mini-swe-agent is a small AI software-engineering agent that works on GitHub issues or tasks from the command line by operating in a software environment. It is designed for coding-agent research and for developers who want a simple agent that can use different language models and execution environments. The catalogue entries provide instructions and agents for using or extending it.
Borrowing it
Nothing to install: this file belongs to SWE-agent/mini-swe-agent. 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/SWE-agent/mini-swe-agent/main/AGENTS.mdgit clone --depth 1 https://github.com/SWE-agent/mini-swe-agentWrote 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/swe-agent/mini-swe-agent/agents-md)<a href="https://agentmods.dev/instructions/swe-agent/mini-swe-agent/agents-md"><img src="https://agentmods.dev/badge/instructions/swe-agent/mini-swe-agent/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/swe-agent/mini-swe-agent/agents-md"><img src="https://agentmods.dev/badge/instructions/swe-agent/mini-swe-agent/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 | $0.00975 | $0.00975 |
| Opus 5.5 | $0.00390 | $0.00390 |
| Sonnet 5.5 | $0.00195 | $0.00195 |
| Haiku 4.5 | $0.00097 | $0.00097 |
Grade A, and why
mini-swe-agent 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 — 110 lines — stays where its author put it; the contents beside it link to each section on GitHub.
mini-SWE-agent overview
- mini-SWE-agent implements an AI software engineering agent that solves github issues and similar programming challenges
- The idea of this project is to write the simplest, smallest, most readable agent.
The project is structured as
minisweagent/__init__ # Protocols/interfaces for all base classes
minisweagent/agents # Agent control flow & loop
minisweagent/environments # Executing agent actions
minisweagent/models # LM interfaces
minisweagent/run # Run scripts that serve as an entry point
- The project embraces polymorphism: Every individual class should be simple, but we offer alternatives
- Every use case should start with a run script, that picks one agent, environment, and model class to run
Style guide
- Target python 3.10 or higher
- Use python with type annotations. Use
listinstead ofList. - Use
pathlibinstead ofos.path. UsePath.read_text()overwith ...open()constructs. - Use
typerto add interfaces - Keep code comments to a minimum and only highlight particularly logically challenging things
- Do not append to the README unless specifically requested
- Use
jinjafor formatting templates - Use
dataclassfor keeping track config - Do not catch exceptions unless explicitly told to.
- Write concise, short, minimal code.
- In most cases, avoid initializing variables just to pass them to a function. Instead just pass the expression to the function directly.
- Not every exception has to be caught. Exceptions are a good way to show problems to a user.
- This repository rewards minimal code. Try to be as concise as possible.
Here's an example for rule 11:
# bad
a = func()
Class(a)
# good
Class(func())
Test style
- Use
pytest, notunittest. - Do not mock/patch anything that you're not explicitly asked to do
- Avoid writing trivial tests. Every test should test for at least one, preferably multiple points of failure
- Avoid splitting up code in multiple lines like this:
a=func()\n assert a=b. Instead, just doassert func() == b - The first argument to
pytest.mark.parametrizeshould be a tuple (not a string! not a list!), the second argument must be a list (not a tuple!).
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 · 110 lines · 975 tokens per session scan A 5cd7ba3e1eaa
mini-swe-agent AGENTS.md is an instructions file published in the GitHub repository SWE-agent/mini-swe-agent (8,922 stars, last pushed 2d ago), licensed MIT. It adds 975 tokens to every session, about $0.0039 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.
Other instructions, from other repositories
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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.
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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.
vscode copilot-instructions.md
Copilot instructions for microsoft/vscode, covering vs code copilot instructions, project overview, root folders, core architecture (src/ folder) and built-in extensions (extensions/ folder).
deepseek-harness AGENTS.md
AGENTS.md instructions for deepseek-ai/deepseek-harness, covering agents.md, pre-stable apis and released session data, repository layout, commands and host sandbox failures.