DeepAgents vs LangGraph

DeepAgents is an opinionated, full-featured agent harness built on LangGraph. It provides pre-built sub-agents with isolated context windows, pluggable filesystem backends (local, sandboxed, remote), automatic context summarization, sandboxed command execution, persistent memory, and human-in-the-loop approval for tool calls. It also supports reusable skills and custom tools via MCP server integration. In contrast, LangGraph is a lower-level orchestration framework that offers graph-based state management, human-in-the-loop checks, built-in memory, token-by-token streaming, and fault tolerance (retries, timeouts, error handlers). LangGraph requires custom implementation for sub-agents and filesystem access. DeepAgents is model-agnostic, while LangGraph also supports multiple LLM providers. A key difference: DeepAgents comes with a pre-built CLI coding agent (Deep Agents Code), whereas LangGraph provides Rubrics for agent self-evaluation. Notably, a recent security advisory (2026-06-19) warns that LangGraph shares remote code execution vulnerabilities with Langflow and LangChain, which may impact deployment decisions. DeepAgents has no such advisory.

Both DeepAgents and LangGraph are free and open-source, with no usage limits or paid tiers. DeepAgents is MIT-licensed via LangChain, while LangGraph is also MIT-licensed. However, LangGraph integrates with LangSmith for observability and deployment, which may incur costs when used at scale. DeepAgents also relies on LangSmith for tracing and evaluation. There are no hidden fees for either tool, but hosting and LLM API costs apply separately. Given both are free, the decision hinges on feature set and security considerations.