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Spanlens
Open-source LLM observability in one line of code
Spanlens records every OpenAI, Anthropic, Gemini, Azure, and Ollama call with one line of code. Get cost, latency, agent traces with Critical Path, anomaly alerts, PII scan, and model-swap suggestions out of the box. MIT, self-hostable, free tier that doesn't punish growth.
Most LLM observability tools ask you to restructure your code before you see anything useful. Spanlens doesn't. You swap your OpenAI client for createOpenAI from @spanlens/sdk and you're recording traces. Python works the same way with pip install "spanlens[openai]".
Welch t-test on the compare page for statistical significance
Works across OpenAI, Anthropic, Gemini, Azure OpenAI, Ollama, Vercel AI SDK, LangChain, and LlamaIndex
How we run it:
MIT licensed, one Docker image to self-host
Free cloud tier with a hard 429 at 50K requests per month, so a runaway dev loop can't bill you
Public changelog with Atom feed, plus a public status page
To be honest, we're early. If you need a mature eval framework with 30+ integrations today, other tools fit better. But if you want the core 80% to just work and you're tired of instrumenting before you can observe, give us 30 seconds.
“Open-source LLM observability in one line of code”
Spanlens was submitted on Product Hunt and earned 0 upvotes and 1 comments, placing #142 on the daily leaderboard. Spanlens records every OpenAI, Anthropic, Gemini, Azure, and Ollama call with one line of code. Get cost, latency, agent traces with Critical Path, anomaly alerts, PII scan, and model-swap suggestions out of the box. MIT, self-hostable, free tier that doesn't punish growth.
On the analytics side, Spanlens competes within Open Source, Artificial Intelligence and GitHub — topics that collectively have 580.8k followers on Product Hunt. The dashboard above tracks how Spanlens performed against the three products that launched closest to it on the same day.
Who hunted Spanlens?
Spanlens was hunted by Haeseong Jeon. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of Spanlens including community comment highlights and product details, visit the product overview.
Haeseong Jeon
Hey Product Hunt,
I'm Haeseong, the maker of Spanlens.
Most LLM observability tools ask you to restructure your code before you see anything useful. Spanlens doesn't. You swap your OpenAI client for createOpenAI from @spanlens/sdk and you're recording traces. Python works the same way with pip install "spanlens[openai]".
That's the whole setup.
What you get out of the box:
Cost, latency, and full agent span trees
Prompt version tracking and PII scanning
Critical Path analysis (which span actually dominated wall-clock time)
LangGraph topology view on trace detail pages
Welch t-test on the compare page for statistical significance
Works across OpenAI, Anthropic, Gemini, Azure OpenAI, Ollama, Vercel AI SDK, LangChain, and LlamaIndex
How we run it:
MIT licensed, one Docker image to self-host
Free cloud tier with a hard 429 at 50K requests per month, so a runaway dev loop can't bill you
Public changelog with Atom feed, plus a public status page
To be honest, we're early. If you need a mature eval framework with 30+ integrations today, other tools fit better. But if you want the core 80% to just work and you're tired of instrumenting before you can observe, give us 30 seconds.
Repo: github.com/spanlens/Spanlens
Docs: spanlens.io/docs
Changelog: spanlens.io/changelog
I'll reply to every comment today.
Haeseong