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AgentLens
Full observability for AI agents. Zero code changes.
AI agents in production lack the observability they need. When something breaks, developers have no trace of which call failed, what the model received, or why costs spiked. AgentLens solves this. Full session traces across every LLM call — costs, latency, errors, and prompt/completion logs. Framework-agnostic. Proxy-based integration requires zero code changes. Self-hostable via Docker Compose. TypeScript and Python SDKs included. MIT licensed.
Hey Product Hunt 👋
Developers building AI agents in production face a problem
most observability tools don't solve — visibility into full
agent runs, not just individual API calls.
AgentLens is built for that gap. The proxy approach means
complete observability with a single environment variable
change. No SDK required to get started. Works with OpenAI,
Anthropic, LangChain, LlamaIndex, or any custom agent.
What's included:
→ Full session trace viewer with span hierarchy
→ Cost analytics by model, agent, and date
→ Real-time live feed via WebSocket
→ Session replay for any past agent run
→ PII scrubbing before data leaves your infrastructure
→ Slack/email failure alerts
→ TypeScript + Python SDKs
Fully self-hostable. MIT licensed. No vendor lock-in.
Would love feedback from anyone running agents in
production — what visibility matters most to your team?
GitHub: https://github.com/farzanhossan/...
Docs: https://agentlens.techmatbd.com
About AgentLens on Product Hunt
“Full observability for AI agents. Zero code changes.”
AgentLens was submitted on Product Hunt and earned 0 upvotes and 1 comments, placing #38 on the daily leaderboard. AI agents in production lack the observability they need. When something breaks, developers have no trace of which call failed, what the model received, or why costs spiked. AgentLens solves this. Full session traces across every LLM call — costs, latency, errors, and prompt/completion logs. Framework-agnostic. Proxy-based integration requires zero code changes. Self-hostable via Docker Compose. TypeScript and Python SDKs included. MIT licensed.
On the analytics side, AgentLens competes within Open Source, Developer Tools, Artificial Intelligence and GitHub — topics that collectively have 1.1M followers on Product Hunt. The dashboard above tracks how AgentLens performed against the three products that launched closest to it on the same day.
Who hunted AgentLens?
AgentLens was hunted by Farzan Hossan Shaikat. 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 AgentLens including community comment highlights and product details, visit the product overview.
Farzan Hossan Shaikat