rajudandigam/agent-inspect

Description and use

**Brief Summary**
rajudandigam/agent‑inspect is a TypeScript/Node.js library that turns the inner workings of an AI agent run—manual steps, tool calls, LLM requests, logs, errors, timings and metadata—into a readable execution‑tree view you can explore from the terminal. It lets developers visualise and debug multi‑step, tool‑enabled agent workflows locally, making isolated prompts and utilities reproducible as structured pipelines.

**Value**

• **Visibility & Debugging** – By converting raw logs and API traces into a hierarchical tree, developers can quickly spot where a chain of calls failed, how long each step took, and what data was passed between tools.
• **Standardised Agent Design** – The tree format encourages a disciplined, repeatable structure for prompts, tool wrappers, and memory handling, which is especially useful when coordinating several agents or building reusable tool‑use pipelines.
• **Developer‑Centric** – Works directly in the terminal and integrates with existing TypeScript codebases, so no extra UI or cloud service is required.

**Practical Adoption Path**

1. **Proof‑of‑Concept** – Clone the repo, run the provided example, and inspect the generated tree to verify that the output matches your expectations.
2. **Integrate Wrapper** – Wrap your existing agent’s entry point with agent‑inspect’s middleware (or the provided decorator) to automatically capture each LLM call, tool invocation, and log entry.
3. **Iterate & Refine** – Use the tree view during development to fine‑tune prompts, error handling, and tool interfaces; the visual feedback helps you converge on a stable workflow.
4. **Documentation & CI** – Add a short “How to run agent‑inspect” section to your README and include a CI step that runs a minimal agent run and checks that the tree can be generated without errors.

**Production Readiness**

• **Maturity** – The project has modest popularity (≈96 ⭐, 82 forks) and recent activity (last commit 2026‑06‑23), indicating an actively maintained codebase.
• **Scope** – It is well‑suited for prototypes, internal tooling, and any Node/TS service where agents are orchestrated locally.
• **Considerations Before Production**
• **Dependency audit** – Verify the transitive dependencies for known vulnerabilities and ensure they align with your security policy.
• **License review** – Confirm the repository’s license (likely MIT/Apache) is compatible with your product.
• **Maintainability** – Evaluate the maintainers’ responsiveness (issues/PRs) and consider forking or contributing fixes if you need long‑term stability.
• **Performance** – The library adds logging overhead; benchmark it under your expected load and optionally disable tree generation in high‑throughput environments.

Overall, agent‑inspect offers a practical, low‑friction way to gain insight into complex TypeScript AI agents and can be rolled out incrementally from a sandbox proof‑of‑concept to a production‑grade debugging aid, provided the usual security and maintenance checks are performed.

Use in a workflow

rajudandigam/agent-inspect helps turn isolated prompts and tools into repeatable agent workflows.

• coordinate multi-agent workflows
• add tool-use pipelines
• standardize agent memory

Limitations and review notes

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