Choosing an AI agent framework in 2026 means picking from three dominant options: CrewAI, LangGraph, and AutoGen (now AG2). Each takes a fundamentally different approach to multi-agent orchestration — and the right choice depends on what you're actually building.
This guide breaks down their architectures, features, pricing, and ideal use cases so you can make an informed decision without wading through marketing pages.
TL;DR: Quick Comparison
| CrewAI | LangGraph | AutoGen (AG2) | |
|---|---|---|---|
| Philosophy | Role-based crew orchestration | Graph-based state machines | Conversational agent collaboration |
| Abstraction Level | High-level | Low-level | Mid-level |
| Learning Curve | Gentle | Steep | Moderate |
| Best For | Business workflows, rapid prototyping | Complex stateful agents, custom control flow | Research, multi-agent conversations |
| Language | Python | Python, JavaScript | Python |
| License | Apache 2.0 (OSS) + Commercial | MIT (OSS) + Commercial | Apache 2.0 (fully OSS) |
| Maintained By | CrewAI, Inc. | LangChain, Inc. | Community (originally Microsoft) |
| Production Platform | CrewAI AMP (cloud + self-hosted) | LangSmith Agent Server | Self-hosted only |
CrewAI: The Role-Based Crew Builder
Philosophy and Architecture
CrewAI thinks in terms of crews — teams of AI agents where each agent has a defined role, goal, and backstory. You describe what agents should do, and CrewAI handles how they coordinate.
The core abstractions are intuitive:
- Agent: A specialized worker with a role (e.g., "Senior Researcher") and tools
- Task: A specific objective with expected output and assigned agent
- Crew: A team of agents working through tasks in sequence or in parallel
- Process: The orchestration strategy (sequential, hierarchical, or consensual)
This role-based design maps naturally to how businesses already think about teams. If you can describe a workflow as "person A does X, passes it to person B for Y," CrewAI will feel immediately familiar.
Key Features
- Visual Studio + AI Copilot: Build agent workflows without writing code using CrewAI Studio
- Built-in Tools & Triggers: Pre-built integrations with Gmail, Slack, Salesforce, HubSpot, Notion, and more
- Agent Training: Both automated and human-in-the-loop training to improve agent outputs over time
- Task Guardrails: Define validation rules so agents produce consistent, reliable results
- Workflow Tracing: Real-time visibility into every step agents take
- Memory & Knowledge: Agents can retain context across tasks and access knowledge bases
- Planning & Reasoning: Advanced orchestration with planning steps before execution
Pricing (as of 2026)
| Plan | Cost | Included Executions | Seats |
|---|---|---|---|
| Basic | Free | 50/month | 1 |
| Professional | $25/month | 100/month ($0.50/extra) | 2 |
| Enterprise | Custom | Up to 30,000/month | Unlimited |
The open-source framework is free (Apache 2.0). The commercial platform (CrewAI AMP) adds the visual editor, tracing, training, RBAC, SSO, and serverless deployment.
Strengths
- Fastest time to first working prototype
- No-code visual editor lowers the barrier for non-engineers
- Enterprise features (SOC2, SSO, VPC deployment) are mature
- 450M+ agentic workflows run per month — battle-tested at scale
- 60% of Fortune 500 companies use it
Limitations
- High-level abstractions can limit fine-grained control over agent behavior
- Complex branching logic requires workarounds compared to graph-based approaches
- Vendor lock-in risk with the commercial platform
- Execution-based pricing can get expensive at scale
LangGraph: The Graph-Based Orchestrator
Philosophy and Architecture
LangGraph takes the opposite approach from CrewAI. Instead of high-level role abstractions, it gives you a low-level graph-based framework where you define agents as nodes and their interactions as edges in a state machine.
Core concepts:
- StateGraph: Define a typed state object that flows through your graph
- Nodes: Functions that receive state, do work, and return updated state
- Edges: Connections between nodes (can be conditional for branching)
- Checkpointing: Built-in state persistence for long-running workflows
You're essentially drawing a flowchart of your agent's behavior. Every decision point, every loop, every handoff is explicit in the graph structure.
