What it does
Core capabilities at a glance
- Composable chain and agent abstractions
- 500+ integrations with models, vector stores, tools
- LangGraph for stateful multi-agent workflows
- LangSmith for debugging and monitoring
- Streaming and async support
- RAG pipelines and document retrieval chains
Deep dive
The full breakdown - performance, comparisons, and setup
LangChain
LangChain is the foundational framework for building applications on top of LLMs. It's not always the most elegant tool, but it has the largest ecosystem, the most integrations, and the biggest community.
What it is
LangChain is a Python and JavaScript framework that provides abstractions for working with LLMs. Its core concepts - chains, agents, retrievers, tools, and memory - have become the standard vocabulary of LLM application development.
Performance you'll see
LangChain itself has no inference cost. It's an orchestration layer. The overhead per chain invocation is ~50-100ms in Python, negligible for most use cases.
How it stacks up
| LangChain | Haystack | Semantic Kernel | Mastra | |
|---|---|---|---|---|
| Ecosystem size | Largest | Medium | Medium | Small |
| Language support | Python, JS | Python | C#, Python, Java | TypeScript |
| Agent support | ✓ (LangGraph) | ✓ | ✓ | ✓ |
| Learning curve | Steep | Moderate | Moderate | Low |
| Best for | Maximum flexibility | Production RAG | Microsoft ecosystem | TypeScript devs |
Get started
pip install langchain langchain-community langchain-ollamafrom langchain_ollama import ChatOllama
llm = ChatOllama(model="qwen3:30b")
result = llm.invoke("Hello!")When to use something else
From the community
What users are saying
“It often took quite a long time for those historic frameworks to get the abstraction right. Survivorship bias sees us forget all the failed attempts. I'm unconvinced there is no room for a framework here because LLMs are somehow special. LangChain just missed the mark.”
“I had a similar experience; looked into using it for something, and felt that it would be easier to recreate the things I would use langchain for in Jinja2, than it would be to reshape my code to the interface that langchain wanted.”
“This is great! I love seeing how rapidly in the past 6 months these ideas are evolving. I've been internally calling these systems "prompt machines". I'm a strong believer that chaining together language model prompts is core to extracting real, and reproducible value from language models.”
Frequently asked
Quick answers to common questions
What is LangChain?
LangChain is a agent-framework tool for local AI workloads. Framework for developing LLM-powered applications with composable chains, agents, and 500+ integrations.
Is LangChain free and open source?
Yes, LangChain has 148,043 GitHub stars and is licensed under MIT. You can self-host it for free on linux, macos, windows.
What platforms does LangChain support?
LangChain runs on linux, macos, windows.
What hardware do I need for LangChain?
The hardware requirements depend on which models you run. Check our hardware directory for compatible GPUs and systems. LangChain has 148,043 GitHub stars and an active community.
Does LangChain support GPU acceleration?
LangChain supports GPU acceleration via CUDA, Metal, or Vulkan depending on your platform. For the best performance, pair it with an NVIDIA RTX 4090 or 5090.
What are the best alternatives to LangChain?
Popular alternatives include other agent-framework tools in our directory. Browse our full collection at /tool for comparisons, community reviews, and benchmark data to find the right fit for your workflow.
How much does LangChain cost?
LangChain is free-tier. There is a free tier available with paid upgrades for advanced features.
Pairs well with
Complementary tools, models, and hardware