Deepagents

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

ls

Pattern 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

deepagents

Example 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

This skill includes comprehensive documentation in references/:

• capabilities.md - Capabilities documentation
• core.md - Core documentation
• getting_started.md - Getting Started documentation
• tools.md - Tools documentation

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For Specific Features

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For Code Examples

The quick reference section above contains common patterns extracted from the official docs.

Resources

references/

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• Detailed explanations
• Code examples with language annotations
• Links to original documentation
• Table of contents for quick navigation

scripts/

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assets/

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Notes

• This skill was automatically generated from official documentation
• Reference files preserve the structure and examples from source docs
• Code examples include language detection for better syntax highlighting
• Quick reference patterns are extracted from common usage examples in the docs

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