Heron
Wireshark for AI Agents: passive eBPF observability
Heron launched on June 25, 2026 with 105 votes, #362 of 22,647 launches that month and more than 95% of that year's launches.
What it does
Heron is a passive network analyzer that reconstructs what your AI agents are actually doing. Zero SDKs. Zero proxy. Hook eBPF to see TLS-encrypted LLM calls and identify which agent process made them.
Agent and LLM API performance monitoring via network packet probe. Measures performance of OpenClaw, Claude, Codex, DeepAgents and more — deployed on the provider side, no SDK changes required. - Netis/heron
Does a similar job
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Raindrop2025 · raindrop.ai · ▲241Monitor your AI Agent the right way. Get alerted when your agent fails in production, trace exactly what went wrong, and prove your fix worked.
Alaz: Open-Source, Self-Hosted, eBPF-Based K8s Monitoring2023 · github.com · ▲101Hello Everyone, I'm excited to introduce a new open-source observability platform and would love to hear your feedback. We are aware that there are lots of open-source/commercial tools out there. However, we believe that monitoring the clusters and extracting actionable insights requires deep know-how about the tools/domain. We mainly focused on this problem. - Alaz is an eBPF agent installed on your K8s cluster as DaemonSet. Thanks to eBPF, Alaz collects traces directly from Linux kernels. This means there's no need for sidecars, instrumentations, or service restarts. - The UI not…
AI Observability by OpenObserveAug 2026 · openobserve.ai · ▲317OpenTelemetry-native observability for agents and LLMs
AgentSight – eBPF observability for AI agents, no code changesAug 2026 · github.com · ▲17
Progress AI ObservabilityAug 2026 · telerik.com · ▲168Hi HN! We’re the team behind Fiddler, and we recently built an Agent Cache feature. It works by capturing an LLM request/response pair and, once caching is enabled, serving subsequent matching calls locally so duplicate requests never hit the provider. It’s meant to reduce three pains that show up together in agent development: token cost, feedback-loop latency, and output non-determinism. We’d love to hear your thoughts.
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