DeepAgents are a next-generation agent harness developed within the LangChain ecosystem for building autonomous, long-running, LLM-powered agents that can tackle complex, open-ended workflows. Unlike simple agent loops that call tools step by step, DeepAgents bring a suite of powerful capabilities—planning, context handling, memory, subagents, and flexible backends—to enable agents that are durable, structured, and capable of sophisticated reasoning and task decomposition.
What Makes DeepAgents Different?
DeepAgents sit above the basic LangChain agent framework and below LangGraph’s execution runtime in the stack:
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LangGraph: The low-level runtime and state system for executing workflows and agents.
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LangChain: A general framework for building and customizing agents.
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DeepAgents: An agent harness that builds on top of LangChain to provide batteries-included capabilities for autonomous agents.
This positioning makes them particularly suitable for long-horizon tasks where context and memory management become critical.
Core Capabilities
🧠 1. Planning & Task Decomposition
DeepAgents include a built-in planning tool (e.g., write_todos) that allows agents to break down complex tasks into manageable subtasks, track progress, and dynamically adapt as new information emerges—perfect for workflows that span many steps or require checkpointing.
📁 2. Context & File System Management
To handle large amounts of context that wouldn’t fit in a single LLM prompt, DeepAgents provide file system tools like:
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ls,read_file,write_file,edit_file -
Offloading large outputs to file storage
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Searching and querying file contents
These features help agents manage context growth without overwhelming the LLM’s memory window.
🤖 3. Subagents: Delegation for Clean Context
DeepAgents can spawn subagents—specialized agents for focused subtasks—and isolate their internal context so the main agent doesn’t become bloated with intermediate tool calls. This makes workflows more efficient and easier to manage.
Example: Delegate research-intensive tasks to a subagent so the main agent can focus on coordination and summary.
💾 4. Long-Term Memory Across Sessions
DeepAgents support persistent memory using LangGraph’s memory store or filesystem backends. This lets an agent learn and recall information across sessions—a big step toward continuity in long-running tasks.
🛠️ 5. Pluggable Backends
The 0.2 release of DeepAgents introduced a backend abstraction that lets you swap out how and where the agent stores files, memory, or state:
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Local filesystem
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LangGraph Store for cross-thread persistence
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Composite backends that mix multiple storage approaches
This makes DeepAgents flexible for both experimentation and production use.
🧪 6. Safety & Sandboxed Execution
DeepAgents can run code in remote sandboxes (via integrations like Runloop, Daytona, or Modal), allowing safe execution of arbitrary code, long-running processes, and heavy computation without exposing your local environment.
DeepAgents CLI — Terminal-Driven Autonomous Agents
To make DeepAgents more accessible for developers, LangChain released the DeepAgents CLI—a command-line tool that lets you:
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Initialize and run DeepAgents locally
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Leverage persistent memory and context
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Execute shell commands (with human approval workflows)
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Search the web and call APIs
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Visualize and manage task lists
The CLI also continues receiving updates to enhance observability, like fast thread switching and LangSmith integration for debugging and tracing agent runs.
Context Management & Compression
As agents work through long tasks, maintaining context efficiently becomes critical. DeepAgents implement techniques such as:
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Offloading large tool inputs and outputs to the filesystem
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Summarizing history when context grows too large
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Strategically deciding what to keep active in memory
These methods help ensure the agent stays efficient and scalable over long interactions.
Use Cases — What DeepAgents Are Good For
DeepAgents shine in scenarios where workflows are complex, dynamic, and long-running:
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Research agents that aggregate and summarize external information
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Developers’ coding assistants that manage tasks and edit code
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Workflow automation across APIs and tools
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Agents that require continuity across days or sessions
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Multi-step pipelines with branching and sub-task delegation
How DeepAgents Fit Into the LangChain Landscape
Here’s a quick comparison to help you decide when to use each component:
| Tool | Best For |
|---|---|
| LangChain | Fast agent prototyping, custom prompts & tool loops |
| LangGraph | Production execution, workflow orchestration |
| DeepAgents | Autonomous, long-horizon agents with built-in tooling and memory |
Getting Started
To start building with DeepAgents, you can:
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Install the library via
pip install deepagents(Python) or use the CLI. -
Use the SDK to define an agent with custom tools, subagents, and memory backends.
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Leverage sandboxes for safe code execution.
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Add LangSmith tracing to monitor runs in production.
Final Thoughts
DeepAgents represent a major evolution in how we build autonomous AI systems within the LangChain ecosystem. They bridge the gap between simple LLM tooling and fully autonomous, contextful agents capable of reasoning over long tasks, spawning subagents, and managing real-world context. With ongoing updates like pluggable backends, sandbox execution, CLI enhancements, and deeper LangSmith integration, DeepAgents are becoming one of the most powerful tools for agent-centric AI development in 2026.
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