GPTtrace
Open-source CLI that turns plain-English tracing requests into eBPF programs with ChatGPT
GPTtrace is worth a look if you write eBPF by hand and want a faster first draft, and not much else. It generates kprobes, tracepoints, and other program types from a natural-language prompt, runs them through eunomia-bpf for CO-RE, and leans on the separate Kgent project for Z3-based symbolic verification at roughly 80% semantic correctness on its test sets. That last number is the honest headline: one in five programs may not mean what you intended. It is free and open source, so the downside is mostly your review time. If you need verified, production-grade instrumentation today, hand-written eBPF or a commercial observability agent remains the safer path.
Verified 15d ago · liveness 45/100 · cite: rightaichoice.com/tools/gpttrace
- System administrators automating kernel tracing tasks with natural language
- DevOps engineers needing quick eBPF probes for performance issues
- Kernel developers exploring eBPF without deep eBPF expertise
- AI/ML engineers tracing GPU workloads in kernel space
- Teams that need production-grade, battle-tested eBPF programs without manual review
- Developers who want a visual GUI for eBPF development
- Beginners unfamiliar with Linux kernel concepts
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Skip GPTtrace if you need a probe you can deploy to production without reading and verifying the generated code yourself, or if you want a GUI or a model backend other than ChatGPT.
The tool itself is free, but every generation call goes to ChatGPT, so you pay OpenAI's API or subscription costs separately from using GPTtrace.
GPTtrace is free and open source, so pricing is not the comparison axis — the eunomia ecosystem is community-run with enterprise support offered separately for its products. The cost that actually scales is engineer review time: every generated probe needs a human pass, so a team of one and a team of twenty pay the same license price and very different review bills. Compared with commercial observability agents that charge per host or per gigabyte, GPTtrace is effectively free software with an
In short
GPTtrace — Open-source CLI that turns plain-English tracing requests into eBPF programs with ChatGPT. Best for System administrators automating kernel tracing tasks with natural language, DevOps engineers needing quick eBPF probes for performance issues, Kernel developers exploring eBPF without deep eBPF expertise. Free to use.
What people actually say about GPTtrace — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
3 mentions across 1 source (GitHub) · researched Jul 3, 2026.
Average across the 1 source that answered — each source counts once, not each post.
- +Lowers the barrier for writing eBPF programs using natural language.
- +Integrates with eunomia-bpf for CO-RE and easy execution.
- +Supports various eBPF program types like kprobes and tracepoints.
- +Reduces boilerplate and syntax errors in eBPF development.
- +Part of an innovative eBPF+AI ecosystem with dual-direction synergy.
- −Generated code often fails due to kernel version mismatches.
- −Still relies on old ChatGPT API, not yet migrated to GPT-4.
- −Limited community feedback and slow issue resolution.
- −No clear documentation on supported kernel versions.
- −Early-stage project with uncertain long-term maintenance.
- • Requires own ChatGPT API subscription if using GPT-4 after API switch
Viability Score
How well maintained and how widely used is GPTtrace? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: September 2026
How we score →Key Features
- Natural-language to eBPF code generation using ChatGPT as the LLM backend
- Support for kprobes, tracepoints, and other eBPF program types
- Integration with eunomia-bpf for CO-RE (Compile Once, Run Everywhere)
- CLI-based interaction with no web or GUI interface
- Companion Kgent project with Z3-based symbolic verification and tests (~80% semantic correctness)
- Free and open-source under the eunomia community
- Part of the broader eunomia eBPF × AI ecosystem
- Related project AgentSight for zero-instrumentation LLM and AI agent observability
- Related project bpftime for GPU offload via PTX/SPIR-V injection
- Related project MCPtrace for LLM-based kernel tracing
- Related project SchedCP for AI agent scheduler tuning
- eBPF TLS interception combined with kernel signals for agent tracing (AgentSight)
- GPU tracing documentation covering CUDA, CUPTI, and NVBit tooling
About GPTtrace
GPTtrace is an open-source command-line tool from the eunomia community that converts natural-language descriptions of kernel tracing tasks into working eBPF code. You describe what you want to observe — "trace malloc calls and log allocation sizes" — and GPTtrace drafts the corresponding kprobe, tracepoint, or other eBPF program. It pairs with eunomia-bpf for CO-RE (Compile Once, Run Everywhere), so compiled programs run across kernel versions without recompilation. A companion research project, Kgent, adds Z3-based symbolic checks and tests to the generated code and reports roughly 80% semantic correctness on its test sets, which is the closest this project gets to a verification story. GPTtrace is built for people who already understand Linux internals and want a fast shortcut for eBPF prototyping or a way to learn eBPF by example. It is research-grade rather than commercial: the generated code is experimental, it relies on ChatGPT as the LLM backend, and there is no plug-and-play support for other models. Treat output as a draft you review before it touches production.
