8 Best Open-Source eBPF Tracing Tools in 2026

eBPF (extended Berkeley Packet Filter) lets you run custom programs inside the Linux kernel without changing kernel code or loading kernel modules. Originally designed for packet filtering, eBPF now powers everything from performance monitoring to security enforcement.

The kernel's built-in verifier ensures eBPF programs are safe—they can't crash your system, access arbitrary memory, or create infinite loops. This makes eBPF practical for production environments where traditional kernel instrumentation would be too risky.

Why Use eBPF for Observability?

Traditional monitoring approaches require you to instrument your applications with SDKs, restart services to enable profiling, or accept the overhead of system-wide tracing. eBPF flips this model by instrumenting the kernel itself.

When you trace system calls, network packets, or filesystem operations with eBPF, you get visibility into every application on your system without touching their code. A database you can't modify? Trace it. A third-party binary with no metrics? Monitor it. A performance regression you can't reproduce locally? Profile it in production.

eBPF programs aggregate data in the kernel before sending it to userspace, dramatically reducing overhead. Instead of capturing every event and processing it later, you can calculate histograms, count occurrences, or sample at specific rates right where events happen.

eBPF Kernel Support

Modern eBPF features require recent Linux kernels:

Linux 4.4 - Basic kprobe and histogram support
Linux 4.9 - Stack traces and efficient event filtering
Linux 5.2 - BTF (BPF Type Format) for portable programs
Linux 5.8 - Better tracepoint and ring buffer support

Most distributions ship kernels that support eBPF, but newer kernels unlock more capabilities. Ubuntu 20.04, RHEL 8, and Debian 11 all have sufficient kernel versions for production eBPF tracing.

The Best Open-Source eBPF Tracing Tools in 2026

1. BCC (BPF Compiler Collection)

BCC offers the most extensive collection of ready-to-run eBPF tracing tools. The project includes over 100 command-line utilities that each target specific performance questions, from tracking slow filesystem operations to counting TCP retransmits.

BCC offers the most extensive collection of ready-to-run eBPF tracing tools. The project includes over 100 command-line utilities that each target specific performance questions, from tracking slow filesystem operations to counting TCP retransmits. Need to see which files your application opens? Run opensnoop. Want a histogram of disk I/O latency? Use biolatency. Each tool focuses on one aspect of system behavior with detailed man pages and practical examples.

Beyond pre-built utilities, BCC provides Python and C APIs for custom eBPF programs. You write kernel-side logic in C, then use Python to control the program and format output. Major tech companies rely on BCC in production. The libbpf-tools directory contains newer CO-RE versions that compile once and run anywhere, eliminating the need for kernel headers on production systems.

Main Benefits:

Over 100 production-ready tracing tools included
Detailed man pages and examples for each tool
Python and C APIs for custom development
Battle-tested at companies like Facebook and Netflix
CO-RE support for portable binaries
Extensive documentation and large community

2. bpftrace

bpftrace is a high-level tracing language that makes eBPF accessible through concise one-liners. If you've used awk or DTrace, bpftrace will feel familiar—it's designed for quick investigations where you need an answer in seconds, not hours.

bpftrace excels at answering specific questions immediately with concise one-liners. Want to see all file opens system-wide? One command. Need a histogram of read() sizes? One line. The syntax is minimal—you specify what to trace, optional filters, and the action. bpftrace handles compiling to eBPF bytecode, loading into the kernel, and displaying results.

When troubleshooting, you often don't know exactly what to measure. bpftrace makes it easy to try different approaches rapidly. Variables starting with @ automatically become maps for aggregating data without boilerplate code. bpftrace ships with a tools/ directory containing scripts for common investigations—most under 50 lines of code.