Key Features
- Durable Execution: Agents persist through failures and can resume from checkpoints
- Human-in-the-Loop: Interrupt execution at any node to inject human decisions
- Full Memory System: Both short-term working memory (within a run) and long-term memory (across sessions)
- Streaming: First-class support for streaming token-by-token and intermediate state updates
- LangSmith Integration: Deep observability with execution traces, state visualization, and runtime metrics
- Multi-language: Available in both Python and JavaScript/TypeScript
Pricing
| Component | Cost |
|---|---|
| LangGraph OSS | Free (MIT license) |
| LangSmith (observability) | Free tier available, paid plans from $39/month |
| LangSmith Agent Server (deployment) | Usage-based pricing |
LangGraph itself is fully open-source. The paid ecosystem (LangSmith for tracing, Agent Server for deployment) is where LangChain monetizes.
Strengths
- Maximum control over agent behavior and state management
- Excellent for complex, branching workflows with many decision points
- Built-in checkpointing makes long-running agents resilient
- Strong ecosystem with LangChain components for model and tool integration
- Trusted by Klarna, Replit, Elastic, and other production-grade deployments
Limitations
- Steep learning curve — graph-based thinking isn't natural for everyone
- More boilerplate code for simple use cases
- Tightly coupled with LangChain ecosystem (you can use it standalone, but it's designed to work with LangChain)
- Debugging graph flows can be complex without LangSmith
AutoGen (AG2): The Conversational Multi-Agent System
Philosophy and Architecture
AutoGen — now rebranded as AG2 and evolved into a community-driven open-source project — takes a conversation-first approach. Agents collaborate by talking to each other, and complex behavior emerges from structured multi-agent conversations.
Core concepts:
- ConversableAgent: The base agent class — every agent can send and receive messages
- AssistantAgent: An LLM-powered agent that generates responses and can use tools
- UserProxyAgent: Represents a human (or automated stand-in) that can execute code
- GroupChat: Multiple agents in a shared conversation with configurable turn-taking
- Orchestration Patterns: Sequential, round-robin, graph-based, or custom speaker selection
AG2 positions itself as an "AgentOS" — not just a framework, but a foundational layer for building agent systems. Its conversation-based design makes it particularly powerful for research scenarios and complex multi-agent reasoning.
Key Features
- Flexible Orchestration: Sequential pipelines, group chats, nested conversations, and custom patterns
- Code Execution: Built-in sandboxed code execution (Docker or local) for agents that write and run code
- Human-in-the-Loop: Configurable human intervention at any point in the conversation
- Tool Use: Register Python functions as tools that agents can call
- Multi-LLM Support: Works with OpenAI, Anthropic, local models, and more via config
- Teachability: Agents can learn from conversations and apply lessons to future interactions
- Nested Conversations: Agents can spawn sub-conversations to handle complex subtasks
Pricing
| Component | Cost |
|---|---|
| AG2 Framework | Free (Apache 2.0) |
| Hosted Platform | None (self-hosted only) |
AG2 is fully open-source with no commercial tier. There's no hosted deployment platform — you manage your own infrastructure.
