The KnowledgeTools toolkit enables Agents to search, retrieve, and analyze information from knowledge bases. This toolkit integrates with AgentKnowledge and provides a structured workflow for finding and evaluating relevant information before responding to users. The toolkit implements a “Think → Search → Analyze” cycle that allows an Agent to:
  1. Think through the problem and plan search queries
  2. Search the knowledge base for relevant information
  3. Analyze the results to determine if they are sufficient or if additional searches are needed
This approach significantly improves an Agent’s ability to provide accurate information by giving it tools to find, evaluate, and synthesize knowledge. The toolkit includes the following tools:
  • think: A scratchpad for planning, brainstorming keywords, and refining approaches. These thoughts remain internal to the Agent and are not shown to users.
  • search: Executes queries against the knowledge base to retrieve relevant documents.
  • analyze: Evaluates whether the returned documents are correct and sufficient, determining if further searches are needed.

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Example

Here’s an example of how to use the KnowledgeTools toolkit: 
from agno.agent import Agent
from agno.embedder.openai import OpenAIEmbedder
from agno.knowledge.url import UrlKnowledge
from agno.models.openai import OpenAIChat
from agno.tools.knowledge import KnowledgeTools
from agno.vectordb.lancedb import LanceDb, SearchType

# Create a knowledge base containing information from a URL
agno_docs = UrlKnowledge(
    urls=["https://docs.agno.com/llms-full.txt"],
    # Use LanceDB as the vector database and store embeddings in the `agno_docs` table
    vector_db=LanceDb(
        uri="tmp/lancedb",
        table_name="agno_docs",
        search_type=SearchType.hybrid,
        embedder=OpenAIEmbedder(id="text-embedding-3-small"),
    ),
)

knowledge_tools = KnowledgeTools(
    knowledge=agno_docs,
    think=True,
    search=True,
    analyze=True,
    add_few_shot=True,
)

agent = Agent(
    model=OpenAIChat(id="gpt-4o"),
    tools=[knowledge_tools],
    show_tool_calls=True,
    markdown=True,
)

if __name__ == "__main__":
    # Load the knowledge base, comment after first run
    agno_docs.load(recreate=True)
    agent.print_response("How do I build multi-agent teams with Agno?", stream=True)
The toolkit comes with default instructions and few-shot examples to help the Agent use the tools effectively. Here is how you can configure them: 
from agno.tools.knowledge import KnowledgeTools

knowledge_tools = KnowledgeTools(
    knowledge=my_knowledge_base,
    think=True,                # Enable the think tool
    search=True,               # Enable the search tool
    analyze=True,              # Enable the analyze tool
    add_instructions=True,     # Add default instructions
    add_few_shot=True,         # Add few-shot examples
    few_shot_examples=None,    # Optional custom few-shot examples
)