ouroboros-ai
Pins an acceptance spec and omits any verify command or expected output from the worker's contract. Works with Claude Code, Codex CLI, OpenCode and 10 more runtimes.
What it is and what it does
Ouroboros is a local-first runtime layer that transforms non-deterministic AI coding work into deterministic, auditable workflows. Instead of ad-hoc prompting, it enforces a specification-first approach: a Socratic interview exposes hidden assumptions, an immutable seed spec locks intent before any code generation, and a three-stage automated evaluation gate replaces manual QA. The package orchestrates multiple LLM runtimes—Claude Code, Codex CLI, OpenCode, Hermes, Gemini, and others—through a unified contract, so the same workflow can run across different AI agents while remaining replayable and policy-bound.
The core stack splits into three layers: the OS kernel (this package) owns the contract and ledger; plugins add domain workflows (PR review, Jira sync, incident response); and a terminal shell (ourocode) provides a unified TUI. You can use Ouroboros standalone with any supported CLI, layer plugins for domain-specific tasks, or install the full stack for a unified cockpit. The package is actively maintained, supports Python 3.12–3.14, and carries no known vulnerabilities.
Use it for:
- Turn a vague product idea into a verified, working codebase by running a Socratic interview to lock the spec before any agent code generation.
- Run the same AI coding workflow across multiple LLM runtimes (Claude, Codex, Gemini, etc.) with full auditability and replay capability.
- Automate domain workflows like PR review, Jira ticket triage, or release management with scoped permissions and provenance tracking.
- Reduce prompt engineering overhead by replacing manual QA with a three-stage automated evaluation gate that evolves the agent's behavior.
- Integrate AI agents into CI/CD or incident-response pipelines where reproducibility and policy enforcement are non-negotiable.
Worth the install?
AI-flagged interpretation of the facts on this page — verify before relying
Ouroboros is an Agent OS that turns vague AI coding tasks into replayable, observable workflows by locking a specification before execution, then orchestrating multiple LLM runtimes (Claude Code, Codex, OpenCode, and others) through a structured interview-crystallize-execute-evaluate-evolve cycle.
Yes. Ouroboros is actively maintained, has low install friction, carries no known vulnerabilities, and solves a real problem—turning vague AI tasks into deterministic, auditable workflows. The MIT license imposes no restrictions. Install it if you need structured, replayable AI coding workflows across multiple LLM runtimes; skip it if you only need ad-hoc prompting or single-agent integration.
Install
ouroboros-ai on PyPI
pip
pip install ouroboros-aiuv
uv add ouroboros-aipoetry
poetry add ouroboros-aiInstalling ouroboros-ai
Before you install
Low friction: pure Python wheel with no compiled dependencies. Active maintenance as of 2026-08-14 with 5412 GitHub stars. Requires Python 3.12+. Twelve runtime dependencies are all well-established (pydantic, sqlalchemy, click, rich, structlog, etc.), suggesting a stable, production-oriented codebase.
License in practice
MIT license (permissive). No restrictions on commercial or private use; you may modify and redistribute freely under the same license terms.
Quickstart
pip install ouroboros-ai
from ouroboros import setup, interview
# One-time setup
await setup()
# Start a Socratic interview to clarify the task
await interview("I want to build a task management CLI")
Requires Python 3.12 or later. Designed to work with an installed LLM runtime (Claude Code, Codex CLI, OpenCode, or one of 10 others); without one, the core CLI (`ouroboros init start`) still works but agent integration requires a supported host.
Verify before relying
- Whether the package works offline or requires network access to LLM APIs during normal operation.
- Performance characteristics and resource overhead of the replayable event ledger for long-running workflows.
- Maturity and stability of the MCP (Model Context Protocol) integration across all 13 supported runtimes.
Package facts
| License | not declared (permissive) |
| Python support | supports the current Python release (>=3.12) |
| Install friction | low — pure-Python wheel |
| Runtime dependencies | 12 — aiosqlite, anyio, click, jsonschema, prompt-toolkit, pydantic, python-dotenv, pyyaml, rich, sqlalchemy, structlog, typer |
| Maintenance | actively maintained — 0 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 105,045/month — #12,724 on PyPI (30-day window, as of 2026-08-14) |
| Known vulnerabilities | none known (OSV.dev, checked 2026-08-14) |
Evidence: ouroboros_ai-0.51.5-py3-none-any.whl
Keywords: agent-os, agentic-loop, ai-agent, ai-coding-agent, claude-code, cli, developer-tools, llm-orchestration, loop-engineering, mcp, prompt-engineering, socratic-method, spec-driven-development
Tags
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