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LangChain Alternatives: 9 Frameworks Compared

2026-11-14

If you want more control than LangChain gives you, start with LangGraph. If you want a ready-made agent instead of a framework, install POLYROB. We build POLYROB, and its dependency list in [email protected] has no agent framework in it: the agent loop and the model layer are native code ([email protected]), so we know what you end up writing yourself when you leave a framework behind.

This page compares nine LangChain alternatives on what their own repositories say. Every fact about another project was read from its GitHub README or LICENSE file on 2026-11-14 and links to it. POLYROB goes last, with a section on where it loses.

What are the best LangChain alternatives?

AutoGen is not in the table on purpose. Its README now says the project is archived and no longer maintained by Microsoft, and points new users to Microsoft Agent Framework. More on that below.

Why look for a LangChain alternative?

Three reasons come up most often, and none of them is “LangChain is bad”.

The first is control. LangChain’s own README says that for more advanced customization or agent orchestration you should look at LangGraph. If your agent needs durable state, a pause for human review, or branching, you are already looking at a second library.

The second is shape. LangChain gives you components: models, embeddings, vector stores and tools behind a standard interface. You assemble the agent, the storage, the scheduler and the chat front end. For some teams that is the point. For others it is the cost.

The third is language. If your product is a TypeScript app, a Python-first framework adds a second runtime. Mastra exists for that case.

Which frameworks are real alternatives to LangChain?

LangGraph

LangGraph is the lower-level layer under LangChain agents. Its README lists durable execution, human-in-the-loop, short-term and long-term memory, LangSmith debugging and deployment support. It says companies such as Klarna, Replit and Elastic trust it.

Pick it if you want to own the control flow of a long-running, stateful agent and are happy to stay in the LangChain ecosystem. Our LangChain vs LangGraph comparison goes through the difference in detail.

CrewAI

CrewAI describes itself as a framework for production-ready multi-agent workflows. Its README names two pieces: Crews, role-based agents that collaborate, and Flows, event-driven control that can include single LLM calls and Crews. A commercial suite, CrewAI AMP, adds managed deployment, observability and governance around it. The README gives Python 3.10 up to but not including 3.14 as the requirement.

Pick it if your problem maps onto a team of role-based agents and you want a Python framework built around that idea.

LlamaIndex

LlamaIndex started as a RAG toolkit. Its README now carries a note that the company’s primary focus has shifted to LlamaParse, document parsing and extraction, while the open-source framework stays available as an open toolkit. The framework installs as a starter package or as a core package plus the integrations you choose.

Pick it if your agent mostly works over documents and retrieval, and read that note before you commit a new project to the framework.

Haystack

Haystack, from deepset, calls itself an open-source AI orchestration framework for production-ready LLM applications in Python. Its banner describes it as built for production RAG and agents. The license is Apache 2.0.

Pick it if you want a pipeline-style framework with retrieval at the center and a permissive license.

DSPy

DSPy takes a different route. Its README says you write compositional Python code instead of brittle prompts, and DSPy offers algorithms to optimize prompts and weights, for classifiers, RAG pipelines and agent loops. Install is pip install dspy.

Pick it if you want to measure and optimize prompts as part of the program rather than hand-tune them. It is a programming model, not an agent you run.

Mastra

Mastra is a TypeScript framework. Its README lists model routing across 40+ providers, agents, a graph-based workflow engine with .then(), .branch() and .parallel(), suspend-and-resume for human approval, memory, and MCP servers. The LICENSE file puts anything under an ee/ directory, including the auth and agent-builder packages, under a separate license, and the rest under Apache 2.0.

Pick it if your stack is TypeScript, React or Next.js. Read ee/LICENSE before you rely on its auth packages in production.

OpenAI Agents SDK

The SDK’s README lists agents, handoffs, tools including MCP, guardrails, human-in-the-loop, sessions and tracing. It says the SDK is provider-agnostic and supports the OpenAI Responses and Chat Completions APIs as well as 100+ other LLMs. A sandbox-agent mode runs agents against a container.

Pick it if you want a small set of primitives and you already build on OpenAI’s APIs.

Agno

Agno’s README describes an agent platform: you build agents, run them as durable services on AgentOS with an API and an MCP server, and automate company operations. It lists authentication, role-based access, resume and replay, human approvals and scheduling as AgentOS features, and PostgreSQL for storage.

