DeepAgents
Open-source agent harness with sub-agents, filesystem, and human-in-the-loop
The most complete open-source agent harness for long-horizon tasks, with built-in sub-agents and filesystem access. Ideal if you want a Claude Code-like experience with any LLM, but its opinionated defaults can be overkill for simple loops.
Verified 17d ago · liveness 87/100 · cite: rightaichoice.com/tools/deepagents
- Developers needing a full-featured agent harness for complex, multi-step tasks out of the box
- Teams building production agents with sub-delegation, filesystem access, and human oversight
- Those wanting a model-agnostic alternative to Claude Code or Cursor for coding agents
- Rapid prototyping of agentic workflows with built-in context management and persistence
- Users who only need a simple agent loop without filesystem, sub-agents, or middleware
- Scenarios requiring a minimal dependency footprint or no LangGraph stack
- Projects where the 'trust the LLM' security model is unacceptable and custom sandboxing is not feasible
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Skip Deep Agents if you only need a simple single-step agent loop without filesystem access, sub-agents, or human-in-the-loop — LangGraph's create_agent is lighter for that.
Running frontier models (GPT-5.5, Claude Opus 4.7) on multi-step plans can cost $0.50–$2 per run; there's no built-in cost cap.
Deep Agents is free and open-source (MIT license). You only pay for LLM API usage and optional LangSmith monitoring. This is dramatically cheaper than managed services like Claude Code ($20/mo) or Cursor ($20/mo Pro) — but you self-host and manage infrastructure. Best for development teams who can justify the ops overhead to avoid per-seat fees.
In short
DeepAgents — Open-source agent harness with sub-agents, filesystem, and human-in-the-loop. Best for Developers needing a full-featured agent harness for complex, multi-step tasks out of the box, Teams building production agents with sub-delegation, filesystem access, and human oversight, Those wanting a model-agnostic alternative to Claude Code or Cursor for coding agents. Free to use.
Viability Score
How likely is DeepAgents to still be operational in 12 months? Based on 4 signals — momentum (how recently it shipped), wrapper dependency, revenue model, and web presence.
Last calculated: July 2026
How we score →Key Features
- Sub-agents with isolated context windows
- Pluggable filesystem backends (local, sandboxed, remote)
- Automatic context summarization and output offloading
- Sandboxed shell command execution
- Persistent memory for cross-session recall
- Human-in-the-loop approve, edit, reject tool calls
- Reusable skills loaded on demand
- Custom tools and MCP server integration
- Model-agnostic: any LLM with tool calling
- Built on LangGraph for streaming, persistence, checkpointing
- First-class tracing, evaluation, deployment via LangSmith
- Deep Agents Code: pre-built CLI coding agent
- Available as Python library (pip install deepagents)
- JavaScript/TypeScript library (deepagents.js)
- Production-ready with LangGraph's deployment features
About DeepAgents
Deep Agents is an opinionated, production-ready agent harness built on LangGraph by LangChain. It provides an out-of-the-box agent with sub-agents, filesystem access, context management, shell access, human-in-the-loop approval, and reusable skills. Designed for developers who need a full-featured agent for complex, multi-step workflows without wiring everything from scratch. It is model-agnostic, supporting any LLM with tool calling (frontier, open-weight, or local). Features include delegated sub-agents with isolated contexts, pluggable filesystem backends (local, sandboxed, remote), context summarization and output offloading, sandboxed command execution, persistent cross-session memory, human-in-the-loop tool call review, and a skills system. It also supports custom tools and any MCP server. First-class tracing, evaluation, and monitoring via LangSmith. Deep Agents is available as a Python library (`pip install deepagents`), a JavaScript/TypeScript library (`deepagents.js`), and a pre-built CLI coding agent (Deep Agents Code, installable via `curl -LsSf https://langch.in/dcode`). It contrasts with lighter harnesses like LangChain's `create_agent` (when you want the full bundle) and custom graphs in LangGraph (when the agent loop shape needs changing).