Main Benefits:

Extremely concise syntax for quick investigations
No compilation step—write and run immediately
Built-in aggregation functions and histograms
Supports all major probe types (kprobes, tracepoints, USDT)
Growing collection of example scripts
Ideal for ad-hoc troubleshooting

3. Cilium

Cilium brings eBPF-powered networking and observability to Kubernetes. While it's primarily known for replacing kube-proxy and implementing network policies, Cilium's Hubble component provides deep visibility into cluster networking without sidecars or service mesh overhead.

Hubble monitors network flows at both packet level and application level (HTTP, gRPC, Kafka, DNS). You get visibility into service communication, response codes, and latency without instrumenting applications or deploying proxies. Cilium's eBPF programs run in the kernel's network stack, seeing every packet without extra hops. The same programs that enforce network policy also extract observability data.

When pods can't communicate, Hubble shows exactly why. See if connections are dropped by network policy, DNS resolution failures, or response issues. The Hubble UI visualizes service dependencies and traffic flows in real-time based on actual observed behavior, not configuration.

Main Benefits:

Zero instrumentation network observability for Kubernetes
HTTP, gRPC, Kafka, and DNS protocol visibility
Real-time service dependency mapping
Network policy debugging with detailed flow logs
CNCF graduated project with strong adoption
Works without service mesh or sidecar containers

4. Pixie

Pixie automatically instruments Kubernetes applications using eBPF to capture distributed traces, metrics, and logs. You deploy it with one command and immediately get observability into your cluster without changing application code.

Pixie's eBPF programs automatically trace network system calls and library functions to reconstruct application behavior. This captures HTTP requests, database queries with parameters, gRPC calls, and DNS lookups—all from observing traffic without explicit instrumentation. Legacy applications, third-party services, and compiled binaries all get monitored automatically.

Unlike traditional platforms that stream telemetry centrally, Pixie stores recent data directly on cluster nodes. Queries run against local storage, returning results in milliseconds. This reduces costs (no data egress charges) and keeps sensitive data in your cluster. Pixie uses PxL (Pixie Language), which resembles pandas DataFrames for familiar data operations.

Main Benefits:

Automatic tracing without code changes or SDK installation
Protocol visibility (HTTP, gRPC, PostgreSQL, MySQL, Redis, Kafka)
Edge storage for fast queries and low costs
PxL query language familiar to Python developers
Open-source with community edition available
Acquired by New Relic with continued development

5. Inspektor Gadget

Inspektor Gadget packages eBPF tracing tools as Kubernetes-native "gadgets" that you run with kubectl. Instead of SSH-ing into nodes to run BCC tools or bpftrace scripts, you target pods or namespaces directly from your terminal.

Inspektor Gadget automatically enriches trace data with Kubernetes context: pod names, namespaces, container IDs, and labels. When you trace DNS queries or TCP connections, results show which pod made them, not just process IDs. Run gadgets with kubectl syntax: kubectl gadget trace tcp monitors connections, kubectl gadget profile cpu profiles CPU usage.

Inspektor Gadget includes gadgets for tracing DNS, TCP, filesystem operations, and more. Each gadget wraps an eBPF program in a container-aware interface. You can develop custom gadgets by writing eBPF programs, packaging as OCI images, and deploying through the framework.

Main Benefits:

kubectl integration for running eBPF tools
Automatic Kubernetes context enrichment (pod, namespace, labels)
No SSH access to nodes required
Collection of ready-to-use gadgets
Framework for custom gadget development
CNCF sandbox project

6. Tracee

Tracee captures system events with eBPF for runtime security and forensics. Built by Aqua Security, it traces hundreds of kernel event types and applies security signatures to detect exploitation attempts and suspicious behavior.

Tracee records system calls, process events, file operations, and network activity with all arguments and return values. This creates a detailed audit trail for investigating security incidents or debugging complex issues. Event capture runs continuously with configurable filters to reduce volume—filter by container ID, process name, or event type.

Tracee includes detection signatures mapped to the MITRE ATT&CK framework. These identify privilege escalation techniques, container escapes, credential theft, and evasion tactics. Tracee can replay captured events to reconstruct incidents—which processes ran, files accessed, and network connections made.