Pick it if you are building agents for a company or a product and want a service layer around them.

AutoGen

AutoGen was Microsoft’s multi-agent framework. The README on 2026-11-14 carries a notice that the project is archived, with no bug fixes, security fixes or new features, and tells new users to start with Microsoft Agent Framework. Check that notice before any new work on it.

POLYROB

POLYROB is the odd one out because it is not a framework you import to build an agent. It is the agent. You install it, give it a task or a standing goal in plain language, and it plans the steps, uses tools (web, files, code, a shell, mail, MCP servers) and reports back, in a terminal, a web console, a REST API or a chat app ([email protected]). It is MIT licensed ([email protected]) and needs Python 3.11 or newer, though the installer fetches a private copy if you lack it.

curl -fsSL https://polyrob.dev/install.sh | bash
polyrob doctor

What you get without writing it, with the file behind each point:

• A model layer with a native client per provider: OpenAI, Anthropic, Gemini, DeepSeek, OpenRouter and NVIDIA (modules/llm/*[email protected]). Any OpenAI-compatible endpoint, such as Ollama, works through a providers file (docs/[email protected]). See running an LLM locally.
• An MCP client, so the agent can call tools from any MCP server (tools/mcp/[email protected]). Our MCP client explainer shows the setup.
• Goals and scheduled jobs that survive a restart (docs/guide/[email protected]).
• Sub-agents for delegated work (agents/task/agent/[email protected]).
• Cross-session memory kept in local SQLite files (modules/memory/[email protected]).
• A cap on every autonomous run: 150 steps and $2.00 by default, with the stop reason reported (docs/[email protected]). Autonomous loops are off until you run polyrob autonomy on (docs/[email protected]).

Pick it if the job is “keep working on this and ask me before high-impact steps” and you would rather configure an agent than assemble one. Our self-hosted AI agent guide covers the full install.

Where does POLYROB lose to LangChain and the other frameworks?

Plainly:

• It is not a component library. If you need to embed one retrieval step in your own application code and choose every model and vector store yourself, a framework is the right tool.
• Ecosystem size. LangChain has integrations, a docs site, a forum and courses. POLYROB is the newer project with a smaller community (docs/[email protected]).
• Language. POLYROB is Python and you work in a terminal or a chat app. It will not drop into a TypeScript codebase the way Mastra does.
• No migration tool. There are manual migration guides from Hermes and OpenClaw, and none from LangChain (docs/guide/migration/). Moving a LangChain app means rebuilding it.
• No hosted version. The README says there is none, so there is no managed platform like LangSmith Deployment or CrewAI AMP.

How do you choose?

Frequently asked questions

Is DSPy better than LangChain?

They solve different problems. DSPy’s README describes programming and optimizing prompts and weights, and LangChain’s describes components for building agents and LLM applications. Many teams could use both. Neither README claims to be better than the other.

Is AutoGen better than LangChain?

On 2026-11-14 the AutoGen README says the project is archived and unmaintained, so for new work it is not a safe pick. Its README points to Microsoft Agent Framework as the successor.

Is LangChain still popular?

We did not measure usage, so we do not rank popularity. What the README shows today is an active project: it describes a family of products, namely Deep Agents, LangGraph, integrations, LangSmith and LangSmith Deployment.

Do you need a framework to build an AI agent?

Not always. POLYROB’s agent loop and model layer are native code, and its pyproject.toml lists no agent framework among its dependencies ([email protected]). A framework saves time when you are building your own product around an agent. A ready-made agent saves more when you want to use one.

Sources

Fetched 2026-11-14: the READMEs of LangChain, LangGraph, CrewAI, LlamaIndex, Haystack, DSPy, Mastra, OpenAI Agents SDK, Agno and AutoGen, plus the LICENSE files of LlamaIndex, Haystack, DSPy and Mastra. POLYROB facts are from the public repository at tag v1.7.9. Projects change quickly: check each link before you decide.

To try an agent that is already built, read how to install POLYROB. For a comparison with other personal agents, see OpenClaw alternatives and n8n alternatives.

POLYROB is an open-source AI agent you run and own. Install it or read the docs.