Why people look for LangChain alternatives
LangChain has the most GitHub stars of any agent framework we track, but it draws some common complaints:
- Abstraction layers. It can be harder to debug than calling a model provider’s SDK directly.
- Many packages. Integrations are spread across separate packages, and their maintenance varies.
- Breaking changes. LangChain has a history of API changes between major versions.
Before switching, it’s worth knowing how LangChain is built today. Its main entry point, create_agent, is a configurable harness that runs on LangGraph. If your problem is “LangChain is too high-level”, the fix may be LangGraph, not a different vendor. See LangChain vs LangGraph.
Quick picks
| If you want… | Pick | Languages | License |
|---|---|---|---|
| More control, same ecosystem | LangGraph | Python, TypeScript | MIT |
| Typed agents and validated outputs | Pydantic AI | Python | MIT |
| Retrieval over your documents | LlamaIndex | Python | MIT |
| A team of role-based agents | CrewAI | Python | MIT |
| One framework across many languages | Google ADK | Python, TypeScript, Go, Java, Kotlin | Apache-2.0 |
| A minimal agent loop, good on AWS | Strands Agents | Python, TypeScript | Apache-2.0 |
The alternatives in more detail
LangGraph. LangChain’s own low-level orchestration framework, and what LangChain agents run on. You build the agent as a graph of nodes and edges with shared state, and get durable execution, human-in-the-loop and memory built in. It can be used without LangChain.
Pydantic AI. A type-safe Python agent framework from the Pydantic team. Agent inputs and outputs are typed and validated, so IDEs and type checkers understand them, and you can switch model providers by changing a string. It supports durable execution through Temporal, DBOS or Prefect, plus Pydantic Graph and Pydantic Evals. MCP is in the core package.
LlamaIndex. Built for retrieval: load documents, build an index, query it. It has a large catalog of data loaders and vector store integrations, and event-driven Workflows for multi-step agents. It’s Python-only now: the TypeScript port is deprecated. LlamaIndex says its focus has shifted to LlamaParse. See LangChain vs LlamaIndex.
CrewAI. A standalone Python framework, not built on LangChain. You define agents by role, goal and backstory, group them into crews, and add event-driven Flows with persistence and human feedback when you need control. New projects can be defined in JSON config. See CrewAI vs LangGraph.
Google ADK. Google’s Agent Development Kit has official SDKs in Python, TypeScript, Go, Java and Kotlin. It includes a local dev UI, evaluation tooling and multi-agent composition, and deploys to Google Cloud or any container host. It works best with Gemini on Google Cloud; other providers go through adapters such as LiteLLM.
Strands Agents. AWS’s model-driven SDK. The model plans and calls tools in a built-in loop, so a working agent takes very little code. It’s model-agnostic, with first-class Amazon Bedrock, Anthropic, OpenAI and Gemini support, and has built-in MCP, multi-agent patterns, hooks and tracing. Python and TypeScript.
What about AutoGen? Microsoft’s README now says AutoGen is archived and no longer maintained, and points to Microsoft Agent Framework as the successor. We’ve left it out of the list below.
