Snapshot · The MongoDB Podcast
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.
Where this was said
At 8:57 · 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.
Sam's initial MVP was coded in approximately one week using ChatGPT voice mode and copy-pasting code, with no prior technical experience.
Sam argues Discord is 10x better than email for building relationships with younger users who rarely check their inbox.
Sam's monthly operating costs include Cursor ($200), AI image generation ($100), AI video generation ($200), hosting ($100), email marketing ($80), and AI compute ($300–$500).
Sam recommends copying days of Discord chat history into ChatGPT and prompting it to list recurring pain points as a fast, free market research technique.
Bhanu and his team built approximately 50 free tools to attract search traffic, each linked back to SiteGPT.
With AI coding tools like Cursor, Bhanu can now create a new free marketing tool in less than 5 minutes by referencing existing tools.
Bhanu filters Ahrefs keyword results to show only those with a keyword difficulty below 10, making them realistic ranking targets for any decent website.
Bhanu sets a minimum search volume of 1,000 monthly searches when selecting keywords to target with free tools.
PropGPT averaged 20 downloads per day right after launching on the App Store through influencer marketing.
We use essential and analytics cookies to run Vuci. To understand how the site is used: Privacy Policy.
Install Vuci on your phone
Add it to your home screen for a faster, app-like experience.
Install Vuci on your phone
Tap the Share button, then “Add to Home Screen”.
A new version is available
Reload to get the latest Vuci.