deepagents-filesystem

name	deepagents-filesystem
description	Using FilesystemMiddleware with virtual filesystems, backends (State, Store, Filesystem, Composite), and context management for Deep Agents.
language	python

deepagents-filesystem (Python)

Overview

FilesystemMiddleware solves context engineering challenges by providing file operations through a pluggable backend system. It allows agents to offload large context to filesystem storage, preventing context window overflow.

Built-in Filesystem Tools:

• ls - List files in a directory
• read_file - Read entire files or specific line ranges
• write_file - Create new files
• edit_file - Edit existing files with exact string replacement
• glob - Find files matching patterns
• grep - Search for text across files

When to Use Filesystem Middleware

Use Filesystem Tools When	Alternative Approach
Tool results are variable-length (web_search, RAG)	Keep in message history (if small)
Working with large documents or code	Use specialized tools
Need persistent storage across turns	Use short-term message history
Multiple files need coordination	Single-turn operations

Backend Types

StateBackend (Default)

Ephemeral storage in agent state - persists within a thread only.

from deepagents import create_deep_agent

# Default backend (StateBackend)
agent = create_deep_agent()

result = agent.invoke({
    "messages": [{"role": "user", "content": "Write notes to /draft.txt"}]
})
# File exists only within this thread

FilesystemBackend (Local Disk)

Direct access to local filesystem.

from deepagents import create_deep_agent
from deepagents.backends import FilesystemBackend

agent = create_deep_agent(
    backend=FilesystemBackend(
        root_dir=".",  # Root directory
        virtual_mode=True  # Enable path restrictions
    )
)

# Agent can now read/write to actual files on disk
result = agent.invoke({
    "messages": [{"role": "user", "content": "Read the README.md file"}]
})

Security Considerations:

• Use virtual_mode=True to prevent .., ~, and absolute path access
• Enable Human-in-the-Loop for sensitive operations
• Never use in web servers - use StateBackend or sandbox instead
• Secrets (API keys, .env) are readable by the agent

StoreBackend (Persistent Cross-Thread)

Storage that persists across threads using LangGraph's Store.

from deepagents import create_deep_agent
from deepagents.backends import StoreBackend
from langgraph.store.memory import InMemoryStore

store = InMemoryStore()

agent = create_deep_agent(
    backend=lambda rt: StoreBackend(rt),
    store=store
)

# Files persist across different thread_ids

CompositeBackend (Hybrid Storage)

Route different paths to different backends.

from deepagents import create_deep_agent
from deepagents.backends import CompositeBackend, StateBackend, StoreBackend
from langgraph.store.memory import InMemoryStore

store = InMemoryStore()

composite_backend = lambda rt: CompositeBackend(
    default=StateBackend(rt),
    routes={
        "/memories/": StoreBackend(rt),  # Persistent storage
    }
)

agent = create_deep_agent(
    backend=composite_backend,
    store=store
)

# /draft.txt -> ephemeral (StateBackend)
# /memories/user-prefs.txt -> persistent (StoreBackend)

Decision Table: Which Backend to Use

Use Case	Backend	Why
Temporary working files	StateBackend	Default, no setup needed
Local development CLI	FilesystemBackend	Direct disk access
Cross-session memory	StoreBackend	Persists across threads
Hybrid storage	CompositeBackend	Mix ephemeral + persistent
Production web app	StateBackend or Sandbox	Never use FilesystemBackend

Code Examples

Example 1: Managing Large Context

from deepagents import create_deep_agent

agent = create_deep_agent()

# Agent offloads search results to filesystem
result = agent.invoke({
    "messages": [{
        "role": "user",
        "content": "Search for information about Python asyncio and save the results for later analysis"
    }]
})

# Agent workflow:
# 1. Use search tool -> large results
# 2. write_file("/search-results.txt", results)
# 3. Continue with compact context
# 4. Later: read_file("/search-results.txt") when needed

