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[FileInfo]: # List objects under _key(path); build FileInfo entries (path, size, modified_at)... def read(self, file_path: str, offset: int = 0, limit: int = 2000) -> str: # Fetch object; return numbered content or an error string... def grep_raw(self, pattern: str, path: str | None = None, glob: str | None = None) -> list[GrepMatch] | str: # Optionally filter server‑side; else list and scan content... def glob_info(self, pattern: str, path: str = "/") -> list[FileInfo]: # Apply glob relative to path across keys... def write(self, file_path: str, content: str) -> WriteResult: # Enforce create‑only semantics; return WriteResult(path=file_path, files_update=None)... def edit(self, file_path: str, old_string: str, new_string: str, replace_all: bool = False) -> EditResult: # Read → replace (respect uniqueness vs replace_all) → write → return occurrences... Postgres-style outline: Table files(path text primary key, content text, created_at timestamptz, modified_at timestamptz) Map tool operations onto SQL: ls_info uses WHERE path LIKE $1 || '%' glob_info filter in SQL or fetch then apply glob in Python grep_raw can fetch candidate rows by extension or last modified time, then scan lines Add policy hooks Enforce enterprise rules by subclassing or wrapping a backend. Block writes/edits under selected prefixes (subclass): Copyfrom deepagents.backends.filesystem import FilesystemBackend from deepagents.backends.protocol import WriteResult, EditResult class GuardedBackend(FilesystemBackend): def init(self, *, deny_prefixes: list[str], kwargs): super().init(kwargs) self.deny_prefixes = [p if p.endswith("/") else p + "/" for p in deny_prefixes] def write(self, file_path: str, content: str) -> WriteResult: if any(file_path.startswith(p) for p in self.deny_prefixes): return WriteResult(error=f"Writes are not allowed under {file_path}") return super().write(file_path, content) def edit(self, file_path: str, old_string: str, new_string: str, replace_all: bool = False) -> EditResult: if any(file_path.startswith(p) for p in self.deny_prefixes): return EditResult(error=f"Edits are not allowed under {file_path}") return super().edit(file_path, old_string, new_string, replace_all) Generic wrapper (works with any backend): Copyfrom deepagents.backends.protocol import BackendProtocol, WriteResult, EditResult from deepagents.backends.utils import FileInfo, GrepMatch class PolicyWrapper(BackendProtocol): def init(self, inner: BackendProtocol, deny_prefixes: list[str] | None = None): self.inner = inner self.deny_prefixes = [p if p.endswith("/") else p + "/" for p in (deny_prefixes or [])] def _deny(self, path: str) -> bool: return any(path.startswith(p) for p in self.deny_prefixes) def ls_info(self, path: str) -> list[FileInfo]: return self.inner.ls_info(path) def read(self, file_path: str, offset: int = 0, limit: int = 2000) -> str: return self.inner.read(file_path, offset=offset, limit=limit) def grep_raw(self, pattern: str, path: str | None = None, glob: str | None = None) -> list[GrepMatch] | str: return self.inner.grep_raw(pattern, path, glob) def glob_info(self, pattern: str, path: str = "/") -> list[FileInfo]: return self.inner.glob_info(pattern, path) def write(self, file_path: str, content: str) -> WriteResult: if self._deny(file_path): return WriteResult(error=f"Writes are not allowed under {file_path}") return self.inner.write(file_path, content) def edit(self, file_path: str, old_string: str, new_string: str, replace_all: bool = False) -> EditResult: if self._deny(file_path): return EditResult(error=f"Edits are not allowed under {file_path}") return self.inner.edit(file_path, old_string, new_string, replace_all) Protocol reference Backends must implement the BackendProtocol. Required endpoints: ls_info(path: str) -> list[FileInfo] Return entries with at least path. Include is_dir, size, modified_at when available. Sort by path for deterministic output. read(file_path: str, offset: int = 0, limit: int = 2000) -> str Return numbered content. On missing file, return "Error: File '/x' not found". grep_raw(pattern: str, path: Optional[str] = None, glob: Optional[str] = None) -> list[GrepMatch] | str Return structured matches. For an invalid regex, return a string like "Invalid regex pattern:..." (do not raise). glob_info(pattern: str, path: str = "/") -> list[FileInfo] Return matched files as FileInfo entries (empty list if none). write(file_path: str, content: str) -> WriteResult Create-only. On conflict, return WriteResult(error=...). On success, set path and for state backends set files_update={...