Fetch Webpage

The fetch_webpage tool enables Letta agents to fetch and convert webpages into readable text or markdown format. Useful for reading documentation, articles, and web content.

Quick Start

Python
TypeScript

from letta import Letta

client = Letta(token="LETTA_API_KEY")

agent = client.agents.create(
model="openai/gpt-4o",
tools=["fetch_webpage"],
memory_blocks=[{
"label": "persona",
"value": "I can fetch and read webpages to answer questions about online content."
}]
)

import { LettaClient } from "@letta-ai/letta-client";

const client = new LettaClient({ token: "LETTA_API_KEY" });

const agent = await client.agents.create({
  model: "openai/gpt-4o",
  tools: ["fetch_webpage"],
  memoryBlocks: [
    {
      label: "persona",
      value:
        "I can fetch and read webpages to answer questions about online content.",
    },
  ],
});

Tool Parameters

Parameter	Type	Description
`url`	`str`	The URL of the webpage to fetch

Return Format

The tool returns webpage content as text/markdown.

With Exa API (if configured):

{
  "title": "Page title",
  "published_date": "2025-01-15",
  "author": "Author name",
  "text": "Full page content in markdown"
}

Fallback (without Exa): Returns markdown-formatted text extracted from the HTML.

How It Works

The tool uses a multi-tier approach:

Exa API (if EXA_API_KEY is configured): Uses Exa’s content extraction
Trafilatura (fallback): Open-source text extraction to markdown
Readability + html2text (final fallback): HTML cleaning and conversion

Self-Hosted Setup

For enhanced fetching on self-hosted servers, optionally configure an Exa API key. Without it, the tool still works using open-source extraction.

Optional: Configure Exa

docker run \
  -e EXA_API_KEY="your_exa_api_key" \
  letta/letta:latest

services:
  letta:
    environment:
      - EXA_API_KEY=your_exa_api_key

agent = client.agents.create(
    tools=["fetch_webpage"],
    tool_env_vars={
        "EXA_API_KEY": "your_exa_api_key"
    }
)

Common Patterns

Documentation Reader

agent = client.agents.create(
    model="openai/gpt-4o",
    tools=["fetch_webpage", "web_search"],
    memory_blocks=[{
        "label": "persona",
        "value": "I search for documentation with web_search and read it with fetch_webpage."
    }]
)

Research Assistant

agent = client.agents.create(
    model="openai/gpt-4o",
    tools=["fetch_webpage", "archival_memory_insert"],
    memory_blocks=[{
        "label": "persona",
        "value": "I fetch articles and store key insights in archival memory for later reference."
    }]
)

Content Summarizer

agent = client.agents.create(
    model="openai/gpt-4o",
    tools=["fetch_webpage"],
    memory_blocks=[{
        "label": "persona",
        "value": "I fetch webpages and provide summaries of their content."
    }]
)

When to Use

Use Case	Tool	Why
Read specific webpage	`fetch_webpage`	Direct URL access
Find webpages to read	`web_search`	Discovery first
Read + search in one	`web_search` with `include_text=true`	Combined operation
Multiple pages	`fetch_webpage`	Iterate over URLs