> ## Documentation Index
> Fetch the complete documentation index at: https://langchain-5e9cc07a-preview-opensw-1782332329-96d87c7.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# AgentMail

> Load messages from an AgentMail inbox as LangChain Documents.

`AgentMailLoader` streams messages from an [AgentMail](https://agentmail.to) inbox as LangChain `Document`s—one document per message, plain-text body as `page_content`, sender / subject / labels / thread / attachment metadata on `metadata`. Useful for indexing an inbox into a vector store for RAG over email.

## Overview

| Class             | Package                                                                |
| :---------------- | :--------------------------------------------------------------------- |
| `AgentMailLoader` | [`langchain-agentmail`](https://pypi.org/project/langchain-agentmail/) |

## Setup

Install the package:

```bash theme={null}
pip install -qU langchain-agentmail
```

Set your AgentMail API key (get one at [agentmail.to](https://agentmail.to)):

```python theme={null}
import getpass
import os

if not os.environ.get("AGENTMAIL_API_KEY"):
    os.environ["AGENTMAIL_API_KEY"] = getpass.getpass("AgentMail API key:\n")
```

## Instantiation

```python theme={null}
from langchain_agentmail import AgentMailLoader

loader = AgentMailLoader(
    inbox_id="ib_abc123",
    labels=["inbox"],  # optional — filter messages by label
    limit=100,         # optional — cap the number of messages loaded
)
```

## Load

```python theme={null}
docs = loader.load()
print(docs[0].page_content[:200])
print(docs[0].metadata)
```

Each `Document` includes the following metadata keys (when present):

* `inbox_id`, `message_id`, `thread_id`
* `from`, `to`, `cc`, `subject`, `labels`, `timestamp`
* `has_attachments`, `attachments`: a list of `{attachment_id, filename, content_type, size}`

To download attachment bytes, pair the loader with `AgentMailGetAttachmentTool` from the toolkit—it returns a presigned download URL.

## Lazy load

For larger inboxes, stream documents one at a time instead of materializing the full list:

```python theme={null}
for doc in loader.lazy_load():
    print(doc.metadata["subject"])
```

## Indexing into a vector store

```python theme={null}
from langchain_core.vectorstores import InMemoryVectorStore
from langchain_openai import OpenAIEmbeddings

docs = AgentMailLoader(inbox_id="ib_abc123", limit=200).load()

store = InMemoryVectorStore.from_documents(docs, OpenAIEmbeddings())
retriever = store.as_retriever(search_kwargs={"k": 5})

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

## API reference

The package source lives at [github.com/agentmail-to/langchain-agentmail](https://github.com/agentmail-to/langchain-agentmail).

***

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