> ## Documentation Index
> Fetch the complete documentation index at: https://docs.agno.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Managed Vector Databases: Pinecone

> Pinecone is a fully managed, serverless vector database for production workloads where you want zero infrastructure management.

```python managed.py theme={null}
"""
Managed Vector Databases: Pinecone
====================================
Pinecone is a fully managed, serverless vector database for
production workloads where you want zero infrastructure management.

Features:
- Fully managed, serverless option available
- Automatic scaling and high availability
- Metadata filtering
- Namespaces for multi-tenancy

Requires: pip install pinecone

See also: 01_qdrant.py for recommended default, 04_pgvector.py for PostgreSQL.
"""

from os import getenv

from agno.agent import Agent
from agno.knowledge.embedder.openai import OpenAIEmbedder
from agno.knowledge.knowledge import Knowledge
from agno.models.openai import OpenAIResponses

# ---------------------------------------------------------------------------
# Pinecone Setup
# ---------------------------------------------------------------------------

try:
    from agno.vectordb.pineconedb import PineconeDb

    knowledge_pinecone = Knowledge(
        vector_db=PineconeDb(
            name="knowledge-demo",
            api_key=getenv("PINECONE_API_KEY"),
            embedder=OpenAIEmbedder(id="text-embedding-3-small"),
        ),
    )
except ImportError:
    knowledge_pinecone = None
    print("Pinecone not installed. Run: pip install pinecone")

# ---------------------------------------------------------------------------
# Run Demo
# ---------------------------------------------------------------------------

if __name__ == "__main__":
    if knowledge_pinecone:
        print("\n" + "=" * 60)
        print("Pinecone: managed serverless vector database")
        print("=" * 60 + "\n")

        knowledge_pinecone.insert(
            url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"
        )
        agent = Agent(
            model=OpenAIResponses(id="gpt-5.2"),
            knowledge=knowledge_pinecone,
            search_knowledge=True,
            markdown=True,
        )
        agent.print_response("What Thai recipes do you know?", stream=True)
    else:
        print("Skipping demo: Pinecone not installed.")
```

## Run the Example

<Steps>
  <Snippet file="create-venv-step.mdx" />

  <Step title="Install dependencies">
    ```bash theme={null}
    uv pip install -U agno openai pinecone pinecone-text pinecone==5.4.2
    ```
  </Step>

  <Step title="Export your API keys">
    <CodeGroup>
      ```bash Mac/Linux theme={null}
      export OPENAI_API_KEY="your_openai_api_key_here"
      export PINECONE_API_KEY="your_pinecone_api_key_here"
      ```

      ```bash Windows theme={null}
      $Env:OPENAI_API_KEY="your_openai_api_key_here"
      $Env:PINECONE_API_KEY="your_pinecone_api_key_here"
      ```
    </CodeGroup>
  </Step>

  <Step title="Run the example">
    Save the code above as `managed.py`, then run:

    ```bash theme={null}
    python managed.py
    ```
  </Step>
</Steps>

Full source: [cookbook/07\_knowledge/05\_integrations/vector\_dbs/03\_managed.py](https://github.com/agno-agi/agno/blob/main/cookbook/07_knowledge/05_integrations/vector_dbs/03_managed.py)