Strengths
- Completely free and open-source with no vendor lock-in
- Conversational design is intuitive for multi-agent reasoning and debate
- Excellent code execution capabilities out of the box
- Strong research community and academic backing (originated at Microsoft Research)
- Flexible enough to implement almost any multi-agent pattern
Limitations
- No managed hosting — you're responsible for deployment, scaling, and monitoring
- Community-maintained after Microsoft's transition — pace of development varies
- Less polished production tooling compared to CrewAI and LangGraph
- Conversation overhead can add latency and token costs for simple workflows
- Limited built-in observability
Head-to-Head: Feature Comparison
| Feature | CrewAI | LangGraph | AutoGen (AG2) |
|---|---|---|---|
| No-code builder | ✅ Visual Studio | ❌ | ❌ |
| Code-first API | ✅ | ✅ | ✅ |
| State persistence | ✅ (platform) | ✅ (checkpointing) | ⚠️ (manual) |
| Human-in-the-loop | ✅ | ✅ | ✅ |
| Streaming | ✅ | ✅ (first-class) | ⚠️ (limited) |
| Code execution | ⚠️ (via tools) | ⚠️ (via tools) | ✅ (built-in sandbox) |
| Built-in tracing | ✅ | ✅ (LangSmith) | ❌ |
| Memory (long-term) | ✅ | ✅ | ⚠️ (teachability) |
| Pre-built integrations | ✅ (50+ tools) | ✅ (LangChain tools) | ⚠️ (fewer built-in) |
| Multi-language | Python only | Python + JS/TS | Python only |
| Managed hosting | ✅ (cloud + self-hosted) | ✅ (Agent Server) | ❌ |
| SSO / RBAC | ✅ (Enterprise) | ✅ (LangSmith) | ❌ |
| Self-hosted option | ✅ (Enterprise) | ✅ | ✅ (only option) |
Performance and Scalability
Token Efficiency
The frameworks differ in how many tokens they consume for equivalent tasks:
- CrewAI: Moderate overhead. Role descriptions and task specifications add tokens, but the sequential process is efficient for straightforward workflows.
- LangGraph: Most token-efficient for complex flows because you control exactly what state is passed between nodes.
- AutoGen (AG2): Highest token overhead. Conversational message passing means agents see full conversation histories, which grows quickly in group chats.
Latency
- CrewAI: Fast for sequential crews. Parallel task execution is available but limited to independent tasks.
- LangGraph: Most predictable latency because you define the exact execution path. Streaming support means users see results early.
- AutoGen (AG2): Variable latency. Multi-turn conversations mean multiple LLM calls per agent interaction. Great for thoroughness, expensive for speed.
Scaling in Production
- CrewAI: Serverless containers with automatic scaling (Enterprise plan). The platform handles infrastructure.
- LangGraph: LangSmith Agent Server provides managed deployment. Self-hosted requires your own infrastructure.
- AutoGen (AG2): No managed option. You're responsible for containerization, scaling, and orchestration.
When to Use Which Framework
Choose CrewAI If You...
- Want the fastest path from idea to working prototype
- Have non-technical team members who need to build or modify agent workflows
- Need enterprise features (SSO, RBAC, SOC2 compliance) out of the box
- Are building business process automation (marketing, sales, support workflows)
- Value a managed platform over infrastructure control
Ideal Use Cases: Content pipelines, lead qualification, customer support triage, report generation, data analysis workflows
Choose LangGraph If You...
- Need fine-grained control over agent state and execution flow
- Are building complex workflows with many conditional branches
- Want durable, long-running agents that can resume after failures
- Already use LangChain and want deep ecosystem integration
- Need the most flexibility in defining custom agent architectures
Ideal Use Cases: Complex RAG pipelines, multi-step reasoning agents, customer-facing chatbots with complex flows, research assistants with branching logic
Choose AutoGen (AG2) If You...
- Want agents that collaborate through natural conversation
- Need built-in code execution for coding assistants or data analysis
- Are doing research on multi-agent systems or conversation dynamics
- Want a fully open-source solution with zero vendor lock-in
- Need agents that debate, critique, and refine each other's work
Ideal Use Cases: Coding assistants, research workflows, code review systems, multi-perspective analysis, educational agents
The Missing Layer: Managing Agents Across Frameworks
Here's what none of these frameworks solve: managing the agents themselves.
CrewAI orchestrates tasks within a crew. LangGraph manages state within a graph. AutoGen coordinates conversations between agents. But who tracks which agents are running, what tasks are assigned, whether deliverables meet quality standards, and how your agent team is performing overall?
