Moss is a sub-10 ms semantic search runtime built for Conversational AI agents. Hybrid retrieval (semantic + Keyword Search), built-in embeddings, metadata filtering, and a WebAssembly build that runs in the browser - all from a single SDK that embeds in your application.
No network hop on the hot path. No clusters to tune. Point the SDK at Moss Cloud, load your index, and query it in under 10 ms. Python, TypeScript, Elixir, and C.
Quickstart
Before you start: sign up at moss.dev for project_id and project_key - free tier available.
The snippets below need Python 3.10+ or Node.js 20+.
Python
from moss import MossClient, QueryOptions client = MossClient("your_project_id", "your_project_key") # Create an index and add documents 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."}, ]) # Load and query — results in <10 ms await client.load_index("support-docs") results = await client.query("support-docs", "how long do refunds take?", QueryOptions(top_k=3)) for doc in results.docs: print(f"[{doc.score:.3f}] {doc.text}") # Returned in {results.time_taken_ms}ms
TypeScript
npm install @moss-dev/moss
import { MossClient } from "@moss-dev/moss"; const client = new MossClient("your_project_id", "your_project_key"); // Create an index and add documents 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." }, ]); // Load and query — results in <10 ms await client.loadIndex("support-docs"); const results = await client.query("support-docs", "how long do refunds take?", { topK: 3 }); results.docs.forEach((doc) => { console.log(`[${doc.score.toFixed(3)}] ${doc.text}`); // Returned in ${results.timeTakenInMs}ms });
Why Moss?
Most retrieval stacks call out to a remote vector database. The round trip alone runs 200–500 ms - enough to break a real-time conversation.
Moss runs search and embedding inside your process. There's no network hop on the hot path, so query latency lands in the single digits - fast enough that retrieval disappears from the latency budget. If you're building a voice bot, a copilot, or any agent that talks to humans, that's the difference between a tool that feels alive and one that feels laggy.
Benchmarks
End-to-end query latency (embedding + search) on 100,000 documents, 750 measured queries, top_k=5. Tested with Macbook pro (M4 Pro, 24GB).
| System | P50 | P95 | P99 | Mean |
|---|---|---|---|---|
| Moss | 3.1 ms | 4.3 ms | 5.4 ms | 3.3 ms |
| Pinecone | 432.6 ms | 732.1 ms | 934.2 ms | 485.8 ms |
| Qdrant | 597.6 ms | 682.0 ms | 771.4 ms | 596.5 ms |
| ChromaDB | 351.8 ms | 423.5 ms | 538.5 ms | 358.0 ms |
Moss includes embedding in the measurement — competitors use an external embedding service (modal). Pinecone and Qdrant use cloud search.
Moss isn't a database! It's a search runtime. You don't manage clusters, tune HNSW parameters, or worry about sharding. You index documents, load them into the runtime, and query. That's it.
Features
- Sub-10 ms semantic search - single-digit-ms p99 in our benchmarks
- Hybrid search - semantic + keyword in a single query
- Built-in embedding models - no OpenAI key required (or bring your own)
- Metadata filtering -
$eq,$and,$in,$nearoperators - Runs in the browser too - separate WebAssembly SDK (
@moss-dev/moss-web) for client-side semantic search with no server - Database connectors - ingest directly from SQLite, MongoDB, MySQL, and Supabase (
packages/moss-data-connector/) - CLI - manage indexes and query from the terminal (
packages/moss-cli/) - SDKs - Python (3.10+), TypeScript / Node.js (20+), Elixir, and C (
libmoss) - Framework integrations - LangChain, DSPy, LlamaIndex, Pipecat, LiveKit, Vapi, ElevenLabs, Strands Agents
Examples
This repo contains working examples you can copy straight into your project:
examples/
├── python/ # Python SDK samples
│ ├── load_and_query_sample.py
│ ├── comprehensive_sample.py
│ ├── custom_embedding_sample.py
│ └── metadata_filtering.py
├── python-classification/ # Classification example
├── javascript/ # TypeScript SDK samples
│ ├── load_and_query_sample.ts
│ ├── comprehensive_sample.ts
│ └── custom_embedding_sample.ts
├── javascript-web/ # Browser / WASM SDK samples
├── c/ # C SDK samples (libmoss)
├── go/ # Go SDK samples
├── voice-agents/ # End-to-end voice agents (ambient + multi-agent)
│ ├── airline-pnr/ # Ambient retrieval; per-PNR Moss indexes, swap mid-call
│ └── mortgage-lending/ # Multi-agent flow with shared session state
└── cookbook/ # Framework integrations
├── langchain/ # LangChain retriever
├── dspy/ # DSPy module
├── crewai/ # CrewAI integration
├── haystack/ # Haystack retriever
├── autogen/ # AutoGen integration
├── mastra/ # Mastra retriever
├── pydantic-ai/ # Pydantic AI integration
└── daytona/ # Daytona sandbox example
apps/
├── next-js/ # Next.js semantic search UI
