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SkillsApr 6, 2026·2 min de lecture

Optio — Workflow Orchestrator for AI Coding Agents

Automates the full AI development lifecycle from task planning to merged PR. Orchestrates AI agents through planning, execution, code review, and merge. 800+ GitHub stars.

Prêt pour agents

Installation agent prête

Cet actif peut être installé après choix du runtime, vérification du plan et exécution de la commande adaptée.

Native · 98/100Policy : autoriser
Surface agent
Tout agent MCP/CLI
Type
Skill
Installation
Single
Confiance
Confiance : Established
Point d'entrée
Optio — Workflow Orchestrator for AI Coding Agents
Commande d'installation directe
npx -y tokrepo@latest install 393e64de-c90a-40d7-b161-a522cf116ee9 --target codex

À exécuter après confirmation du plan en dry-run.

TL;DR
Optio orchestrates AI coding agents through the full lifecycle: planning, execution, review, and merge.
§01

What it is

Optio is a workflow orchestrator that automates the full AI-assisted development lifecycle. It takes a task from planning through execution, code review, and PR merge by coordinating AI coding agents at each stage.

Optio targets engineering teams that use AI coding agents but need structured orchestration rather than ad-hoc prompting. It manages the handoff between planning, implementation, and review phases so the human stays in the approval loop without doing manual coordination.

The project is actively maintained and suitable for both individual developers and teams looking to integrate it into their existing toolchain. Documentation and community support are available for onboarding.

§02

How it saves time or tokens

Optio reduces the context-switching overhead of manually prompting agents at each development stage. By defining the workflow once, it routes tasks through planning, coding, and review agents automatically. The estimated token budget is around 2,400 tokens per workflow execution. Teams report faster PR turnaround because the review step runs immediately after code generation.

§03

How to use

  1. Define your workflow stages (plan, implement, review, merge) in the Optio configuration file.
  2. Submit a task description or issue reference to Optio.
  3. Optio dispatches the task to a planning agent, then routes the plan to an implementation agent.
  4. The generated code goes through an automated review agent. If approved, Optio opens a PR or merges directly based on your rules.
§04

Example

# optio.config.yaml
workflow:
  stages:
    - name: plan
      agent: planner
      output: task_spec
    - name: implement
      agent: coder
      input: task_spec
      output: pull_request
    - name: review
      agent: reviewer
      input: pull_request
      approval_required: true
    - name: merge
      auto: true
      condition: review.approved
§05

Related on TokRepo

§06

Common pitfalls

  • Skipping the review stage to move faster. Automated code generation without review introduces bugs and security issues. Keep the review agent or human approval in the loop.
  • Configuring overly broad task descriptions. Optio works best with well-scoped tasks. Break large features into smaller units before submitting.
  • Not setting token or cost limits per stage. Without limits, a verbose planning agent can consume excessive tokens before implementation even starts.
  • Not reading the changelog before upgrading. Breaking changes between versions can cause unexpected failures in production. Pin your version and review release notes.

Questions fréquentes

What AI agents does Optio support?+

Optio is agent-agnostic. It orchestrates any agent that can receive prompts and produce structured output. Common setups use Claude, GPT-4, or open-source models for different stages.

Does Optio require a specific Git hosting platform?+

Optio integrates with GitHub for PR creation and merge workflows. GitLab and Bitbucket support is available through community plugins.

Can I customize the workflow stages?+

Yes. The workflow is fully configurable. You can add, remove, or reorder stages, add conditional branching, and set approval gates at any point in the pipeline.

How does Optio handle failures?+

Each stage has configurable retry logic and fallback behavior. If the review agent rejects code, Optio routes the feedback back to the implementation agent for revision. You set the maximum number of revision cycles.

Is Optio open source?+

Yes. Optio is open-source and available on GitHub. The project accepts community contributions for new agent integrations and workflow templates.

Sources citées (3)
🙏

Source et remerciements

Created by Jon Wiggins. Licensed under MIT.

optio — ⭐ 800+

Thanks for automating the tedious parts of AI-assisted development.

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