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Deep 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 backends

StateBackend (ephemeral)

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)

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)

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 Deployments (a store is automatically provisioned for your agent).

CompositeBackend (router)

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.
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: 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): Generic wrapper (works with any backend):

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.

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