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Module 04 · Page 5 of 20 · 9 sections
Agent Frameworks and Tools
LangChain, LangGraph, AutoGen, CrewAI and the others, and how to pick between them.
What are Agent Frameworks?
- Agent frameworks are libraries and platforms that provide building blocks to create, configure and run AI agents.
- They simplify development by handling common tasks like tool integration, memory management, planning and orchestration.
- Developers can focus on application logic instead of building everything from scratch.
Popular Agent Frameworks
| Framework | Language | Key features | Use cases |
|---|---|---|---|
| LangChain | Python | Chains, agents, tools, memory | RAG, chatbots, custom agents |
| LangGraph | Python | Graph-based agents, multi-agent workflows | Complex workflows, state management |
| AutoGen | Python | Multi-agent conversation | Collaborative agents, automation |
| CrewAI | Python | Role-based agents, easy configuration | Team of agents, business automation |
| Semantic Kernel | Python / C# / JS | Enterprise ready, plugins and tools | Enterprise apps, tool integration |
| LlamaIndex | Python | Data ingestion, RAG and agents | Knowledge apps, document Q&A |
LangChain Architecture (Overview)
User Query
LangChain
Agent / Chain
ToolsAPIs, DB, search, etc.
LLMe.g. GPT-4, Claude
MemoryConversation + context
Response to User
LangChain Components
- LLM
- Interface to interact with language models (e.g. OpenAI, Anthropic, local models).
- Prompt Templates
- Reusable templates for instructions.
- Chains
- Combine multiple steps together.
- Agents
- Use tools to decide and take actions.
- Tools
- External integrations (search, APIs, database, code).
- Memory
- Maintain conversation history and context.
- Output Parsers
- Structure the model output.
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LangGraph (Graph based Agent)
- LangGraph allows building stateful, graph-based multi-agent workflows.
- It is useful for complex tasks where agents need to collaborate, loop or maintain state.
- Each node in the graph can represent an agent, tool or logic step.
Start
Agent 1Planner
Agent 3Reviewer
End
Agent 2Tool user
AutoGen (Multi-Agent Framework)
- Developed by Microsoft for building multi-agent conversational systems.
- Supports different types of agents (Assistant, User, Critic, etc.).
- Agents can communicate, share context and work together to solve tasks.
User AgentProvides goal
Assistant AgentPlans & executes
Critic AgentReviews output
Shared MemoryConversation history, task state
CrewAI (Role Based Agents)
- CrewAI makes it easy to create a team of agents with specific roles and goals.
- Each agent has a role, backstory and set of tools.
- Useful for real-world business workflows.
Researcher AgentResearch topic
Writer AgentGenerate content
Reviewer AgentCheck quality
Choosing the Right Framework
| Criteria | LangChain | LangGraph | AutoGen | CrewAI |
|---|---|---|---|---|
| Best for | General purpose | Complex workflows | Multi-agent conversations | Role-based teams |
| Ease of use | Easy | Moderate | Moderate | Easy |
| Multi-agent support | Yes | Yes (advanced) | Yes | Yes |
| Tool integration | Wide support | Wide support | Good | Good |
| Ideal use case | RAG, chatbots | Stateful agents | Collaborative agents | Business workflows |
Key Takeaways
- Use frameworks to speed up agent development.
- Choose the right framework based on your use case.
- All frameworks support tool integration and memory.
- For complex multi-agent workflows, LangGraph or AutoGen are better options.