Deep Agents vs LangChain
Which should you use? A side-by-side look at what each one does, who it suits, what it costs, how hard it is to set up, and where each falls short.
Open source · MIT
An open-source Python agent harness for multi-step AI work, built on LangGraph.
Open source · MIT
A Python framework for building AI agents and apps powered by language models.
The short answer
Choose Deep Agents if you need…
- Developers building AI agents that run multi-step tasks such as research and writing
- Teams that want a working agent harness they can customize instead of building one from scratch
- People who want to use either hosted or self-hosted language models with the same code
- Developers already using LangChain or LangGraph who want a more opinionated setup
Choose LangChain if you need…
- Python developers building LLM-powered applications
- Teams that want to switch between model providers without rewriting code
- Developers connecting LLMs to external data sources and tools
- Prototyping LLM workflows with reusable components
Side-by-side
| Deep Agents | LangChain | |
|---|---|---|
| Type | Library | Framework |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 30.9k | 149k |
| Setup difficulty | Intermediate | Intermediate |
| How you get it | Install with a package manager | Install with a package manager |
| Main language | Python | Python |
| Last code update | 4 days ago | 4 days ago |
What each one is
Deep Agents
Deep Agents is a Python (and JavaScript/TypeScript) library that gives you a ready-made AI agent with planning, file access, sub-agents, and context management already included. You can swap in any tool-calling language model and override or replace parts of it. It is built on LangGraph, a framework for agent workflows.
LangChain
LangChain is a Python framework for building applications powered by large language models (LLMs) and agents. It provides a standard interface for models, embeddings, and vector stores, plus a library of integrations with model providers and tools. Developers use it to connect LLMs to their own data and systems and to swap models without rewriting their application.
Best for
Deep Agents
- Developers building AI agents that run multi-step tasks such as research and writing
- Teams that want a working agent harness they can customize instead of building one from scratch
- People who want to use either hosted or self-hosted language models with the same code
- Developers already using LangChain or LangGraph who want a more opinionated setup
LangChain
- Python developers building LLM-powered applications
- Teams that want to switch between model providers without rewriting code
- Developers connecting LLMs to external data sources and tools
- Prototyping LLM workflows with reusable components
Strengths
Deep Agents
- Ships with planning, filesystem, sub-agent, and context management features out of the box
- Works with any LLM that supports tool calling, including local and open-weight models
- Built on LangGraph, which the README describes as providing streaming, persistence, and checkpointing
LangChain
- Standard interface lets you swap model providers with limited code changes
- Large library of integrations with model providers, tools, and vector stores
- Open source under the MIT license with a documentation site and free courses
Not ideal for
Deep Agents
- Non-programmers looking for a finished app: the README centers on writing Python code, and the terminal coding agent is a separate feature
- Anyone who needs strict guarantees the agent will not misuse tools, since the README says it follows a "trust the LLM" model and boundaries must be enforced at the tool or sandbox level
- Projects that only need a simple single-call LLM request, since the harness adds more structure than that requires
LangChain
- People who want a finished end-user app rather than a library to code against
- Developers who need low-level control over agent orchestration; the README points to LangGraph for that
- JavaScript or TypeScript projects, which the README directs to the separate LangChain.js library
Trade-offs
Deep Agents
- The README is truncated and does not document every option, so details of configuration depend on the external docs
- Its security model places responsibility on the developer to restrict what tools and sandboxes can do
- It is tied to the LangChain ecosystem, so adopting it means adopting those dependencies
LangChain
- The README does not detail the API surface, so you rely on external docs for depth
- Several related products (LangGraph, Deep Agents, LangSmith) are needed for advanced or production work, which adds to the learning curve
- The README's quickstart targets a specific model string, so you must have access to that provider's API
Getting started
Deep Agents
Add deepagents to a uv project
uv add deepagentsFrequently asked questions
What is the difference between Deep Agents and LangChain?
Deep Agents: An open-source Python agent harness for multi-step AI work, built on LangGraph. LangChain: A Python framework for building AI agents and apps powered by language models. Deep Agents suits developers building ai agents that run multi-step tasks such as research and writing, while LangChain suits python developers building llm-powered applications.
Are Deep Agents and LangChain free?
Deep Agents: Free and open source. LangChain: Free and open source.
Is Deep Agents or LangChain more popular?
LangChain has more GitHub stars (149k) than Deep Agents (30.9k). Popularity isn't the same as fit — check which one matches your use case above.
What are other alternatives to Deep Agents and LangChain?
Other options people compare them with include LangGraph, CrewAI, Pydantic AI, OpenAI Agents SDK, LEANN.
Keep comparing
Built from our catalogue entries for both products: open-source details come from each project's README (AI-assisted, refreshed weekly) and GitHub data; AI-tool details are editorial. Spotted something wrong? Let us know from either product's page.