AI-Powered Production Debugging

Debug production failures with AI.

PathFlow investigates production errors, traces execution paths, isolates root causes with evidence, and helps you get to a fix faster.

Open PathFlow See how it works

✓ No credit card required·Connects via Python SDK, REST API, or OpenTelemetry

Investigation #8492500 Error

412ms $0.004production (iad1)

POST/api/checkout/process

TypeError: Cannot read properties of null (reading 'customer_id')

Span Execution HierarchyLatency & Status

1. AuthMiddleware.verifySessionJWT Auth

18ms

2. CartService.calculateTotalsPostgreSQL

45ms

3. StripeCustomerLookupCRITICAL FAILURE

184ms

4. PaymentIntent.createSkipped

Production Application UI

Real engineering tooling for production agents.

Explore high-resolution captures of the live PathFlow platform—from real-time execution waterfall traces to automated root cause investigations.

https://thepathflow.online/app/runs

Runs Dashboard

Click to enlarge full preview

Real-Time Execution Runs & Token Economics

See every production execution in one place. Monitor status, model family, duration, token usage, and quality scores at a glance.

Explore Interactive Run

Built around the signals engineers already use to debug production

Runtime Logs

Structured errors & console streams

Traces

Waterfall spans & critical paths

Code Context

AST traversal & function scopes

Git Commits

Recent diffs & code regressions

Deployments

Release tags & runtime configs

The Problem

Production failures rarely have one obvious cause.

When something breaks in production, context is scattered across half a dozen tools.

An alert tells you something broke.

PagerDuty or Slack notifies you of an incident.

Logs tell you what was recorded.

Grepping megabytes of stdout across containers.

Traces tell you what executed.

Browsing disconnected waterfall spans.

Git tells you what changed.

Reviewing recent PRs and deployments manually.

Someone still has to connect the dots under incident pressure.

PathFlow connects that context into a single investigation.

Instead of jumping between disconnected dashboards, PathFlow automatically correlates runtime telemetry, execution paths, and recent code changes to determine exactly why your system failed.

Zero guesswork•Evidence-backed•Direct to fix

The Workflow

From production failure to root cause.

How PathFlow isolates issues step-by-step.

01 — Trigger

Production failure happens

An unhandled exception, HTTP 500, or tool timeout triggers automatic telemetry capture.

Ingress capture

02 — Investigate

Collects relevant context

Captures runtime stack frames, variable state, span latencies, and deployment metadata.

Context correlation

03 — Trace

Reconstructs execution path

Builds the step-by-step DAG of function calls, database queries, and external API requests.

Path reconstruction

04 — Explain

Isolates root cause

Identifies the exact bug with verifiable telemetry evidence and confidence ratings.

Evidence reasoning

05 — Fix

Moves toward a fix

Proposes the code patch or configuration adjustment needed to resolve the incident permanently.

Actionable patch

Interactive Demo

See what happened, not just that it failed.

Switch between real production scenarios to see how PathFlow diagnoses incidents.

POST/api/checkout/process(500 Internal Error)

Uncaught null customer reference in payment handler

⏱ 412ms💰 $0.004

AuthMiddleware.verifySessionMIDDLEWARE

18ms

CartService.calculateTotalsDB_QUERY

45ms

StripeCustomerLookupEXTERNAL_APINULL_CUSTOMER_ID

184ms

∅

PaymentIntent.createPAYMENT_GATEWAY

165ms

Capabilities

Engineered for rapid production diagnosis.

Everything you need to turn raw failures into actionable fixes.

Understand every execution

See what happened across the entire request or workflow. Trace complex multi-step pipelines from initial HTTP request to internal function calls.

Full execution graph

Investigate failures automatically

Bring logs, traces, errors, stack frames, and runtime variable context together into a unified incident investigation without manual log writing.

Unified context capture

Find the root cause

PathFlow correlates evidence and isolates the exact failure point with high confidence, distinguishing symptoms from the true root cause.

Evidence-backed diagnosis

Understand recent changes

Connect runtime failures directly with recent Git commits, code migrations, and deployment revisions that introduced the regression.

Git & deployment diffing

Reconstruct failures step-by-step

Turn production exceptions into concrete debugging paths you can inspect, step through, and compare with successful baseline runs.

PR-style run comparison

Get to a fix faster

Move directly from diagnosis toward an actionable code change with concrete diff patches ready for developer review and verification.

Direct patch proposals

Integration

Works with the systems your team already runs.

Zero migration needed. PathFlow instruments your Python microservices, FastAPI backends, or agentic frameworks in 2 lines of code.

Python SDKLangChainCrewAIAutoGenOpenTelemetryREST API

app.py

import pathflow

# 1. Initialize PathFlow client
pathflow.init(
    api_key="pf_live_secret_key",
    endpoint="https://thepathflow.online/app/api/v1"
)

# 2. Trace critical execution paths automatically
@pathflow.trace()
def process_checkout(user_id, cart):
    return billing_service.charge(user_id, cart)

pip install pathflowZero overhead runtime wrapper

Comparison

Observability tells you something is wrong.
PathFlow helps you debug why.

Traditional dashboards aggregate metrics. PathFlow reconstructs individual failures with code context.

Traditional Observability PathFlow AI Debugger

Alert fires (PagerDuty / Slack)Incident captured automatically with full stack state

Search through disconnected logs manuallyCorrelated execution path mapped from entry point

Inspect raw distributed waterfall spansCritical path & bottleneck highlighting

Browse Git history to guess regressionsDirect commit & AST diff correlation

Guess the cause under incident pressureEvidence-backed root cause with confidence score

Manual trial-and-error debuggingMove directly toward verified patch

Who It Is For

Built for the people who own production.

Designed for developers and engineering teams operating real production systems.

Backend Engineers

Triage elusive 500s, database deadlock spikes, and external service timeout cascades without manual reproduction steps.

Full-Stack Teams

Connect user-facing frontend error reports directly to the downstream API call and database query that caused the breakdown.

AI & Agent Teams

Inspect multi-step LLM chains, diagnose tool schema mismatches, prompt drift, token cost spikes, and execution loops.

Startup Teams

Slash incident MTTR so your small team can spend time building customer features rather than digging through log archives.

Transparent INR Pricing

Simple, transparent plans in Indian Rupees.

Start free for personal prototypes. Upgrade seamlessly with PhonePe Payment Gateway Standard Checkout.

Free Developer

For personal projects, solo experimentation, and small prototypes.

₹0 / month

1 Active Project
500 Runs / month
7-day Trace Retention
Basic Error Logging & Spans
Python & TypeScript SDKs
Community Discord Support

Start Free

Pro Engineer

For developers and creators running autonomous agents in production.

₹2,499 / month

Unlimited Projects
10,000 Runs / month
30-day Trace Retention
AI Root Cause Investigation
Automated Failure Detections
Git Diff & Commit Attribution
Priority Email & Chat Support

Upgrade to Pro →

⚡ Instant PhonePe Checkout

Engineering Team

For engineering teams debugging complex multi-agent workflows together.

₹7,999 / month

5 Included Team Seats
50,000 Runs / month
90-day Trace Retention
Real-time Incident Webhooks & Slack Alerts
Multi-Agent Comparative Profiler
Custom Alerting Thresholds
Dedicated Slack Channel Support

Upgrade to Team →