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TraceRoot.AI is an AI-native observability tool that automates bug fixing by analyzing telemetry data, source code, and repository history.
Key Features:
- AI agents for bug fixing
- OSS integration with leading frameworks
- Comprehensive visualizations
- Multi-agent collaboration
- Custom integration options
Use Cases:
- Debugging production issues
- Integrating with development tools
- Analyzing error logs
- Visualizing code performance
- Collaborating on issue resolution
Benefits:
- Faster bug resolution
- Improved debugging efficiency
- Enhanced team collaboration
- Streamlined development workflow
- Increased code reliability
Capabilities
- Ingests traces and logs via TraceRoot SDK (Python and TypeScript) to Cloud or self-hosted instance
- Visualizes execution traces, spans, and log clusters in a trace explorer UI
- Runs AI agents that analyze traces, logs, metrics and repository context for root-cause reasoning
- Produces natural-language root-cause summaries and execution-tree explanations in chat/agent UI
- Generates GitHub issues and drafts or creates GitHub PRs with suggested code changes
- Auto-triages production errors by detecting recent error logs and surfacing prioritized incidents
- Correlates Slack conversations and Notion docs with telemetry to add investigative context
- Supports bring-your-own LLMs (OpenAI, Anthropic) and local models for agent reasoning
- Provides self-host deployment via one-line Docker script; UI serves at localhost:3000, API at :8000
- Publishes open-source SDKs and examples on PyPI and npm for instrumenting apps and multi-agent flows
- Allows selecting spans or trace paths to guide agents and focus analysis on specific execution paths
- Operates multi-agent workflows (GitHub agent, Slack agent, Notion agent) that collaborate on fixes
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