> For clean Markdown content of this page, append .md to this URL. For the complete documentation index, see https://docs.agentmail.to/llms.txt. For full content including API reference and SDK examples, see https://docs.agentmail.to/llms-full.txt.

# LangChain

> AgentMail's LangChain integration

## Getting started

[LangChain](https://www.langchain.com/) is the most widely used framework for building LLM-powered agents. The [`langchain-agentmail`](https://github.com/agentmail-to/langchain-agentmail) package wraps the AgentMail SDK as standard LangChain tools, plus a document loader and a retriever — so a LangGraph agent can send, reply, draft, label, and search email through a real inbox without any glue code.

## Use cases

* **Give agents their own inboxes:** Provision a dedicated email address per agent so it can send and receive mail independently.
* **Triage and reply:** Read recent threads, summarize what's new, and reply inside the same thread with the right In-Reply-To headers.
* **Stage and schedule sends:** Use the draft tools to compose iteratively or schedule a delivery time via `send_at`.
* **RAG over email:** Load messages as LangChain `Document`s and index them into a vector store for semantic search across the inbox.

## Prerequisites

1. An [AgentMail account](https://agentmail.to/) with an API key from the [AgentMail Console](https://console.agentmail.to).
2. Python 3.10+ and a LangChain-compatible model provider (e.g. an `OPENAI_API_KEY` or `ANTHROPIC_API_KEY`).

## Setup

Install the integration package:

```bash
pip install langchain-agentmail
```

Set your API key:

```bash
export AGENTMAIL_API_KEY="your-api-key"
```

## Quickstart

Build a ReAct agent with the full toolkit in a few lines:

**`Python`**

```python title="Python"
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent

from langchain_agentmail import AgentMailToolkit

toolkit = AgentMailToolkit.from_api_key()
agent = create_react_agent(
    ChatOpenAI(model="gpt-4o-mini"),
    tools=toolkit.get_tools(),
)

result = agent.invoke(
    {"messages": [("user", "Summarize my most recent email thread.")]}
)
print(result["messages"][-1].content)
```

You can also pull a single tool in if you don't need the whole toolkit:

**`Python`**

```python title="Python"
from langchain_agentmail import AgentMailClient, AgentMailSendTool

send = AgentMailSendTool(client=AgentMailClient())
send.invoke({
    "inbox_id": "ib_...",
    "to": "alice@example.com",
    "subject": "Ping",
    "text": "Hello from my agent.",
})
```

## Available tools

The toolkit exposes one tool per AgentMail operation.

### Inbox and thread management

| Tool                     | Description                                            |
| ------------------------ | ------------------------------------------------------ |
| `agentmail_list_inboxes` | List inboxes the account owns                          |
| `agentmail_create_inbox` | Create a new inbox (random or custom username)         |
| `agentmail_list_threads` | List threads across an inbox with label / time filters |
| `agentmail_get_thread`   | Pull every message in a thread                         |

### Message operations

| Tool                              | Description                                               |
| --------------------------------- | --------------------------------------------------------- |
| `agentmail_list_messages`         | List messages inside an inbox                             |
| `agentmail_get_message`           | Fetch one message with its full plain-text body           |
| `agentmail_send_message`          | Send a new email                                          |
| `agentmail_reply_to_message`      | Reply inside an existing thread (with optional reply-all) |
| `agentmail_update_message_labels` | Add or remove labels (archive, follow-up, etc.)           |
| `agentmail_get_attachment`        | Get a presigned URL to download a message attachment      |

### Draft management

| Tool                     | Description                                                    |
| ------------------------ | -------------------------------------------------------------- |
| `agentmail_create_draft` | Stage a draft (with optional `send_at` for scheduled delivery) |
| `agentmail_update_draft` | Revise an existing draft                                       |
| `agentmail_send_draft`   | Send a previously created draft                                |
| `agentmail_delete_draft` | Permanently delete a draft                                     |

## RAG over an inbox

`AgentMailLoader` streams messages as LangChain `Document`s — one per message, plain-text body as `page_content`, sender / subject / labels / thread / attachment metadata on `metadata`. Pair it with any vector store for semantic search:

**`Python`**

```python title="Python"
from langchain_core.vectorstores import InMemoryVectorStore
from langchain_openai import OpenAIEmbeddings

from langchain_agentmail import AgentMailLoader

docs = AgentMailLoader(inbox_id="ib_...", limit=200).load()
store = InMemoryVectorStore.from_documents(docs, OpenAIEmbeddings())
retriever = store.as_retriever(search_kwargs={"k": 5})

retriever.invoke("Q3 invoice from acme")
```

For a quick keyword search without embeddings, use the bundled `AgentMailRetriever` instead.

## Inbound email via webhooks

The `webhooks` extra ships a FastAPI router with svix-compatible signature verification so a LangGraph agent can react to inbound mail:

```bash
pip install 'langchain-agentmail[webhooks]'
```

**`Python`**

```python title="Python"
from fastapi import FastAPI
from langchain_agentmail.webhooks import AgentMailEvent, create_fastapi_router

async def on_event(event: AgentMailEvent) -> None:
    if event.event_type == "message.received":
        # drive your LangGraph agent here
        ...

app = FastAPI()
app.include_router(
    create_fastapi_router(on_event),  # reads AGENTMAIL_WEBHOOK_SECRET
    prefix="/agentmail",
)
```

## Resources

* **Source code:** [github.com/agentmail-to/langchain-agentmail](https://github.com/agentmail-to/langchain-agentmail)
* **PyPI:** [pypi.org/project/langchain-agentmail/](https://pypi.org/project/langchain-agentmail/)

> AgentMail is an email API built for AI agents. Create inboxes, send and receive messages, manage threads, and handle webhooks programmatically.