Gartner reports a 1,445% surge in multi-agent system inquiries. The AI agent framework market is projected to grow from $7.8B to $52B by 2030. Everyone wants to build AI agents — but which framework should you use?
Here's an honest comparison of the four most popular options in 2026.
Quick Overview
| Framework | Type | Best For | Language | Difficulty |
|---|---|---|---|---|
| CrewAI | Multi-agent orchestration | Team-based AI workflows | Python | Medium |
| LangGraph | Stateful agent graphs | Complex, branching workflows | Python/JS | Hard |
| AutoGen | Multi-agent conversation | Research & complex reasoning | Python | Medium |
| OpenClaw | Personal AI agent | Daily assistance & automation | TypeScript | Easy |
CrewAI
What It Is
CrewAI is a framework for orchestrating multiple AI agents that work together as a "crew." Each agent has a role, goal, and backstory. They collaborate on tasks like a team.
Strengths
- Intuitive mental model — Agents as team members with roles
- Built-in tools — Web search, file I/O, code execution
- CrewAI Studio — Visual builder for non-coders
- Enterprise features — CrewAI Enterprise with SSO, audit logs
- Growing community — One of the fastest-growing AI frameworks
Limitations
- Python only — No JavaScript/TypeScript SDK
- Orchestration focused — Not a standalone agent you can chat with
- Requires coding — Even with Studio, complex crews need Python
- No messaging integration — Doesn't live in Telegram/Slack natively
- No persistent memory — State resets between executions
Best For
Teams building automated workflows where multiple specialized agents need to collaborate — content pipelines, research teams, data analysis crews.
LangGraph
What It Is
LangGraph (by LangChain) is a framework for building stateful, multi-step AI agent workflows as directed graphs. It's the most flexible but also the most complex.
Strengths
- Maximum flexibility — Build any workflow topology
- Stateful execution — Maintains state across steps
- Human-in-the-loop — Built-in checkpoints for human approval
- LangSmith integration — Debugging and monitoring
- Production-ready — Used by enterprise teams
Limitations
- Steep learning curve — Graph-based thinking isn't intuitive
- Verbose — Simple tasks require significant boilerplate
- LangChain dependency — Tied to the LangChain ecosystem
- No end-user interface — You build the interface yourself
- Developer-only — Requires strong programming skills
Best For
Developers building complex, production-grade agent workflows that need precise control over execution flow, branching logic, and state management.
AutoGen
What It Is
AutoGen (by Microsoft) is a framework for building multi-agent conversations. Agents talk to each other to solve problems — like a panel of experts debating.
Strengths
- Conversational agents — Natural multi-agent dialogue
- Code execution — Agents can write and run code
- Flexible topologies — Group chats, sequential, nested conversations
- Microsoft backing — Strong corporate support and development
- Research-oriented — Excellent for complex reasoning tasks
Limitations
- Complex setup — Configuration can be overwhelming
- Token-heavy — Multi-agent conversations consume many tokens
- No consumer interface — Built for developers, not end users
- No messaging integration — Doesn't connect to Telegram/Slack
- Overkill for simple tasks — Multiple agents debating a simple question wastes resources
Best For
Research teams and developers building multi-agent systems for complex problem-solving — code generation, analysis, research synthesis.
OpenClaw
What It Is
OpenClaw is an open-source personal AI agent that runs 24/7 in your messaging apps. It's not a framework for building agents — it's a ready-to-use agent.
Strengths
- Ready to use — Deploy in under 1 minute, no coding
- Messaging-native — Lives in Telegram, Slack, Discord, WhatsApp
- Persistent memory — Remembers everything across weeks and months
- Any model — OpenAI, Claude, DeepSeek, local models via Ollama
- MCP support — Connect to any tool via Model Context Protocol
- Proactive — Sends daily briefings, reminders, alerts without being asked
- Self-hosted — Your data stays on your server
- Open source — Full code transparency
Limitations
- Single agent — Not a multi-agent orchestration framework
- Personal scale — Designed for individuals and small teams
- Requires hosting — Needs a server or managed hosting (ClawTank)
Best For
Individuals and small teams who want a personal AI assistant that actually runs 24/7, remembers context, automates tasks, and lives in their daily messaging apps.
Detailed Comparison
Ease of Use
| Framework | Setup Time | Coding Required | Non-Dev Friendly |
|---|---|---|---|
| OpenClaw | 1 minute | No | Yes |
| CrewAI | 30-60 min | Python basics | Studio: partial |
| AutoGen | 1-2 hours | Python | No |
| LangGraph | 2-4 hours | Python/JS (advanced) | No |
Integration & Deployment
| Framework | Messaging Apps | Self-Hosted | Cloud Option | MCP Support |
|---|---|---|---|---|
| OpenClaw | Telegram, Slack, Discord, WhatsApp | Yes | ClawTank | Full |
| CrewAI | No (API only) | Yes | CrewAI Enterprise | Limited |
| AutoGen | No (API only) | Yes | Azure | No |
| LangGraph | No (build yourself) | Yes | LangSmith Cloud | Via LangChain |
Memory & State
| Framework | Persistent Memory | Cross-Session | Long-Term Context |
|---|---|---|---|
| OpenClaw | Yes — weeks/months | Yes | Yes (daily logs, preferences) |
| CrewAI | No — per execution | No | No |
| AutoGen | Limited | Limited | No |
| LangGraph | Checkpoints | Yes (with setup) | Manual implementation |
Cost to Run
| Framework | Hosting | API Costs | Total Monthly |
|---|---|---|---|
| OpenClaw | $5-8 (ClawTank) | $1-20 (model API) | $6-28 |
| CrewAI | Your infra | High (multi-agent) | $20-100+ |
| AutoGen | Your infra | Very high (conversations) | $30-200+ |
| LangGraph | Your infra | Medium-high | $15-100+ |
Multi-agent frameworks consume significantly more tokens because agents talk to each other. OpenClaw's single-agent approach is dramatically cheaper.
When to Use What
Use OpenClaw When:
- You want a personal AI assistant, not a dev framework
- You need 24/7 availability in messaging apps
- Persistent memory matters for your use case
- You want the lowest cost and simplest setup
- Privacy and self-hosting are priorities
Use CrewAI When:
- You need multiple specialized agents collaborating
- You're building automated workflows (content, research, analysis)
- You have Python skills and want role-based agent design
- You need enterprise features (SSO, audit logs)
Use LangGraph When:
- You need maximum control over agent workflow logic
- Your use case requires complex branching and state management
- You're already in the LangChain ecosystem
- You're building production-grade enterprise applications
Use AutoGen When:
- Your problem benefits from multi-agent debate/discussion
- You're doing research that needs diverse AI perspectives
- Code generation and execution are core requirements
- You have Microsoft/Azure infrastructure
Can You Combine Them?
Yes. They solve different problems:
- OpenClaw as your daily personal agent (messaging, memory, automation)
- CrewAI/LangGraph for specific complex workflows (triggered by OpenClaw or scheduled)
- AutoGen for research tasks requiring multi-perspective analysis
OpenClaw can trigger external workflows via MCP servers, acting as the user-facing layer while specialized frameworks handle complex backend tasks.
The Bottom Line
If you're a developer building complex multi-agent systems → LangGraph or CrewAI.
If you want a personal AI agent that just works → OpenClaw.
Deploy OpenClaw on ClawTank in under 1 minute. No framework knowledge required. Just your own AI agent, running 24/7, in your Telegram.
