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Modern AIOps: What It Takes to Build Reliable AI Products
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The Origin Story: From Coinbase Blockchain Observability to Langtrace
At 7:52 · chapter starts 4:00
The Langtrace origin story is a classic case of a developer solving their own problem and discovering it was everyone else's problem too. Karthik and his co-founder left Coinbase in August 2022, raised a seed round from Redpoint Ventures, and built Scale3 — an observability platform for blockchain nodes. The challenge was formidable: blockchains lack the observability standards of traditional software, so the team had to adapt Web2 monitoring tools to a Web3 world. When ChatGPT launched during this period, excitement swept through the team. They built two LLM-powered features: a RAG chatbot over open-source blockchain documentation and a log-analysis assistant that explained errors in context. [1] — Karthik Kalyanamaran "Langtrace wasn't designed to monitor LLMs — it was built to fix hallucinating chatbot features inside a blockchain observability product. W…" 04:00 After launch, user feedback split sharply — some loved it, some said it was flat-out wrong and hallucinating. The team had no way to tell why. Being observability engineers at heart, they recognized the gap immediately: they needed eyes on the model. They duct-taped together an internal SDK based on OpenTelemetry, piped traces into Grafana, and within weeks were able to baseline performance and meaningfully improve accuracy. They shared this story with other AI developers, found immediate interest, and decided to open-source the tool. Langtrace went live in February 2024.
Langtrace wasn't designed to monitor LLMs — it was built to fix hallucinating chatbot features inside a blockchain observability product. When users reported the AI features were giving wrong answers, the team had zero visibility into what was going wrong, so they duct-taped an OpenTelemetry SDK together internally. That internal tool became Langtrace.
Langtrace can be added to any Python or TypeScript project with just 2 lines of initialization code, automatically patching libraries at runtime via OpenTelemetry.
While Karthik's team was building blockchain observability at Scale3, ChatGPT launched and they immediately started integrating LLM features, which led to the discovery that AI needed its own observability layer.
Langtrace was open-sourced in February 2024 after approximately two months of productionizing the internal observability SDK the team had built for their own AI features.
