Agents - Docs by LangChain

An agent is a model calling tools in a loop until a given task is complete.

Agent = Model + HarnessThe job of a harness: get the model the right context at the right time for the given task.

A harness is everything around that loop: the model, its prompt, its tools, and any middleware that shapes its behavior. create_agent is a highly configurable harness. At its simplest, you can create one with:

from langchain.agents import create_agent

agent = create_agent(model="google_genai:gemini-3.5-flash", tools=tools)

Building on that, you can configure the basics directly with the model=, tools=, and system_prompt= parameters. For more advanced capabilities, extend the harness with middleware.

Core components

Model

Pass a model identifier string ("provider:model") or an initialized model instance to select the model for your agent. See Models for parameters, provider setup, and dynamic model selection.

from langchain.agents import create_agent

agent = create_agent(model="google_genai:gemini-3.5-flash", tools=tools)

Tools

To provide the agent with tools, pass any Python callable, LangChain tool, or tool dict. See Tools for tool definition, context access, and dynamic tool selection.

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


@tool
def search(query: str) -> str:
    """Search for information."""
    return f"Results for: {query}"


agent = create_agent(model="google_genai:gemini-3.5-flash", tools=[search])

System prompt

Shape how the agent approaches tasks. The system prompt parameter accepts a string or SystemMessage. For dynamic prompts at runtime, use middleware.

agent = create_agent(
    model="google_genai:gemini-3.5-flash",
    tools=tools,
    system_prompt="You are a helpful assistant. Be concise and accurate.",
)

Structured output

Return a validated schema from the agent using response_format=. See Structured output for strategies and examples.

from pydantic import BaseModel
from langchain.agents import create_agent


class Answer(BaseModel):
    summary: str
    confidence: float


agent = create_agent(model="google_genai:gemini-3.5-flash", tools=tools, response_format=Answer)
result = agent.invoke({"messages": [{"role": "user", "content": "Summarize AI trends"}]})
result["structured_response"]  # Answer(summary=..., confidence=...)

Invocation

Trace each step of this loop, debug tool calls, and evaluate agent outputs with LangSmith. Follow the tracing quickstart to get set up. We recommend you also set up LangSmith Engine which monitors your traces, detects issues, and proposes fixes.

You can invoke an agent with a message. Behind the scenes that passes an update to the agent’s State. All agents include a sequence of messages in their state; to invoke the agent, pass a new message along with a thread_id so the agent can persist and resume conversation history:

from langchain.agents import create_agent
from langchain_core.utils.uuid import uuid7
from langgraph.checkpoint.memory import InMemorySaver

agent = create_agent(
    model="google_genai:gemini-3.5-flash",
    tools=[],
    checkpointer=InMemorySaver(),
)

config = {"configurable": {"thread_id": str(uuid7())}}

result = agent.invoke(
    {"messages": [{"role": "user", "content": "What's the weather in San Francisco?"}]},
    config=config,
)

# A follow-up turn on the same conversation: reuse the same thread_id to keep history
result = agent.invoke(
    {"messages": [{"role": "user", "content": "What about tomorrow?"}]},
    config=config,
)

Persisting conversation history with thread_id requires the agent to be configured with a checkpointer. When deployed on LangSmith, a checkpointer is provisioned automatically. Locally, pass one explicitly, for example create_agent(..., checkpointer=InMemorySaver()).

If you also need to pass per-run configuration (such as a user ID, API keys, or feature flags) to tools and middleware, pass it as context alongside config. Define the shape of that data with context_schema and access it through runtime.context:

from dataclasses import dataclass

from langchain.agents import create_agent
from langchain_core.utils.uuid import uuid7
from langgraph.checkpoint.memory import InMemorySaver


@dataclass
class Context:
    user_id: str


agent = create_agent(
    model="google_genai:gemini-3.5-flash",
    tools=[],
    context_schema=Context,
    checkpointer=InMemorySaver(),
)

result = agent.invoke(
    {"messages": [{"role": "user", "content": "What's the weather in San Francisco?"}]},
    config={"configurable": {"thread_id": str(uuid7())}},
    context=Context(user_id="user-123"),
)

thread_id scopes the conversation (message history, checkpoints), while context carries per-run data your tools and middleware read at invocation time. Both are commonly passed together. See tool context and Runtime for more.