Behind the Verdict
GPTtrace occupies a narrow but real niche: the blank-page problem in eBPF development. Writing a kprobe means knowing the right hook point, the right helper functions, and the right verifier-safe memory access patterns. GPTtrace collapses that into a prompt and gives you something to react to. For learning, that is genuinely useful — you can ask for a tracepoint on system-call entry to monitor file-open events and then read the generated code to see how it is structured. For prototyping, it gets you to a running probe faster than starting from scratch. The weaknesses are structural, not cosmetic. The generated programs are experimental and need manual verification; the seed documentation says plainly that output may contain errors or inefficiencies. GPTtrace depends on ChatGPT's understanding of kernel internals, which is inconsistent for complex cases. There is no API and no web interface — everything happens on the CLI. And model support is effectively fixed: no custom models, no fine-tuning, no plug-and-play alternative backends in the core tool. The verification story sits outside the tool. Kgent is a separate research project, not part of GPTtrace's core, so the ~80% correctness figure applies to that pipeline rather than to what you get from a bare GPTtrace run. If you adopt GPTtrace, adopt the habit of reviewing every probe before it runs anywhere that matters. Where it fits: kernel developers and sysadmins who want rapid exploration, and AI/ML engineers tracing GPU-side workloads in kernel space who can tolerate rough output. Where it does not: anyone who needs a GUI, anyone without Linux kernel background, and anyone who needs a guaranteed-correct probe in a production fleet. The wider eunomia ecosystem — AgentSight for zero-instrumentation observability of LLM agents, bpftime for GPU offload via PTX/SPIR-V injection, MCPtrace for LLM-based kernel tracing — shows the direction the community is heading, but GPTtrace itself remains a research-grade starting point rather than a finished product.
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Real-world workflow fit
Concrete scenarios for the personas GPTtrace actually fits — and what changes day-one when you adopt it.
You need to know how often a particular syscall is entered during a workload. Instead of looking up the tracepoint format and helper functions, you describe the tracepoint for system-call entry in natural language and let GPTtrace draft the program.
Outcome: You get a working draft in minutes rather than starting from an empty file, then hand-edit the parts the LLM got wrong and compile with eunomia-bpf for CO-RE.
A shared box is suffering from unexplained latency spikes. You ask GPTtrace for a program that traces context switches so you can identify which processes are waking most often.
Outcome: A context-switch tracing program is generated and you attach it to the running kernel to gather evidence, treating the output as a lead rather than a final answer.
Kernel launch latency is suspected as the bottleneck in a training pipeline. You use GPTtrace to draft a probe against GPU driver functions, and consult the eunomia GPU tracing material covering CUDA, CUPTI, and NVBit.
Outcome: You obtain a starting probe for GPU driver instrumentation and an entry point into the broader eunomia eBPF × AI tooling, including bpftime's GPU offload work.
Use Cases
- Generate a kprobe to trace malloc calls and log allocation sizes in real time
- Create a tracepoint for system-call entry to monitor file open events
- Produce an eBPF program that counts TCP connections per second
- Draft a probe for GPU driver functions to measure kernel launch latencies
- Develop a tool to trace context switches and identify noisy neighbors
- Learn eBPF program structure by reading generated kprobe and tracepoint code before hand-editing it
- Prototype an instrumentation probe quickly when you know the kernel hook point you want but not the helper syntax
Models Under the Hood
as of 2026-09-23
Limitations
- GPTtrace is experimental.
