LangChain 1.0 Knowledge Patch Skill

LangChain 1.0 Knowledge Patch

Claude's baseline knowledge covers LangChain 0.2.x and LangGraph 0.1.x. This skill provides changes from the LangChain 1.0 / LangGraph 1.0 release (2025-10-22).

Quick Reference

Agent Creation

| Old API | New API | Notes | |---------|---------|-------| | langgraph.prebuilt.create_react_agent | langchain.agents.create_agent | Full replacement | | prompt= | system_prompt= | Parameter renamed | | config["configurable"] | context=Context(...) | Typed runtime context | | Pydantic/dataclass state | TypedDict only | state_schema constraint | | Streaming node "agent" | "model" | Node name changed | | model.bind_tools(...) then pass | Pass unbound model + tools= | Pre-bound models rejected |

create_agent — Essential Pattern

from langchain.agents import create_agent

agent = create_agent(
    model="anthropic:claude-sonnet-4-6",  # or model instance
    tools=[my_tool],
    system_prompt="You are a helpful assistant",
    middleware=[...],           # replaces pre/post hooks
    response_format=...,       # ToolStrategy or ProviderStrategy
    state_schema=CustomState,  # must be TypedDict, not Pydantic
    context_schema=Context,    # typed runtime context
    name="my_agent",
)

result = agent.invoke(
    {"messages": [{"role": "user", "content": "Hello"}]},
    context=Context(user_id="123")  # replaces config["configurable"]
)

See references/agent-creation.md for full migration guide.

Middleware System

| Decorator | Purpose | |-----------|---------| | @before_model | Modify state before model call | | @after_model | Modify state after model response | | @wrap_model_call | Wrap entire model call (try/except, retries) | | @wrap_tool_call | Wrap tool execution | | @dynamic_prompt | Generate system prompt dynamically |

Built-in: SummarizationMiddleware, HumanInTheLoopMiddleware, ModelCallLimitMiddleware, ToolCallLimitMiddleware, ModelFallbackMiddleware, PIIMiddleware, TodoListMiddleware, LLMToolSelectorMiddleware, ToolRetryMiddleware, ContextEditingMiddleware.

All middleware imports from langchain.agents.middleware. See references/middleware.md for decorator and class patterns.

Middleware — Quick Example

from langchain.agents.middleware import before_model, wrap_tool_call

@before_model
def trim_context(state, runtime):
    return {"messages": state["messages"][-20:]}

@wrap_tool_call
def handle_errors(request, handler):
    try:
        return handler(request)
    except Exception as e:
        return ToolMessage(content=f"Error: {e}", tool_call_id=request.tool_call["id"])

agent = create_agent(
    model="claude-sonnet-4-6",
    tools=tools,
    middleware=[trim_context, handle_errors],
)

Tools & Runtime

ToolRuntime gives tools access to agent state, context, and store:

from langchain.tools import tool, ToolRuntime

@tool
def greet(runtime: ToolRuntime[Context, CustomState]) -> str:
    """Greet the user."""
    name = runtime.state.get("user_name", "Unknown")
    return f"Hello {name} (id: {runtime.context.user_id})!"

Reserved parameter names: config, runtime cannot be used as tool argument names.

See references/tools-and-runtime.md.

Content Blocks & Structured Output

| Feature | API | |---------|-----| | Provider-agnostic content | response.content_blocks (list of typed dicts) | | Multimodal input | HumanMessage(content_blocks=[...]) | | Serialization opt-in | LC_OUTPUT_VERSION=v1 or output_version="v1" | | Tool-based structured output | ToolStrategy(MySchema) | | Native structured output | ProviderStrategy(MySchema) | | Auto-fallback | Pass schema directly to response_format= |

Structured Output — Quick Example

from langchain.agents.structured_output import ToolStrategy, ProviderStrategy

# ToolStrategy: works with any model supporting tool calling
agent = create_agent(model="gpt-4.1", tools=tools, response_format=ToolStrategy(MySchema))

# ProviderStrategy: uses native structured output (more reliable, fewer providers)
agent = create_agent(model="gpt-4.1", response_format=ProviderStrategy(MySchema))

# Passing schema directly defaults to ProviderStrategy with ToolStrategy fallback
agent = create_agent(model="gpt-4.1", response_format=MySchema)

result["structured_response"]  # access the typed output

Prompted output is removed. Structured output now happens in the main agent loop (no extra LLM call).

See references/content-and-output.md for content blocks and multimodal messages.

Namespace Changes

| New location | Old location | |-------------|-------------| | langchain.agents | langgraph.prebuilt | | langchain.messages | langchain_core.messages | | langchain.tools | langchain_core.tools | | langchain.chat_models | langchain_community.chat_models | | langchain.embeddings | langchain_community.embeddings | | langchain_classic.* | Legacy chains, retrievers, hub, indexing |

langchain-classic

Legacy chains, retrievers, indexing, hub, and community re-exports:

# pip install langchain-classic
from langchain_classic.chains import LLMChain
from langchain_classic.retrievers import ...
from langchain_classic import hub

See references/namespace-and-migration.md for full details.

Breaking Changes (1.0)

Python 3.10+ required (3.9 dropped)
message.text is now a property — use response.text not response.text()
example parameter removed from AIMessage
langchain-anthropic max_tokens default increased (was 1024)
Chat model return type fixed to AIMessage (was BaseMessage)
OpenAI Responses API now defaults to storing response items in message content

Deep Agents SDK

New deepagents package for complex multi-step tasks:

from deepagents import create_deep_agent

agent = create_deep_agent(
    tools=[my_tool],
    system_prompt="You are a helpful assistant",
)

Built-in capabilities: write_todos (planning), ls/read_file/write_file/edit_file (context management), task (subagent spawning). Pluggable filesystem backends (in-memory, local disk, LangGraph store, sandboxes).

See references/namespace-and-migration.md for package details.

Reference Files

| File | Contents | |------|----------| | agent-creation.md | create_agent API, migration from create_react_agent, parameters, invocation | | middleware.md | Decorator and class middleware patterns, built-in middleware, SummarizationMiddleware, HumanInTheLoopMiddleware | | tools-and-runtime.md | ToolRuntime typed access to state/context/store, reserved parameter names | | content-and-output.md | content_blocks property, multimodal messages, ToolStrategy, ProviderStrategy, structured output in agent loop | | namespace-and-migration.md | Simplified namespace, langchain-classic package, Deep Agents SDK |

Agent Skills: LangChain 1.0 Knowledge Patch

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