Main Benefits:

Forensic-grade event tracing with full argument capture
Security signatures detecting known attack patterns
MITRE ATT&CK framework mapping
Event capture and replay for incident investigation
Container and Kubernetes context awareness
Open-source with commercial support available

7. Parca

Parca provides continuous profiling with eBPF, sampling stack traces across your entire infrastructure to show where CPU time goes. Unlike traditional profilers that you enable temporarily, Parca runs constantly with low overhead.

Parca's eBPF profiler samples at 19Hz by default, capturing stack traces from all processes system-wide. This creates a continuous record of what your code was doing, making it possible to investigate performance issues after they occur. The eBPF approach means no language-specific agents. Parca profiles Go, Rust, C/C++, Java, Python from a single collector.

Profile data compresses well, and Parca's storage format optimizes for this. Query profiles by time range, service, pod, or custom labels. Compare profiles across deployments to identify performance regressions or validate optimizations. Differential flamegraphs highlight exactly which functions consumed more or less CPU between two profiles.

Main Benefits:

Continuous CPU profiling with eBPF
Multi-language support without per-language agents
Low overhead suitable for always-on production profiling
Interactive flamegraph visualization
Differential profiling for before/after comparisons
Open-source project with active development

8. kubectl-trace

kubectl-trace lets you run bpftrace programs on Kubernetes nodes from kubectl. It handles deploying the bpftrace runtime, executing your script on the target node, and streaming results back to your terminal.

kubectl-trace eliminates the need for SSH access by packaging bpftrace as a Job that runs on specific nodes or against specific pods. You provide the bpftrace script, kubectl-trace handles deployment and cleanup. This makes powerful eBPF tracing accessible to anyone with kubectl access, even without node SSH keys. Target traces to specific pods, nodes, or namespaces using familiar Kubernetes selectors.

Main Benefits:

Run bpftrace programs through kubectl
No SSH access to nodes required
Target specific pods, nodes, or namespaces
Automatic deployment and cleanup
Integrates with Kubernetes RBAC
Open-source from the BCC/bpftrace community

Getting Started with eBPF Tracing

Here's a practical approach to adopting eBPF tools:

1. Check your kernel version
Run uname -r to verify you have at least Linux 4.9, preferably 5.x or newer. Most modern distributions meet this requirement.

2. Start with pre-built tools
Install BCC or Inspektor Gadget and run their ready-made utilities before writing custom eBPF programs. This builds intuition for what eBPF can do.

3. Learn bpftrace basics
Spend an hour trying bpftrace one-liners. The skill pays off when you need to investigate issues that existing tools don't cover.

4. Add instrumentation gradually
Don't instrument everything at once. Start with high-value targets: services with unknown performance characteristics, security-critical workloads, or systems you're actively optimizing.

5. Understand overhead characteristics
eBPF overhead is low but not zero. High-frequency events (like scheduler context switches) can impact performance if you trace every occurrence. Sample or filter intelligently.

eBPF-Based Observability Platforms

While the open-source tools above excel for hands-on debugging and investigation, production observability at scale often requires managed platforms that handle data storage, querying, alerting, and team collaboration.

Better Stack

Better Stack combines eBPF auto-instrumentation with OpenTelemetry to deliver end-to-end observability without code changes. It provides the low-level visibility of tools like BCC or Cilium, paired with managed infrastructure, unified dashboards, and built-in alerting.

Better Stack’s collector uses eBPF to automatically instrument Kubernetes and Docker environments. Deploy it once to start collecting distributed traces, logs, and metrics—no application restarts, code changes, or language-specific SDKs required.

Operating at the kernel level, eBPF captures network-level telemetry that reflects real application behavior. This includes HTTP requests with latency and status codes, database queries with execution times and parameters, gRPC calls, message queues, and cache interactions. The approach works for any application, including legacy or third-party systems. Better Stack automatically detects PostgreSQL, MySQL, Redis, Memcached, and MongoDB, exposing query performance, slow queries, and connection pool utilization without database agents.

