Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add instructions/usemoss/moss/agents-mdgit clone --depth 1 https://github.com/usemoss/mossWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.04222 | $0.04222 |
| Opus 5 | $0.02111 | $0.02111 |
| Sonnet 5 | $0.00844 | $0.00844 |
| Haiku 4.5 | $0.00422 | $0.00422 |
Grade A, and why
moss AGENTS.md scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 287 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AGENTS.md
This file provides guidance to AI agents when working with code in this repository.
What This Repo Is
Moss is a real-time semantic search runtime for AI agents targeting sub-10ms query latency. The runtime runs on-device using bundled embedding models (moss-minilm) no external embedding API calls are needed. The cloud layer handles project management and index distribution; the local layer handles querying.
This repository contains the multi-language SDKs, framework integrations (cookbooks), and application examples.
Repository Layout
sdks/
python/sdk/ — Python SDK (PyPI: moss), Python 3.10+
javascript/sdk/ — JS/TS SDK (npm: @moss-dev/moss), ESM-only
elixir/sdk/ — Elixir SDK (Hex: moss)
examples/
python/ — Standalone Python usage examples
javascript/ — Standalone TS usage examples
javascript-web/ — Browser/Vite examples (no Node runtime)
c/ — C binding examples
go/ — Standalone Go SDK usage examples
bun/ — Bun runtime example
python-classification/ — Text classification with Moss
voice-agents/ - End-to-end voice agents (LiveKit-based)
airline-pnr/ - Ambient retrieval; per-caller Moss index, swap mid-call
mortgage-lending/ - Multi-agent flow with shared session state
cookbook/ — Framework integrations (one subdirectory per framework)
autogen/ — AutoGen multi-agent e-commerce support
crewai/ — CrewAI retrieval tool
daytona/ — Log Ingestion Q&A Agent on Daytona sandboxes
dspy/ — DSPy notebook
haystack/ — Haystack RAG pipeline integration
langchain/ — LangChain retriever + tool integration
langflow/ — Langflow drag-and-drop retriever + search components
langgraph/ - LangGraph stateful retrieval node
mastra/ — Mastra agent createTool() integration
pydantic-ai/ - Pydantic AI integration
moss-cognee-daytona/ — Claude Code + Cognee + Moss on Daytona (shared memory)
apps/
agora-moss/ — Agora Conversational AI voice agent (MCP server demo)
agora-custom-llm-moss/ — Agora custom-llm middleware: ambient prepend + in-process tool loop
docker/ — Dockerized Python + JS SDK examples (ECS/K8s pattern)
elevenlabs-moss/ — ElevenLabs voice agent with Moss knowledge base
livekit-moss-vercel/ — LiveKit voice agent + React frontend on Vercel
moss-bun/ — Production Bun semantic search application
moss-llamaindex/ — LlamaIndex + Liteparse full-stack PDF search demo
next-js/ — Next.js 16 browser-based semantic search UI (@moss-dev/moss-web)
pipecat-moss/ — Pipecat voice agent (three variants below)
pipecat-quickstart/ — Cloud-deployable quickstart bot
ollama-local/ — Local LLM + Moss + Pipecat via docker compose
hume-ollama-local/ — Local LLM + Hume AI TTS + Moss + Pipecat
ten-moss/ — TEN Framework voice agent with Moss session-scoped grounding
vapi-moss/ — VAPI Custom Tool webhook server
packages/
agora-moss/ — Agora Conversational AI MCP server package
elevenlabs-moss/ — ElevenLabs integration package
moss-cli/ — CLI for index/document management (no-code workflows)
moss-data-connector/ — Database source connectors
moss-connector-mongodb/ — MongoDB connector
moss-connector-mysql/ — MySQL / MariaDB connector
moss-connector-sqlite/ — SQLite connector
moss-connector-supabase/ — Supabase (PostgREST) connector
moss-md-indexer/ — Markdown docs → Moss index builder
pipecat-moss/ — Pipecat Python integration package
strands-agents-moss/ — AWS Strands Agents integration package
ten-moss/ — TEN Framework Moss session manager (MossSessionManager)
vapi-moss/ — VAPI Custom Knowledge Base webhook adapter
n8n-nodes-moss/ — n8n community node for Moss index + query
vercel-sdk/ — Vercel AI SDK tool wrappers (@moss-tools/vercel-sdk)
vitepress-plugin-moss/ — VitePress search plugin (on-device fallback after cloud)
zo-computer/ — Zo computer skill for Moss search
moss-live-labs/ - Experimental zone: prototypes and community demos (APIs can change)
python/ - Minimal Python quickstart + advanced query example
typescript/ - Minimal TypeScript quickstart + advanced query example
examples/
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 data
community-demos/
voice-agents/
bharat-benefits/ - Voice RAG over Indian public-benefit schemes (Sarvam STT/TTS)
shoplabs-voice-agent/ - Pipecat WebRTC ecommerce support agent
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 287 lines · 4,222 tokens per session scan A bbab9974ab47
moss AGENTS.md is an instructions file published in the GitHub repository usemoss/moss (669 stars, last pushed 9d ago), licensed BSD-2-Clause. It adds 4,222 tokens to every session, about $0.0211 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other instructions, from other repositories
pipecat AGENTS.md
AGENTS.md instructions for pipecat-ai/pipecat, covering agents.md, project overview, common commands, setup development environment and install pre-commit hooks.
OpenSandbox CLAUDE.md
Claude Code instructions for opensandbox-group/OpenSandbox: See AGENTS.md for all rules, routing, and conventions.
next-sanity AGENTS.md
Instructions for sanity-io/next-sanity, covering agent notes, next-sanity/live export conditions, runtime nuance, ai literacy framework (ailf) and cursor cloud specific instructions.
AI-fundamentals AGENTS.md
AGENTS.md instructions for ForceInjection/AI-fundamentals, covering 项目概览与工具使用说明 and 1 核心技术模块.
relay AGENTS.md
Instructions for AgentWorkforce/relay, covering git workflow rules, never push directly to main, correct workflow, ... do work .. and stop here - let user merge.
OpenKB AGENTS.md
Instructions for VectifyAI/OpenKB, covering agents.md — openkb map for coding agents, read next, dev commands, module map (openkb/) and hard invariants.