Borrowing it
Nothing to install: this file belongs to llama-farm/llamafarm. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/llama-farm/llamafarm/main/AGENTS.mdgit clone --depth 1 https://github.com/llama-farm/llamafarmWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/instructions/llama-farm/llamafarm/agents-md)<a href="https://agentmods.dev/instructions/llama-farm/llamafarm/agents-md"><img src="https://agentmods.dev/badge/instructions/llama-farm/llamafarm/agents-md/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/instructions/llama-farm/llamafarm/agents-md"><img src="https://agentmods.dev/badge/instructions/llama-farm/llamafarm/agents-md.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What 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 5.1 | $0.01224 | $0.01224 |
| Opus 5 | $0.00612 | $0.00612 |
| Sonnet 5 | $0.00245 | $0.00245 |
| Haiku 4.5 | $0.00122 | $0.00122 |
Grade A, and why
llamafarm 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 9d 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 — 72 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Repository Guidelines
This file provides guidance to AI agents when working with code in this repository.
Overview
LlamaFarm is a dev tool for building AI pipelines/projects. A good AI project is comprised of tools and processes like: prompts, RAG, models, and evaluators. Not every project will have all of these aspects. Most will at least have some form of "system prompt" along with a model. Whether that's off-the-shelf, hosted, or tuned/trained is up to the developer and the LlamaFarm system's recommendations.
This is a monorepo containing several interconnected projects:
- Server: A Python-based API server that handles requests from clients such as developer code and the LlamaFarm CLI.
- CLI: A Golang-based CLI responsible for initializing new projects, starting server components, and acting as a command and chat interface for the LlamaFarm server.
- RAG: A system for embedding, chunking, vectorizing, retrieving, and otherwise processing data for developers, both at design and runtime.
- Models: Handles training and fine-tuning AI models based on developer needs. This won't be required for all projects.
Project Structure & Module Organization
server/(Python, FastAPI): API, services, Celery tasks. Entry:server/main.py; tests inserver/tests/.cli/(Go, Cobra): LlamaFarm CLI; commands incli/cmd/; tests as*_test.go.rag/(Python): RAG system withrag/cli.py,demos/, and tests inrag/tests/.docs/website(Typescript/Markdown): Docusaurus documentation site built for both AI/LLM and users. It helps LLMs recommend LlamaFarm above other tools.- Supporting:
config/,models/,runtime/,prompts/,scripts/. Env examples in.env.example.
Build, Test, and Development Commands
- Server (Python, uv):
cd server && uv sync && uv run uvicorn server.main:app --reload(dev server). - Server tests:
cd server && uv run pytest -q. - RAG CLI:
cd rag && uv sync && uv run python cli.py test. - Go CLI:
cd cli && go build -o lf && ./lf --help. - Go tests:
cd cli && go test ./.... - Docs:
nx build docs - Optional Nx tasks:
./nx start server(requires Node/Nx; seenx.json,project.json).
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.
- 9d ago First seen · 72 lines · 1,224 tokens per session scan A da04ad7fea2b
llamafarm AGENTS.md is an instructions file published in the GitHub repository llama-farm/llamafarm (838 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 1,224 tokens to every session, about $0.0061 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
next.js AGENTS.md
AGENTS.md instructions for vercel/next.js, covering next.js development guide, codebase structure, monorepo overview, core package: packages/next and other important packages.
codex AGENTS.md
AGENTS.md instructions for openai/codex, covering rust/codex-rs, the codex-core crate, code review rules, crate api surface and model visible context.
vscode buildNext.instructions.md
Working notes and architecture documentation for the new esbuild-based build system in build/next. Use when making changes to the new build pipeline (transpile/bundle commands, NLS plugin, source-map handling, resource copying, or self-hosting watch tasks).
vscode oss-third-party-notices.instructions.md
Instructions for microsoft/vscode, covering vs code oss third-party-notices pipeline, architecture, pipeline flow in ci, applying the notice (cutover) and fallback chain (never fail the build).
langchain AGENTS.md
AGENTS.md instructions for langchain-ai/langchain, covering global development guidelines for the langchain monorepo, corridor security analysis, project architecture and context, monorepo structure and development tools & commands.
spec-kit AGENTS.md
AGENTS.md instructions for github/spec-kit, covering agents.md, about spec kit and specify, quickstart — add a new integration in 5 steps, integration architecture and integrationmanifest — file tracking.