Libraries and SDKs that provide abstractions for building AI-powered applications including agent orchestration, tool calling, retrieval-augmented generation, and structured LLM interactions.
Top pick for Solo Dev
Simplest integration for adding AI to a Next.js or React app with minimal boilerplate.
Vercel AI SDK provides the cleanest TypeScript-first abstraction for integrating LLMs into web applications. It offers first-class streaming UI support for React and Next.js, a unified provider interface that works with OpenAI, Anthropic, Google, and others, and structured output with Zod schemas. Its focused scope avoids the complexity of larger frameworks while covering the most common patterns: chat, tool calling, and structured generation. For most web development teams, it is the fastest path to production AI features.
Comparison matrix
Comparison matrix
| Tool | Rag Support | Streaming Ui | Tool Calling | Multi Provider | Typescript First | Abstraction Level | Ecosystem Maturity | Bundle Friendliness | Pick |
|---|---|---|---|---|---|---|---|---|---|
| langchain | High | High | High | High | Low | ||||
| crewai | Medium | High | High | Low | Low | ||||
| autogen | Medium | High | High | Low | Low | ||||
| vercel-ai-sdk | Medium | High | Medium | Medium | High | Pick | |||
| llamaindex | High | Medium | High | Medium | Low |
Also great
Teams building complex agent systems with multi-step reasoning, RAG pipelines, and production observability via LangSmith.
You gain the broadest ecosystem and most advanced agent patterns but accept significant abstraction complexity and steeper learning curve.
Teams focused on RAG and document-heavy AI applications that need structured data extraction and retrieval pipelines.
You gain purpose-built RAG tooling and data connectors but accept a narrower scope beyond retrieval use cases.
Teams that want a role-based multi-agent framework for orchestrating specialized AI agents with clear responsibilities.
You gain intuitive multi-agent patterns but accept a younger ecosystem and less production-hardened infrastructure.
Avoid
AutoGen's multi-agent conversation model introduces significant complexity for use cases that simpler frameworks handle well.
Better alternatives: vercel-ai-sdk, langchain
Methodology
Hands-on onboarding runs with the best-supported SDK path.
Pricing snapshots captured at publish time and reviewed on drift.
Benchmarks recorded in a repeatable environment with notes on tradeoffs.
Use this in your agent
npx @vetted/cli recommend ai-agent-frameworks --context solo-dev
# or query from code
recommend({
category: "ai-agent-frameworks",
context: "solo-dev"
})Top pick for Startup
Best streaming UI, cleanest TypeScript types, and fastest path to a polished AI-powered product.
Vercel AI SDK provides the cleanest TypeScript-first abstraction for integrating LLMs into web applications. It offers first-class streaming UI support for React and Next.js, a unified provider interface that works with OpenAI, Anthropic, Google, and others, and structured output with Zod schemas. Its focused scope avoids the complexity of larger frameworks while covering the most common patterns: chat, tool calling, and structured generation. For most web development teams, it is the fastest path to production AI features.
Comparison matrix
Comparison matrix
| Tool | Rag Support | Streaming Ui | Tool Calling | Multi Provider | Typescript First | Abstraction Level | Ecosystem Maturity | Bundle Friendliness | Pick |
|---|---|---|---|---|---|---|---|---|---|
| langchain | High | High | High | High | Low | ||||
| crewai | Medium | High | High | Low | Low | ||||
| autogen | Medium | High | High | Low | Low | ||||
| vercel-ai-sdk | Medium | High | Medium | Medium | High | Pick | |||
| llamaindex | High | Medium | High | Medium | Low |
Also great
Teams building complex agent systems with multi-step reasoning, RAG pipelines, and production observability via LangSmith.
You gain the broadest ecosystem and most advanced agent patterns but accept significant abstraction complexity and steeper learning curve.
Teams focused on RAG and document-heavy AI applications that need structured data extraction and retrieval pipelines.
You gain purpose-built RAG tooling and data connectors but accept a narrower scope beyond retrieval use cases.
Teams that want a role-based multi-agent framework for orchestrating specialized AI agents with clear responsibilities.
You gain intuitive multi-agent patterns but accept a younger ecosystem and less production-hardened infrastructure.
Avoid
AutoGen's multi-agent conversation model introduces significant complexity for use cases that simpler frameworks handle well.