- The eBPF programs it generates may contain errors or inefficiencies and require manual verification before use.
- It depends on ChatGPT's understanding of eBPF and kernel internals, which is inconsistent for complex cases.
- There is no API and no web interface — interaction is limited to the CLI.
- There is currently no support for custom models or fine-tuning.
- The Kgent project, which adds Z3-based symbolic verification and reports roughly 80% semantic correctness on its test sets, is a companion research effort rather than part of the core GPTtrace tool, so that verification is not automatic when you run GPTtrace itself.
as of 2026-09-14
Verification history
We have re-verified GPTtrace 8 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-checked, vendor evidence unchanged
- — re-checked, vendor evidence unchanged
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Showing the 6 most recent of 8 verification passes.
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published GPTtrace tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Open Source
$0/mo
Ideal for
Individual kernel developers, sysadmins, and researchers who can supply their own ChatGPT credentials and review generated code before use
What this tier adds
Starting tier and only tier — free entry point covering natural-language eBPF generation, kprobe and tracepoint support, eunomia-bpf CO-RE integration, and CLI use
Where the pricing makes sense
The company stage and team size where GPTtrace's pricing actually pencils out — and where peers do it cheaper.
GPTtrace is free and open source, so pricing is not the comparison axis — the eunomia ecosystem is community-run with enterprise support offered separately for its products. The cost that actually scales is engineer review time: every generated probe needs a human pass, so a team of one and a team of twenty pay the same license price and very different review bills. Compared with commercial observability agents that charge per host or per gigabyte, GPTtrace is effectively free software with an
Setup time & first value
How long it actually takes to get something useful out of GPTtrace — broken out by persona, not the marketing-page minute.
CLI-savvy kernel developers can install GPTtrace and generate a first probe in well under an hour, provided they already have an eBPF toolchain and a ChatGPT credential configured. The longer timeline is verification: expect to spend additional time reviewing generated code, and more again if you wire up the separate Kgent project for Z3-based checks. Beginners without Linux kernel background
Integrations
Resources & Guides
Tutorials & Learning
YouTube returned 6 videos for “GPTtrace”, and we withheld 6: 6 could not be judged, because “GPTtrace” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about GPTtrace.
Official links
Tools that pair well with GPTtrace
Common stack mates teams adopt alongside GPTtrace, with the specific reason each pairing earns its keep.
Replit Agent
Replit Agent turns a plain-English prompt into a runnable full-stack app inside your browser, then deploys it to a live URL.
Screenshot To Code
Open-source screenshot to code converter turns mockups, Figma designs, and screen recordings into HTML, React, and Vue.
MarsX
Open-source dev platform uniting AI, NoCode, Code, and reusable MicroApps.
Featured Head-to-Head Comparisons
Gpttrace vs Spider Cloud
If you need to feed real-time web data into AI agents or RAG pipelines, Spider Cloud is the obvious choice with its low-cost, high-success API and rich integrations. If your work involves Linux kernel tracing and you want to quickly prototype eBPF programs without deep expertise, GPTtrace is a free, handy tool. They solve entirely different problems, so choose based on your primary need: web data or kernel observability.
Gpttrace vs Voyage Ai
Choose Voyage AI if your priority is high-accuracy RAG with domain-specific embeddings and long-context support (32K tokens) for enterprise compliance. Choose GPTtrace if you need a free, open-source tool to quickly generate eBPF programs from natural language for kernel tracing. They serve completely different purposes.
Gpttrace vs Temporal Ai
Temporal AI and GPTtrace are fundamentally different tools. Temporal is a comprehensive durable execution platform for building resilient AI agents and workflows, ideal for teams needing stateful orchestration with retries and rollbacks. GPTtrace is a niche utility for generating eBPF tracing programs from English prompts, perfect for kernel debugging but not for general-purpose workflow automation. Choose based on your domain: reliable long-running processes vs. quick kernel-level observability probes.
Alternatives to GPTtrace
View all
Replit Agent
Replit Agent turns a plain-English prompt into a runnable full-stack app inside your browser, then deploys it to a live URL.
Screenshot To Code
Open-source screenshot to code converter turns mockups, Figma designs, and screen recordings into HTML, React, and Vue.
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