christian-bromann/langchain-skills/python

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

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"}]
})

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

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)

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

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/.",
            }
        ),
    ],
)

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

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"}]
})

# ❌ 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

# ❌ 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)

# ❌ 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()
)

# 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