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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.

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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.

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Required tools

  • Moss project credentials from the Moss Portal
  • Python 3.10+
  • An Agno-compatible model provider, such as OpenAI or Anthropic

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Integration guide

1

Install

pip install agno-moss
# or
uv add agno-moss
2

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"
3

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)

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Configuration

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MossRuntime

ParameterDefaultDescription
index_nameRequiredName of the Moss index
project_idMOSS_PROJECT_ID env varMoss project ID
project_keyMOSS_PROJECT_KEY env varMoss project key
embedding_model"moss-minilm""moss-minilm" for speed or "moss-mediumlm" for higher accuracy
alpha0.8Hybrid search blend. 1.0 = semantic only, 0.0 = keyword only
auto_refreshFalseAuto-refresh the in-memory index when new docs are added
polling_interval_in_seconds600Refresh interval when auto_refresh=True

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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,
)