> ## Documentation Index
> Fetch the complete documentation index at: https://langchain-5e9cc07a-preview-devupd-1765394015-eccef47.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Quickstart

> Build your first deep agent in minutes

This guide walks you through creating your first deep agent with planning, file system tools, and subagent capabilities. You'll build a research agent that can conduct research and write reports.

## Prerequisites

Before you begin, make sure you have an API key from a model provider (e.g., Anthropic, OpenAI).

### Step 1: Install dependencies

<CodeGroup>
  ```bash pip theme={null}
  pip install deepagents tavily-python
  ```

  ```bash uv theme={null}
  uv add deepagents tavily-python
  ```

  ```bash poetry theme={null}
  poetry add deepagents tavily-python
  ```
</CodeGroup>

### Step 2: Set up your API keys

```bash theme={null}
export ANTHROPIC_API_KEY="your-api-key"
export TAVILY_API_KEY="your-tavily-api-key"
```

### Step 3: Create a search tool

```python theme={null}
import os
from typing import Literal
from tavily import TavilyClient
from deepagents import create_deep_agent

tavily_client = TavilyClient(api_key=os.environ["TAVILY_API_KEY"])

def internet_search(
    query: str,
    max_results: int = 5,
    topic: Literal["general", "news", "finance"] = "general",
    include_raw_content: bool = False,
):
    """Run a web search"""
    return tavily_client.search(
        query,
        max_results=max_results,
        include_raw_content=include_raw_content,
        topic=topic,
    )
```

### Step 4: Create a deep agent

```python theme={null}
# System prompt to steer the agent to be an expert researcher
research_instructions = """You are an expert researcher. Your job is to conduct thorough research and then write a polished report.

You have access to an internet search tool as your primary means of gathering information.

## `internet_search`

Use this to run an internet search for a given query. You can specify the max number of results to return, the topic, and whether raw content should be included.
"""

agent = create_deep_agent(
    tools=[internet_search],
    system_prompt=research_instructions
)
```

### Step 5: Run the agent

```python theme={null}
result = agent.invoke({"messages": [{"role": "user", "content": "What is langgraph?"}]})

# Print the agent's response
print(result["messages"][-1].content)
```

## What happened?

Your deep agent automatically:

1. **Planned its approach**: Used the built-in `write_todos` tool to break down the research task
2. **Conducted research**: Called the `internet_search` tool to gather information
3. **Managed context**: Used file system tools (`write_file`, `read_file`) to offload large search results
4. **Spawned subagents** (if needed): Delegated complex subtasks to specialized subagents
5. **Synthesized a report**: Compiled findings into a coherent response

## Next steps

Now that you've built your first deep agent:

* **Customize your agent**: Learn about [customization options](/oss/python/deepagents/customization), including custom system prompts, tools, and subagents.
* **Understand middleware**: Dive into the [middleware architecture](/oss/python/deepagents/middleware) that powers deep agents.
* **Add long-term memory**: Enable [persistent memory](/oss/python/deepagents/long-term-memory) across conversations.
* **Deploy to production**: Learn about [deployment options](/oss/python/langgraph/deploy) for LangGraph applications.

***

<Callout icon="pen-to-square" iconType="regular">
  [Edit the source of this page on GitHub.](https://github.com/langchain-ai/docs/edit/main/src/oss/deepagents/quickstart.mdx)
</Callout>

<Tip icon="terminal" iconType="regular">
  [Connect these docs programmatically](/use-these-docs) to Claude, VSCode, and more via MCP for real-time answers.
</Tip>