AReaL is an infrastructure system for training large language models with reinforcement learning, connecting model training to applications built around AI agents. Researchers and developers use it to train reasoning and agentic models through asynchronous workflows, and the catalogue add-ons support working with AReaL.
Borrowing it
Nothing to install: this file belongs to areal-project/AReaL. 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/areal-project/AReaL/main/AGENTS.mdgit clone --depth 1 https://github.com/areal-project/AReaLWrote 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/areal-project/areal/agents-md)<a href="https://agentmods.dev/instructions/areal-project/areal/agents-md"><img src="https://agentmods.dev/badge/instructions/areal-project/areal/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/areal-project/areal/agents-md"><img src="https://agentmods.dev/badge/instructions/areal-project/areal/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 | $0.02812 | $0.02812 |
| Opus 5.5 | $0.01125 | $0.01125 |
| Sonnet 5.5 | $0.00562 | $0.00562 |
| Haiku 4.5 | $0.00281 | $0.00281 |
Grade A, and why
AReaL 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 2mo 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 — 261 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AGENTS.md -- AReaL Agent Operations Guide
Quick reference
Tech stack: Python 3.12+ | PyTorch | FSDP2 / Megatron / Archon | SGLang / vLLM
# Environment
uv sync --extra cuda # CUDA + SGLang inference (default); for vLLM: cp pyproject.vllm.toml pyproject.toml && cp uv.vllm.lock uv.lock && uv sync --extra cuda
uv sync --extra sandbox # Daytona cloud sandbox backend (optional)
source .venv/bin/activate # activate venv BEFORE pre-commit or git commit if venv exists
pre-commit install --install-hooks # hooks: Ruff, clang-format, mdformat, nbstripout, conventional-commits
pre-commit run --all-files # lint + format everything
# Tests
uv run pytest tests/test_<topic>.py
# CLI docs
uv run python docs/generate_cli_docs.py
# Docs build (canonical, release-aligned)
./docs/build_all.sh
# Do NOT use `jupyter-book build docs/en|docs/zh` directly for final preview/release,
# because it skips AReaL-specific static setup and output packaging.
Hard rules -- never violate:
- No wildcard imports (
from x import *). - No hardcoded secrets, paths, or endpoints.
- No skipping pre-commit hooks.
- No guessing cluster configs or rebuilding CUDA/driver stacks.
- Integration tests require multi-node hardware -- explain skips explicitly.
Always do:
- Read relevant files before modifying code.
- Run
pre-commit run --all-filesbefore committing. - Follow existing code patterns in the same module.
- Add tests for new functionality.
- Ask for decisions and clarifications with short, structured options instead of broad open-ended questions. Use the platform's native question/clarification tool if available.
Ask first before:
- Modifying config structures in
areal/api/cli_args.py. - Adding new dependencies.
- Changing launcher or scheduler logic.
- Deleting or renaming public APIs.
When unsure, leave a TODO(agent) comment and note the constraint in your response.
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.
- 2mo ago First seen · 261 lines · 2,812 tokens per session scan A 6b91da3c8360
AReaL AGENTS.md is an instructions file published in the GitHub repository areal-project/AReaL (5,888 stars, last pushed today), licensed Apache-2.0. It adds 2,812 tokens to every session, about $0.0112 per session on Opus 5.5. 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.
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