Better alternatives: vercel-ai-sdk, langchain
Methodology
Hands-on onboarding runs with the best-supported SDK path.
Pricing snapshots captured at publish time and reviewed on drift.
Benchmarks recorded in a repeatable environment with notes on tradeoffs.
Use this in your agent
npx @vetted/cli recommend ai-agent-frameworks --context startup
# or query from code
recommend({
category: "ai-agent-frameworks",
context: "startup"
})Top pick for Scale Up
Most comprehensive framework for complex RAG pipelines, multi-step agents, and advanced orchestration patterns.
Most comprehensive framework for complex RAG pipelines, multi-step agents, and advanced orchestration patterns. Teams building complex agent systems with multi-step reasoning, RAG pipelines, and production observability via LangSmith. You gain the broadest ecosystem and most advanced agent patterns but accept significant abstraction complexity and steeper learning curve.
Comparison matrix
Comparison matrix
| Tool | Rag Support | Streaming Ui | Tool Calling | Multi Provider | Typescript First | Abstraction Level | Ecosystem Maturity | Bundle Friendliness | Pick |
|---|---|---|---|---|---|---|---|---|---|
| langchain | High | High | High | High | Low | Pick | |||
| crewai | Medium | High | High | Low | Low | ||||
| autogen | Medium | High | High | Low | Low | ||||
| vercel-ai-sdk | Medium | High | Medium | Medium | High | ||||
| llamaindex | High | Medium | High | Medium | Low |
Also great
Teams focused on RAG and document-heavy AI applications that need structured data extraction and retrieval pipelines.
You gain purpose-built RAG tooling and data connectors but accept a narrower scope beyond retrieval use cases.
Teams that want a role-based multi-agent framework for orchestrating specialized AI agents with clear responsibilities.
You gain intuitive multi-agent patterns but accept a younger ecosystem and less production-hardened infrastructure.
Avoid
AutoGen's multi-agent conversation model introduces significant complexity for use cases that simpler frameworks handle well.
Better alternatives: vercel-ai-sdk, langchain
Methodology
Hands-on onboarding runs with the best-supported SDK path.
Pricing snapshots captured at publish time and reviewed on drift.
Benchmarks recorded in a repeatable environment with notes on tradeoffs.
Use this in your agent
npx @vetted/cli recommend ai-agent-frameworks --context scale-up
# or query from code
recommend({
category: "ai-agent-frameworks",
context: "scale-up"
})Top pick for Enterprise
Largest ecosystem with LangSmith observability, LangGraph for agent workflows, and broadest integration coverage.
Largest ecosystem with LangSmith observability, LangGraph for agent workflows, and broadest integration coverage. Teams building complex agent systems with multi-step reasoning, RAG pipelines, and production observability via LangSmith. You gain the broadest ecosystem and most advanced agent patterns but accept significant abstraction complexity and steeper learning curve.
Comparison matrix
Comparison matrix
| Tool | Rag Support | Streaming Ui | Tool Calling | Multi Provider | Typescript First | Abstraction Level | Ecosystem Maturity | Bundle Friendliness | Pick |
|---|---|---|---|---|---|---|---|---|---|
| langchain | High | High | High | High | Low | Pick | |||
| crewai | Medium | High | High | Low | Low | ||||
| autogen | Medium | High | High | Low | Low | ||||
| vercel-ai-sdk | Medium | High | Medium | Medium | High | ||||
| llamaindex | High | Medium | High | Medium | Low |
Also great
Teams focused on RAG and document-heavy AI applications that need structured data extraction and retrieval pipelines.
You gain purpose-built RAG tooling and data connectors but accept a narrower scope beyond retrieval use cases.
Teams that want a role-based multi-agent framework for orchestrating specialized AI agents with clear responsibilities.
You gain intuitive multi-agent patterns but accept a younger ecosystem and less production-hardened infrastructure.
Avoid
AutoGen's multi-agent conversation model introduces significant complexity for use cases that simpler frameworks handle well.
Better alternatives: vercel-ai-sdk, langchain
Methodology
Hands-on onboarding runs with the best-supported SDK path.
Pricing snapshots captured at publish time and reviewed on drift.
Benchmarks recorded in a repeatable environment with notes on tradeoffs.
Use this in your agent
npx @vetted/cli recommend ai-agent-frameworks --context enterprise
# or query from code
recommend({
category: "ai-agent-frameworks",
context: "enterprise"
})