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.

EOL Dataset by HeroDevs — Find every EOL dependency in your stack. Free. In 5 minutes.

Find every EOL dependency in your stack. Free. In 5 minutes.

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

EOL Dataset by HeroDevs — Find every EOL dependency in your stack. Free. In 5 minutes.

Find every EOL dependency in your stack. Free. In 5 minutes.

AgentLens

Full observability for AI agents. Zero code changes.

Full observability for AI agents. Zero code changes.