LogoLogo
TwitterWebsite
  • Getting Started
    • Introduction
    • Human UI
    • Examples
    • Monitoring
    • Workflows
    • Getting Help
  • Documentation
    • Humans
      • Introduction
      • Prompts
      • Tools
      • Knowledge
      • Memory
      • Storage
      • Structured Output
      • Reasoning
      • Teams
    • Models
      • Introduction
      • Open AI
      • Open AI Like
      • Anthropic Claude
      • AWS Bedrock Claude
      • Azure
      • Cohere
      • DeepSeek
      • Fireworks
      • Gemini
      • Gemini - VertexAI
      • Groq
      • HuggingFace
      • Mistral
      • Nvidia
      • Ollama
      • OpenRouter
      • Sambanova
      • Together
      • xAI
    • Tools
      • Introduction
      • Functions
      • Writing your own Toolkit
      • Airflow
      • Apify
      • Arxiv
      • AWS Lambda
      • BaiduSearch
      • Calculator
      • Cal.com
      • Composio
      • Crawl4AI
      • CSV
      • Dalle
      • DuckDb
      • DuckDuckGo
      • Email
      • Exa
      • Fal
      • File
      • Firecrawl
      • Giphy
      • Github
      • Google Calendar
      • Google Search
      • Hacker News
      • Jina Reader
      • Jira
      • Linear
      • Lumalabs
      • MLX Transcribe
      • ModelsLabs
      • Newspaper
      • Newspaper4k
      • OpenBB
      • Bitca
      • Postgres
      • Pubmed
      • Pyton
      • Replicate
      • Resend
      • Searxng
      • Serpapi
      • Shell
      • Slack
      • Sleep
      • Spider
      • SQL
      • Tavily
      • Twitter
      • Website
      • Yfinance
      • Zendesk
    • Knowledges
      • Introduction
      • ArXiv Knowledge Base
      • Combined KnowledgeBase
      • CSV Knowledge Base
      • CSV URL Knowledge Base
      • Docx Knowledge Base
      • Document Knowledge Base
      • JSON Knowledge Base
      • LangChain Knowledge Base
      • LlamaIndex Knowledge Base
      • PDF Knowledge Base
      • PDF URL Knowledge Base
      • S3 PDF Knowledge Base
      • S3 Text Knowledge Base
      • Text Knowledge Base
      • Website Knowledge Base
    • Chunking
      • Fixed Size Chunking
      • Agentic Chunking
      • Semantic Chunking
      • Recursive Chunking
      • Document Chunking
    • VectorDBS
      • Introduction
      • PgVector Agent Knowledge
      • Qdrant Agent Knowledge
      • Pinecone Agent Knowledge
      • LanceDB Agent Knowledge
      • ChromaDB Agent Knowledge
      • SingleStore Agent Knowledge
    • Storage
      • Introduction
      • Postgres Agent Storage
      • Sqlite Agent Storage
      • Singlestore Agent Storage
      • DynamoDB Agent Storage
      • JSON Agent Storage
      • YAML Agent Storage
    • Embeddings
      • Introduction
      • OpenAI Embedder
      • Gemini Embedder
      • Ollama Embedder
      • Voyage AI Embedder
      • Azure OpenAI Embedder
      • Mistral Embedder
      • Fireworks Embedder
      • Together Embedder
      • HuggingFace Embedder
      • Qdrant FastEmbed Embedder
      • SentenceTransformers Embedder
    • Workflows
      • Introduction
      • Session State
      • Streaming
      • Advanced Example - News Report Generator
  • How To
    • Install & Upgrade
    • Upgrade to v2.5.0
Powered by GitBook
LogoLogo

© 2025 Bitca. All rights reserved.

On this page
  • ​Prerequisites
  • ​Example
  • ​Toolkit Params
  • ​Toolkit Functions
Export as PDF
  1. Documentation
  2. Tools

Tavily

PreviousSQLNextTwitter

Last updated 4 months ago

TavilyTools enable an Agent to search the web using the Tavily API.

Prerequisites

The following examples requires the tavily-python library and an API key from .

pip install -U tavily-python
export TAVILY_API_KEY=***

Example

The following agent will run a search on Tavily for “language models” and print the response.

cookbook/tools/tavily_tools.py

from bitca.agent import Agent
from bitca.tools.tavily import TavilyTools

agent = Agent(tools=[TavilyTools()], show_tool_calls=True)
agent.print_response("Search tavily for 'language models'", markdown=True)

Toolkit Params

Parameter
Type
Default
Description

api_key

str

-

API key for authentication. If not provided, will check TAVILY_API_KEY environment variable.

search

bool

True

Enables search functionality.

max_tokens

int

6000

Maximum number of tokens to use in search results.

include_answer

bool

True

Whether to include an AI-generated answer summary in the response.

search_depth

Literal['basic', 'advanced']

'advanced'

Depth of search - ‘basic’ for faster results or ‘advanced’ for more comprehensive search.

format

Literal['json', 'markdown']

'markdown'

Output format - ‘json’ for raw data or ‘markdown’ for formatted text.

use_search_context

bool

False

Whether to use Tavily’s search context API instead of regular search.

Function
Description

web_search_using_tavily

Searches the web for a query using Tavily API. Takes a query string and optional max_results parameter (default 5). Returns results in specified format with titles, URLs, content and relevance scores.

web_search_with_tavily

Alternative search function that uses Tavily’s search context API. Takes a query string and returns contextualized search results. Only available if use_search_context is True.

Toolkit Functions

​
Tavily
​
​
​