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NeoVandNeoVand/op-xy-agent/.agents/skills/deep-agents-memory/SKILL.md

deep-agents-memory

Skill1 starsChanged 50 days agoMITView on GitHub

INVOKE THIS SKILL when your Deep Agent needs memory, persistence, or filesystem access. Covers StateBackend (ephemeral), StoreBackend (persistent), FilesystemMiddleware, and CompositeBackend for routing.

8 other projects use a version of this, 3 of them edited.

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Projects with it also use managed-deep-agents, deepagents-python-quickstart and 2 more.

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Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
  • LangChainSkill

    managed-deep-agents

    INVOKE THIS SKILL when building, testing, or deploying Managed Deep Agents in LangSmith. Walks a user through their first agent end to end — interviewing them about what they want to build, mapping it onto what MDA can actually do, then scaffolding and deploying it. Covers the file-based project layout; define_deep_agent / defineDeepAgent; instructions, skills, memory, identity, tools, MCP servers, connections, middleware, sandboxes, schedules, channels, and evals; the mda CLI; GitHub deployments; and Context Hub.

    Also in 5 projects that use this

  • LangChainSkill

    deepagents-python-quickstart

    Scaffold a minimal local Deep Agent in Python by following the official quickstart, using provider-native web search instead of Tavily. Use when the user wants to quickly build or try a Deep Agent locally.

    Also in 3 projects that use this

  • +2 more from langchain-ai

The file

We split this skill into the rules it gives an agent

3 rules. About 0 are common, 3 are distinctive, and 0 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈3

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Default StateBackend stores files ephemerally within a thread.

    line 29
  2. Distinctive:

    Use FilesystemBackend for local development with real disk access and human-in-the-loop.

    line 132
  3. Distinctive:

    Use PostgresStore for production (InMemoryStore lost on restart).

    line 284

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md
<overview> Deep Agents use pluggable backends for file operations and memory:

Short-term (StateBackend): Persists within a single thread, lost when thread ends Long-term (StoreBackend): Persists across threads and sessions Hybrid (CompositeBackend): Route different paths to different backends

FilesystemMiddleware provides tools: ls, read_file, write_file, edit_file, glob, grep </overview>

<backend-selection>
Use CaseBackendWhy
Temporary working filesStateBackendDefault, no setup
Local development CLIFilesystemBackendDirect disk access
Cross-session memoryStoreBackendPersists across threads
Hybrid storageCompositeBackendMix ephemeral + persistent
</backend-selection><ex-default-state-backend> <python> Default StateBackend stores files ephemerally within a thread.
from deepagents import create_deep_agent

agent = create_deep_agent()  # Default: StateBackend
result = agent.invoke({
    "messages": [{"role": "user", "content": "Write notes to /draft.txt"}]
}, config={"configurable": {"thread_id": "thread-1"}})
# /draft.txt is lost when thread ends
</python> <typescript> Default StateBackend stores files ephemerally within a thread.
import { createDeepAgent } from "deepagents";

const agent = await createDeepAgent();  // Default: StateBackend
const result = await agent.invoke({
  messages: [{ role: "user", content: "Write notes to /draft.txt" }]
}, { configurable: { thread_id: "thread-1" } });
// /draft.txt is lost when thread ends
</typescript> </ex-default-state-backend><ex-composite-backend-for-hybrid> <python> Configure CompositeBackend to route paths to different storage 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)}
)

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

# /draft.txt -> ephemeral (StateBackend)
# /memories/user-prefs.txt -> persistent (StoreBackend)
</python> <typescript> Configure CompositeBackend to route paths to different storage backends.
import { createDeepAgent, CompositeBackend, StateBackend, StoreBackend } from "deepagents";
import { InMemoryStore } from "@langchain/langgraph";

const store = new InMemoryStore();

const agent = await createDeepAgent({
  backend: (config) => new CompositeBackend(
    new StateBackend(config),
    { "/memories/": new StoreBackend(config) }
  ),
  store
});

// /draft.txt -> ephemeral (StateBackend)
// /memories/user-prefs.txt -> persistent (StoreBackend)
</typescript> </ex-composite-backend-for-hybrid><ex-cross-session-memory> <python> Files in /memories/ persist across threads via StoreBackend routing.
# Using CompositeBackend from previous example
config1 = {"configurable": {"thread_id": "thread-1"}}
agent.invoke({"messages": [{"role": "user", "content": "Save to /memories/style.txt"}]}, config=config1)

config2 = {"configurable": {"thread_id": "thread-2"}}
agent.invoke({"messages": [{"role": "user", "content": "Read /memories/style.txt"}]}, config=config2)
# Thread 2 can read file saved by Thread 1
</python> <typescript> Files in /memories/ persist across threads via StoreBackend routing.
// Using CompositeBackend from previous example
const config1 = { configurable: { thread_id: "thread-1" } };
await agent.invoke({ messages: [{ role: "user", content: "Save to /memories/style.txt" }] }, config1);

const config2 = { configurable: { thread_id: "thread-2" } };
await agent.invoke({ messages: [{ role: "user", content: "Read /memories/style.txt" }] }, config2);
// Thread 2 can read file saved by Thread 1
</typescript> </ex-cross-session-memory><ex-filesystem-backend-local-dev> <python> Use FilesystemBackend for local development with real disk access and human-in-the-loop.
from deepagents import create_deep_agent
from deepagents.backends import FilesystemBackend
from langgraph.checkpoint.memory import MemorySaver

agent = create_deep_agent(
    backend=FilesystemBackend(root_dir=".", virtual_mode=True),  # Restrict access
    interrupt_on={"write_file": True, "edit_file": True},
    checkpointer=MemorySaver()
)

