Deep Agents vs LangGraph

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

LangGraph

• Python developers building multi-step AI agents that must survive failures or long waits
• Teams that want fine-grained control over agent state and execution flow
• Projects that need a human to review or edit an agent's state partway through a run
• Developers who want a lower-level base instead of a higher-level agent package

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

LangGraph

• Built-in durable execution that resumes workflows from where they stopped, per the README
• Supports human-in-the-loop checkpoints and both short-term and long-term memory
• Open source under the MIT license with extensive documentation and a free course

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

LangGraph

• Beginners who just want a ready-made agent: the README points to Deep Agents, a higher-level package built on LangGraph, for quicker starts
• Non-programmers, since it is a Python library that requires writing code
• JavaScript/TypeScript-only teams, who are pointed to the separate LangGraph.js project

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

LangGraph

• It is a low-level framework, so it has a steeper learning curve than higher-level tools
• Several of the most useful features (debugging, deployment) point to LangSmith, a separate product
• The README is truncated and does not show full API details, so the depth of specific features is hard to verify from it alone