---
id: ouroboros-ai
version: "0.51.5"
license: unclear
license_treatment: permissive
maintenance: active
---

# 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.

License: permissive · Maintenance: active · Downloads: 105.0K/mo

## 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 above — 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

pip install ouroboros-ai
uv add ouroboros-ai
poetry add ouroboros-ai

## Installing 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_current
- Install friction: low
- Maintenance: active
- Downloads: 105.0K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

ai agent orchestration framework, llm coding workflow automation, spec-driven ai development, multi-runtime agent os, replayable ai coding loops, agentic loop engineering, prompt-free agent specification, ai-agent-orchestration, llm-workflow-automation, spec-driven-development

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