Open Source AI Agent Frameworks Compared: CrewAI vs LangGraph vs AutoGen vs OpenAgents (2026)

The AI agent ecosystem has exploded. There are now dozens of open source frameworks for building agents that reason, collaborate, and take actions autonomously. But if you're evaluating your options for a multi-agent project, the sheer number of choices can be overwhelming.

In this guide, we compare four of the most prominent open source AI agent frameworks — CrewAI, LangGraph, AutoGen, and OpenAgents — across the dimensions that actually matter for production use: architecture, multi-agent collaboration, protocol support, and what each one is best suited for.

The Frameworks at a Glance

Let's dig into each one.

CrewAI: Role-Based Autonomous Teams

CrewAI models multi-agent collaboration as a team ("crew") of role-playing agents. You define each agent's role, backstory, and goal, then assemble them into a crew with a set of tasks. The agents communicate naturally and can delegate work to each other.

How It Works

CrewAI offers two architecture modes. Crews are autonomous teams where agents have true agency — they decide when to delegate, when to ask questions, and how to approach their tasks. Flows are event-driven pipelines for production workloads that need more predictability.

A distinctive feature is the hierarchical process mode, which auto-generates a manager agent that oversees task delegation and reviews outputs — similar to how a team lead manages a group of specialists.

Strengths

• Intuitive role-based agent design that maps well to real-world team structures
• Minimal dependencies — lean and fast without heavy external libraries
• Growing A2A protocol support for agent interoperability
• Large certified developer community (100K+ developers)

Limitations

• Primarily designed for task-oriented workflows rather than persistent agent communities
• Framework-specific abstractions can limit flexibility for advanced use cases
• Agents are tied to the crew lifecycle rather than operating independently across sessions

Best For

Complex business workflows where agents need clear roles — think research teams, content pipelines, or customer service workflows with handoffs between specialists.

LangGraph: Graph-Based State Machines

LangGraph takes a fundamentally different approach. Rather than defining agents as personas, you model your application as a directed graph where agents are nodes and edges define the flow of shared state between them.

How It Works

Each agent reads from and writes to a central state object. LangGraph manages state persistence and uses reducer logic to merge concurrent updates. This makes it especially powerful for workflows where you need precise control over execution order, branching, and error recovery.

The framework reached v1.0 in late 2025 and has become the default runtime for all LangChain agents. It supports both Python and JavaScript.

Strengths

• Durable execution — agents can persist through failures and resume automatically
• Human-in-the-loop support lets you inspect and modify state at any point
• Comprehensive memory system (short-term working memory + long-term persistent memory)
• Part of the LangChain ecosystem with access to its extensive integrations
• Available in both Python and JavaScript

Limitations

• The graph-based paradigm has a steep learning curve compared to more intuitive agent abstractions
• Tightly coupled to the LangChain ecosystem
• No native A2A or MCP protocol support — interoperability requires custom integration

Best For

Production-grade agents requiring fault tolerance and precise state management. Strong choice for teams already invested in LangChain who need durable, long-running workflows with human oversight.

AutoGen: Conversation-Driven Multi-Agent Systems

AutoGen, created by Microsoft Research, pioneered the idea of multi-agent conversation patterns. Agents interact through structured dialogues — two-agent chats, group chats, sequential conversations, and nested chat patterns.

How It Works

In a group chat, a centralized Group Chat Manager (itself an LLM-powered agent) orchestrates which agent speaks next. Speaker selection can be round-robin, random, manual, or auto-determined by the LLM based on conversation context. The v0.4 release in 2025 introduced asynchronous messaging with both event-driven and request/response patterns.

AutoGen also ships with AutoGen Studio, a visual interface for building agents without code.

Strengths

• The most diverse set of conversation patterns among any framework
• Huge community and GitHub presence (50K+ stars)
• Both code-based and no-code (Studio) approaches to building agents
• .NET support in addition to Python

Limitations

• Microsoft is shifting strategic focus toward the broader Microsoft Agent Framework — AutoGen will receive bug fixes and security patches but is unlikely to see major new features
• Centralized orchestration via the Group Chat Manager can become a bottleneck
• Less emphasis on open protocol interoperability (MCP/A2A)

Best For

Conversational multi-agent scenarios like group decision-making, debate, and consensus-building. Also a good option if you need a no-code interface for non-technical users.

