Track agents with user intent and behavior, right where you code.
Bridge the gap between product analytics and LLM observability.
Kubit maps user behavior, intent, and sentiment directly to LLM traces, giving you the full context and skills needed to detect hallucinations, optimize performance, and solve issues right where you build.
The pain of non-deterministic AI
If you are building AI agents, you already know the context-switching nightmare. Standard observability tools tell what broke (latency, token limits, system errors), but they can’t tell you why the user was frustrated. To get the full picture, you have to manually stitch together clickstream data, prompt versions, and raw logs across six different tabs. It is slow, disjointed, and leaves you guessing.
Kubit fixes this.
We map how users behave directly to how your agents reason, feeding that context straight into Claude Code or Cursor via MCP. Instead of drowning in raw JSON traces, your coding agent instantly sees:
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User intent and sentiment
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Friction and delight signals
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Exact tool call trajectories
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User actions before and after agent interactions
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We give you the exact context you need to debug and ship reliable AI, right where you code.
Connecting the dots
Kubit isn't just another dashboard. It is an end-to-end Agent Analytics platform that feeds actionable insights directly into your workflow so you can debug, optimize, and self-fix without leaving your editor.
Conversation Intelligence
Go beyond simple LLM observability. Extract intent, sentiment, and resolution directly from your traces to understand the root cause of user frustration—insights that normally stay buried deep within raw JSON logs.
Total Visibility
Full context, all in one place. Correlate user behavior directly with your agent's reasoning chain. We stitch together clickstream events, LLM traces, tool calls, prompt versions, and user intent—so you can stop manually piecing the puzzle together across six different tools.
Actionable context, solve it where you build it
Turn unpredictable AI into a measurable product. Kubit feeds exact user intent and friction signals straight into Claude Code or Cursor via MCP, allowing your coding agents to trigger auto-fixes and optimize performance without ever opening a browser.
Built for the AI product engineer
Product Analytics for LLM Observability
Go beyond simple performance monitoring and error tracking. With Kubit, you get advanced, self-service analytics to discover patterns at scale. Track token costs and error rates by intent and friction signals, explore escalation funnels, and follow user retention by cohort before and after agent interactions.
Support Langfuse, OTel, Vercel and Langchain
Integrate seamlessly with all major LLM observability tools (Langfuse, Braintrust, LangSmith or Arize) and AI frameworks (Langchain, Vercel). With native OpenTelemetry support, Kubit does the heavy lifting for you and automatically applies industry best practices.
Conversation Intelligence Without Limits
Understand the why behind your metrics by analyzing the intent and sentiment of users interacting with your agent. Unlike expensive LLM-as-a-Judge or BYOM approaches, Kubit efficiently enriches 100% of your traces with zero sampling.
Coding Agent Integration and Self-Healing
The magic happens right where you code. Embedded directly into Claude Code and Cursor via MCP, Kubit equips your coding agents with the live context and skills needed to debug and auto-fix without ever opening a browser tab.
Stop guessing. Start optimizing.
Give your engineering team and your coding agents the skills and complete context they need to build, debug, and scale reliable AI products. Just install the Kubit Claude Code plugin and follow its lead.
