deep-agents-memory

द्वारा langchain-ai

डीप एजेंट्स के लिए प्लग करने योग्य मेमोरी और फ़ाइल बैकएंड, जिसमें एफेमरल, पर्सिस्टेंट और हाइब्रिड रूटिंग विकल्प हैं। चार बैकएंड प्रकार: स्टेटबैकएंड (थ्रेड-स्कोप्ड, एफेमरल), स्टोरबैकएंड (क्रॉस-सेशन पर्सिस्टेंट), फ़ाइलसिस्टमबैकएंड (स्थानीय डेव के लिए वास्तविक डिस्क एक्सेस), और कम्पोजिटबैकएंड (विभिन्न पथों को विभिन्न बैकएंड पर रूट करता है)। फ़

npx skills add https://github.com/langchain-ai/langchain-skills --skill deep-agents-memory
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

Use CaseBackendWhy
Temporary working filesStateBackendDefault, no setup
Local development CLIFilesystemBackendDirect disk access
Cross-session memoryStoreBackendPersists across threads
Hybrid storageCompositeBackendMix ephemeral + persistent
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
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
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)
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)
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
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
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
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()
});

Security: Never use FilesystemBackend in web servers - use StateBackend or sandbox instead.

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
)
### 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
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())
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() });
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!
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!
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)
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)
Use PostgresStore for production (InMemoryStore lost on restart).
# WRONG                              # CORRECT
store = InMemoryStore()              store = PostgresStore(connection_string="postgresql://...")
Use PostgresStore for production (InMemoryStore lost on restart).
// WRONG                                    // CORRECT
const store = new InMemoryStore();          const store = new PostgresStore({ connectionString: "..." });
Enable virtual_mode=True to restrict path access (prevents ../ and ~/ escapes).
backend = FilesystemBackend(root_dir="/project", virtual_mode=True)  # Secure
CompositeBackend matches longest prefix first.
routes = {"/mem/": StoreBackend(rt), "/mem/temp/": StateBackend(rt)}
# /mem/file.txt -> StoreBackend, /mem/temp/file.txt -> StateBackend (longer match)

langchain-ai की और Skills

deepagents-thread-inspector
langchain-ai
स्थानीय Deep Agents Code SQLite सत्र भंडार में वार्तालापों का निरीक्षण और व्याख्या करें। LangSmith ट्रेस टूलिंग अनुपलब्ध होने पर फ़ॉलबैक के रूप में उपयोग करें, इसके लिए…
deepagents-python-quickstart
langchain-ai
आधिकारिक क्विकस्टार्ट का पालन करके पायथन में एक न्यूनतम स्थानीय डीप एजेंट तैयार करें, टैविली के बजाय प्रदाता-मूल वेब खोज का उपयोग करें। उपयोग करें जब उपयोगकर्ता चाहता है…
deepagents-typescript-quickstart
langchain-ai
आधिकारिक क्विकस्टार्ट का पालन करके TypeScript में एक न्यूनतम स्थानीय Deep Agent तैयार करें, Tavily के बजाय प्रोवाइडर-नेटिव वेब खोज का उपयोग करें। उपयोग करें जब उपयोगकर्ता…
eval-engineering
langchain-ai
किसी एजेंट रिपॉजिटरी और वैकल्पिक उपयोगकर्ता-प्रदत्त ट्रेस का पुनरावृत्त रूप से निरीक्षण करें, उपयोगकर्ता का साक्षात्कार लें, और एक-एक करके Harbor मूल्यांकन बनाएं, चलाएं, और ऑडिट करें। इसके लिए उपयोग करें…
LangChain RAG Pipeline
langchain-ai
इस कौशल का उपयोग किसी भी पुनर्प्राप्ति-संवर्धित पीढ़ी (RAG) प्रणाली के निर्माण में करें। इसमें दस्तावेज़ लोडर, RecursiveCharacterTextSplitter, एम्बेडिंग (OpenAI),… शामिल हैं।
LangChain Structured Output & HITL
langchain-ai
langchain-structured-output-&-hitl — AI एजेंटों के लिए एक इंस्टॉल करने योग्य कौशल, जो langchain-ai/langchain-skills द्वारा प्रकाशित है।
LangSmith Datasets
langchain-ai
इस कौशल का उपयोग करें जब ट्रेस से मूल्यांकन डेटासेट बनाना हो या LangSmith पर डेटासेट अपलोड करना हो या डेटासेट क्वेरी करना हो। इसमें डेटासेट प्रकार शामिल हैं (final_response,…
langsmith-evaluator
langchain-ai
लैंगस्मिथ के लिए मूल्यांकन पाइपलाइन बनाते समय इस कौशल का आह्वान करें। इसमें तीन मुख्य घटक शामिल हैं: (1) मूल्यांकनकर्ता बनाना - LLM-एक-न्यायाधीश, कस्टम कोड; (2)…