DeepAgents
DeepAgents is an open-source agent harness for building long-horizon agents with sub-agents and a pluggable filesystem.
Few open-source harnesses ship isolated-context sub-agents, a swappable filesystem, and tool-level human approval in one install — that combination is the reason to pick this over a thin prompt wrapper. Reach for it when your agent needs to plan and delegate across many steps on any tool-calling model, frontier or self-hosted. Skip it if a two-tool loop is all you need or the LangGraph dependency is a dealbreaker.
Verified 7h ago · liveness 82/100 · cite: rightaichoice.com/tools/deepagents
- Developers who want sub-agents, filesystem, and human oversight pre-assembled in one harness
- Teams building long-horizon, multi-step agent workflows that outgrow a single context window
- Anyone wanting a Claude Code or Cursor-style terminal agent on any tool-calling LLM
- LangChain and LangGraph users who want an opinionated layer with planning and delegation pre-wired
- Simple agent loops — LangChain's create_agent is a lighter harness without the bundled middleware
- Projects that must keep a minimal dependency footprint or can't adopt the LangGraph stack
- Production environments where the trust-the-LLM security model is unacceptable and you can't build tool-level sandboxing
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Skip Deep Agents if you only need a simple two-tool agent loop — LangChain's create_agent is a lighter harness without the bundled sub-agent, filesystem, and context-management middleware you'd be paying dependency weight for.
Sub-agents, filesystem operations, and shell commands each burn LLM tokens, so a long research job costs far more in inference than a single-thread agent loop — budget for it before you scale usage.
Deep Agents itself is MIT-licensed open source at $0, which puts it below commercial agent platforms and managed SaaS agent services that charge per seat or per run. The real cost sits next to it: LLM inference, whether you pay a frontier API per token or run open-weight models on Baseten, Fireworks, Ollama, vLLM, or llama.cpp, and optional LangSmith for tracing, evaluation, and deployment.
In short
DeepAgents — DeepAgents is an open-source agent harness for building long-horizon agents with sub-agents and a pluggable filesystem. Best for Developers who want sub-agents, filesystem, and human oversight pre-assembled in one harness, Teams building long-horizon, multi-step agent workflows that outgrow a single context window, Anyone wanting a Claude Code or Cursor-style terminal agent on any tool-calling LLM. Free to use.
What people actually say about DeepAgents — is it worth it?
We scanned public community sources for DeepAgents on Oct 8, 2026 and could not establish that the discussion we found is about this tool rather than something else sharing its name. Only 0 of the posts we fetched could be positively tied to DeepAgents. Rather than publish a sentiment score built on the wrong subject, we publish nothing here and re-run the scan.
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: October 2026
How we score →Key Features
- Sub-agents that delegate tasks with isolated context windows
- Pluggable filesystem: read, write, edit, and search over local, sandboxed, or remote backends
- Context management: summarizes long threads and offloads tool outputs to disk
- Shell access for running commands in your sandbox of choice
- Human-in-the-loop: approve, edit, or reject tool calls before they run
- Persistent memory via pluggable state and store backends for cross-session recall
- Skills: reusable agent behaviors the agent loads on demand
- Bring your own functions or connect any MCP server
- Model-agnostic: works with any LLM that supports tool calling
- Frontier, open-weight, and local model support (Ollama, vLLM, llama.cpp, Baseten, Fireworks)
- Built on LangGraph with streaming, persistence, and checkpointing
- Tracing, evaluation, and deployment via LangSmith
- Deep Agents Code: pre-built terminal coding agent installed with one curl command
- Python library installed with uv add deepagents
- JavaScript/TypeScript library via deepagents.js
About DeepAgents
DeepAgents is LangChain's MIT-licensed agent harness on GitHub — an opinionated agent that plans, reads and writes files, and delegates work out of the box. It is aimed at developers building long-horizon, multi-step agent workflows who would rather not assemble delegation, file handling, context management, and human oversight from scratch. Install with `uv add deepagents`, then call `create_deep_agent`; a JavaScript/TypeScript library, deepagents.js, mirrors the same surface. The bundled middleware is the draw. Sub-agents absorb delegated tasks in isolated context windows so a sprawling research job doesn't collapse a single thread. A pluggable filesystem reads, writes, edits, and searches across local, sandboxed, or remote backends, while context management summarizes long threads and spills tool output to disk. Shell access runs commands in your chosen sandbox, human-in-the-loop lets you approve, edit, or reject tool calls before execution, and skills load on demand as reusable behaviors. Persistent memory rides on pluggable state and store backends. Models are a non-issue: anything supporting tool calling works — frontier APIs from OpenAI, Anthropic, and Google, open-weight models on Baseten or Fireworks, or self-hosted ones via Ollama, vLLM, and llama.cpp. It sits on LangGraph, inheriting streaming, persistence, and checkpointing, and pairs with LangSmith for tracing, evaluation, and deployment. A pre-built terminal coding agent, Deep Agents Code, installs with a single curl command. The layering is deliberate: LangGraph is the graph runtime, LangChain's create_agent is a minimal harness above it, and DeepAgents is the more opinionated layer that bundles filesystem, sub-agents, context management, and skills. Any LangGraph CompiledStateGraph can be passed in as a sub-agent, so custom orchestration plugs in alongside the defaults rather than replacing them.
