DeepAgents vs LangGraph
Side-by-side comparison of features, pricing, and ratings
At a glance
| Dimension | DeepAgents | LangGraph |
|---|---|---|
| Pricing | Free (MIT License) | Free (MIT License) |
| Core Focus | Production-ready agent harness with sub-agents, filesystem, human-in-the-loop | Low-level orchestration framework for custom agent workflows |
| Sub-agents | Built-in sub-agents with isolated context windows | Supported via custom graph design, no built-in isolation |
| Human-in-the-Loop | Approve, edit, reject tool calls | Checkpoint-and-resume via graph interrupts |
| Filesystem Access | Pluggable backends: local, sandboxed, remote | Not built-in; can be added via custom nodes |
| Latest News Impact | Prompt caching added (2026-06-26); Box AI case study (2026-06-12) | LangSmith Engine for evaluation (2026-05-13) |
Choose DeepAgents if you want a full-featured agent out of the box—with sub-agents, filesystem access, and human approval—without wiring everything from scratch. Choose LangGraph if you need low-level control to build custom agent architectures and are comfortable assembling your own stack from primitives.
Open-source framework for building reliable, stateful AI agents with low-level control.
Visit WebsiteWho should pick which
- Solo developer building a coding agentPick: DeepAgents
DeepAgents includes a pre-built CLI coding agent (Deep Agents Code) and skills system, getting you started quickly without assembling low-level graph nodes.
- DevOps engineer needing custom agent orchestrationPick: LangGraph
LangGraph's graph primitives give you full control over state transitions, error handling, and multi-agent coordination, ideal for bespoke workflows.
- Team requiring human oversight on every tool callPick: DeepAgents
DeepAgents' built-in approve/edit/reject mechanism is turn-key, whereas LangGraph requires building custom interrupt nodes.
- Enterprise scaling multi-agent systemsPick: LangGraph
LangGraph's fault tolerance, retries, and integration with LangSmith for observability make it suitable for large-scale production loads.
- Rapid prototype of an agent with filesystem accessPick: DeepAgents
Pluggable filesystem backends (local, sandboxed, remote) are included, avoiding the need to implement file I/O from scratch.
Frequently Asked Questions
DeepAgents vs LangGraph: which should you choose?
Choose DeepAgents if you want a full-featured agent out of the box—with sub-agents, filesystem access, and human approval—without wiring everything from scratch. Choose LangGraph if you need low-level control to build custom agent architectures and are comfortable assembling your own stack from primitives.
Can I use DeepAgents without LangGraph?
No, DeepAgents is built on LangGraph and requires it as a dependency.
Does LangGraph come with a pre-built agent?
No, LangGraph provides low-level components; you must compose your own agent logic.
Which tool supports sub-agents out of the box?
DeepAgents has built-in sub-agents with isolated context windows. LangGraph can support sub-agents but requires manual graph design.
Is there a managed cloud version?
LangGraph offers LangGraph Platform (paid) for managed hosting. DeepAgents can be self-hosted or deployed via LangSmith.
How does human-in-the-loop differ?
DeepAgents provides approve/edit/reject on tool calls. LangGraph uses graph interrupts and checkpointing for human review.
Can I use any LLM with both?
Yes, both are model-agnostic, supporting OpenAI, Anthropic, Google, Ollama, and more.
Which has better memory features?
LangGraph has built-in memory for cross-session context. DeepAgents also offers persistent memory but relies on LangGraph's state management.
What does the latest news mean?
DeepAgents added prompt caching (2026-06-26) to reduce latency/cost. LangGraph's LangSmith Engine (2026-05-13) improves autonomous evaluation. Choose based on whether you need caching or eval tools.
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Last reviewed: May 12, 2026