agno-agent-starter: Instructions file for Codex

AGENTS.md

agno-agent-starter AGENTS.md is an instructions file for Codex, OpenCode from linhai0872/agno-agent-starter. It costs 2,129 tokens per session, scanned A, original, MIT.

A set of instructions for developing services with Agno, an agent framework for building AI applications. It explains the project layout, how to separate framework code from user code, and conventions for agents, tools, workflows, and tests.

In plain words
What is it for?
It is for developing Agno agents and services, registering tools, organizing teams or workflows, configuring models, adding hooks, and writing matching tests.
Why use it?
It gives the coding agent clear boundaries and project conventions to follow. This helps prevent unnecessary changes to the scaffold while keeping custom application code organized.

Instructions file for CodexOpenCode

Written for Codex and OpenCode: the file is AGENTS.md. Also seen: mentions Claude Code.

This is linhai0872/agno-agent-starter's own configuration. It tells Codex and OpenCode how to work on agno-agent-starter itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything agno-agent-starter configures →

Reuse

Borrowing it

Nothing to install: this file belongs to linhai0872/agno-agent-starter. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/linhai0872/agno-agent-starter/main/AGENTS.md
Clone the repo
git clone --depth 1 https://github.com/linhai0872/agno-agent-starter

Made for: Codex, OpenCode.

Wrote this? Show the measurements

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README.md
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Per session 2,129 This file is loaded in full into every session.
When invoked 2,129 The same file — it is already loaded in full.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.02129 $0.02129
Opus 5 $0.01064 $0.01064
Sonnet 5 $0.00426 $0.00426
Haiku 4.5 $0.00213 $0.00213

Measured 7d ago against content hash 5fb5bb2f9606, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-07, from the pricing page.

Security

Grade A, and why

agno-agent-starter 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 7d 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.

AGENTS.md · 277 lines

How it starts

The opening of the file, as written. The whole thing — 277 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Agno Agent Service 开发规范

AI 工具主规范文档。所有 AI 辅助开发工具(Cursor、Claude Code 等)应参考本文档。


核心哲学

  1. AgentOS First — 使用 AgentOS 标准 API,不手写 FastAPI 路由
  2. Single Agent 优先 — 90% 场景用单 Agent + 工具解决,避免过度使用 Team/Workflow
  3. 配置与代码分离 — 模型参数用 ModelConfig 配置,不硬编码

项目结构概览

agno-agent-starter/
├── app/                     # 应用代码(User Code 区域)
│   ├── main.py              # AgentOS 入口(脚手架核心,慎改)
│   ├── config.py            # 三层配置加载器(脚手架核心)
│   ├── agents/              # ✏️ Agent 实现(用户扩展区)
│   ├── teams/               # ✏️ Team 实现(用户扩展区)
│   ├── workflows/           # ✏️ Workflow 实现(用户扩展区)
│   ├── models/              # 模型抽象层(脚手架提供)
│   ├── tools/               # 三层工具注册表
│   │   ├── framework/       # 框架级工具(脚手架提供)
│   │   ├── project/         # ✏️ 项目级工具(用户扩展区)
│   │   └── user/            # Agent 专用工具
│   ├── hooks/               # 护栏与生命周期钩子
│   └── mcp/                 # MCP Server(AI Agent 开发工具)
├── api/                     # OpenAPI 规格(自动生成)
├── tests/                   # 单元测试(镜像 app/ 结构)
├── scripts/                 # 脚本工具(含 agno CLI)
└── .cursor/rules/           # Vibe Coding 规则

User Code vs Scaffold Core:

  • ✏️ 标记 = 用户扩展区,自由修改
  • 无标记 = 脚手架核心,谨慎修改

开发规范

命名规则

元素 规则 示例
Agent ID kebab-case github-analyzer
文件名 snake_case.py github_analyzer.py
类名 PascalCase GitHubAnalyzer
函数/变量 snake_case analyze_repo
常量 UPPER_SNAKE_CASE MAX_RETRIES

性能要求

  • 禁止在循环中创建 Agent,Agent 实例是重量级对象
  • 创建一次,复用多次
  • 异步操作使用 async def

代码风格

  • 禁止在 print/log 中使用 emoji
  • 工具函数返回 str,让模型处理
  • 每个模块必须在 __init__.py 注册

模型配置

基础用法

from app.models import ModelConfig, create_model

AGENT_MODEL_CONFIG = ModelConfig(
    model_id="google/gemini-2.5-flash-preview-09-2025",
    temperature=0.1,
    max_tokens=16384,
)

model = create_model(AGENT_MODEL_CONFIG)

Read the full file on GitHub · 277 lines

Changes

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.

  1. 7d ago First seen · 277 lines · 2,129 tokens per session scan A 5fb5bb2f9606

Subscribe to this mod's changes

agno-agent-starter AGENTS.md is an instructions file published in the GitHub repository linhai0872/agno-agent-starter (6 stars, last pushed 7mo ago), licensed MIT. It adds 2,129 tokens to every session, about $0.0106 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 2026-08-31.

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