Behind the Verdict
Here's the honest framing: DeepAgents is not a new model or a new runtime. It's a curated set of agent middleware that LangChain bundled so you stop rebuilding delegation and context handling on every project. If you've hand-rolled sub-agent spawning and file offloading more than once, this saves you that work. Pick it when your agent genuinely has long-horizon work — research runs, multi-file refactors, any task that outgrows one context window. The isolated-context sub-agents are the feature that earns the install; they keep a sprawling job from collapsing a single thread. The pluggable filesystem and on-demand skills are the supporting cast that make the delegation usable. Pass when you need a simple agent loop. LangChain's create_agent is the lighter harness without the bundled middleware, and dropping to LangGraph is the right move when the planning-and-delegation shape doesn't fit your problem at all. The three layers compose, so there's no penalty for starting lower and climbing. Model flexibility is real, not marketing — any tool-calling LLM works, including local and open-weight setups via Ollama, vLLM, or llama.cpp. Teams running agents on self-hosted models get the same harness as frontier-API users. In practice, watch the security model. DeepAgents follows a trust-the-LLM approach: the agent can do anything its tools allow. If that's unacceptable in your environment, the burden is on you to enforce boundaries at the tool and sandbox level. That's not a bug, but it is a real operational caveat for production teams. The closest alternative depends on your stack. If you're already all-in on LangChain and LangGraph, the comparison is internal — pick the layer that matches your complexity. If you're outside that ecosystem, adopting this means adopting
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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.
Installs the Python library with `uv add deepagents`, creates an agent with `create_deep_agent`, points it at a frontier model, and writes a custom tool that queries internal documents.
Outcome: A multi-step research agent that plans, reads and writes files, and keeps its own context clean without hand-built delegation logic.
Runs an open-weight model behind vLLM or Ollama and wires it into create_deep_agent, then turns on human-in-the-loop approval for every shell command.
Outcome: A long-horizon agent on self-hosted infrastructure with tool calls reviewed before execution.
Installs Deep Agents Code with the published curl command and points it at the LLM of their choice.
Outcome: A Claude Code or Cursor-style coding agent in the terminal running on whichever tool-calling model they already pay for.
Use Cases
- Ship a Deep Research-style feature in your product with citations and multi-step planning
- Research competitive landscapes by spawning a sub-agent per competitor
- Generate long-form market analyses from a short prompt plus internal documents
- Prototype a legal or medical research assistant over curated sources
- Use the terminal 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
Limitations
- Deep Agents is an open-source Python/JavaScript library, so practical use requires developer setup and familiarity with harness concepts such as sub-agents and middleware.
- It is not a hosted product — you run it and own deployment, scaling, and observability.
- The evidence describes it as model-agnostic (any tool-calling LLM) and names no specific underlying model, so models is positively empty rather than a specific model.
as of 2026-10-01
Verification history
We have re-verified DeepAgents 21 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 21 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 to self-host a long-horizon agent harness and pay only for the LLM inference they use
What this tier adds
Starting tier and the only tier — MIT-licensed source on GitHub with sub-agents, filesystem, context management, and skills included, plus Python and JavaScript/TypeScript libraries
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 itself is MIT-licensed open source at $0, which puts it below commercial agent platforms and managed SaaS agent services that charge per seat or per run. The real cost sits next to it: LLM inference, whether you pay a frontier API per token or run open-weight models on Baseten, Fireworks, Ollama, vLLM, or llama.cpp, and optional LangSmith for tracing, evaluation, and deployment.
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 get a first agent running in minutes: `uv add deepagents`, then `create_deep_agent` with a model and tools. Expect an hour or two to wire custom tools, sub-agents, and a filesystem backend, and longer if you're also standing up a self-hosted model via vLLM, Ollama, or llama.cpp. Deep Agents Code installs with a single curl command but still needs a model endpoint.
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 create_agent: move your tools and prompt into create_deep_agent to pick up filesystem, sub-agents, context management, and skills
- →From LangGraph hand-rolled graphs: any LangGraph CompiledStateGraph can be passed in as a sub-agent, and LangGraph primitives carry over directly
- →From an ad-hoc custom agent loop: replace hand-written delegation and context handling with the bundled sub-agent and context-management middleware
- ↗To LangChain create_agent: drop the middleware and keep the same tools when your loop is simple enough not to need delegation or filesystem
- ↗To raw LangGraph: keep the graph runtime and persistence while removing the opinionated harness layer
- ↗To a managed SaaS agent platform: rebuild the workflow on a hosted service if you no longer want to own deployment and scaling
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
YouTube returned 6 videos for “DeepAgents”, and we withheld 6: 6 could not be judged, because “DeepAgents” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about DeepAgents.
Official links
Tools that pair well with DeepAgents
Common stack mates teams adopt alongside DeepAgents, with the specific reason each pairing earns its keep.
LangChain
Open-source agent frameworks (langchain, langgraph, deepagents) plus LangSmith for tracing, evals, deployment and governance.
Microsoft Agent Framework
Microsoft Agent Framework is an open-source SDK for building agentic AI with workflows, checkpoints, and the GA Agent Harness runtime.
Haystack
Haystack is deepset's open-source Python framework for building inspectable RAG pipelines and production-grade AI agents
Featured Head-to-Head Comparisons
Crewai vs Deepagents
Deepagents vs Langgraph
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 allLangChain
Open-source agent frameworks (langchain, langgraph, deepagents) plus LangSmith for tracing, evals, deployment and governance.
Microsoft Agent Framework
Microsoft Agent Framework is an open-source SDK for building agentic AI with workflows, checkpoints, and the GA Agent Harness runtime.
Frequently Asked Questions
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