AI tool comparison
CodeBurn vs Kelet
Which one should you ship with? Here is the side-by-side panel verdict, pricing read, reviewer split, and community vote comparison.
Developer Tools
CodeBurn
Token cost analytics and waste finder for AI coding tools
75%
Panel ship
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Community
Paid
Entry
CodeBurn is an open-source terminal dashboard that tracks and analyzes your token spend across Claude Code, OpenAI Codex, Cursor, OpenCode, and GitHub Copilot. It classifies coding sessions into 13 activity types — architecture, debugging, refactoring, code review, and more — and shows you exactly where your tokens are going. The standout feature is the optimizer: CodeBurn identifies wasteful patterns in your workflow — like repeatedly re-reading the same files, bloated context files, or MCP servers that are loaded but never used — and suggests concrete changes with estimated savings. It also tracks one-shot success rates per task type, helping you understand where AI is genuinely saving time vs. where you're fighting the tool. A macOS menu bar widget shows live token spend as you work, with a daily budget alert. Built by indie developer AgentSeal and shared as a Show HN, it picked up 80 upvotes and significant interest from developers who didn't realize how much they were spending on context re-reads alone. Open source under MIT license.
Developer Tools
Kelet
AI agent that diagnoses why your LLM app failed in production
75%
Panel ship
—
Community
Free
Entry
Kelet is a production monitoring platform that automatically diagnoses and fixes failures in LLM applications and AI agents. Rather than requiring engineers to manually sift through thousands of traces, Kelet reads production agent traces, clusters failure patterns across sessions, and surfaces root causes with supporting evidence. The platform's standout feature is credit assignment for multi-agent architectures — when a LangChain, CrewAI, or PydanticAI pipeline fails, Kelet pinpoints exactly which agent in the chain caused the failure rather than returning a vague error message. It then generates targeted prompt patches with measurable before/after reliability improvements, so fixes ship with proof they work. Setup takes approximately five minutes via the Kelet SDK or installer skill, with full OpenTelemetry compliance for teams already running observability infrastructure. Kelet covers the LLM token costs for its own analysis, and a free tier requires no credit card — making it accessible to indie builders before they've committed to paid tooling.
Reviewer scorecard
“I ran this on a week of Claude Code sessions and immediately found I was spending 30% of my tokens re-reading the same five config files. The menu bar widget is the killer feature — seeing the cost counter tick up while you work changes your behavior instantly. Instant install for anyone serious about AI coding.”
“Kelet solves the specific hell of debugging AI agents in production: thousands of traces, failure patterns scattered across sessions, and no clear signal about which prompt, which agent, or which data caused the issue. The credit assignment for multi-agent chains is the killer feature — knowing exactly which subagent in a CrewAI or LangGraph chain broke is worth the integration cost alone. Five-minute setup via SDK and OpenTelemetry compliance means it plugs into what you're already running.”
“The 13 activity categories feel arbitrary and require calibration. More importantly, this is fundamentally a symptom-treating tool — the real fix is better context management built into the AI tools themselves. And if you're on a flat-rate API plan, cost tracking is largely irrelevant.”
“Kelet is an LLM analyzing LLM failures, which is a charming recursion problem. When your agent monitoring agent hallucinates a root cause, you've added a failure mode that's harder to debug than the original. The 'evidence-backed fixes with before/after reliability measurements' pitch sounds airtight, but those measurements depend on the LLM evaluation being correct — which is exactly what you can't assume in production. A solid structured logging + tracing setup with deterministic replay would catch most of these failures without adding another probabilistic layer.”
“Observability for AI token usage is an entire category about to explode. As agentic workflows scale from individual developers to teams and enterprises, understanding where tokens go becomes as important as understanding where CPU cycles go. CodeBurn is early but directionally correct.”
“Observability tooling for AI agents is a category that barely exists and desperately needs to. As agent deployments move from side projects to production infrastructure, teams need the same root cause analysis discipline that SRE culture built for traditional services. Kelet is early in a space that will be massive — expect DataDog, Grafana, and every APM vendor to build versions of this within 18 months.”
“Even for non-coding creative work — writing, research, brainstorming — understanding which prompting patterns are wasteful vs. effective is valuable. The one-shot success rate tracking by task type is a genuinely novel idea I haven't seen anywhere else.”
“For indie builders shipping AI products to paying customers, Kelet is exactly the kind of tooling that turns 'my agent sometimes fails and I don't know why' into a real support workflow. The free tier with no credit card means you can actually test whether it's useful before committing.”
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