MIT. Pydantic AI centers on typed agents: validated outputs, dependency injection and provider-prefixed model strings, with durable execution integrations and AG-UI built in. The OpenAI Agents SDK centers on handoffs, guardrails, sessions and built-in tracing, and integrates most closely with OpenAI's hosted tools and Responses API.
- [Ruflo vs Claude Code subagents](https://multiagentguide.top/compare/ruflo-vs-claude-code-subagents.md): Claude Code subagents are built in: the main agent hands a focused task to a helper with its own context and gets a summary back. Ruflo is a third-party layer installed around Claude Code or Codex that adds swarm topologies, on-disk vector memory, hooks and an MCP server. Start with subagents; add Ruflo for what they lack.
- [Semantic Kernel vs Microsoft Agent Framework](https://multiagentguide.top/compare/semantic-kernel-vs-agent-framework.md): Semantic Kernel is Microsoft's SDK for plugins and agents in C#, Python and Java. Microsoft Agent Framework is its stated successor, combining Semantic Kernel and AutoGen ideas with graph workflows and checkpoints in Python and .NET. Microsoft says most new features go to Agent Framework and that Semantic Kernel keeps receiving critical fixes.
- [smolagents vs Pydantic AI](https://multiagentguide.top/compare/smolagents-vs-pydantic-ai.md): smolagents is Hugging Face's small Apache-2.0 library whose CodeAgent writes each action as Python code, with manager agents calling managed sub-agents. Pydantic AI is a typed MIT framework: validated structured outputs, dependency injection, deferred tools and durable execution. Choose smolagents for code-acting agents and quick experiments; Pydantic AI for typed application code.
- [Superpowers vs gstack](https://multiagentguide.top/compare/superpowers-vs-gstack.md): Superpowers and gstack are MIT-licensed skill packs that give a coding agent a fixed development process; neither is a runtime. Superpowers enforces design approval, a written plan, then execution by subagents with reviews, across many harnesses. gstack is a set of role-based slash commands for Claude Code that pass documents along a plan, build, review, test and ship sequence.
- [Swarms vs CrewAI](https://multiagentguide.top/compare/swarms-vs-crewai.md): Swarms offers a catalog of multi-agent structures (sequential, concurrent, graph, hierarchical, group chat, mixture-of-agents) behind a SwarmRouter, so the pattern is a parameter. CrewAI offers two models: role-based crews and event-driven Flows, with documented human review and checkpointing. Swarms gives more patterns; CrewAI gives more documented control around each run.

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Data as of 2026-09-30. Not affiliated with listed projects. HTML version: https://multiagentguide.top/compare