}; external backends should use files_update=None. edit(file_path: str, old_string: str, new_string: str, replace_all: bool = False) -> EditResult Enforce uniqueness of old_string unless replace_all=True. If not found, return error. Include occurrences on success. Supporting types: WriteResult(error, path, files_update) EditResult(error, path, files_update, occurrences) FileInfo with fields: path (required), optionally is_dir, size, modified_at. GrepMatch with fields: path, line, text. Edit this page on GitHub or file an issue. Connect these docs to Claude, VSCode, and more via MCP for real-time answers.Was this page helpful?YesNoAgent harness capabilitiesPreviousSubagentsNext⌘IDocs by LangChain home pagegithubxlinkedinyoutubeResourcesForumChangelogLangChain AcademyTrust CenterCompanyAboutCareersBloggithubxlinkedinyoutubePowered by
lsPattern 2: Docs by LangChain home pageLangChain + LangGraphSearch...⌘KSupportGitHubTry LangSmithTry LangSmithSearch...NavigationCore capabilitiesAgent harness capabilitiesLangChainLangGraphDeep AgentsIntegrationsLearnReferenceContributePythonOverviewGet startedQuickstartCustomizationCore capabilitiesAgent harnessBackendsSubagentsHuman-in-the-loopLong-term memoryMiddlewareCommand line interfaceUse the CLIOn this pageFile system accessLarge tool result evictionPluggable storage backendsTask delegation (subagents)Conversation history summarizationDangling tool call repairTo-do list trackingHuman-in-the-loopPrompt caching (Anthropic)Core capabilitiesAgent harness capabilitiesCopy pageCopy pageWe think of deepagents as an “agent harness”. It is the same core tool calling loop as other agent frameworks, but with built-in tools and capabilities. This page lists out the components that make up the agent harness. File system access The harness provides six tools for file system operations, making files first-class citizens in the agent’s environment: ToolDescriptionlsList files in a directory with metadata (size, modified time)read_fileRead file contents with line numbers, supports offset/limit for large fileswrite_fileCreate new filesedit_filePerform exact string replacements in files (with global replace mode)globFind files matching patterns (e.g., **/*.py)grepSearch file contents with multiple output modes (files only, content with context, or counts) Large tool result eviction The FilesystemMiddleware automatically evicts large tool results to the file system when they exceed a token threshold, preventing context window saturation. How it works: Monitors tool call results for size (default threshold: 20,000 tokens, configurable via tool_token_limit_before_evict) When exceeded, writes the result using the configured backend Replaces the tool result with a truncated preview and file reference Agent can read the full result from the file system as needed Pluggable storage backends The harness abstracts file system operations behind a protocol, allowing different storage strategies for different use cases. Available backends: StateBackend - Ephemeral in-memory storage Files live in the agent’s state (checkpointed with conversation) Persists within a thread but not across threads Useful for temporary working files FilesystemBackend - Real filesystem access Read/write from actual disk Supports virtual mode (sandboxed to a root directory) Integrates with system tools (ripgrep for grep) Security features: path validation, size limits, symlink prevention StoreBackend - Persistent cross-conversation storage Uses LangGraph’s BaseStore for durability Namespaced per assistant_id Files persist across conversations Useful for long-term memory or knowledge bases CompositeBackend - Route different paths to different backends Example: / → StateBackend, /memories/ → StoreBackend Longest-prefix matching for routing Enables hybrid storage strategies See backends for configuration details and examples. Task delegation (subagents) The harness allows the main agent to create ephemeral “subagents” for isolated multi-step tasks. Why it’s useful: Context isolation - Subagent’s work doesn’t clutter main agent’s context