Tools

Agents use tools to take actions and interact with external systems.

Tools are functions that an Agent can run to achieve tasks. For example: searching the web, running SQL, sending an email or calling APIs. You can use any python function as a tool or use a pre-built toolkit. The general syntax is:

from bitca.agent import Agent

agent = Agent(
    # Add functions or Toolkits
    tools=[...],
    # Show tool calls in the Agent response
    show_tool_calls=True
)

Using a Toolkit

Bitca provides many pre-built toolkits that you can add to your Agents. For example, let’s use the DuckDuckGo toolkit to search the web.

1

Create Web Search Agent

Create a file web_search.py

web_search.py

from bitca.agent import Agent
from bitca.tools.duckduckgo import DuckDuckGo

agent = Agent(tools=[DuckDuckGo()], show_tool_calls=True, markdown=True)
agent.print_response("Whats happening in France?", stream=True)

2

Run the agent

Install libraries

pip install openai duckduckgo-search bitca

Run the agent

python web_search.py

Writing your own Tools

For more control, write your own python functions and add them as tools to an Agent. For example, here’s how to add a get_top_hackernews_stories tool to an Agent.

hn_agent.py

import json
import httpx

from bitca.agent import Agent


def get_top_hackernews_stories(num_stories: int = 10) -> str:
    """Use this function to get top stories from Hacker News.

    Args:
        num_stories (int): Number of stories to return. Defaults to 10.

    Returns:
        str: JSON string of top stories.
    """

    # Fetch top story IDs
    response = httpx.get('https://hacker-news.firebaseio.com/v0/topstories.json')
    story_ids = response.json()

    # Fetch story details
    stories = []
    for story_id in story_ids[:num_stories]:
        story_response = httpx.get(f'https://hacker-news.firebaseio.com/v0/item/{story_id}.json')
        story = story_response.json()
        if "text" in story:
            story.pop("text", None)
        stories.append(story)
    return json.dumps(stories)

agent = Agent(tools=[get_top_hackernews_stories], show_tool_calls=True, markdown=True)
agent.print_response("Summarize the top 5 stories on hackernews?", stream=True)

Read more about:

• Available toolkits

• Using functions as tools

Attributes

The following attributes allow an Agent to use tools

Parameter	Type	Default	Description
tools	List[Union[Tool, Toolkit, Callable, Dict, Function]]	-	A list of tools provided to the Model. Tools are functions the model may generate JSON inputs for.
show_tool_calls	bool	False	Print the signature of the tool calls in the Model response.
tool_call_limit	int	-	Maximum number of tool calls allowed.
tool_choice	Union[str, Dict[str, Any]]	-	Controls which (if any) tool is called by the model. “none” means the model will not call a tool and instead generates a message. “auto” means the model can pick between generating a message or calling a tool. Specifying a particular function via {"type": "function", "function": {"name": "my_function"}} forces the model to call that tool. “none” is the default when no tools are present. “auto” is the default if tools are present.
read_chat_history	bool	False	Add a tool that allows the Model to read the chat history.
search_knowledge	bool	False	Add a tool that allows the Model to search the knowledge base (aka Agentic RAG).
update_knowledge	bool	False	Add a tool that allows the Model to update the knowledge base.
read_tool_call_history	bool	False	Add a tool that allows the Model to get the tool call history.