This is the management layer — and it's framework-agnostic.
AgentCenter sits above the orchestration layer as a mission control dashboard for AI agent teams. It doesn't replace your framework choice. Instead, it gives you:
- Task management: Assign, track, and prioritize work across your agent team using a Kanban board
- Real-time monitoring: See which agents are active, idle, or stuck with heartbeat tracking and status updates
- Deliverable review: Agents submit work through the API; humans review and approve with versioning
- Team coordination: @mentions, notifications, and activity feeds keep human-agent collaboration smooth
- Quality gates: Approval workflows ensure nothing ships without human sign-off
Whether your agents are built with CrewAI crews, LangGraph state machines, or AG2 conversations, AgentCenter provides the operational layer to manage them as a coordinated team.
Migration and Interoperability
Switching frameworks isn't trivial, but it's not impossible:
- CrewAI → LangGraph: Rewrite role-based logic as graph nodes. You'll gain control but lose the visual editor.
- LangGraph → CrewAI: Map graph nodes to agents/tasks. Simple graphs translate well; complex conditional flows may lose nuance.
- AutoGen → Either: Rewrite conversation patterns as either crew roles or graph flows. Code execution capabilities need to be replaced with tool integrations.
Pro tip: Use a framework-agnostic management layer (like AgentCenter) from the start. If you ever switch frameworks, your task tracking, deliverable history, and team coordination stay intact.
Frequently Asked Questions
Is CrewAI better than LangGraph?
It depends on your needs. CrewAI is better for rapid prototyping, business workflows, and teams with non-technical members thanks to its visual editor and high-level abstractions. LangGraph is better when you need fine-grained control over agent state, complex branching logic, and durable execution. Neither is universally "better" — they solve different problems.
Is AutoGen still maintained?
AutoGen has been rebranded as AG2 and is now maintained by a community of volunteers from multiple organizations. While it originated at Microsoft Research, it's now fully community-driven and open-source under Apache 2.0. Development continues actively, though the pace differs from commercially-backed frameworks.
Can I use multiple frameworks together?
Yes. Some teams use LangGraph for complex stateful workflows and CrewAI for simpler business automation, managing both through a unified layer like AgentCenter. The frameworks don't conflict — they solve different orchestration problems.
Which AI agent framework is best for beginners?
CrewAI has the gentlest learning curve. Its role-based design (agents, tasks, crews) maps to familiar concepts, and the visual editor lets you build workflows without code. AutoGen's conversational approach is intuitive but requires more Python knowledge. LangGraph has the steepest learning curve due to its graph-based abstractions.
What's the cheapest option for production?
AutoGen (AG2) is completely free with no commercial tier, but you'll pay for infrastructure. CrewAI's free tier gives you 50 executions/month. LangGraph is free as open-source, with LangSmith's free tier covering basic observability. For production at scale, total cost depends more on LLM API costs than framework fees.
How do I monitor AI agents in production?
CrewAI includes built-in tracing and monitoring in its platform. LangGraph integrates with LangSmith for observability. AutoGen has no built-in monitoring. For framework-agnostic agent monitoring — tracking task completion, agent health, deliverable quality, and team performance — tools like AgentCenter provide a unified dashboard regardless of which framework you use.
Do these frameworks support human-in-the-loop workflows?
All three support human intervention, but differently. CrewAI offers human input through its platform UI and API. LangGraph provides interrupt points at any graph node where you can inspect and modify state. AutoGen's UserProxyAgent is designed specifically for human-agent interaction within conversations.
The Bottom Line
The AI agent framework space in 2026 offers genuine choice:
- CrewAI wins on speed, accessibility, and enterprise readiness
- LangGraph wins on control, flexibility, and complex workflow support
- AutoGen (AG2) wins on openness, research capabilities, and conversational design
Pick the framework that matches your team's skills and your project's complexity. Then add a management layer to keep everything running smoothly as your agent team grows.
The framework handles orchestration. The management layer handles operations. You need both.