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docs: add llms.txt for AI tool discoverability (category 7)
Create a structured llms.txt summary at the docs site root so LLMs and AI-powered tools can quickly understand what ralphify is, its core concepts, CLI commands, and where to find detailed documentation. Co-authored-by: Ralphify <noreply@ralphify.co>
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# Ralphify
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> A minimal CLI harness for autonomous AI coding loops. Run commands, assemble a prompt, pipe it to an agent, and repeat with fresh context each iteration.
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Ralphify puts your AI coding agent in a `while True` loop. Each iteration: run commands (tests, lint, git log), inject their output into a prompt via placeholders, pipe the prompt to an agent, and repeat. The agent starts fresh every cycle — no context decay, no conversation bloat.
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## Quick install
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```bash
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uv tool install ralphify
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```
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Requires Python 3.11+ and an AI agent CLI (Claude Code recommended).
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## Core concept
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A **ralph** is a directory containing a `RALPH.md` file. That file has YAML frontmatter (agent command, commands, args) and a prompt body with `{{ placeholders }}`.
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```markdown
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---
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agent: claude -p --dangerously-skip-permissions
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commands:
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- name: tests
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run: uv run pytest -x
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---
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{{ commands.tests }}
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You are an autonomous coding agent running in a loop.
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Fix failing tests before starting new work.
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Read TODO.md and implement the next task.
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```
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Run it:
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```bash
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ralph run my-ralph # Loop forever (Ctrl+C to stop)
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ralph run my-ralph -n 5 # Run 5 iterations
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```
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## Key features
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- **Self-healing feedback loop**: Command output (test failures, lint errors) feeds into each iteration — the agent sees and fixes its own mistakes.
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- **Live steering**: Edit `RALPH.md` while the loop runs. Changes take effect on the next iteration.
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- **Fresh context every cycle**: No conversation bloat or hallucinated memories. The agent reads current codebase state each time.
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- **Progress in git**: Every iteration commits to git. Roll back with `git reset` if needed.
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- **Any agent**: Works with Claude Code, Aider, Codex CLI, or any CLI that reads stdin.
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- **Shareable ralphs**: Install pre-built ralphs from GitHub with `ralph add owner/repo`.
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## Placeholder types
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- `{{ commands.<name> }}` — replaced with command output (stdout + stderr)
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- `{{ args.<name> }}` — replaced with user argument values from CLI flags
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- `{{ ralph.name }}` — ralph directory name
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- `{{ ralph.iteration }}` — current iteration number (1-based)
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- `{{ ralph.max_iterations }}` — total iterations if `-n` was set
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## CLI commands
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- `ralph run <path> [-n N] [--log-dir DIR] [--stop-on-error] [--timeout SEC] [--delay SEC]` — run the loop
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- `ralph init <name>` — scaffold a ralph from template
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- `ralph new [name]` — AI-guided ralph creation
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- `ralph add <source>` — install ralph(s) from GitHub
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## Python API
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```python
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from ralphify import run_loop, RunConfig, RunState
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```
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Embed the loop in automation pipelines. See the API docs for details.
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## Documentation
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- [Getting Started](https://ralphify.co/docs/getting-started/): Install to running loop in 10 minutes
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- [When to Use](https://ralphify.co/docs/when-to-use/): Task suitability guide
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- [How it Works](https://ralphify.co/docs/how-it-works/): Iteration lifecycle details
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- [Writing Prompts](https://ralphify.co/docs/writing-prompts/): Patterns for effective loop prompts
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- [Using with Different Agents](https://ralphify.co/docs/agents/): Claude Code, Aider, Codex CLI setup
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- [Cookbook](https://ralphify.co/docs/cookbook/): Copy-pasteable recipes for coding, docs, research
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- [CLI Reference](https://ralphify.co/docs/cli/): All commands and options
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- [Python API](https://ralphify.co/docs/api/): Programmatic usage
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- [Quick Reference](https://ralphify.co/docs/quick-reference/): Cheat sheet
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- [Troubleshooting](https://ralphify.co/docs/troubleshooting/): Common issues and fixes
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## Source
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- PyPI: https://pypi.org/project/ralphify/
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- GitHub: https://github.com/computerlovetech/ralphify

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