Moss

What does this agent do, and when should you use it?

Moss is a retrieval runtime that runs search and embedding inside your own process instead of calling a remote vector database. It has three parts: Moss Cloud handles ingestion, embedding, storage, and distribution; an Index packages your documents and their vectors as a single artifact; and the Runtime is embedded in your application, pulls indexes over HTTPS, holds them in memory, and serves queries locally. SDKs expose create_index, load_index, and query, with hybrid semantic plus keyword retrieval, built-in embedding models, metadata filtering ($eq, $and, $in, $near), and a WebAssembly build (@moss-dev/moss-web) that runs entirely client-side. The project's benchmark on 100,000 documents reports 3.1 ms P50 end-to-end latency, versus hundreds of milliseconds for remote vector databases. The repository also ships a CLI, database connectors, and integrations for LangChain, LlamaIndex, Pipecat, LiveKit, Vapi, ElevenLabs and more, under BSD-2-Clause.

The end-to-end flow starts at moss.dev, where you sign up for a project_id and project_key. You then call MossClient.create_index with documents that carry an id and text; Moss Cloud generates embeddings and packages them into a distributable index. Calling load_index pulls that index over HTTPS into local memory, and query performs embedding plus search inside the same process, returning a docs list together with time_taken_ms. Queries accept QueryOptions(top_k=...) or { topK } to control result count, and metadata operators filter results. For browser use, @moss-dev/moss-web is a separate WebAssembly build that downloads the index and answers queries client-side with no server. The repository additionally contains the moss-cli package for terminal index management, moss-data-connector for ingesting from SQLite, MongoDB, MySQL and Supabase, and runnable examples and apps covering multiple languages, voice agents, and framework cookbooks.

1. Developers building voice customer-support agents where retrieval latency above a few milliseconds makes conversation feel laggy.
2. Backend teams assembling a RAG stack who would rather embed the runtime than operate a vector database cluster.
3. Frontend teams building static sites or browser extensions that need client-side semantic search via the WebAssembly SDK.
4. Teams already on LangChain, LlamaIndex, DSPy, or CrewAI who want to swap in a lower-latency retriever.
5. Builders of Pipecat, LiveKit, Vapi, or ElevenLabs voice pipelines that need retrieval inside a real-time audio loop.
6. Teams syncing existing SQLite, MongoDB, MySQL, or Supabase data who want a ready ingestion connector.

How do you install or deploy this agent?

Server-side, install the SDK for your language and register at moss.dev for a project_id and project_key. Python requires 3.10+:

pip install moss

TypeScript / Node.js requires 20+:

npm install @moss-dev/moss

For the browser, use the separate WebAssembly package:

npm install @moss-dev/moss-web

To run a bundled example, copy the env template and fill in your credentials:

cd examples/python
pip install -r requirements.txt
cp ../../.env.example .env
python load_and_query_sample.py

How do you use this agent?

End-to-end Python usage:

from moss import MossClient, QueryOptions

client = MossClient("your_project_id", "your_project_key")

await client.create_index("support-docs", [
    {"id": "1", "text": "Refunds are processed within 3-5 business days."},
    {"id": "2", "text": "You can track your order on the dashboard."},
    {"id": "3", "text": "We offer 24/7 live chat support."},
])

await client.load_index("support-docs")
results = await client.query("support-docs", "how long do refunds take?", QueryOptions(top_k=3))

End-to-end TypeScript usage:

import { MossClient } from "@moss-dev/moss";

const client = new MossClient("your_project_id", "your_project_key");

await client.createIndex("support-docs", [
  { id: "1", text: "Refunds are processed within 3-5 business days." },
  { id: "2", text: "You can track your order on the dashboard." },
  { id: "3", text: "We offer 24/7 live chat support." },
]);

await client.loadIndex("support-docs");
const results = await client.query("support-docs", "how long do refunds take?", { topK: 3 });

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