DeepAgents
Open-source, model-agnostic agent harness with sub-agents, filesystem, and human-in-the-loop.
Deep Agents is the most complete open-source harness for long-horizon agent tasks, with sub-agents, filesystem access, and human oversight built in. It's a strong pick if you want a Claude Code-like experience with any LLM, but the opinionated defaults may be overkill for simple loops. Compare with `create_agent` for minimal needs or LangGraph for full control.
Verified 7d ago · liveness 76/100 · cite: rightaichoice.com/tools/deepagents
- Developers who want a production-ready, full-featured agent harness with sub-agents, filesystem, and human oversight
- Teams building complex multi-step agent workflows that require context management and delegation
- Users seeking a model-agnostic alternative to Claude Code or Cursor for terminal-based coding agents
- LangChain/LangGraph users who want an opinionated layer with built-in security checkpoints
- Simple agent loops where you don't need filesystem, sub-agents, or middleware—use LangChain's create_agent instead
- Projects that require a minimal dependency footprint or cannot adopt the LangGraph stack
- High-security environments where the 'trust the LLM' model is unacceptable and custom sandboxing is not feasible
We scan live Reddit threads, YouTube comments, X posts, G2 reviews and other communities — and hand you an honest verdict in under a minute.
- Honest verdict, not marketing
- Real pros & cons from real users
- Attributed quotes with receipts
3 free scans · no card needed
Skip DeepAgents if you only need a simple agent loop without filesystem, sub-agents, or context management—LangChain's create_agent is lighter and sufficient.
Sub-agents and context management can increase token usage, potentially raising your LLM API costs significantly on high-volume tasks.
DeepAgents is free and open-source (MIT), so the cost is zero for the harness itself—you only pay for LLM API calls. Compared to managed agents like Claude Code or Cursor (which charge per seat), DeepAgents is cheaper for teams with high usage but requires more engineering to deploy.
In short
DeepAgents — Open-source, model-agnostic agent harness with sub-agents, filesystem, and human-in-the-loop. Best for Developers who want a production-ready, full-featured agent harness with sub-agents, filesystem, and human oversight, Teams building complex multi-step agent workflows that require context management and delegation, Users seeking a model-agnostic alternative to Claude Code or Cursor for terminal-based coding agents. Free to use.
Viability Score
How well maintained and how widely used is DeepAgents? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: September 2026
How we score →Key Features
- Sub-agents with isolated context windows
- Pluggable filesystem backends (local, sandboxed, remote)
- Automatic context summarization and output offloading to disk
- Sandboxed shell command execution
- Persistent memory for cross-session recall
- Human-in-the-loop approve, edit, or reject tool calls
- Reusable skills loaded on demand
- Custom tools and MCP server integration
- Model-agnostic: works with any tool-calling LLM
- Built on LangGraph: streaming, persistence, checkpointing
- First-class tracing, evaluation, deployment via LangSmith
- Deep Agents Code: pre-built CLI coding agent
- Python library (uv add deepagents)
- JavaScript/TypeScript library (deepagents.js)
- Extensible: override or replace any component without forking
About DeepAgents
Deep Agents is LangChain's open-source agent harness built on LangGraph, designed for developers who need a production-ready agent without assembling every piece themselves. It ships with sub-agents that maintain isolated context windows, a pluggable filesystem for reading, writing, and searching local or remote backends, automatic context summarization that offloads tool outputs to disk, and shell access in your sandbox. Human-in-the-loop review lets you approve, edit, or reject tool calls before they run. Persistent memory keeps state across sessions, and reusable skills load on demand. The harness is model-agnostic, working with any LLM that supports tool calling—frontier APIs like OpenAI, Anthropic, or Google; open-weight models hosted on Baseten or Fireworks; or self-hosted via Ollama, vLLM, or llama.cpp. It's available as a Python library (`uv add deepagents`), a JavaScript/TypeScript library (`deepagents.js`), and a pre-built CLI coding agent called Deep Agents Code, installable with a single curl command. Everything is extensible: you can override or replace any component without forking, and it integrates with LangSmith for tracing, evaluation, and monitoring. The project has 28.7k GitHub stars and 3,601 commits, reflecting strong community momentum. Compared to LangChain's `create_agent`, which is a lighter harness, Deep Agents bundles the full feature set—filesystem, sub-agents, context management, and skills—making it ideal for complex, multi-step workflows. It sits above LangGraph as an opinionated layer, so you can use it as-is or drop down to custom graphs when the default loop isn't the right fit.
Behind the Verdict
Deep Agents impresses as a 'batteries-included' harness that saves you from assembling filesystem, context management, and sub-agents yourself. The isolated context windows for sub-agents are a standout—they let you delegate research or file-reading tasks without bloating the main context. The pluggable filesystem backends (local, sandboxed, remote) add flexibility for different environments, and the human-in-the-loop review is a critical safety feature for production use. However, it's an opinionated layer. If you only need a simple loop, LangChain's `create_agent` is lighter. Also, because it's open-source, production support is community-driven—you'll rely on GitHub issues and discussions. Token usage can increase with sub-agents, and the model-agnostic design means quality depends on the underlying LLM's tool-calling ability. Where it fits: teams building complex multi-step agents, developers wanting a CLI coding assistant similar to Claude Code but with any model, and LangChain users needing security checkpoints. Where it doesn't: minimal-dependency projects, teams preferring a fully managed SaaS, or high-security environments that can't adopt the 'trust the LLM' model without custom sandboxing.