# Agent can read/write actual files on disk
</python> <typescript> Use FilesystemBackend for local development with real disk access and human-in-the-loop.
import { createDeepAgent, FilesystemBackend } from "deepagents";
import { MemorySaver } from "@langchain/langgraph";

const agent = await createDeepAgent({
  backend: new FilesystemBackend({ rootDir: ".", virtualMode: true }),
  interruptOn: { write_file: true, edit_file: true },
  checkpointer: new MemorySaver()
});
</typescript>

Security: Never use FilesystemBackend in web servers - use StateBackend or sandbox instead. </ex-filesystem-backend-local-dev>

<ex-store-in-custom-tools> <python> Access the store directly in custom tools for long-term memory operations.
from langchain.tools import tool, ToolRuntime
from langchain.agents import create_agent
from langgraph.store.memory import InMemoryStore

@tool
def get_user_preference(key: str, runtime: ToolRuntime) -> str:
    """Get a user preference from long-term storage."""
    store = runtime.store
    result = store.get(("user_prefs",), key)
    return str(result.value) if result else "Not found"

@tool
def save_user_preference(key: str, value: str, runtime: ToolRuntime) -> str:
    """Save a user preference to long-term storage."""
    store = runtime.store
    store.put(("user_prefs",), key, {"value": value})
    return f"Saved {key}={value}"

store = InMemoryStore()

agent = create_agent(
    model="gpt-4.1",
    tools=[get_user_preference, save_user_preference],
    store=store
)
</python> </ex-store-in-custom-tools><boundaries> ### What Agents CAN Configure
  • Backend type and configuration
  • Routing rules for CompositeBackend
  • Root directory for FilesystemBackend
  • Human-in-the-loop for file operations

What Agents CANNOT Configure

  • Tool names (ls, read_file, write_file, edit_file, glob, grep)
  • Access files outside virtual_mode restrictions
  • Cross-thread file access without proper backend setup</boundaries>
<fix-storebackend-requires-store> <python> StoreBackend requires a store instance.
# WRONG
agent = create_deep_agent(backend=lambda rt: StoreBackend(rt))

# CORRECT
agent = create_deep_agent(backend=lambda rt: StoreBackend(rt), store=InMemoryStore())
</python> <typescript> StoreBackend requires a store instance.
// WRONG
const agent = await createDeepAgent({ backend: (c) => new StoreBackend(c) });

// CORRECT
const agent = await createDeepAgent({ backend: (c) => new StoreBackend(c), store: new InMemoryStore() });
</typescript> </fix-storebackend-requires-store><fix-statebackend-files-dont-persist> <python> StateBackend files are thread-scoped - use same thread_id or StoreBackend for cross-thread access.
# WRONG: thread-2 can't read file from thread-1
agent.invoke({"messages": [...]}, config={"configurable": {"thread_id": "thread-1"}})  # Write
agent.invoke({"messages": [...]}, config={"configurable": {"thread_id": "thread-2"}})  # File not found!
</python> <typescript> StateBackend files are thread-scoped - use same thread_id or StoreBackend for cross-thread access.
// WRONG: thread-2 can't read file from thread-1
await agent.invoke({ messages: [...] }, { configurable: { thread_id: "thread-1" } });  // Write
await agent.invoke({ messages: [...] }, { configurable: { thread_id: "thread-2" } });  // File not found!
</typescript> </fix-statebackend-files-dont-persist><fix-path-prefix-for-persistence> <python> Path must match CompositeBackend route prefix for persistence.
# With routes={"/memories/": StoreBackend(rt)}:
agent.invoke(...)  # /prefs.txt -> ephemeral (no match)
agent.invoke(...)  # /memories/prefs.txt -> persistent (matches route)
</python> <typescript> Path must match CompositeBackend route prefix for persistence.
// With routes: { "/memories/": StoreBackend }:
await agent.invoke(...);  // /prefs.txt -> ephemeral (no match)
await agent.invoke(...);  // /memories/prefs.txt -> persistent (matches route)
</typescript> </fix-path-prefix-for-persistence><fix-production-store> <python> Use PostgresStore for production (InMemoryStore lost on restart).
# WRONG                              # CORRECT
store = InMemoryStore()              store = PostgresStore(connection_string="postgresql://...")
</python> <typescript> Use PostgresStore for production (InMemoryStore lost on restart).
// WRONG                                    // CORRECT
const store = new InMemoryStore();          const store = new PostgresStore({ connectionString: "..." });
</typescript> </fix-production-store><fix-filesystem-backend-needs-virtual-mode> <python> Enable virtual_mode=True to restrict path access (prevents ../ and ~/ escapes).
backend = FilesystemBackend(root_dir="/project", virtual_mode=True)  # Secure
</python> </fix-filesystem-backend-needs-virtual-mode><fix-longest-prefix-match> <python> CompositeBackend matches longest prefix first.
routes = {"/mem/": StoreBackend(rt), "/mem/temp/": StateBackend(rt)}
# /mem/file.txt -> StoreBackend, /mem/temp/file.txt -> StateBackend (longer match)
</python> </fix-longest-prefix-match>

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