deepagents

What this skill does

# Deepagents Skill

Langchain deep agents framework for building autonomous coding agents. use for agent harness, backends, subagents, human-in-the-loop, long-term memory, middleware, and cli-based agent development., generated from official documentation.

## When to Use This Skill

This skill should be triggered when:
- Working with deepagents
- Asking about deepagents features or APIs
- Implementing deepagents solutions
- Debugging deepagents code
- Learning deepagents best practices

## Quick Reference

### Common Patterns

**Pattern 1:** Docs by LangChain home pageLangChain + LangGraphSearch...⌘KSupportGitHubTry LangSmithTry LangSmithSearch...NavigationCore capabilitiesBackendsLangChainLangGraphDeep AgentsIntegrationsLearnReferenceContributePythonOverviewGet startedQuickstartCustomizationCore capabilitiesAgent harnessBackendsSubagentsHuman-in-the-loopLong-term memoryMiddlewareCommand line interfaceUse the CLIOn this pageQuickstartBuilt-in backendsStateBackend (ephemeral)FilesystemBackend (local disk)StoreBackend (LangGraph store)CompositeBackend (router)Specify a backendRoute to different backendsUse a virtual filesystemAdd policy hooksProtocol referenceCore capabilitiesBackendsCopy pageChoose and configure filesystem backends for deep agents. You can specify routes to different backends, implement virtual filesystems, and enforce policies.Copy pageDeep agents expose a filesystem surface to the agent via tools like ls, read_file, write_file, edit_file, glob, and grep. These tools operate through a pluggable backend. This page explains how to choose a backend, route different paths to different backends, implement your own virtual filesystem (e.g., S3 or Postgres), add policy hooks, and comply with the backend protocol. ​Quickstart Here are a few pre-built filesystem backends that you can quickly use with your deep agent: Built-in backendDescriptionDefaultagent = create_deep_agent() Ephemeral in state. The default filesystem backend for an agent is stored in langgraph state. Note that this filesystem only persists for a single thread.Local filesystem persistenceagent = create_deep_agent(backend=FilesystemBackend(root_dir="/Users/nh/Desktop/")) This gives the deep agent access to your local machine’s filesystem. You can specify the root directory that the agent has access to. Note that any provided root_dir must be an absolute path.Durable store (LangGraph store)agent = create_deep_agent(backend=lambda rt: StoreBackend(rt)) This gives the agent access to long-term storage that is persisted across threads. This is great for storing longer term memories or instructions that are applicable to the agent over multiple executions.CompositeEphemeral by default, /memories/ persisted. The Composite backend is maximally flexible. You can specify different routes in the filesystem to point towards different backends. See Composite routing below for a ready-to-paste example. ​Built-in backends ​StateBackend (ephemeral) Copy# By default we provide a StateBackend agent = create_deep_agent() # Under the hood, it looks like from deepagents.backends import StateBackend agent = create_deep_agent( backend=(lambda rt: StateBackend(rt)) # Note that the tools access State through the runtime.state ) How it works: Stores files in LangGraph agent state for the current thread. Persists across multiple agent turns on the same thread via checkpoints. Best for: A scratch pad for the agent to write intermediate results. Automatic eviction of large tool outputs which the agent can then read back in piece by piece. ​FilesystemBackend (local disk) Copyfrom deepagents.backends import FilesystemBackend agent = create_deep_agent( backend=FilesystemBackend(root_dir=".", virtual_mode=True) ) How it works: Reads/writes real files under a configurable root_dir. You can optionally set virtual_mode=True to sandbox and normalize paths under root_dir. Uses secure path resolution, prevents unsafe symlink traversal when possible, can use ripgrep for fast grep. Best for: Local projects on your machine CI sandboxes Mounted persistent volumes ​StoreBackend (LangGraph store) Copyfrom langgraph.store.memory import InMemoryStore from deepagents.backends import StoreBackend agent = create_deep_agent( backend=(lambda rt: StoreBackend(rt)), # Note that the tools access Store through the runtime.store store=InMemoryStore() ) How it works: Stores files in a LangGraph BaseStore provided by the runtime, enabling cross‑thread durable storage. Best for: When you already run with a configured LangGraph store (for example, Redis, Postgres, or cloud implementations behind BaseStore). When you’re deploying your agent through LangSmith Deployment (a store is automatically provisioned for your agent). ​CompositeBackend (router) Copyfrom deepagents import create_deep_agent from deepagents.backends import CompositeBackend, StateBackend, StoreBackend from langgraph.store.memory import InMemoryStore composite_backend = lambda rt: CompositeBackend( default=StateBackend(rt), routes={ "/memories/": StoreBackend(rt), } ) agent = create_deep_agent( backend=composite_backend, store=InMemoryStore() # Store passed to create_deep_agent, not backend ) How it works: Routes file operations to different backends based on path prefix. Preserves the original path prefixes in listings and search results. Best for: When you want to give your agent both ephemeral and cross-thread storage, a CompositeBackend allows you provide both a StateBackend and StoreBackend When you have multiple sources of information that you want to provide to your agent as part of a single filesystem. e.g. You have long-term memories stored under /memories/ in one Store and you also have a custom backend that has documentation accessible at /docs/. ​Specify a backend Pass a backend to create_deep_agent(backend=...). The filesystem middleware uses it for all tooling. You can pass either: An instance implementing BackendProtocol (for example, FilesystemBackend(root_dir=".")), or A factory BackendFactory = Callable[[ToolRuntime], BackendProtocol] (for backends that need runtime like StateBackend or StoreBackend). If omitted, the default is lambda rt: StateBackend(rt). ​Route to different backends Route parts of the namespace to different backends. Commonly used to persist /memories/* and keep everything else ephemeral. Copyfrom deepagents import create_deep_agent from deepagents.backends import CompositeBackend, StateBackend, FilesystemBackend composite_backend = lambda rt: CompositeBackend( default=StateBackend(rt), routes={ "/memories/": FilesystemBackend(root_dir="/deepagents/myagent", virtual_mode=True), }, ) agent = create_deep_agent(backend=composite_backend) Behavior: /workspace/plan.md → StateBackend (ephemeral) /memories/agent.md → FilesystemBackend under /deepagents/myagent ls, glob, grep aggregate results and show original path prefixes. Notes: Longer prefixes win (for example, route "/memories/projects/" can override "/memories/"). For StoreBackend routing, ensure the agent runtime provides a store (runtime.store). ​Use a virtual filesystem Build a custom backend to project a remote or database filesystem (e.g., S3 or Postgres) into the tools namespace. Design guidelines: Paths are absolute (/x/y.txt). Decide how to map them to your storage keys/rows. Implement ls_info and glob_info efficiently (server-side listing where available, otherwise local filter). Return user-readable error strings for missing files or invalid regex patterns. For external persistence, set files_update=None in results; only in-state backends should return a files_update dict. S3-style outline: Copyfrom deepagents.backends.protocol import BackendProtocol, WriteResult, EditResult from deepagents.backends.utils import FileInfo, GrepMatch class S3Backend(BackendProtocol): def __init__(self, bucket: str, prefix: str = ""): self.bucket = bucket self.prefix = prefix.rstrip("/") def _key(self, path: str) -> str: return f"{self.prefix}{path}" def ls_info(self, path: str) -> list[F