GPTtrace
Generate eBPF programs from natural language using LLMs
GPTtrace is an innovative prototype that makes eBPF accessible via LLMs, but generated code must be reviewed. Ideal for rapid exploration and learning, not production without scrutiny.
- 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
- Users requiring production-grade, battle-tested eBPF programs without manual review
- Those seeking a visual GUI for eBPF development
- Beginners unfamiliar with Linux kernel concepts
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In short
GPTtrace — Generate eBPF programs from natural language using LLMs. 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.
Viability Score
How likely is GPTtrace to still be operational in 12 months? Based on 4 signals — momentum (how recently it shipped), wrapper dependency, revenue model, and web presence.
Last calculated: July 2026
How we score →Key Features
- Generate eBPF programs from natural language prompts using ChatGPT
- Kernel tracing with plain English descriptions
- Supports eBPF program types: kprobes, tracepoints, etc.
- Integration with eunomia-bpf for CO-RE (Compile Once, Run Everywhere)
- Automatic generation of correct eBPF code for common tracing tasks
- Reduces boilerplate and syntax errors in eBPF development
- Enables rapid prototyping of system observability probes
- Part of eBPF+AI ecosystem for dual-direction synergy
- Zero-instrumentation tracing of AI/GPU workloads (related projects)
- Open-source with community contributions
About GPTtrace
GPTtrace is an open-source tool that uses large language models (LLMs) to generate eBPF programs and trace the Linux kernel from natural language prompts. Targeted at system administrators, DevOps engineers, and kernel developers, it automates the creation of eBPF code for performance monitoring, debugging, and security analysis. The tool integrates with the eunomia-bpf ecosystem and supports various eBPF program types like kprobes and tracepoints. Key features include natural language-to-eBPF code generation, kernel tracing with plain English descriptions, automatic CO-RE (Compile Once, Run Everywhere) support, and integration with eunomia-bpf for easy compilation and execution. GPTtrace also reduces boilerplate and syntax errors, enabling rapid prototyping of system observability probes. The project is part of a broader symbiotic loop between AI and eBPF, where eBPF also traces AI workloads (e.g., GPU inference) via related tools like Agentsight and MCPtrace. GPTtrace is free, open-source, and fosters community contributions. Compared to manual eBPF coding, GPTtrace drastically lowers the entry barrier but generated programs require human review before production use. It is best suited for exploration, learning, and quick prototyping rather than mission-critical deployments.
Behind the Verdict
GPTtrace fills a real gap: eBPF is powerful but notoriously hard to write. By letting you describe tracing goals in plain English, it cuts prototyping time from hours to minutes. We'd reach for this when debugging a kernel performance issue and need a quick probe without deep eBPF knowledge. But—and it's a big but—the generated code is not guaranteed correct or safe. The Kgent research mentioned on the site shows ~80% semantic correctness with symbolic checks, so you cannot skip manual review. For production, you're better off with hand-tuned eBPF or using GPTtrace as a starting point. Where GPTtrace truly shines is in learning. Newcomers can describe what they want and see how the LLM translates it into eBPF, effectively using GPTtrace as a tutor. The ecosystem (eunomia-bpf, bpftime) makes running these programs straightforward. Compared to writing eBPF manually, GPTtrace wins on speed. Compared to a library of pre-canned probes, it offers more flexibility. But for stable, audited observability, traditional tools like BCC or libbpf are safer. In practice, we see GPTtrace used by system admins for ad-hoc troubleshooting and by developers exploring eBPF. Just don't trust it blindly—always verify before attaching to production kernels.
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Use Cases
- Generate a kprobe to trace malloc calls and log sizes in real time.
- Create a tracepoint for system call entry to monitor file open events.
- Produce an eBPF program to count 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.
Models Under the Hood
as of 2026-07-15
Limitations
- The tool is experimental; generated eBPF programs may contain errors or inefficiencies and require manual verification.
- It relies on ChatGPT's understanding of eBPF, which can be inconsistent for complex kernel internals.
- No API or web interface is available; use is limited to CLI.
- Currently no support for custom models or fine-tuning.
Integrations
Resources & Guides
Official links
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