Streaming

invoke returns the final response at the end of a run. If an agent executes multiple tool calls, users often need progress updates before completion. Use streaming to surface intermediate messages and tool activity as they happen.

from langchain.messages import AIMessage, HumanMessage


stream = agent.stream_events(
    {"messages": [{"role": "user", "content": "Search for AI news and summarize the findings"}]},
    version="v3",
)
for snapshot in stream.values:
    # Each snapshot contains the full state at that point
    latest_message = snapshot["messages"][-1]
    if latest_message.content:
        if isinstance(latest_message, HumanMessage):
            print(f"User: {latest_message.content}")
        elif isinstance(latest_message, AIMessage):
            print(f"Agent: {latest_message.content}")
    elif latest_message.tool_calls:
        print(f"Calling tools: {[tc['name'] for tc in latest_message.tool_calls]}")

For streaming modes, event types, and UI patterns, see Streaming.

Configure the harness

create_agent is highly extensible. Middleware is the primitive for customization: each piece handles one concern, hooks into the agent loop at the right moment, and composes freely with any other. Take exactly what your use case needs and skip the rest. Common patterns are prebuilt as first-class middleware. You can build anything else as custom middleware.

As agents take on complex work, they need support across a few key areas. The middleware ecosystem provides:

Execution environment

Tools, filesystem, sandboxes, and code execution

Context management

Summarization, memory, skills, and prompt caching

Planning and delegation

Todo lists and subagents for parallel, isolated work

Fault tolerance

Retries, fallbacks, and call limits

Guardrails

PII detection and content controls

Steering

Human-in-the-loop approval before high-impact actions

create_deep_agent pre-assembles this stack for long-running coding and research tasks (filesystem, summarization, subagents, and prompt caching included by default). See Deep Agents for the full prebuilt harness.

Execution environment

Agents are especially useful when they can take action rather than just generate text. The execution environment gives the agent a workspace: tools it can call, a filesystem for reading and writing files across turns, and code execution for running scripts or shell commands.

from langchain.agents import create_agent
from deepagents.backends import StateBackend
from deepagents.middleware import FilesystemMiddleware

agent = create_agent(
    model="google_genai:gemini-3.5-flash",
    tools=[search],
    middleware=[FilesystemMiddleware(backend=StateBackend())],
)

See FilesystemMiddleware, Sandboxes, Interpreters.

Context management

Every model call has a fixed context window. As an agent runs, that window fills with accumulating history, tool results, and intermediate steps. Summarization compresses history before overflow hits; memory loads persistent instructions at startup so knowledge carries across sessions; skills surface domain knowledge on demand rather than loading everything upfront.

from deepagents.backends import StateBackend
from deepagents.middleware import FilesystemMiddleware, MemoryMiddleware, SkillsMiddleware, SummarizationMiddleware

backend = StateBackend()
model="google_genai:gemini-3.5-flash"

agent = create_agent(
    model=model,
    tools=[search],
    middleware=[
        FilesystemMiddleware(backend=backend),
        SummarizationMiddleware(model=model, backend=backend),
        MemoryMiddleware(backend=backend, sources=["./AGENTS.md"]),
        SkillsMiddleware(backend=backend, sources=["./skills/"]),
    ],
)

See SummarizationMiddleware, MemoryMiddleware, Skills, Context engineering.

