Explore/observability/In-IDE Toolkit for Developers of AI-Based Features
I

Yaroslav Sokolov, Yury Khudyakov, Lenar Sharipov, Andrei Gasparian, Parth Tiwary, Artem Trofimov/In-IDE Toolkit for Developers of AI-Based FeaturesUnknown

AI-enabled features built on LLMs and agentic workflows are difficult to test, debug, and reproduce, especially for product-focused software engineers without a machine learning background. We present the AI Toolkit plugin for JetBrains IDEs, which brings tracing and evaluation directly into the Run/Debug loop. A mixed methods study with practitioners presents three consistent needs: (1) make evaluation regular and repeatable, (2) expose traces at the moment of execution, and (3) minimize setup and context switching. Guided by these needs, the AI Toolkit introduces an IDE-native workflow: run-triggered trace capture; immediate, hierarchical inspection; one-click "Add to Dataset" from traces; and unit-test-like evaluations with pluggable metrics. The first release in PyCharm shows promising early signals - strong conversion when promoted at Run, sustained usage among those who capture traces, and low churn - suggesting that IDE-native observability lowers activation energy and helps developers adopt disciplined practices. We detail the design and implementation of the AI Agents Debugger and AI Evaluation, report initial adoption telemetry, and outline next steps to broaden framework coverage and scale evaluations. Together, these results indicate that integrating AI observability and evaluation into everyday IDE workflows can make modern AI development accessible to non-ML specialists while preserving software-engineering practices.

observability
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AgentHub Score
55 / 100
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Active project
55Score
Growth
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Activity
30C
Documentation
70C+
Maturity
45C
Community
42C
Production
58C
GitHub stars · 77 days observed0 not enough history
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Repository activity · 77 days observedReal snapshots from pushed_at
inactivepushed
2026-07-262026-10-12
Practical assessment
Should you use it?

✓ Best for

  • Agent tracing in production
  • LLM cost tracking
  • Debugging agent failures

◎ Strengths

  • Integrates with major frameworks
  • OpenTelemetry compatible

✕ Not ideal for

  • Small prototype projects
  • Teams without existing observability culture

⚠ Watch-outs

  • Data retention costs at scale
  • Privacy implications of storing LLM traces
Technical details
What's inside
Language—
License—
Sourcearxiv
Open source✗ No
Commercial use—
Docs—
Demo—

AgentHub Score

55
Score 55/100
Below average

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