├── pipecat-moss/ # Pipecat voice agent with Moss retrieval
├── vapi-moss/ # Vapi voice agent with Moss retrieval
├── elevenlabs-moss/ # ElevenLabs voice agent with Moss retrieval
├── livekit-moss-vercel/ # LiveKit voice agent on Vercel
├── agora-moss/ # Agora Conversational AI MCP server with Moss retrieval
├── moss-llamaindex/ # LlamaIndex RAG backend + frontend
├── moss-bun/ # Bun runtime example
└── docker/ # Dockerized examples (ECS/K8s pattern)
moss-live-labs/ # Experimental zone: prototypes and community demos
├── python/ # Minimal Python quickstart + advanced query example
├── typescript/ # Minimal TypeScript quickstart + advanced query example
├── examples/ # Larger experiments (image search, voice agents)
│ ├── voice-agent/ # LiveKit + Moss voice assistant
│ ├── advanced-voice-agent/ # Persona impersonator built on a PDF knowledge base
│ └── image-search/ # FastAPI + React image search over COCO
└── community-demos/ # Community-contributed projects
└── voice-agents/ # bharat-benefits, shoplabs-voice-agent
Run the Python examples
cd examples/python pip install -r requirements.txt cp ../../.env.example .env # Add your credentials python load_and_query_sample.py
Run the TypeScript examples
cd examples/javascript npm install cp ../../.env.example .env # Add your credentials npx tsx load_and_query_sample.ts
Run the Next.js app
cd apps/next-js npm install cp ../../.env.example .env # Add your credentials npm run dev # Open http://localhost:3000
Run the Pipecat voice agent
Sub-10 ms retrieval plugged into Pipecat's real-time voice pipeline — a customer support agent that actually keeps up with conversation.
cd apps/pipecat-moss/pipecat-quickstart # See README for setup and Pipecat Cloud deployment
Run the fully-local voice agent (Ollama + Moss + Pipecat)
A privacy-first voice AI stack: Ollama for LLM inference, Moss for retrieval, Pipecat for real-time audio - the LLM and retrieval both run on your machine.
cd apps/pipecat-moss/ollama-local
docker compose upFull API reference: docs.moss.dev.
Integrations
| Framework | Status | Example |
|---|---|---|
| LangChain | Available | examples/cookbook/langchain/ |
| DSPy | Available | examples/cookbook/dspy/ |
| LlamaIndex | Available | apps/moss-llamaindex/ |
| CrewAI | Available | examples/cookbook/crewai/ |
| AutoGen | Available | examples/cookbook/autogen/ |
| Haystack | Available | examples/cookbook/haystack/ |
| Mastra | Available | examples/cookbook/mastra/ |
| Pydantic AI | Available | examples/cookbook/pydantic-ai/ |
| Pipecat | Available | apps/pipecat-moss/ |
| LiveKit | Available | apps/livekit-moss-vercel/ |
| Vapi | Available | apps/vapi-moss/ |
| ElevenLabs | Available | apps/elevenlabs-moss/ |
| Agora | Available | apps/agora-moss/ |
| Strands Agents | Available | packages/strands-agents-moss/ |
| Next.js | Available | apps/next-js/ |
| VitePress | Available | packages/vitepress-plugin-moss/ |
| Vercel AI SDK | Available | packages/vercel-sdk/ |
Architecture
Three parts:
- Moss Cloud - handles ingestion, document embedding, storage, and distribution. Point the SDK at it with a project ID and key.
- Index - your documents and their vectors, packaged as a single artifact that lives on Moss Cloud.
- Runtime - embedded in your application. It pulls indexes over HTTPS, holds them in memory, and serves queries locally.
Once an index is loaded, queries don't leave your process - that's where the sub-10 ms latency comes from. Document changes flow through Moss Cloud and the runtime stays in sync.
Two ways to run the runtime
- Server-side -
moss(Python) and@moss-dev/moss(Node.js 20+) embed the runtime in your backend. Use this when your agent runs on a server. - Browser -
@moss-dev/moss-webis a WebAssembly build that downloads the index and runs queries entirely client-side, no server required. Use this for static sites, browser extensions, and offline-first apps. Seeexamples/javascript-web/.
Full Python SDK source code is available at sdks/python/.
Contributing
Here's where the community can have the most impact:
- New SDK bindings — Swift, Go, Elixir,...
- Framework integrations — CrewAI, Haystack, AutoGen
- Reranking support — plug in cross-encoder rerankers
- Doc-parsing connectors — PDF, DOCX, HTML, Markdown ingestion
- Examples and tutorials — if you build something with Moss, we'd love to feature it
See our Contributing Guide for setup instructions and our Roadmap for what's planned.
Check out issues labeled good first issue to get started.
Contributors
Community
- Discord — ask questions, share what you're building
- GitHub Issues — bug reports and feature requests
- Twitter — announcements and updates
License
BSD 2-Clause License — the SDKs, examples, and integrations in this repo are fully open source.