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Symphony
Symphony is an OpenAI engineering preview and specification for orchestrating coding agents from project work queues into isolated autonomous implementation runs.
OpenAI publishes the specification and an Elixir reference implementation. The current release supports Linear and adapters for GitHub Issues, Jira Cloud, Asana and GitLab Issues; the project remains an engineering preview for trusted environments. Use this as a first read, not a recommendation. Open the original project before trusting details like terms, limits, privacy, cost, setup, or safety.
What it is
A coding-agent orchestration spec
Symphony is framed around moving coding-agent work from interactive sessions into issue-driven, isolated implementation runs that can be tracked and reviewed.
Why it stands out
Work queues instead of session babysitting
The official materials focus on managing work at the task-board level, with agents handling implementation runs and returning evidence such as CI status, review feedback, analysis, and walkthroughs.
Availability
Spec, reference implementation, and engineering post
The public materials include the GitHub repository, SPEC.md, an experimental Elixir implementation, setup notes, a demo video reference, and the OpenAI engineering post explaining the workflow.
Why it matters
What makes it useful
Symphony picks up issue-tracker work and gives each coding-agent run an isolated workspace. It turns an issue queue into implementation runs whose status and returned work can be reviewed.
What to know
Where it fits
It sits between the issue tracker and the coding agent. A repository-owned workflow file supplies the work policy and agent instructions, while the service coordinates runs. If the work already lives in GitHub Issues, choose that tracker adapter rather than moving the queue to match the Linear demo. Keep the repository’s dispatch rules and agent instructions in its workflow file: changing the tracker connection does not replace those work policies.
Notable points
What stands out
The workflow separates task status from the evidence returned by a run, such as CI results and walkthroughs. A run finishing is therefore only one part of deciding whether its implementation is ready to accept.
Before using
What to review
Treat it as the project’s engineering preview for trusted environments, with its documented isolation and approval limits.
Configure the tracker credentials, repository workflow and coding-agent environment before dispatching issues. Check whether the repository has suitable tests, CI, workflow rules and a review process before giving it autonomous implementation work.
Reader fit
Who may find it relevant
Teams experimenting with queue-driven coding work and reviewable implementation runs. It needs repository and tracker setup; it is less relevant to an occasional chat-based coding task.
Editorial note
Why LifeHubber lists it
For teams adjusting the next queued coding runs, the specification supports reloading dispatch settings and prompts from the workflow file while the service keeps running. The new workflow applies to future work; it does not require restarting in-flight sessions, and some listener or resource changes can still require a restart.
Source links
Source materials
Reader note
Before relying on this entry
LifeHubber lists entries to help readers inspect AI projects, not to endorse them or prove they are safe, suitable, accurate, maintained, or right for a specific use. We do not verify every entry in depth. Before relying on anything listed, review the original materials, terms, privacy practices, limits, and risks that matter for your situation.
What to explore next
Compare the specification with a working task board.
Symphony explains the orchestration pattern. Multica shows a practical control center for tracking issues, people, agents, and run status together.
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