TaewoooPark/Agent-Blackbox

Description and use

**Brief Summary (2‑3 sentences)**
Agent‑Blackbox is a local‑first “flight recorder” for coding agents that captures every execution, replays runs as an interactive session map, scores the context usage, and automatically writes fixes back into an AGENTS.md file—all with a single npx command and no external API keys. It lets developers prototype AI‑enhanced features, build RAG or agent workflows, and evaluate model tooling without having to assemble a full model stack from scratch.

**Value Proposition**

• **Zero‑setup AI augmentation** – By recording and replaying agent actions locally, teams can add observability and debugging to any OpenCode‑style agent without provisioning cloud APIs.
• **Rapid iteration** – The live session map visualises the agent’s decision path, while the built‑in scoring highlights context‑bill inefficiencies, enabling developers to spot and fix problems instantly.
• **Documentation‑as‑code** – Automatic updates to AGENTS.md keep the knowledge base in sync with the latest fixes, reducing manual bookkeeping and improving team onboarding.

**Practical Adoption Path**

| Step | Action | Outcome |
|------|--------|---------|
| 1️⃣ | **Clone & install** – git clone https://github.com/TaewoooPark/Agent-Blackbox && cd Agent-Blackbox | Source ready for local use. |
| 2️⃣ | **Run the recorder** – npx agent-blackbox inside the project that contains your coding agents | A local flight‑recording server starts and begins capturing every agent call. |
| 3️⃣ | **Execute your agent** – run your usual OpenCode scripts (e.g., node runAgent.js) | The recorder logs API/SDK calls, language metadata, and execution flow. |
| 4️⃣ | **Review the live session map** – open http://localhost:xxxx in a browser | Interactive visualization of the run, with context‑bill scores displayed. |
| 5️⃣ | **Apply suggested fixes** – click “Write fix” to have Agent‑Blackbox append the correction to AGENTS.md | Documentation stays up‑to‑date automatically. |
| 6️⃣ | **Integrate into CI** – add the npx agent-blackbox command to your CI pipeline (e.g., GitHub Actions) to enforce observability on every PR | Continuous quality gate for AI‑driven code. |

**Production Readiness**

• **Maturity**: Medium. The project is actively maintained (last update 2026‑06‑25) and has modest community traction (23 ★, 3 forks). It is suitable for prototypes, internal tooling, or staged roll‑outs, but a production deployment should include a security audit (license compliance, dependency scanning) and a fallback plan if the local recorder encounters performance bottlenecks.
• **Dependencies**: Pure TypeScript/Node.js stack; no external API keys are required, which simplifies compliance and reduces operational cost.
• **Scalability**: Designed for local execution; for high‑throughput environments you may need to containerise the recorder and allocate sufficient CPU/memory, or offload persisted session maps to a dedicated observability store.
• **Risk considerations**: Verify the repository’s license (e.g., MIT, Apache) aligns with your policy, run a static‑analysis security scan on the dependencies, and ensure an active maintainer is reachable for critical issues.

**Bottom line** – Agent‑Blackbox offers a low‑friction way to add observability, debugging, and self‑documenting fixes to AI‑powered coding agents. It can be adopted quickly with a single npx command, making it ideal for early‑stage prototypes and internal workflows, while a modest amount of due‑diligence (security, licensing, performance testing) is advisable before scaling to production.

Use in a workflow

TaewoooPark/Agent-Blackbox 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.