enuno/langchain

LangChain Skill

Expert assistance for the langchain Python package's new agent API: create_agent() — a high-level factory that builds a LangGraph-backed agent with composable middleware for cross-cutting concerns (HITL, PII, fallback, limits, summarization, etc.).

Install: pip install -U langchain

Reference: references/api.md (500 KB — full API reference).

When to Use This Skill

Activate when:

• Creating an agent with create_agent() — using model strings, tools, and middleware
• Using init_chat_model strings — e.g. "anthropic:claude-sonnet-4-5" as the model param
• Adding HITL approval gates — using HumanInTheLoopMiddleware per tool
• Redacting PII — using PIIMiddleware to detect/redact/mask/hash PII in input or output
• Adding model fallback — using ModelFallbackMiddleware for automatic failover
• Limiting model or tool calls — using ModelCallLimitMiddleware or ToolCallLimitMiddleware
• Auto-summarizing long conversations — using SummarizationMiddleware on token/message threshold
• Retrying failed calls — using ModelRetryMiddleware or ToolRetryMiddleware
• Structured agent output — using response_format param on create_agent()
• Composing multiple middleware — stacking middleware in the middleware list

Quick Reference

create_agent() — minimal agent

from langchain.agents import create_agent
from langchain_core.tools import tool

@tool
def check_weather(location: str) -> str:
    """Return the weather forecast for a location."""
    return f"It's sunny and 22°C in {location}."

# model can be a string (uses init_chat_model) or a BaseChatModel instance
agent = create_agent(
    model="anthropic:claude-sonnet-4-6",   # or "openai:gpt-4o", "google:gemini-2.0-flash"
    tools=[check_weather],
    system_prompt="You are a helpful assistant.",
)

# Returns a CompiledStateGraph (standard LangGraph interface)
for chunk in agent.stream(
    {"messages": [{"role": "user", "content": "What's the weather in Paris?"}]},
    stream_mode="updates",
):
    print(chunk)

HumanInTheLoopMiddleware — per-tool approval gates

from langchain.agents import create_agent
from langchain.agents.middleware import HumanInTheLoopMiddleware
from langchain_core.tools import tool

@tool
def delete_file(path: str) -> str:
    """Delete a file from the filesystem."""
    import os; os.remove(path); return f"Deleted {path}"

@tool
def read_file(path: str) -> str:
    """Read file contents."""
    return open(path).read()

hitl = HumanInTheLoopMiddleware(
    interrupt_on={
        "delete_file": True,    # all decisions: approve/edit/reject/respond
        "read_file": False,     # auto-approve (no interrupt)
        # "write_file": InterruptOnConfig(approve=True, reject=True, description="Approve write?")
    }
)

agent = create_agent(
    model="anthropic:claude-sonnet-4-6",
    tools=[delete_file, read_file],
    middleware=[hitl],
    checkpointer=...,   # required for HITL interrupts
)

# Resume after interrupt (same as LangGraph interrupt pattern)
from langchain_core.messages import HumanMessage
from langgraph.types import Command
agent.invoke(Command(resume="approve"), config={"configurable": {"thread_id": "1"}})

PIIMiddleware — detect and handle PII

from langchain.agents import create_agent
from langchain.agents.middleware import PIIMiddleware

# Redact emails and credit cards from user input
pii = PIIMiddleware(
    "email",
    strategy="redact",        # "block" | "redact" | "mask" | "hash"
    apply_to_input=True,      # scan user messages
    apply_to_output=False,    # don't scan agent responses
    apply_to_tool_results=False,
)

# Stack multiple PII middleware
agent = create_agent(
    model="openai:gpt-4o",
    tools=[],
    middleware=[
        PIIMiddleware("email", strategy="redact"),
        PIIMiddleware("credit_card", strategy="mask"),    # ****-****-****-1234
        PIIMiddleware("ip", strategy="hash"),
    ],
)

ModelFallbackMiddleware — automatic model failover

from langchain.agents import create_agent
from langchain.agents.middleware import ModelFallbackMiddleware

fallback = ModelFallbackMiddleware(
    "openai:gpt-4o-mini",                    # try first on error
    "anthropic:claude-haiku-4-5-20251001",   # then this
    # additional fallbacks...
)

agent = create_agent(
    model="openai:gpt-4o",    # primary model
    tools=[...],
    middleware=[fallback],
)

SummarizationMiddleware — auto-compress long conversations

from langchain.agents import create_agent
from langchain.agents.middleware import SummarizationMiddleware

summarizer = SummarizationMiddleware(
    model="openai:gpt-4o-mini",    # model to generate summaries
    trigger=[
        ("fraction", 0.8),         # trigger at 80% of model's context window
        ("messages", 100),         # or at 100 messages, whichever first
    ],
    keep=("messages", 20),         # keep most recent 20 messages after summary
)

agent = create_agent(
    model="anthropic:claude-sonnet-4-6",
    tools=[...],
    middleware=[summarizer],
)

