Moss - Real-time Semantic Search for Conversational AI

Connect Agno 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
Python 3.10+
An Agno-compatible model provider, such as OpenAI or Anthropic

Integration guide

Install

pip install agno-moss
# or
uv add agno-moss

Configure credentials

Set your Moss credentials in the environment. MossRuntime reads these automatically when project_id and project_key are omitted.

export MOSS_PROJECT_ID="your_project_id"
export MOSS_PROJECT_KEY="your_project_key"

Create Agno knowledge backed by Moss

Point Agno Knowledge at MossRuntime, then enable search_knowledge on your agent.

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)

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:

from agno.models.openai import OpenAIChat

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

Voice & Real-time

AI Frameworks

Developer Tools

Agno

Why use Moss with Agno?

Required tools

Integration guide

Configuration

MossRuntime

Model providers

​Why use Moss with Agno?

​Required tools

​Integration guide

​Configuration

​MossRuntime

​Model providers

Why use Moss with Agno?

Required tools

Integration guide

Configuration

MossRuntime

Model providers