OpenAgents: Network-Based Agent Communities

OpenAgents approaches multi-agent AI differently from the frameworks above. Rather than defining isolated teams or conversations, OpenAgents builds persistent agent networks — self-sustaining communities where agents discover peers, collaborate on tasks, and grow over time.

How It Works

You create a network, add agents to it, and the agents can discover each other's capabilities and collaborate autonomously. Networks are persistent — they keep running, and agents can join or leave at any time. This is closer to how the internet works (nodes joining and leaving a network) than how a traditional task pipeline operates.

OpenAgents provides both a Python SDK for developers and OpenAgents Studio, a visual web interface for configuring and monitoring networks. Agents communicate through open protocols — MCP for tool and context sharing, and A2A for agent-to-agent communication — making them interoperable with agents built in other frameworks.

Strengths

• Native MCP + A2A: The only framework with first-class support for both major open agent protocols, enabling true cross-framework interoperability
• Persistent networks: Agents operate in long-lived communities rather than one-shot task pipelines
• LLM-agnostic: Agents can use any language model provider without vendor lock-in
• Open protocol design: A LangGraph agent, a CrewAI agent, and a custom Python agent can all participate in the same OpenAgents network via A2A
• Modular architecture: Extend functionality through mods — community-built modules for task delegation, shared artifacts, workspace management, and more

Limitations

• Younger framework with a smaller community compared to AutoGen or LangGraph
• The network paradigm is powerful but conceptually different — teams familiar with task-pipeline thinking may need time to adapt
• Fewer out-of-the-box integrations compared to LangChain ecosystem

Best For

Building large-scale, persistent agent ecosystems that need open interoperability. If you want agents from different frameworks to discover and collaborate with each other through standard protocols, or if you're building a long-lived community of specialized agents, OpenAgents is purpose-built for that.

Choosing the Right Framework

The right framework depends on what you're building:

Choose CrewAI if you want an intuitive way to model agent teams with clear roles and need something up and running quickly. The role-based abstraction is the easiest to reason about for business workflow automation.

Choose LangGraph if you need production-grade durability, precise state management, and are already using LangChain. The graph-based model gives you fine-grained control over complex, stateful workflows.

Choose AutoGen if you're building conversational multi-agent systems with diverse interaction patterns — especially group decision-making or debate scenarios. The no-code Studio option is a plus for mixed technical/non-technical teams.

Choose OpenAgents if you're building persistent, interoperable agent networks at scale. It's the natural choice when you need agents from different frameworks to work together through open protocols (MCP + A2A), or when you're building a community of agents that evolves over time rather than running one-off tasks.

The Bigger Picture: Interoperability Is the Future

Perhaps the most important trend in the AI agent space isn't any single framework — it's the emergence of open protocols that let agents work together regardless of which framework built them.

MCP (Model Context Protocol), contributed by Anthropic to the Linux Foundation's Agentic AI Foundation, is becoming the standard for how agents connect to tools and data sources. A2A (Agent2Agent Protocol), launched by Google with 50+ technology partners, standardizes agent-to-agent communication and discovery.

The frameworks that embrace these protocols will define the next generation of multi-agent systems. Today, OpenAgents is the only framework with native support for both MCP and A2A. CrewAI has added A2A support. LangGraph and AutoGen have yet to adopt either standard natively.

As the ecosystem matures, expect these protocol boundaries to dissolve. The winners won't be the frameworks that lock you in — they'll be the ones that let your agents participate in the broader agent economy.

Getting Started

Ready to try OpenAgents? Here's the fastest path:

pip install openagents

Create your first network:

from openagents import Network, Agent
 
network = Network(name="my-first-network")
 
agent = Agent(
    name="researcher",
    instructions="You are a helpful research agent."
)
 
network.add_agent(agent)
network.run()

Then open OpenAgents Studio to see your network in action, or check out the documentation for more advanced setups including A2A integration, MCP tools, and multi-agent workflows.