Exa

Use Exa with Agno agents.

Enable Agno agents to find web content based on its actual meaning and context instead of keyword match with Exa link-prediction transformers and embeddings.

from agno.agent import Agent
from agno.tools.exa import ExaTools

# ---------------------------------------------------------------------------
# Create Agent
# ---------------------------------------------------------------------------

# Example 1: Enable all tools
agent_all = Agent(
    tools=[
        ExaTools(
            all=True,  # Enable all exa tools
            show_results=True,
        )
    ],
    markdown=True,
)

# Example 2: Enable specific tools only
agent_specific = Agent(
    tools=[
        ExaTools(
            enable_search=True,
            enable_answer=True,
            enable_get_contents=False,
            enable_find_similar=False,
            enable_research=False,
            include_domains=["cnbc.com", "reuters.com", "bloomberg.com"],
            show_results=True,
            text=False,
        )
    ],
    markdown=True,
)

# Example 3: Default behavior (most functions enabled by default)
agent = Agent(
    tools=[
        ExaTools(
            include_domains=["cnbc.com", "reuters.com", "bloomberg.com"],
            show_results=True,
            text=False,
        )
    ],
    markdown=True,
)

# ---------------------------------------------------------------------------
# Run Agent
# ---------------------------------------------------------------------------
if __name__ == "__main__":
    agent.print_response("Search for AAPL news", markdown=True)

    agent = Agent(
        tools=[
            ExaTools(
                show_results=True,
            )
        ],
        markdown=True,
    )

    agent.print_response("Search for AAPL news", markdown=True)

    agent.print_response(
        "What is the paper at https://arxiv.org/pdf/2307.06435 about?", markdown=True
    )

    agent.print_response(
        "Find me similar papers to https://arxiv.org/pdf/2307.06435 and provide a summary of what they contain",
        markdown=True,
    )

    agent.print_response(
        "What is the latest valuation of SpaceX?",
        markdown=True,
    )

Run the Example

# Clone and setup repo
git clone https://github.com/agno-agi/agno.git
cd agno/cookbook/91_tools

# Create and activate virtual environment
./scripts/demo_setup.sh
source .venvs/demo/bin/activate

python exa_tools.py

For details, see Exa cookbook.