Proof of Usefulness Report

agenttrace: cost and latency tracking for agent runs

Analysis completed on 5/15/2026

+45
Proof of Usefulness Score
You're In Business

agenttrace addresses a highly practical and relevant problem for developers building multi-step LLM agents by providing per-run cost and latency tracking. While the solution exhibits solid problem-solution fit and excellent market timing, the project is extremely early-stage (v0.1.0 recently published) and currently lacks verifiable traction, active user metrics, or revenue. The minimal current audience reach heavily constrains the overall score, placing it appropriately within the minimal traction tier.

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Score Breakdown

Real World Utility+70
Audience Reach Impact+5
Technical Innovation+60
Evidence Of Traction+5
Market Timing Relevance+80
Functional Completeness+80
Subtotal+40.75
Usefulness Multiplierx1.1
Final Score+45

Project Details

Project URL
github.com/MukundaKatta/agenttrace
Description
A tiny zero-dep JavaScript library that groups LLM calls into runs and reports total cost, p50 and p95 latency, and a by-model breakdown. Composes with prompt-cache observability and per-call cost calculators so you get a per-run report instead of a per-call spreadsheet.
Audience Reach
Early stage. agenttrace v0.1.0 published on npm on 2026-04-28. Author has shipped 30+ open-source libraries (npm/PyPI/crates.io) under @mukundakatta; distribution flywheel established.
Target Users
Teams running multi-step LLM agent pipelines who need to know the cost and tail latency of a complete run, not just per-call. Group calls by run id, get total dollars and p50/p95 latency, drop into existing observability.
Technologies
Other, JavaScript, TypeScript, npm, Node, ESM, GitHub Actions
Traction Evidence
GitHub: https://github.com/MukundaKatta/agenttrace (MIT). npm: https://www.npmjs.com/package/@mukundakatta/agenttrace (v0.1.0 published 2026-04-28). Companion entries: agentmemory, agentguard, agentsnap, agentcast, agentvet. Author maintains 30+ shipped open-source libraries under @mukundakatta.

Algorithm Insights

Market Position
Growing utility with room for optimization
User Engagement
Documented reach suggests active user community
Technical Stack
Modern tech stack aligned with sponsor technologies

Recommendations to Increase Usefulness Score

Document User Growth

Provide specific metrics on user acquisition and retention rates

Showcase Revenue Model

Detail sustainable monetization strategy and current revenue streams

Expand Evidence Base

Include testimonials, case studies, and third-party validation

Technical Roadmap

Share development milestones and feature completion timeline