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
Export as PDF
  1. Documentation
  2. Humans

Teams

We can combine multiple Agents to form a team and tackle tasks as a cohesive unit. Here’s a simple example that uses a team of agents to write an article about the top stories on hackernews.

hn_team.py

from bitca.agent import Agent
from bitca.tools.hackernews import HackerNews
from bitca.tools.duckduckgo import DuckDuckGo
from bitca.tools.newspaper4k import Newspaper4k

hn_researcher = Agent(
    name="HackerNews Researcher",
    role="Gets top stories from hackernews.",
    tools=[HackerNews()],
)

web_searcher = Agent(
    name="Web Searcher",
    role="Searches the web for information on a topic",
    tools=[DuckDuckGo()],
    add_datetime_to_instructions=True,
)

article_reader = Agent(
    name="Article Reader",
    role="Reads articles from URLs.",
    tools=[Newspaper4k()],
)

hn_team = Agent(
    name="Hackernews Team",
    team=[hn_researcher, web_searcher, article_reader],
    instructions=[
        "First, search hackernews for what the user is asking about.",
        "Then, ask the article reader to read the links for the stories to get more information.",
        "Important: you must provide the article reader with the links to read.",
        "Then, ask the web searcher to search for each story to get more information.",
        "Finally, provide a thoughtful and engaging summary.",
    ],
    show_tool_calls=True,
    markdown=True,
)
hn_team.print_response("Write an article about the top 2 stories on hackernews", stream=True)

Run the script to see the output.

pip install -U openai duckduckgo-search newspaper4k lxml_html_clean bitca

python hn_team.py
  1. Add a name and role parameter to the member Agents.

  2. Create a Team Leader that can delegate tasks to team-members.

  3. Use your Agent team just like you would use a regular Agent.

PreviousReasoningNextModels

Last updated 4 months ago

How to build Agent Teams

​