Planning and delegation

Complex tasks often exceed what one context window can handle. Delegation lets the main agent break work into pieces, hand them to subagents that each run in their own isolated context, and stay focused on coordination rather than execution. Work can run in parallel; the main agent’s context stays clean.

from deepagents.backends import StateBackend
from deepagents.middleware import FilesystemMiddleware
from deepagents.middleware.subagents import SubAgentMiddleware
from langchain.agents import create_agent
from langchain.agents.middleware import TodoListMiddleware
from langchain.tools import tool


@tool
def search(query: str) -> str:
    """Search for a query and return a short summary."""
    return f"Search results for: {query}"


backend = StateBackend()

agent = create_agent(
    model="google_genai:gemini-3.5-flash",
    tools=[search],
    middleware=[
        FilesystemMiddleware(backend=backend),
        TodoListMiddleware(),
        SubAgentMiddleware(
            backend=backend,
            subagents=[
                {
                    "name": "researcher",
                    "description": "Searches and returns a structured summary.",
                    "system_prompt": "Use the search tool to research the question and summarize key points.",
                    "tools": [search],
                    "model": "anthropic:claude-sonnet-4-6",
                    "middleware": [],
                }
            ],
        ),
    ],
)

See Subagents.

Name your agent

Optionally use an identifier for the agent. This is especially useful when embedding the agent as a subgraph in multi-agent systems.

agent = create_agent(model="google_genai:gemini-3.5-flash", tools=tools, name="research_assistant")

Fault tolerance

Agents in production encounter failures that rarely appear in development: rate limits, model timeouts, transient API errors. Fault tolerance middleware handles these at the infrastructure level so your tools and business logic don’t need try/catch around every call.

from langchain.agents import create_agent
from langchain.agents.middleware import ModelRetryMiddleware, ToolRetryMiddleware
from langchain.tools import tool


@tool
def search(query: str) -> str:
    """Search for a query and return a short summary."""
    return f"Search results for: {query}"


agent = create_agent(
    model="google_genai:gemini-3.5-flash",
    tools=[search],
    middleware=[
        ModelRetryMiddleware(max_retries=3),
        ToolRetryMiddleware(max_retries=2),
    ],
)

See ModelRetryMiddleware, ToolRetryMiddleware, Prebuilt middleware.

Guardrails

Some policies can’t live in a prompt—they need to be enforced deterministically regardless of what the model does. Guardrails intercept data as it flows through the agent loop, applying compliance rules or content policies before tool results reach the model’s context.

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


@tool
def search(query: str) -> str:
    """Search for a query and return a short summary."""
    return f"Search results for: {query}"


agent = create_agent(
    model="google_genai:gemini-3.5-flash",
    tools=[search],
    middleware=[PIIMiddleware("email")],
)

See PIIMiddleware, Prebuilt middleware.

Steering

Full autonomy isn’t always appropriate. Steering lets you place humans at specific decision points—before destructive writes, expensive API calls, or anything requiring judgment—without restructuring your agent. The agent pauses and waits; a human approves, edits, or rejects; execution continues.

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


@tool
def search(query: str) -> str:
    """Search for a query and return a short summary."""
    return f"Search results for: {query}"


agent = create_agent(
    model="google_genai:gemini-3.5-flash",
    tools=[search],
    middleware=[HumanInTheLoopMiddleware(interrupt_on={"write_file": True})],
)

See HumanInTheLoopMiddleware, Human-in-the-loop.

Middleware resources

Middleware overview

How the middleware stack works and when hooks fire

Prebuilt middleware

Full reference with configuration examples

Custom middleware

Write your own hooks for business logic, PII scrubbing, and more

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​Core components

​Model

​Tools

​System prompt

​Structured output

​Invocation

​Streaming

​Configure the harness

Execution environment

Context management

Planning and delegation

Fault tolerance

Guardrails

Steering

​Execution environment

​Context management

​Planning and delegation

​Name your agent

​Fault tolerance

​Guardrails

​Steering

​Middleware resources

Middleware overview

Prebuilt middleware

Custom middleware

Core components

Model

Tools

System prompt

Structured output

Invocation

Streaming

Configure the harness

Execution environment

Context management

Planning and delegation

Name your agent

Fault tolerance

Guardrails

Steering

Middleware resources