Researching DeepAgents? Get your full AI stack in 60 seconds.
Free, no signup — tell us your goal and get tools matched to your budget & existing stack.
Real-world workflow fit
Concrete scenarios for the personas DeepAgents actually fits — and what changes day-one when you adopt it.
Building a research agent that can browse the web, extract data, and write a report.
Outcome: Using the Python library, you configure sub-agents for web research and a filesystem backend to store findings, with human approval on writes.
Deploying a coding agent in CI to fix bugs.
Outcome: Setting up Deep Agents Code CLI, you connect it to your model API and a sandboxed shell, enabling automated bug fixes with human review before pull requests.
Building a customer support bot that can access internal docs.
Outcome: Using LangGraph persistence and a remote filesystem, the bot reads internal documentation, answers queries, and escalates to humans via human-in-the-loop.
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.
- Delegate file-reading and summarization subtasks to isolated sub-agents to keep the main context clean.
Models Under the Hood
as of 2026-08-30
Limitations
- DeepAgents is an open-source, model-agnostic agent harness that supports any tool-calling LLM.
- The documentation does not name any specific underlying model, so the quality and behavior depend on the chosen LLM's tool-calling capability.
- Being an open-source library, support is community-driven, and advanced usage may require understanding of LangGraph and related concepts.
as of 2026-08-30
Verification history
We have re-verified DeepAgents 17 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Showing the 6 most recent of 17 verification passes.
Free to cite with attribution — this page re-verifies continuously.
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 a free, self-hosted agent harness with full control and no per-seat costs.
What this tier adds
Starting entry point: free and open-source with all features included, but requires self-hosting and management.
Where the pricing makes sense
The company stage and team size where DeepAgents's pricing actually pencils out — and where peers do it cheaper.
DeepAgents is free and open-source (MIT), so the cost is zero for the harness itself—you only pay for LLM API calls. Compared to managed agents like Claude Code or Cursor (which charge per seat), DeepAgents is cheaper for teams with high usage but requires more engineering to deploy.
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.
For developers familiar with Python, you can get DeepAgents running in under an hour: install via pip, write a few lines of code. The CLI can be set up in minutes with a single curl command, but configuring sub-agents and filesystem backends may take additional time.
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 LangChain's create_agent: DeepAgents is built on top, so you can gradually adopt filesystem and sub-agents without rewriting existing graphs.
- →From Claude Code or Cursor: If you want to switch from a managed coding agent to an open-source one, DeepAgents Code provides a similar terminal experience with your own model.
- ↗To LangGraph: If you need full control, you can strip away DeepAgents' opinionated layers and build custom graphs on LangGraph directly.
- ↗To a managed service like LangGraph Platform: You can deploy the same LangGraph-based agents to a fully hosted environment if you outgrow self-hosting.
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
Get started, troubleshoot, and make the most of GitHub. Documentation for new users, developers, administrators, and all of GitHub's products.
Tutorials & Learning
Official links
Tools that pair well with DeepAgents
Common stack mates teams adopt alongside DeepAgents, with the specific reason each pairing earns its keep.
Featured Head-to-Head Comparisons
Crewai vs Deepagents
Choose DeepAgents if you are a developer seeking a free, open-source, model-agnostic agent harness with sub-agents and filesystem access, and you're comfortable with a code-first setup. Choose CrewAI if you are an enterprise team that needs built-in governance (RBAC, audit trails, PII redaction), a discovery engine for automation opportunities, and a no-code visual editor for rapid prototyping.
Deepagents vs Langgraph
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.
Claude vs Deepagents
If you need a self-hosted, flexible agent harness with sub-agents and human oversight, DeepAgents is the clear winner—it's free, model-agnostic, and production-ready. If you're a professional who needs deep document analysis, voice mode, and enterprise integrations (Slack, Salesforce), Claude is the better fit, especially with its recent Cowork and CRM enhancements. Choose based on whether you need customization or out-of-the-box enterprise tools.
Deepagents vs Langchain
If you're building production agents and need deep insight into failures, LangSmith is the enterprise choice—its autonomous issue clustering and fix recommendations pay off at scale. If you want a free, customizable harness to start building complex agents with sub-agents and filesystem access, Deep Agents gives you the foundation without lock-in. Choose based on whether you need managed reliability (LangSmith) or hands-on control (Deep Agents).
Alternatives to DeepAgents
View allPopular in Agent Frameworks & Orchestration
Temporal AI
Durable execution platform keeping AI agents and workflows running through failures with automatic state capture and retries.
Frequently Asked Questions
Used DeepAgents? Help shape our editorial sentiment research.