TraceFlowLens
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AI workflow worked yesterday.Why is it different today?

TraceFlowLens records, replays, and compares AI agent executions to pinpoint what changed across development and production.

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  • Local-first
  • Zero runtime dependencies
  • Push is opt-in
  • Privacy by design
Yesterday (Original)ChangeToday (Replay)User querySameRetrieve contextChangedModel callChangedGenerate answerChangedTool callChanged

Execution diverged at "Retrieve context".TraceFlowLens shows exactly what changed.

Why logs aren't enough

Logs show that something happened. TraceFlowLens shows what changed.

Logs tell you

  • Request succeeded
  • Status code
  • Duration
  • Tool executed
What happened.

TraceFlowLens helps show

  • Which execution step changed
  • Which tool or payload differed
  • Where the execution diverged
  • Why two runs behaved differently
What changed.

How TraceFlowLens works

Record. Replay. Compare. Verify.

Understand AI execution changes in four simple steps.

1

Record an execution

Capture prompts, tool calls, inputs, outputs, metadata, and errors.

2

Replay the recorded execution

Run the same execution again from the recording.

3

Compare both runs

Find exactly where the execution started behaving differently.

4

Verify the fix

Replay again to confirm the issue is resolved.

From development to production

The same debugging workflow across development and production.

Development

Build with confidence

RecordCapture executions during development and testing.
ReplayReplay the recorded execution after code changes.
CompareCompare both executions to find differences.
VerifyVerify the fix before deployment.
OutcomeCatch issues before deployment.
Same workflow

Production

Detect. Diagnose. Resolve.

RecordCapture production executions.
ReplayReplay the recorded execution locally.
CompareCompare with the original execution to find the root cause.
VerifyVerify the fix before redeployment.
OutcomeResolve production issues faster.

One workflow. Every environment.

TraceFlowLens uses the same execution debugging workflow across development, staging, and production.

Built for AI applications

One execution debugging workflow. Any AI application.

Multi-agent systems

Understand agent handoffs, tool calls, and execution differences across complex AI workflows.

RAG applications

Debug retrieval, prompt context, and generated responses across retrieval pipelines.

AI SaaS products

Understand execution differences behind AI-powered SaaS features.

AI infrastructure

Debug model gateways, tool orchestration, and execution pipelines.

Platform engineering

Build reliable internal AI platforms with consistent execution debugging.

AI developer tools

Build AI SDKs, frameworks, libraries, and developer tooling with replay and comparison.

AI operations (AIOps)

Investigate AI execution failures, regressions, and runtime behavior.

AI governance

Investigate AI decision changes, compare execution history, and understand why behavior changed.

One execution workflow. Any AI application.

Whether you are building agents, RAG systems, AI infrastructure, or AI-powered SaaS products, TraceFlowLens helps engineers understand what changed and why.

Works with today's AI development stack

Works with existing AI stacks. No lock-in. Just execution debugging.

OpenAI SDK

Record, replay, and compare OpenAI SDK executions.

Anthropic SDK

Record, replay, and compare Anthropic SDK executions.

Custom AI workflows

Record, replay, and debug custom AI workflows.

Local-first

Execution data stays local by default.

Privacy by design

Full control over prompts, outputs, traces, and tool data.

Push is opt-in

Nothing is uploaded unless explicitly pushed.

Zero runtime dependencies

No TraceFlowLens runtime services required.

Works alongside existing AI tools.

Complements existing AI tooling instead of replacing it.

Apply for early access

Stop chasing logs. Start replaying executions.

Traditional debugging tells you what happened. TraceFlowLens lets you see how it happened.

Traditional debugging

  • Search logs
  • Read stack traces
  • Guess the root cause
  • Reproduce manually
  • Debug after failures
  • Piece together evidence manually
RunLogsSearchGuessRetryRepeat
Slow. Manual. Hard to reproduce.

TraceFlowLens workflow

  • Replay any recorded execution
  • Compare any two runs
  • Step-by-step execution timeline
  • Verify fixes before deployment
  • Local-first debugging
  • Push is opt-in
RunRecordReplayCompareTimelineVerify
Replay. Compare. Verify.

Record. Replay. Compare. Verify.

Spend less time debugging. More time building AI.

Apply for early access

Ready to stop chasing logs?Start understanding AI executions.

Join early access for AI execution debugging.

Apply for early access

Early access is available for selected users.

Why early access?

Local-first

Execution data remains local by default.

Privacy by design

Execution data remains local unless explicitly pushed.

Push is opt-in

Execution data is shared only when explicitly pushed.

Early access

Early access feedback guides product direction.