Show HN: Orchestrate parallel Claude Code and Codex agents on a live map

Description and use

**Brief Summary (2‑3 sentences)**
Show HN: Orchestrate parallel Claude Code and Codex agents on a live map is an open‑source framework that lets you spin up multiple Claude Code and Codex agents, run them concurrently, and visualise their execution on an interactive map. It provides a ready‑made “agent‑as‑a‑service” stack so you can prototype AI‑enhanced features, RAG pipelines, or complex workflow orchestration without building the underlying model infrastructure from scratch.

**Value**

• **Speed to prototype** – The library bundles the plumbing for parallel execution, state sharing, and live visualisation, letting teams focus on business logic rather than low‑level API orchestration.
• **Model‑agnostic agent layer** – By supporting both Claude Code and Codex, it enables quick comparison of model behaviours and hybrid workflows (e.g., using Claude for reasoning and Codex for code generation).
• **Debug‑friendly UI** – The live map shows each agent’s location, status, and messages, which accelerates troubleshooting and stakeholder demos.

**Practical Adoption Path**

1. **Explore the repo** – Clone the project, run the provided Docker‑compose or local dev script, and verify the demo map loads.
2. **Integrate your data** – Replace the sample prompts and knowledge bases with your own RAG corpus or API endpoints; the framework exposes a simple JSON‑based agent definition file.
3. **Add a thin wrapper** – Implement a small adaptor (e.g., a Flask or FastAPI endpoint) that translates your internal request format into the framework’s agent definition.
4. **Validate** – Run unit‑ and integration‑tests for your custom agents, and use the live map to manually inspect execution paths.
5. **Iterate & lock** – Once stable, pin the dependency versions, bake the Docker image into your CI/CD pipeline, and optionally contribute any bug fixes upstream.

**Production Readiness**

• **Readiness level: Medium** – The project is suitable for internal tools, prototypes, or low‑risk production components after a modest vetting effort.
• **Key checks before production**
• Verify the open‑source license (e.g., MIT/Apache) and ensure it aligns with your compliance policy.
• Review the issue tracker and commit history for maintenance activity; the last update is recent (2026‑07‑06) but the repo has few contributors.
• Add comprehensive monitoring (agent health, latency, error rates) and fallback mechanisms because the upstream model APIs can experience throttling or outages.
• Harden security around API keys for Claude and Codex, and consider isolating the live‑map UI behind internal authentication.

If those checks pass, the framework can be promoted to production for internal AI‑augmented services, while external‑facing products should undergo a more rigorous reliability and security audit.

Use in a workflow

Show HN: Orchestrate parallel Claude Code and Codex agents on a live map helps add AI capability without starting from a blank model stack.

• prototype AI features
• build RAG or agent workflows
• evaluate model tooling

Limitations and review notes

Medium: useful for prototypes or internal workflows, with dependency and maintenance checks before production.