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

# Agno

> Use Moss as the in-memory semantic search runtime for Agno agents.

Connect [Agno](https://docs.agno.com) agents to Moss with `agno-moss`. Moss manages embeddings internally and serves queries from an in-memory runtime, so Agno agents get fast response without running a separate embedder or vector database.

## Why use Moss with Agno?

Agno's `Knowledge` interface is the standard way to plug external knowledge into agents. Moss delivers sub-10ms semantic search that slots directly into this interface via `MossRuntime`, giving your agents fast, accurate retrieval without the latency overhead of a standalone vector database.

## Required tools

* Moss project credentials from the [Moss Portal](https://portal.usemoss.dev)
* Python 3.10+
* An Agno-compatible model provider, such as OpenAI or Anthropic

## Integration guide

<Steps>
  <Step title="Install">
    ```bash theme={null}
    pip install agno-moss
    # or
    uv add agno-moss
    ```
  </Step>

  <Step title="Configure credentials">
    Set your Moss credentials in the environment. `MossRuntime` reads these automatically when `project_id` and `project_key` are omitted.

    ```bash theme={null}
    export MOSS_PROJECT_ID="your_project_id"
    export MOSS_PROJECT_KEY="your_project_key"
    ```
  </Step>

  <Step title="Create Agno knowledge backed by Moss">
    Point Agno `Knowledge` at `MossRuntime`, then enable `search_knowledge` on your agent.

    ```python theme={null}
    from agno.agent import Agent
    from agno.knowledge.knowledge import Knowledge
    from agno.models.anthropic import Claude
    from agno_moss import MossRuntime

    knowledge = Knowledge(
        vector_db=MossRuntime(
            index_name="my-index",
            # Falls back to MOSS_PROJECT_ID / MOSS_PROJECT_KEY env vars
        ),
    )

    agent = Agent(
        model=Claude(id="claude-sonnet-4-20250514"),
        knowledge=knowledge,
        search_knowledge=True,
        markdown=True,
    )

    knowledge.load(recreate=False)
    agent.print_response("What do you know about our return policy?", stream=True)
    ```
  </Step>
</Steps>

## Configuration

### MossRuntime

| Parameter                     | Default                    | Description                                                        |
| ----------------------------- | -------------------------- | ------------------------------------------------------------------ |
| `index_name`                  | Required                   | Name of the Moss index                                             |
| `project_id`                  | `MOSS_PROJECT_ID` env var  | Moss project ID                                                    |
| `project_key`                 | `MOSS_PROJECT_KEY` env var | Moss project key                                                   |
| `embedding_model`             | `"moss-minilm"`            | `"moss-minilm"` for speed or `"moss-mediumlm"` for higher accuracy |
| `alpha`                       | `0.8`                      | Hybrid search blend. `1.0` = semantic only, `0.0` = keyword only   |
| `auto_refresh`                | `False`                    | Auto-refresh the in-memory index when new docs are added           |
| `polling_interval_in_seconds` | `600`                      | Refresh interval when `auto_refresh=True`                          |

## Model providers

Use any Agno-compatible model provider. For example:

```python theme={null}
from agno.models.openai import OpenAIChat

agent = Agent(
    model=OpenAIChat(id="gpt-4o"),
    knowledge=knowledge,
    search_knowledge=True,
)
```