Logira
eBPF runtime auditing for AI agent runs
Details
- External ID
- 47211914
- Source
- HN
- Company
- —
- Product
- Logira
- Website domain
- github.com
- Launched
- March 1, 2026
- Cohort
- —
- Upvotes
- 26
- Upvotes percentile
- 0.7755227552275523
- Tags
- —
- Fetched at
- Sept. 7, 2026, 9:26 p.m.
- Updated at
- Sept. 7, 2026, 9:26 p.m.
Description
I started using Claude Code (claude --dangerously-skip-permissions) and Codex (codex --yolo) and realized I had no reliable way to know what they actually did. The agent's own output tells you a story, but it's the agent's story.logira records exec, file, and network events at the OS level via eBPF, scoped per run. Events are saved locally in JSONL and SQLite. It ships with default detection rules for credential access, persistence changes, suspicious exec patterns, and more. Observe-only – it never blocks.https://github.com/melonattacker/logira
Enrichment
- Theme
- Claude integrations and coding agents
- Vertical
- Security
- Function
- Observability & eval
- Audience
- Developer
- AI stance
- AI feature
- Project type
- Commercial product
- Normalized one-liner
- audit ai agent execution with ebpf
- Manually corrected
- False
Could you build this?
No Kernel-level tracing using eBPF programs to securely audit syscalls, network sockets, and file descriptors requires specialized Linux systems programming.
What it would actually take: The implementation requires writing specialized eBPF C programs loaded into the Linux kernel targeting tracepoints and kprobes (e.g., sys_enter_execve, openat, connect) with ring-buffer user-space exporters in Go (using cilium/ebpf) or Rust (Aya). The builder needs deep kernel architecture knowledge, understanding of eBPF verifier constraints, and Linux security auditing internals.
Discussion
3 comments analyzed.
Competitors mentioned: monolithic agent platforms, CodeLeash (self-reflection approach)
Concerns raised: agents can't be trusted to report their actual actions, reviewing agent logs is cognitively burdensome, auditing must be independent of audited system
Feature requests: Stop & PreCompact hooks for self-review, agent reflection against transcript logs, model-generated recommendations for safety improvements
Competitors
Other products that read as similar to this one — 132 launches clear the similarity bar, closest 8 shown.
Attention rank: #34 of 133 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).
Launched 110 days after the earliest competitor.
- NullRun · ph · 2026-09-16 · 1 upvotes · similarity 0.43
- AIOStack · hn · 2026-01-14 · 9 upvotes · similarity 0.43
- Claudescope · ph · 2026-09-17 · 1 upvotes · similarity 0.42
- ML condenses billions of logs into a tiny snapshot your LLM can debug · hn · 2026-06-17 · 13 upvotes · similarity 0.41
- I am running 3 coding agents non-stop over the last 3 days. Here is how · hn · 2026-06-13 · 10 upvotes · similarity 0.41
- K9 Audit · hn · 2026-03-12 · 5 upvotes · similarity 0.41
- Halo · hn · 2026-07-07 · 37 upvotes · similarity 0.41
- Policy enforcement for Claude Code, Cursor, and Codex · hn · 2026-07-09 · 13 upvotes · similarity 0.40
Other launches for this product
- No other launches for this product.
Same idea, different domain
Nobody's really built a observability & eval tool for Media & entertainment yet.