Framework Comparison

If you're building custom AI agent applications (not just using coding tools), you may want a framework. Here's how the major options compare.

Don't reach for a framework first. If your workflow is a straight line (A → B → C → D), plain async/await + direct API calls is simpler, faster, and easier to debug. Use a framework only when you need agent autonomy, branching, or multi-agent orchestration. See When NOT to use a framework below for details.

Quick Comparison

When to Use Each

Claude Agent SDK

Use when you want Claude Code's agent loop as a library in your own app or CI pipeline.

• Same built-in tools as Claude Code (Read/Write/Edit/Bash/Glob/Grep/WebFetch/WebSearch)
• Skills, hooks, sub-agents, sessions, permissions — all programmable
• npm install @anthropic-ai/claude-agent-sdk (TypeScript) or pip install claude-agent-sdk (Python)
• Requires an Anthropic API key; also works via Bedrock / Vertex AI / Azure Foundry

OpenAI Agents SDK

Use when you want a lightweight, OpenAI-native agent framework with very few abstractions.

• Primitives: Agents, Handoffs, Guardrails
• Successor to Swarm; Python-first (pip install openai-agents)
• Built-in agent loop, sessions, tracing, sandbox agents, realtime voice
• Good default if your stack is already OpenAI

Pydantic AI

Use when you want a model-agnostic Python framework that integrates tightly with Pydantic validation.

• Install: pip install pydantic-ai
• Supports Agents (primary), Graphs (complex flows), function-based tools
• Works with Anthropic, OpenAI, Google, and most other providers
• Pairs with Logfire for observability out of the box

Google ADK

Use when you're building production agents inside the Google Cloud ecosystem.

• Multi-language: Python (pip install google-adk), TypeScript (@google/adk), Go, Java
• Deploys to Agent Engine / Cloud Run / GKE
• Strong integrated evaluation + safety tooling
• "Build production agents, not prototypes" framing — heavier than Pydantic AI or OpenAI Agents

LangChain / LangGraph

Use when you need complex, stateful agent workflows with branching logic, parallel execution, and persistence.

• Graph-based orchestration (nodes = steps, edges = transitions)
• Built-in state management and checkpointing
• Huge ecosystem of integrations (200+ tools)
• Can be over-engineered for simple use cases

CrewAI

Use when you need multiple agents with distinct roles collaborating on a task.

• Define agents with roles, goals, and backstories
• Agents delegate tasks to each other
• Good for research, analysis, content generation pipelines
• Higher-level abstraction than LangGraph

AutoGen (Microsoft)

Use when you need multi-agent conversations — agents talking to each other to solve problems.

• Conversational agent framework
• Strong enterprise integration (Azure, Microsoft 365)
• Good for coding tasks (code generation + code execution agents)

Mastra

Use when you're building TypeScript-native agent backends.

• First-class TypeScript support
• Built-in workflow engine
• Integrations with common APIs
• Growing ecosystem

When NOT to Use a Framework

For simple, deterministic pipelines (like a news aggregation pipeline), frameworks add unnecessary abstraction. A custom pipeline with async/await + direct API calls is simpler, faster, and easier to debug.

Rule of thumb: If your pipeline is a straight line (A → B → C → D), use plain code. If it branches, loops, or needs agent autonomy, use a framework.