AgentPulse
Know when your AI agent fails — before users do
AgentPulse launched on May 15, 2026 with 1 votes.
What it does
AgentPulse is observability for AI agents in production. Track full execution timelines, step-level traces, failures, latency, and token costs across your entire agent workflow — not just individual API calls. Built-in secret detection, Slack alerts, weekly reports, and 2-line integration with LangChain, OpenAI, Anthropic, Gemini, and Bedrock.
AgentPulse - LLM Agent Observability Platform
Does a similar job
all alternatives →
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.
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.



AgentPulses – AI Agent ObservabilityFeb 2026 · agentpulses.com · ▲1Stop burning money blind on LLM API calls
More ai this month
the category →



Paintings by AI models: programs run through a simulation of oil paint.
AI · 20d ago · stillwet.art

PixVerse R2▲381A real-time world model you can explore and change
AI · 28d ago · world.pixverse.video
Source: Product Hunt launch ↗
Launched alongside, May 2026
the whole month →

Parallel agents, diff reviewer, and multi-model comparisons
Dev tools · May 2026 · kilo.ai



Hey HN, Henry here from Cactus. We open-sourced Needle, a 26M parameter function-calling (tool use) model. It runs at 6000 tok/s prefill and 1200 tok/s decode on consumer devices. We were always frustrated by the little effort made towards building agentic models that run on budget phones, so we conducted investigations that led to an observation: agentic experiences are built upon tool calling, and massive models are overkill for it. Tool calling is fundamentally retrieval-and-assembly (match query to tool name, extract argument values, emit JSON), not reasoning. Cross-attention…
Life & fun · May 2026 · github.com

🌱 Private, quiet space for thinking. Simple app for .md files. - zakirullin/files.md
Dev tools · May 2026 · github.com