Glassbrain
Glassbrain provides a visual debugging tool that allows developers to quickly identify, replay, and fix AI-powered application bugs using interactive trace trees.
At a Glance
- AI Startups
- Software Engineers
- Enterprise AI Teams
- LLM Application Developers
AI Tools by Glassbrain
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Glassbrain
Visual AI Chain Debugger
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Latest News
Glassbrain launches on Product Hunt with 78+ upvotes in first week.
Hugo Barra joins as key advisor to support AI agent observability.
Glassbrain raises $7.4M Seed round led by Decibel VC.
Released 'Time-Travel Replay' for instant LLM debugging.
Products & Services
A visual debugging platform for AI applications that captures every step of an LLM call as an interactive tree.
Market Position
Positions itself as a developer-first alternative to LangSmith and Arize AI, emphasizing speed (30-second fixes) and visual 'time-travel' debugging over simple log monitoring.
Leadership
Founders
Sai Ram Muthineni
Former Penetration Tester (OSCP, CRTP) and security researcher. Previously founded AgentPier, an AI-driven industrial supplier discovery platform.
Executive Team
Sai Ram Muthineni
Founder & CEO
Security engineer and entrepreneur with expertise in AI-driven procurement and penetration testing.
Hugo Barra
Key Advisor / Executive
Former VP of VR at Meta, VP at Xiaomi, and VP of Product for Android at Google.
Board of Directors
Founding Story
Built after experiencing the frustration of debugging complex AI reasoning chains with traditional text logs. The founders wanted a 'time-travel' debugger that could visualize LLM traces and allow for instant re-runs with modified inputs.
Business Model
Revenue Model
SaaS subscription with tiered usage-based plans.
Pricing Tiers
1,000 traces/mo, 5 replays, 10 AI fix suggestions, 24-hour retention.
50,000 traces/mo, 30-day retention, unlimited replays and fix suggestions.
200,000 traces/mo, 60-day retention, 5 team members, shareable links.
500,000+ traces/mo, 90-day retention, 15+ members, CI/CD, SSO.
Target Markets
- AI Startups
- Software Engineers
- Enterprise AI Teams
- LLM Application Developers
- Debugging incorrect LLM responses
- Identifying performance bottlenecks and rate-limit retries
- Optimizing system prompts and temperature parameters
- Collaborative troubleshooting across engineering teams
- Production AI bug fixing without redeployment
- Indie hackers
- Solopreneurs
- Stealth AI startups