Behind the Verdict
Deep Agents fills the gap between minimal agent wrappers and bespoke orchestration. Its standout strength is the bundling of sub-agents, filesystem access, context management, and human-in-the-loop right out of the box. For teams already on LangChain, it's a natural extension. Security-wise, it explicitly follows a 'trust the LLM' model — you must enforce boundaries at the tool/sandbox level. That's fine for sandboxed or trusted environments but a non-starter for tightly controlled production. The closest alternative is building custom agents directly in LangGraph, which gives you flexibility but trades Deep Agents' prebuilt capabilities. For most practical coding-agent use cases, Deep Agents is a faster start than Cursor or Claude Code when you want to bring your own model. We'd reach for it when we need a full-featured agent quickly and don't mind the LangGraph dependency. Pass if you need a minimal footprint or a fully managed SaaS.
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Real-world workflow fit
Concrete scenarios for the personas DeepAgents actually fits — and what changes day-one when you adopt it.
You need to automate competitive analysis by spawning sub-agents to research each competitor, summarize findings, and write a report with citations.
Outcome: Deep Agents handles sub-agent delegation with isolated contexts, context summarization, and filesystem writes — you get a structured report without manual orchestration.
Your team uses Ollama for local models and wants a terminal-based coding agent like Claude Code but with full control over the model.
Outcome: Install Deep Agents Code CLI, configure Ollama as the backend, and get a coding assistant with sandboxed shell execution and file editing — no vendor lock-in.
You want to let users ask complex questions and get multi-step research results with citations, all within your product.
Outcome: Integrate Deep Agents via its Python/JS library, provide your own search tools, and offer a research feature with sub-agents and human-in-the-loop review calls.
Use Cases
- Ship a Deep Research-style feature in your product with citations and multi-step planning.
- Research competitive landscapes by spawning sub-agents per competitor.
- Generate long-form market analyses from a short prompt and internal documents.
- Prototype a legal or medical research assistant over curated sources.
- Use the CLI as a coding assistant with web search and sandboxed shell execution.
Models Under the Hood
as of 2026-07-06
Limitations
- Sandboxed shell execution is available but limited; sub-agents have isolated contexts that may increase token usage.
- Quality depends on the underlying LLM's tool-calling ability.
- As an open-source library, production support is community-driven.
as of 2026-07-01
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published DeepAgents tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Open Source
$0/mo
Ideal for
Developers and teams who want full control, self-hosting, and no per-seat fees. Best for prototyping, production use with dedicated infrastructure, and customization.
What this tier adds
Free and open-source (MIT license) — you get the full source code, all features, and can modify anything. No paid tiers; you only pay for LLM API costs.
Where the pricing makes sense
The company stage and team size where DeepAgents's pricing actually pencils out — and where peers do it cheaper.
Deep Agents is free and open-source (MIT license). You only pay for LLM API usage and optional LangSmith monitoring. This is dramatically cheaper than managed services like Claude Code ($20/mo) or Cursor ($20/mo Pro) — but you self-host and manage infrastructure. Best for development teams who can justify the ops overhead to avoid per-seat fees.
Setup time & first value
How long it actually takes to get something useful out of DeepAgents — broken out by persona, not the marketing-page minute.
Python developers: pip install deepagents and a basic agent is running in 5 minutes. Full setup with sub-agents, filesystem, and human-in-the-loop takes ~30 minutes of config. For Deep Agents Code CLI, the one-liner curl install takes 2 minutes, then you can start coding immediately.
Switching to or from DeepAgents
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From LangGraph create_agent: Replace create_agent with create_deep_agent and add filesystem, sub-agents, and skills config in the same codebase.
- →From custom LangGraph graph: Refactor your agent loop to use Deep Agents' opinionated graph — you lose custom topology but gain built-in features.
- ↗To LangGraph custom graph: You can extract the LangGraph graph from Deep Agents and customize it — the library is built on LangGraph so migration is incremental.
- ↗To Claude Code or Cursor: Deep Agents Code output can be ported to these proprietary tools if you need their tighter IDE integration.
Integrations
Resources & Guides
- Resourcegithub.com
GitHub - langchain-ai/deepagents: The batteries-included agent harness.
The batteries-included agent harness. Contribute to langchain-ai/deepagents development by creating an account on GitHub.
- Resourcedocs.github.com
GitHub.com Help Documentation
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Official links
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