https://github.com/amitmishrg/agenticlens
Visual debugging, tracing, and replay for agent workflows.
https://github.com/amitmishrg/agenticlens
agent-debugging agent-workflows agentic-ai ai ai-agents ai-observability debugging-tools developer-tools devtools execution-tracing jsonl llm log-visualization nodejs observability reactjs tracing visualizations workflow-visualization
Last synced: 5 days ago
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Visual debugging, tracing, and replay for agent workflows.
- Host: GitHub
- URL: https://github.com/amitmishrg/agenticlens
- Owner: amitmishrg
- Created: 2026-03-25T10:29:33.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2026-03-27T11:28:17.000Z (3 months ago)
- Last Synced: 2026-04-30T22:08:05.424Z (about 1 month ago)
- Topics: agent-debugging, agent-workflows, agentic-ai, ai, ai-agents, ai-observability, debugging-tools, developer-tools, devtools, execution-tracing, jsonl, llm, log-visualization, nodejs, observability, reactjs, tracing, visualizations, workflow-visualization
- Language: JavaScript
- Homepage: https://www.agenticlens.in
- Size: 2.91 MB
- Stars: 1
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# AgenticLens
See inside your AI agents.
AgenticLens is a developer tool for visualizing and debugging AI agent workflows from JSONL logs.
It converts raw traces into an interactive workspace with flow graphs, event trees, timelines, replay controls, and step-level inspection.
> **Beta:** AgenticLens is currently in beta.
> If you find a bug or unexpected behavior, please report it in [GitHub Issues](https://github.com/amitmishrg/agenticlens/issues).
## Why AgenticLens
Agent runs are hard to reason about from raw logs alone:
- Flows are implicit
- Nested tool chains are noisy
- Latency and token issues are easy to miss
AgenticLens helps you:
- Understand reasoning and tool usage
- Visualize full execution flow
- Detect slow or high-token steps quickly
- Replay sessions step by step
- Inspect each event with metadata and raw JSON
## Quick Start
Run directly with npx:
```bash
npx agenticlens path/to/logs.jsonl
```
The app starts and opens in your browser.
You can also launch and upload files/folders directly in the UI.
## Product Demo
Watch the full walkthrough:
- [AgenticLens Demo Video (MP4)](https://agenticlens-assets.vercel.app/gallery/demo.mp4)
[](https://agenticlens-assets.vercel.app/gallery/demo.mp4)
## Screenshots
### Flow Canvas
Visual graph of each run with connected steps, slow/high-token badges, and clear transitions between queue ops, user turns, thinking, tools, and results.
### Event Tree
Hierarchical breakdown of events and sub-steps so you can expand branches and inspect execution structure in context.
### Timeline
Chronological event stream with timing deltas and performance signals for debugging ordering and latency gaps.
### Flow + Inspector
Flow canvas paired with inspector details (metadata + raw JSON) so topology and payload stay aligned.
## Core Features
- **Flow View**: graph of execution steps and transitions
- **Tree View**: nested event hierarchy
- **Timeline View**: ordered event feed with deltas
- **Replay**: step through session progression
- **Session Summary**: totals for time, tokens, steps, nodes, and issues
- **Inspector Panel**: detailed event metadata and raw JSON
- **Anomaly Signals**: slow spans, token-heavy steps, bottlenecks
## Supported Logs
Current:
- Claude Agent SDK JSONL logs
Planned:
- OpenAI Agents
- Custom agent frameworks
- SDK-based integrations
## Use Cases
- Debugging agent workflows
- Understanding reasoning and tool usage
- Optimizing latency and cost
- Comparing different runs
- Building more reliable AI systems
## Roadmap
- SDK integration (`agenticlens.init()`)
- Chrome DevTools extension
- Multi-provider support
- Run comparison
- Cloud dashboard
## Local Development
```bash
npm install
npm run build
node cli/index.js sample-logs.jsonl
```
## License
MIT