Tavily Chat Integration

Try Our Chatbot

Step 1: Get Your API Key

Get your Tavily API key

Step 2: Chat with Tavily

Launch the application

Step 3: Read The Open Source Code

View Github Repository

Features

Fast Results: Tavily’s API delivers quick responses essential for real-time chat experiences.
Intelligent Parameter Selection: Dynamically select API parameters based on conversation context using LangChain integration. Specifically designed for agentic systems. All you need is a natural language input, no need to configure structured JSON for our API.
Content Snippets: Tavily provides compact summaries of search results in the content field, best for maintaining small context sizes in low latency, multi-turn applications.
Source Attribution: All search, extract, and crawl results include URLs, enabling easy implementation of citations for transparency and credibility in responses.

How Does It Work?

The chatbot uses a simple ReAct architecture to manage conversation flow and decision-making. Here’s how the core components work together:

The workflow consists of several key components:

1. Code Snippet: Graph Structure

The chatbot uses LangGraph MemorySaver to manage conversation flow. The graph structure conrtols how messages are processed and routed.

This code snippet is not meant to run standalone. View the full implementation in our github repository.

class WebAgent:
    def __init__(
        self,
    ):
        self.llm = ChatOpenAI(
            model="gpt-4.1-nano", api_key=os.getenv("OPENAI_API_KEY")
        ).with_config({"tags": ["streaming"]})

        # Define the LangChain search tool
        self.search = TavilySearch(
            max_results=10, topic="general", api_key=os.getenv("TAVILY_API_KEY")
        )

        # Define the LangChain extract tool
        self.extract = TavilyExtract(
            extract_depth="advanced", api_key=os.getenv("TAVILY_API_KEY")
        )
        # Define the LangChain crawl tool
        self.crawl = TavilyCrawl(api_key=os.getenv("TAVILY_API_KEY"))
        self.prompt = PROMPT
        self.checkpointer = MemorySaver()

    def build_graph(self):
        """
        Build and compile the LangGraph workflow.
        """
        return create_react_agent(
            prompt=self.prompt,
            model=self.llm,
            tools=[self.search, self.extract, self.crawl],
            checkpointer=self.checkpointer,
        )

2. Routing Logic

3. Memory Management

4. Real-time Search Integration

5. Streaming Updates

On this page

Try Our Chatbot
Step 1: Get Your API Key
Step 2: Chat with Tavily
Step 3: Read The Open Source Code
Features
How Does It Work?

Try Our Chatbot

Step 1: Get Your API Key

Get your Tavily API key

Step 2: Chat with Tavily

Launch the application

Step 3: Read The Open Source Code

View Github Repository

Features

Fast Results: Tavily’s API delivers quick responses essential for real-time chat experiences.
Intelligent Parameter Selection: Dynamically select API parameters based on conversation context using LangChain integration. Specifically designed for agentic systems. All you need is a natural language input, no need to configure structured JSON for our API.
Content Snippets: Tavily provides compact summaries of search results in the content field, best for maintaining small context sizes in low latency, multi-turn applications.
Source Attribution: All search, extract, and crawl results include URLs, enabling easy implementation of citations for transparency and credibility in responses.

How Does It Work?

The chatbot uses a simple ReAct architecture to manage conversation flow and decision-making. Here’s how the core components work together:

The workflow consists of several key components:

1. Code Snippet: Graph Structure

The chatbot uses LangGraph MemorySaver to manage conversation flow. The graph structure conrtols how messages are processed and routed.

This code snippet is not meant to run standalone. View the full implementation in our github repository.

class WebAgent:
    def __init__(
        self,
    ):
        self.llm = ChatOpenAI(
            model="gpt-4.1-nano", api_key=os.getenv("OPENAI_API_KEY")
        ).with_config({"tags": ["streaming"]})

        # Define the LangChain search tool
        self.search = TavilySearch(
            max_results=10, topic="general", api_key=os.getenv("TAVILY_API_KEY")
        )

        # Define the LangChain extract tool
        self.extract = TavilyExtract(
            extract_depth="advanced", api_key=os.getenv("TAVILY_API_KEY")
        )
        # Define the LangChain crawl tool
        self.crawl = TavilyCrawl(api_key=os.getenv("TAVILY_API_KEY"))
        self.prompt = PROMPT
        self.checkpointer = MemorySaver()

    def build_graph(self):
        """
        Build and compile the LangGraph workflow.
        """
        return create_react_agent(
            prompt=self.prompt,
            model=self.llm,
            tools=[self.search, self.extract, self.crawl],
            checkpointer=self.checkpointer,
        )

2. Routing Logic

3. Memory Management

4. Real-time Search Integration

5. Streaming Updates

On this page

Try Our Chatbot
Step 1: Get Your API Key
Step 2: Chat with Tavily
Step 3: Read The Open Source Code
Features
How Does It Work?

​Try Our Chatbot

​Step 1: Get Your API Key

Get your Tavily API key

​Step 2: Chat with Tavily

Launch the application

​Step 3: Read The Open Source Code

View Github Repository

​Features

​How Does It Work?

Changelog

​Try Our Chatbot

​Step 1: Get Your API Key

Get your Tavily API key

​Step 2: Chat with Tavily

Launch the application

​Step 3: Read The Open Source Code

View Github Repository

​Features

​How Does It Work?

Try Our Chatbot

Step 1: Get Your API Key

Step 2: Chat with Tavily

Step 3: Read The Open Source Code

Features

How Does It Work?

Try Our Chatbot

Step 1: Get Your API Key

Step 2: Chat with Tavily

Step 3: Read The Open Source Code

Features

How Does It Work?