ModelCallLimitMiddleware — rate limit model calls

from langchain.agents import create_agent
from langchain.agents.middleware import ModelCallLimitMiddleware

limiter = ModelCallLimitMiddleware(
    thread_limit=50,        # max model calls across all runs in a thread
    run_limit=10,           # max model calls in a single run
    exit_behavior="end",    # "end" (graceful) | "error" (raise exception)
)

agent = create_agent(
    model="openai:gpt-4o",
    tools=[...],
    middleware=[limiter],
)

Structured agent output with response_format

from langchain.agents import create_agent
from pydantic import BaseModel, Field

class ResearchReport(BaseModel):
    title: str = Field(description="Report title")
    summary: str = Field(description="Executive summary")
    key_findings: list[str] = Field(description="List of key findings")
    confidence: float = Field(description="Confidence score 0-1")

agent = create_agent(
    model="anthropic:claude-sonnet-4-6",
    tools=[search_tool],
    response_format=ResearchReport,    # agent returns structured output
    system_prompt="Research the given topic and produce a structured report.",
)

result = agent.invoke({"messages": [{"role": "user", "content": "Research LangGraph"}]})
# result is typed as ResearchReport

Composing multiple middleware

from langchain.agents import create_agent
from langchain.agents.middleware import (
    HumanInTheLoopMiddleware,
    PIIMiddleware,
    ModelFallbackMiddleware,
    ModelCallLimitMiddleware,
    SummarizationMiddleware,
)

agent = create_agent(
    model="anthropic:claude-sonnet-4-6",
    tools=[...],
    middleware=[
        PIIMiddleware("email", strategy="redact"),          # outermost
        ModelCallLimitMiddleware(run_limit=20),
        ModelFallbackMiddleware("openai:gpt-4o-mini"),
        SummarizationMiddleware("openai:gpt-4o-mini", trigger=("fraction", 0.8)),
        HumanInTheLoopMiddleware({"delete_file": True}),    # innermost
    ],
    checkpointer=...,   # needed for HITL + persistence
)

API Reference

create_agent() parameters

| Param | Type | Description | |-------|------|-------------| | model | str \| BaseChatModel | Model string ("provider:model") or instance | | tools | list | Tools, callables, or dicts | | system_prompt | str \| SystemMessage | System prompt | | middleware | list[AgentMiddleware] | Middleware stack (first = outermost) | | response_format | type[BaseModel] \| dict | Structured output schema | | checkpointer | Checkpointer | State persistence (required for HITL) | | store | BaseStore | Cross-thread storage | | interrupt_before | list[str] | Node names to interrupt before | | interrupt_after | list[str] | Node names to interrupt after | | name | str | Name for subgraph use | | debug | bool | Enable verbose logging |

Model string format

"provider:model-name"   # e.g.:
"anthropic:claude-sonnet-4-6"
"openai:gpt-4o"
"openai:gpt-4o-mini"
"google:gemini-2.0-flash"
"ollama:llama3.1"

Middleware catalog

| Middleware | Constructor | Key params | |------------|-------------|------------| | HumanInTheLoopMiddleware | (interrupt_on) | Per-tool approve/edit/reject/respond | | PIIMiddleware | (pii_type, strategy) | block/redact/mask/hash | | ModelFallbackMiddleware | (model1, model2, ...) | Failover chain | | ModelCallLimitMiddleware | (thread_limit, run_limit) | Call counting | | ToolCallLimitMiddleware | (thread_limit, run_limit) | Tool call counting | | ModelRetryMiddleware | (max_retries, ...) | Retry failed model calls | | ToolRetryMiddleware | (max_retries, ...) | Retry failed tool calls | | SummarizationMiddleware | (model, trigger, keep) | Auto-compress context | | ContextEditingMiddleware | (edits) | Modify message history | | ShellToolMiddleware | — | Shell command execution | | TodoListMiddleware | — | Planning with todo lists | | LLMToolSelectorMiddleware | (model) | LLM-based tool selection | | FilesystemFileSearchMiddleware | — | File search tools |

Reference Files

| File | Size | Contents | |------|------|----------| | references/api.md | 500 KB | Full API reference | | references/llms.md | 28 KB | Doc index | | references/llms-full.md | 500 KB | Complete page content |

Source: https://reference.langchain.com/python/langchain
Agent docs: https://docs.langchain.com/oss/python/langchain/agents
Middleware docs: https://docs.langchain.com/oss/python/langchain/middleware