AI Agent Frameworks Compared

The AI agent ecosystem has exploded with frameworks, each taking a different approach to building autonomous AI systems. This guide compares the most popular options to help you choose the right tool for your needs.

1. Quick Overview

2. Feature Comparison Matrix

This matrix compares key features across frameworks. Full support, Partial, Not available

3. LangChain

LangChain is the most popular framework for building LLM applications. It excels at composing chains of operations—connecting prompts, retrievers, tools, and output parsers.

Strengths

• Largest ecosystem of integrations
• Excellent for RAG (Retrieval Augmented Generation)
• Great documentation and community
• LangSmith for observability

Weaknesses

• Abstraction overhead can make debugging hard
• Not designed for multi-agent orchestration
• No built-in governance or approval workflows
• Agent loops can be unpredictable

Best For

Prototyping, RAG applications, single-agent chains, document Q&A

4. AutoGen

AutoGen (by Microsoft) focuses on multi-agent conversations. Agents communicate with each other to solve problems collaboratively.

Strengths

• Sophisticated multi-agent conversations
• Human-in-the-loop built into conversation flow
• Code execution capabilities
• Microsoft backing and enterprise support

Weaknesses

• Conversation-centric (not workflow-centric)
• No policy enforcement
• Limited audit capabilities
• Can be resource-intensive

Best For

Research, collaborative problem-solving, code generation tasks

5. CrewAI

CrewAI organizes agents into "crews" with defined roles. Each agent has a specific job (researcher, writer, reviewer), and they collaborate on tasks.

Strengths

• Intuitive role-based mental model
• Good for content generation workflows
• Easy to understand and set up
• Growing community

Weaknesses

• Limited workflow control (sequential/hierarchical only)
• No policy enforcement or approval gates
• No audit trail
• Not designed for high-stakes operations

Best For

Content generation, research automation, creative workflows

6. Temporal

Temporal is a durable workflow engine (not AI-specific) that guarantees exactly-once execution. It's been adapted for AI workflows by some teams.

Strengths

• Battle-tested durability and reliability
• Strong workflow guarantees
• Excellent observability
• Enterprise-grade

Weaknesses

• Not AI-native (requires custom integration)
• No policy enforcement for AI actions
• Steep learning curve
• Overkill for simple use cases

Best For

Teams already using Temporal, mission-critical workflows

7. Cordum

Cordum is a control plane specifically designed for AI agent governance. It sits between your agents and the actions they take, enforcing policies and providing audit trails.

Strengths

• Policy-before-dispatch enforcement
• Human approval gates for high-risk actions
• Complete audit trail
• MCP-native
• Works with any agent framework

Weaknesses

• Not an agent framework itself (complements others)
• Newer project, smaller community
• Requires infrastructure (NATS, Redis)

Best For

Production deployments, regulated industries, enterprise AI governance

8. Recommendations

Choose LangChain if...

• You're building RAG or document Q&A
• You need the largest integration ecosystem
• You're prototyping and iterating quickly

Choose AutoGen if...

• You need sophisticated multi-agent conversations
• Research or experimental use cases
• Microsoft ecosystem integration

Choose CrewAI if...

• Role-based agent teams make sense for your problem
• Content generation or creative workflows
• You want something easy to understand

Choose Temporal if...

• You already use Temporal for other workflows
• You need strong durability guarantees
• AI is part of a larger workflow system

Choose Cordum if...

• You need governance for AI agents in production
• Compliance and audit trails are required
• You want policy enforcement before agents act
• You're using MCP-compatible tools