Example 2: Custom Tool Descriptions

from langchain.agents import create_agent
from deepagents.middleware.filesystem import FilesystemMiddleware

agent = create_agent(
    model="claude-sonnet-4-5-20250929",
    middleware=[
        FilesystemMiddleware(
            backend=None,  # Use default StateBackend
            system_prompt="Save intermediate results to /workspace/ directory",
            custom_tool_descriptions={
                "read_file": "Read files you've previously written. Use offset/limit for large files.",
                "write_file": "Save data to avoid context overflow. Organize in /workspace/.",
            }
        ),
    ],
)

Example 3: Long-term Memory with CompositeBackend

from deepagents import create_deep_agent
from deepagents.backends import CompositeBackend, StateBackend, StoreBackend
from langgraph.store.memory import InMemoryStore

store = InMemoryStore()

agent = create_deep_agent(
    backend=lambda rt: CompositeBackend(
        default=StateBackend(rt),
        routes={"/memories/": StoreBackend(rt)}
    ),
    store=store
)

# Thread 1: Save user preferences
config1 = {"configurable": {"thread_id": "thread-1"}}
agent.invoke({
    "messages": [{"role": "user", "content": "Save my preference: I like concise explanations to /memories/prefs.txt"}]
}, config=config1)

# Thread 2: Access saved preferences
config2 = {"configurable": {"thread_id": "thread-2"}}
agent.invoke({
    "messages": [{"role": "user", "content": "Read my preferences and explain asyncio"}]
}, config=config2)
# Agent reads /memories/prefs.txt and provides concise explanation

Example 4: FilesystemBackend for Local Development

from deepagents import create_deep_agent
from deepagents.backends import FilesystemBackend

agent = create_deep_agent(
    backend=FilesystemBackend(
        root_dir="/Users/username/project",
        virtual_mode=True
    ),
    interrupt_on={"write_file": True, "edit_file": True}  # Safety
)

# Agent can read actual project files
result = agent.invoke({
    "messages": [{"role": "user", "content": "Analyze the code in src/main.py"}]
})

Boundaries

What Agents CAN Configure

✅ Backend type and configuration ✅ Custom tool descriptions ✅ File paths and organization ✅ Human-in-the-loop for file operations ✅ Root directory for FilesystemBackend ✅ Routing rules for CompositeBackend

What Agents CANNOT Configure

❌ Tool names (ls, read_file, write_file, edit_file, glob, grep) ❌ The fundamental file operation protocol ❌ Disable filesystem tools in create_deep_agent ❌ Access files outside virtual_mode restrictions ❌ Cross-thread file access without proper backend setup

Gotchas

1. StateBackend Files Don't Persist Across Threads

# ❌ Files lost when thread changes
config1 = {"configurable": {"thread_id": "thread-1"}}
agent.invoke({"messages": [{"role": "user", "content": "Write to /notes.txt"}]}, config=config1)

config2 = {"configurable": {"thread_id": "thread-2"}}
agent.invoke({"messages": [{"role": "user", "content": "Read /notes.txt"}]}, config=config2)
# File not found! Different thread

# ✅ Use same thread_id OR use StoreBackend for persistence

2. FilesystemBackend Needs virtual_mode for Security

# ❌ Insecure - agent can access anywhere
backend = FilesystemBackend(root_dir="/project", virtual_mode=False)

# ✅ Secure - agent restricted to /project
backend = FilesystemBackend(root_dir="/project", virtual_mode=True)

3. StoreBackend Requires a Store Instance

# ❌ Missing store
agent = create_deep_agent(
    backend=lambda rt: StoreBackend(rt)
)

# ✅ Provide store
from langgraph.store.memory import InMemoryStore

agent = create_deep_agent(
    backend=lambda rt: StoreBackend(rt),
    store=InMemoryStore()
)

4. edit_file Requires Exact String Match

# The edit_file tool needs exact string matching

# ❌ Won't work - whitespace mismatch
old_string = "def hello():\n  print('hi')"
new_string = "def hello():\n    print('hi')"  # Different indentation

# ✅ Match exactly as it appears in the file
old_string = "  print('hi')"  # Exact match from file
new_string = "    print('hi')"  # New content

Full Documentation

• Filesystem Middleware
• Backends Guide
• Long-term Memory