Parallel execution - Multiple subagents can run concurrently Specialization - Subagents can have different tools/configurations Token efficiency - Large subtask context is compressed into a single result How it works: Main agent has a task tool When invoked, creates a fresh agent instance with its own context Subagent executes autonomously until completion Returns a single final report to the main agent Subagents are stateless (can’t send multiple messages back) Default subagent: “general-purpose” subagent automatically available Has filesystem tools by default Can be customized with additional tools/middleware Custom subagents: Define specialized subagents with specific tools Example: code-reviewer, web-researcher, test-runner Configure via subagents parameter Conversation history summarization The harness automatically compresses old conversation history when token usage becomes excessive. Configuration: Triggers at 85% of the model’s max_input_tokens from its model profile Keeps 10% of tokens as recent context Falls back to 170,000 tokens trigger / 6 messages kept if model profile is unavailable Older messages are summarized by the model Why it’s useful: Enables very long conversations without hitting context limits Preserves recent context while compressing ancient history Transparent to the agent (appears as a special system message) Dangling tool call repair The harness fixes message history when tool calls are interrupted or cancelled before receiving results. The problem: Agent requests tool call: “Please run X” Tool call is interrupted (user cancels, error, etc.) Agent sees tool_call in AIMessage but no corresponding ToolMessage This creates an invalid message sequence The solution: Detects AIMessage objects with tool_calls that have no results Creates synthetic ToolMessage responses indicating the call was cancelled Repairs the message history before agent execution Why it’s useful: Prevents agent confusion from incomplete message chains Gracefully handles interruptions and errors Maintains conversation coherence To-do list tracking The harness provides a write_todos tool that agents can use to maintain a structured task list. Features: Track multiple tasks with statuses ('pending', 'in_progress', 'completed') Persisted in agent state Helps agent organize complex multi-step work Useful for long-running tasks and planning Human-in-the-loop The harness can pause agent execution at specified tool calls to allow human approval or modification. This feature is opt-in via the interrupt_on parameter. Configuration: Pass interrupt_on to create_deep_agent with a mapping of tool names to interrupt configurations Example: interrupt_on={"edit_file": True} pauses before every edit Can provide approval messages or modify tool inputs Why it’s useful: Safety gates for destructive operations User verification before expensive API calls Interactive debugging and guidance Prompt caching (Anthropic) The harness enables Anthropic’s prompt caching feature to reduce redundant token processing. How it works: Caches portions of the prompt that repeat across turns Significantly reduces latency and cost for long system prompts Automatically skipped for non-Anthropic models Why it’s useful: System prompts (especially with filesystem docs) can be 5k+ tokens These repeat every turn without caching Caching provides ~10x speedup and cost reduction Edit this page on GitHub or file an issue. Connect these docs to Claude, VSCode, and more via MCP for real-time answers.Was this page helpful?YesNoCustomize Deep AgentsPreviousBackendsNext⌘IDocs by LangChain home pagegithubxlinkedinyoutubeResourcesForumChangelogLangChain AcademyTrust CenterCompanyAboutCareersBloggithubxlinkedinyoutubePowered by
deepagentsExample Code Patterns
Example 1 (python):
from langchain.chat_models import init_chat_model
from deepagents import create_deep_agent
model = init_chat_model(model="openai:gpt-5")
agent = create_deep_agent(model=model)Example 2 (sql):
from deepagents.backends import FilesystemBackend
agent = create_deep_agent(
backend=FilesystemBackend(root_dir=".", virtual_mode=True)
)Reference Files
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- core.md - Core documentation
- getting_started.md - Getting Started documentation
- tools.md - Tools documentation
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