Agent Frameworks Overview

Overview

Agent frameworks provide the building blocks for creating AI agents — tool integration, memory management, planning, and orchestration. Instead of building everything from scratch, frameworks handle the plumbing so you can focus on agent logic. Understanding the landscape is essential for choosing the right tool for your use case.

Framework Comparison

graph TD
    FRAMEWORKS[Agent Frameworks]
    FRAMEWORKS --> GENERAL[General Purpose]
    FRAMEWORKS --> MULTI[Multi-Agent]
    FRAMEWORKS --> CODE[Code-Focused]
    FRAMEWORKS --> LIGHTWEIGHT[Lightweight]

    GENERAL --> LANGCHAIN[LangChain / LangGraph]
    GENERAL --> LLAMAINDEX[LlamaIndex]

    MULTI --> CREWAI[CrewAI]
    MULTI --> AUTOGEN[AutoGen]
    MULTI --> SWARM[OpenAI Swarm]

    CODE --> OPENDEVIN[OpenDevin]
    CODE --> AIDER[Aider]

    LIGHTWEIGHT --> PYDANTIC[PydanticAI]
    LIGHTWEIGHT --> MARVIN[Marvin]

Key Features to Compare

When to Use What

Framework Architecture Patterns

Graph-Based (LangGraph)

graph TD
    START[Start] --> NODE1[Node 1: Research]
    NODE1 --> CONDITION{Condition}
    CONDITION -->|Need more info| NODE2[Node 2: Search]
    CONDITION -->|Enough info| NODE3[Node 3: Write]
    NODE2 --> NODE1
    NODE3 --> END[End]

Agents as nodes in a state graph. Edges define transitions. Supports cycles, conditions, and human-in-the-loop.

Role-Based (CrewAI)

graph TD
    CREW[Crew]
    CREW --> A1[Agent: Researcher]
    CREW --> A2[Agent: Writer]
    CREW --> A3[Agent: Reviewer]
    A1 --> TASK1[Task: Research topic]
    A2 --> TASK2[Task: Write article]
    A3 --> TASK3[Task: Review article]

Agents have roles, goals, and backstories. Tasks are assigned to agents. Crew orchestrates execution.

Conversation-Based (AutoGen)

graph LR
    A1[Agent 1] <-->|Messages| A2[Agent 2]
    A2 <-->|Messages| A3[Agent 3]
    A1 <-->|Messages| A3

Agents communicate through messages. Group chat manager coordinates. Code execution is built-in.

Interview Questions

Q1: How do you choose an agent framework?

Answer: Consider:

1. Task complexity: Simple → PydanticAI. Complex → LangGraph.
2. Multi-agent needed? Yes → CrewAI or AutoGen. No → LangChain or PydanticAI.
3. Type safety important? Yes → PydanticAI. No → any.
4. RAG-focused? Yes → LlamaIndex. No → general framework.
5. Production readiness? Check community size, maintenance, documentation.
6. Learning curve: CrewAI is easiest. LangGraph is most flexible but complex.

Q2: What are the trade-offs of using a framework vs building from scratch?

Answer:

• Framework pros: Faster development, best practices built-in, community support, integrations
• Framework cons: Abstraction overhead, dependency on framework, may not fit exact needs, version churn
• Scratch pros: Full control, no dependencies, optimized for your use case
• Scratch cons: More development time, must handle edge cases, maintenance burden

Rule: Start with a framework. Only go custom if the framework genuinely can’t handle your use case.

Common Mistakes

• ❌ Choosing a framework based on hype, not fit
• ❌ Over-engineering with a complex framework for simple tasks
• ❌ Not understanding what the framework does under the hood
• ❌ Ignoring framework updates (breaking changes are common)

Summary

Agent frameworks provide building blocks for tool integration, memory, planning, and orchestration. LangGraph offers flexibility, CrewAI simplifies multi-agent, AutoGen enables agent conversations, and PydanticAI provides type safety. Choose based on task complexity, multi-agent needs, and production requirements.

Cross-References

• LangChain → Deep dive
• CrewAI → Deep dive
• AutoGen → Deep dive
• Agent Architecture → Design patterns
• MLOps Platforms