Service dependency maps are generated by observing actual network traffic. These maps show how services communicate in production, including request volumes, latency distributions, and error rates. This makes hidden dependencies visible, highlights chatty services, and helps identify cascading failures in real time.

For logs, Better Stack provides real-time analysis with Live Tail, allowing you to filter by severity, search patterns, and follow user sessions as events happen.

Custom dashboards can be built using drag-and-drop tools or SQL queries, combining metrics, logs, and traces in a single view. Logs-to-metrics automatically extracts structured metrics from logs, reducing the need for manual instrumentation.

eBPF’s in-kernel aggregation significantly reduces data volume, and when paired with ClickHouse storage, enables distributed tracing at up to 30× lower cost than competitors like Datadog. Complete traces can be stored without sampling, and SQL or PromQL queries can be run across billions of spans with sub-second latency.

Main Benefits:

eBPF auto-instrumentation for Kubernetes and Docker
Automatic database monitoring without agents
Service maps generated from eBPF network traces
30x cheaper than Datadog with predictable pricing
OpenTelemetry-native for vendor flexibility
SQL and PromQL queries with sub-second response times
ClickHouse storage for cost-effective retention
AI-powered root cause analysis
Integration with Better Stack Uptime for comprehensive observability
Available in 4 regions with custom deployments
SOC 2 Type 2, GDPR, and ISO 27001 compliant
60-day money-back guarantee

The Future of eBPF

eBPF capabilities continue expanding with each kernel release. CO-RE (Compile Once, Run Everywhere) makes eBPF programs portable across kernel versions, eliminating the need for per-kernel compilation. BTF (BPF Type Format) provides rich type information that tools use for safer, more capable instrumentation.

Language ecosystems are embracing eBPF too. Go, Rust, and other languages now have eBPF libraries for writing programs without C. This lowers the barrier to custom eBPF development.

Cloud providers are building eBPF into their managed services. AWS, GCP, and Azure increasingly use eBPF internally for networking, security, and observability—and expose these capabilities to customers.

Final thoughts

eBPF transformed Linux observability by making kernel instrumentation safe, efficient, and accessible. The tools covered here—BCC, bpftrace, Cilium, Pixie, Inspektor Gadget, Tracee, Parca, and kubectl-trace—represent mature, production-tested solutions for performance analysis, troubleshooting, and security monitoring.

Start with the open-source tools that match your immediate needs. Use BCC or bpftrace for investigations, Cilium for Kubernetes networking visibility, or Inspektor Gadget for pod-level debugging. These tools are free, well-documented, and used daily by engineers at thousands of companies.

For teams requiring managed observability platforms with eBPF capabilities, Better Stack provides automatic instrumentation, unified dashboards, cost-effective storage, and AI-powered analysis. You get the deep visibility of eBPF tracing without operating your own collection infrastructure.

The investment in learning eBPF pays off through faster incident resolution, better performance optimization, and deeper system understanding. With tools becoming more accessible and kernels gaining new capabilities, there's never been a better time to adopt eBPF-based observability.

Got an article suggestion? Let us know

Explore more

14 Best AWS Monitoring Tools

Learn about the best AWS monitoring tools for 2026. Compare features, pros and cons, and choose the right tool for your needs.

10 Best Cron Job Monitoring Tools in 2026

Cron jobs are a programmer's helper in the automation of repetitive yet critical tasks in the life cycle of every program. That's why they should work as they are expected to. However, we know, that flawless program runs and fairies live in the same realm, and in order to keep the magic alive, we need to check our cron jobs on a regular basis. That is achievable with cron job monitoring.

11 Best Distributed Tracing Tools in 2026

Explore the top 11 distributed tracing tools for monitoring and troubleshooting microservices.

10 Best Linux Monitoring Tools in 2026

Linux is a go-to platform for most developers. However, Linux machines are often left paralyzed by their own design and monitoring can help you prevent it.