mirror of
https://github.com/bytedance/deer-flow.git
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Willem Jiang
119 lines
3.2 KiB
Plaintext
119 lines
3.2 KiB
Plaintext
---
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title: Quick Start
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description: Learn how to create and run a DeerFlow agent with create_deerflow_agent, from model setup to streaming responses.
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---
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import { Callout, Cards, Steps } from "nextra/components";
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# Quick Start
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<Callout type="info" emoji="🚀">
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This guide shows you how to build and run a DeerFlow agent in Python with
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<code>create_deerflow_agent</code>.
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</Callout>
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The fastest way to understand DeerFlow Harness is to create an agent directly in code. This quick start walks through model setup, agent creation, and streaming a response.
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## Prerequisites
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DeerFlow Harness requires Python 3.12 or later. The package is part of the `deerflow` repository under `backend/packages/harness`.
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If you are working from the repository clone:
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```bash
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cd backend
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uv sync
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```
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You will also need a chat model instance from the LangChain provider package you want to use.
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## Create your first agent
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<Steps>
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### Import the factory and model
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```python
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from deerflow.agents import create_deerflow_agent
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from langchain_openai import ChatOpenAI
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```
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### Create a model
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```python
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model = ChatOpenAI(
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model="gpt-4o",
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api_key="YOUR_OPENAI_API_KEY",
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)
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```
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### Create an agent
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```python
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agent = create_deerflow_agent(model)
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```
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This returns a compiled LangGraph agent with DeerFlow's default middleware chain.
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### Stream a response
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```python
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for event in agent.stream(
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{"messages": [{"role": "user", "content": "Explain what DeerFlow Harness is."}]},
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stream_mode=["messages", "values"],
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):
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print(event)
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```
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</Steps>
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## Add tools or behavior
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You can customize the agent by passing tools, a system prompt, runtime features, middleware, or a checkpointer.
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```python
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from deerflow.agents import RuntimeFeatures, create_deerflow_agent
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agent = create_deerflow_agent(
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model,
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system_prompt="You are a concise research assistant.",
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features=RuntimeFeatures(subagent=True, memory=False),
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plan_mode=True,
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name="research-agent",
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)
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```
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Common parameters:
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| Parameter | Description |
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| ------------------ | ----------------------------------------------- |
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| `tools` | Additional tools available to the agent |
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| `system_prompt` | Custom system prompt |
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| `features` | Enable or replace built-in runtime features |
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| `extra_middleware` | Insert custom middleware into the default chain |
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| `plan_mode` | Enable Todo-style task tracking |
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| `checkpointer` | Persist agent state across runs |
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| `name` | Logical agent name |
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## When to use DeerFlowClient instead
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`create_deerflow_agent()` is the low-level SDK factory when you want to work directly with the compiled agent graph.
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Use `DeerFlowClient` when you want the higher-level embedded app interface, such as:
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- thread-oriented chat helpers,
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- model / skills / memory management APIs,
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- file uploads and artifacts,
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- Gateway-like response formats.
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## Next steps
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<Cards num={3}>
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<Cards.Card
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title="Design Principles"
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href="/docs/harness/design-principles"
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/>
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<Cards.Card title="Lead Agent" href="/docs/harness/lead-agent" />
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<Cards.Card title="Configuration" href="/docs/harness/configuration" />
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</Cards>
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