Netra

Netra

5.0 out of 5 stars
Kayba Netra

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Kayba:

About

Kayba makes AI agents self-improve from experience. It learns from an agent’s execution traces to detect failures, fix them, and measure whether the fix actually worked. Instead of relying on generic evals that cannot explain why an agent failed, Kayba derives failure modes from the agent’s own traces and builds custom benchmarks for the user’s domain, so teams can measure improvement against real production failure patterns. Kayba wires tracing into an agent with one line of setup, watches it around the clock, and flags the moment a step stops being recorded. Even good tracing rots as teams ship changes, and steps can quietly stop being captured; Kayba checks the tracing users already have, shows exactly what is broken, points to the file that needs attention, and sends the gap to a coding agent through MCP. The coding agent patches the issue, and Kayba verifies that the trace is actually closed.
Netra:

About

AI agents fail silently in production. Wrong answers, broken loops, cost spikes, behavior drift after a prompt change, and no stack trace to explain why. Netra is the single platform to trace, evaluate, simulate and security test your agents. Built for text, voice, image and video agents. SOC2 Type II certified. GDPR and HIPAA compliant. US and EU data residency. Integrates with: LangChain, LangGraph, CrewAI, LlamaIndex, OpenAI, Anthropic, Gemini, AWS Bedrock, and 30+ more.
Kayba:

Platforms Supported

Windows Not Supported
Mac Not Supported
Linux Not Supported
Cloud Supported
On-Premises Not Supported
iPhone Not Supported
iPad Not Supported
Android Not Supported
Chromebook Not Supported
Netra:

Platforms Supported

Windows Not Supported
Mac Not Supported
Linux Not Supported
Cloud Supported
On-Premises Supported
iPhone Not Supported
iPad Not Supported
Android Supported
Chromebook Not Supported
Kayba:

Audience

AI development teams that need to diagnose agent failures, generate reviewable fixes, and track whether those fixes improve performance over time
Netra:

Audience

Teams building AI agents and Agentic AI products
Kayba:

Support

Phone Support Not Supported
24/7 Live Support Not Supported
Online Supported
Netra:

Support

Phone Support Not Supported
24/7 Live Support Not Supported
Online Supported
Kayba:

API

Offers API Not Supported
Netra:

API

Offers API Supported
Kayba:

Screenshots and Videos

Netra:

Screenshots and Videos

No images available
Kayba:

Pricing

Free
Free Version Supported
Free Trial Not Supported
Netra:

Pricing

$39/month
Free Version Not Supported
Free Trial Supported
Kayba:

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

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Netra:

Reviews/Ratings

Overall 5.0 / 5
ease 5.0 / 5
features 5.0 / 5
design 4.0 / 5
support 5.0 / 5
Netra:

Pros & Cons from Real Users

Pros

  • ==Simulation mode lets you stress-test agents before production. You can spin up multi-turn conversations with defined personas and goals, auto-evaluated against fact-checking criteria you set. Instead of manual testing or hoping users find the edge cases, you run realistic scenarios and see where agents fail. Saves weeks of "what if" testing. Evaluation framework is actually repeatable. Not just pass/fail. You're measuring model quality, reasoning accuracy, tool correctness, output reliability—with metrics that stick across runs. That means you can compare versions, track improvements, and catch regressions before they hit users. Alerting catches problems you'd otherwise miss. Cost spikes, performance drops, safety violations, weird agent behavior—real-time notifications instead of discovering issues in production. That's table stakes for shipping anything at scale. Works across the whole ecosystem. OpenTelemetry standards, integrations with OpenAI, Claude, Anthropic, vector databases, LangChain, LlamaIndex. Not locked into one vendor. Multi-tenancy for SaaS applications. Voice traces for voice pipelines too, if that's your stack. Netra handles both text and voice traces end-to-end. Text captures LLM calls, tool executions, vector searches. Voice pipelines trace the full chain: speech-to-text, reasoning, text-to-speech. One platform for observing how your agents think and communicate, no matter the modality. That's rare.

Cons

  • SDK integration is non-negotiable overhead. You can't just drop this in and observe magically. Netra requires explicit instrumentation across your codebase. Multi-language support helps, but you're still looking at integration work across services. That's friction most teams don't have budget for on day one. Less public mindshare than competitors. Datadog, New Relic, LangSmith get more air time. Netra doesn't have the same community gravity or case study library. Fewer public comparisons, less blog coverage, smaller forum. That means fewer answers when you hit problems and harder time recruiting help from people who already know the tool. The documentation is excellent and so is the customer support
Kayba:

Training

Documentation Supported
Webinars Not Supported
Live Online Not Supported
In Person Not Supported
Netra:

Training

Documentation Supported
Webinars Not Supported
Live Online Not Supported
In Person Not Supported
Kayba:

Company Information

Kayba
Founded: 2025
United States
kayba.ai/
Netra:

Company Information

Netra
Founded: 2025
getnetra.ai/
Kayba:

Alternatives

Netra:

Alternatives

LangChain

LangChain

5.0 out of 5 stars
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Kayba:
Netra

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5.0 out of 5 stars
Netra:
Kayba:
Netra:
Traccia

Traccia

Algen AI
5.0 out of 5 stars
Kayba:
Netra:
PRISM

PRISM

Block Convey
Kayba:

Categories

Netra:

Categories

Kayba:

Integrations

CrewAI Not Supported
LangChain Not Supported
LlamaIndex Not Supported
Model Context Protocol (MCP) Supported
OpenTelemetry Not Supported
Netra:

Integrations

CrewAI Supported
LangChain Supported
LlamaIndex Supported
Model Context Protocol (MCP) Not Supported
OpenTelemetry Supported
Kayba:
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Netra:
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