Observyze
Trace, debug, and price every prompt — in real-time
Observyze launched on May 19, 2026 with 3 votes, #3,449 of 20,538 launches that month and more than 50% of that year's launches.
What it does
Observyze is the observability layer for AI teams. Most logging tools add 200ms+ overhead to every LLM request. Observyze’s proxy-first architecture adds under 100ms while capturing high-fidelity traces for prompts, tool calls, and agent workflows. Features include: → Token-level cost attribution → Automated guardrails with Redis-backed circuit breakers → Prompt A/B testing → Regression detection Two lines of code. No infra to manage. 🎉 40% launch discount automatically applied at checkout.
Trace production AI agents, monitor LLM cost and latency, enforce execution budgets, runtime policies and circuit breakers. Explore Observyze free during Early Access.
Trace every LLM call, enforce runtime limits, and investigate failures before they become expensive production incidents. Instrument in-process with the lightweight SDK without being in the provider request path, or route traffic through the proxy gateway to actively block prompt injections and enforce circuit breakers. Point a supported OpenAI-compatible client at the Observyze proxy, provide your project ID, and send a test request using the documented authentication headers. import os from openai import OpenAI # Authenticate with an Observyze project API key client = OpenAI( api_key =os.environ[ "OBSERVYZE_API_KEY" ], base_url = "https://api.observyze.com/api/v1/proxy/openai/v1" ,…from observyze.com
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AI Observability by OpenObserveAug 2026 · openobserve.ai · ▲317OpenTelemetry-native observability for agents and LLMs

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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