Agentic RAG With Reasoning

Demonstrates agentic RAG with reranking and explicit reasoning tools.

agentic_rag_with_reasoning.py
"""
Agentic Rag With Reasoning
=============================

Demonstrates agentic RAG with reranking and explicit reasoning tools.
"""

import asyncio

from agno.agent import Agent
from agno.knowledge.embedder.cohere import CohereEmbedder
from agno.knowledge.knowledge import Knowledge
from agno.knowledge.reranker.cohere import CohereReranker
from agno.models.openai import OpenAIResponses
from agno.tools.reasoning import ReasoningTools
from agno.vectordb.lancedb import LanceDb, SearchType

# ---------------------------------------------------------------------------
# Setup
# ---------------------------------------------------------------------------
knowledge = Knowledge(
    # Use LanceDB as the vector database, store embeddings in the `agno_docs` table
    vector_db=LanceDb(
        uri="tmp/lancedb",
        table_name="agno_docs",
        search_type=SearchType.hybrid,
        embedder=CohereEmbedder(id="embed-v4.0"),
        reranker=CohereReranker(model="rerank-v3.5"),
    ),
)

# ---------------------------------------------------------------------------
# Create Agent
# ---------------------------------------------------------------------------
agent = Agent(
    model=OpenAIResponses(id="gpt-5.2"),
    # Agentic RAG is enabled by default when `knowledge` is provided to the Agent.
    knowledge=knowledge,
    # search_knowledge=True gives the Agent the ability to search on demand
    # search_knowledge is True by default
    search_knowledge=True,
    tools=[ReasoningTools(add_instructions=True)],
    instructions=[
        "Include sources in your response.",
        "Always search your knowledge before answering the question.",
    ],
    markdown=True,
)

# ---------------------------------------------------------------------------
# Run Agent
# ---------------------------------------------------------------------------
if __name__ == "__main__":
    asyncio.run(
        knowledge.ainsert_many(urls=["https://docs.agno.com/agents/overview.md"])
    )
    agent.print_response(
        "What are Agents?",
        stream=True,
        show_full_reasoning=True,
    )

Run the Example

Set up your virtual environment

uv venv --python 3.12
source .venv/bin/activate

Install dependencies

uv pip install -U agno cohere lancedb openai pyarrow

Export your API keys

export CO_API_KEY="your_co_api_key_here"
export OPENAI_API_KEY="your_openai_api_key_here"

Run the example

Save the code above as agentic_rag_with_reasoning.py, then run:

python agentic_rag_with_reasoning.py

Full source: cookbook/02_agents/07_knowledge/agentic_rag_with_reasoning.py