TraceRoot.AI

Product Introduction

1. TraceRoot.AI is an AI-native, open-source observability platform that unifies logs, traces, metrics, source code, and team communications into a single interface. It automates root cause analysis and remediation by integrating directly with developer workflows through GitHub issue and pull request generation.
2. The core value lies in its ability to reduce debugging time by combining structured data analysis with AI-driven fixes, enabling engineering teams to resolve production issues faster while maintaining full visibility into their systems.

Main Features

1. TraceRoot SDK: Provides lightweight instrumentation for capturing structured logs and traces directly from codebases, supporting Python (via PyPI) and Node.js (via npm) with decorator-based tracing. The SDK automatically forwards data to TraceRoot’s cloud for analysis while preserving context like function parameters and execution paths.
2. Multi-Tool Integration: Connects with GitHub, Slack, Notion, and monitoring tools to unify codebases, team discussions, and operational data. This enables AI agents to correlate errors with recent code changes, RFCs, or Slack conversations for context-aware troubleshooting.
3. AI Remediation Agents: Deploys specialized AI agents that analyze traces and logs using large language models (LLMs), identify root causes, and autonomously generate GitHub issues or PRs with code fixes. Agents collaborate across platforms (e.g., fetching source code from GitHub, validating fixes via CI/CD pipelines) to implement solutions.
4. Structured Visualization: Offers flame graphs, dependency maps, and temporal trace visualizations with embedded error hotspots and LLM-generated annotations. Engineers can drill into specific traces to view correlated logs, metrics, and AI-generated hypotheses about failure paths.

Problems Solved

1. Manual Debugging Overhead: Eliminates hours spent correlating logs across disjointed tools and hypothesizing root causes by automating trace analysis and providing code-level fixes.
2. Target Users: DevOps engineers, SREs, and software development teams managing cloud-native applications with complex microservice architectures or frequent production incidents.
3. Use Cases: Resolving intermittent production outages caused by race conditions, diagnosing performance degradation in distributed systems, and addressing inconsistent logging practices across services.

Unique Advantages

1. Code-to-Remediation Automation: Unlike traditional APM tools (e.g., Datadog, New Relic), TraceRoot.AI closes the loop between observability and action by deploying AI agents that write and submit code fixes, not just surface alerts.
2. Open-Source SDK with Extensible Tracing: The SDK’s open-source design allows customization of trace sampling, log enrichment, and context propagation, while prebuilt integrations for frameworks like FastAPI and Express reduce setup time.
3. Compliance-Ready Enterprise Tier: The Startups and Growth plans offer SOC2, ISO27001, and HIPAA-compliant deployments with granular access controls, audit logs, and private LLM hosting options for regulated industries.

Frequently Asked Questions (FAQ)

1. What programming languages do you support? TraceRoot.AI currently supports Python and Node.js via PyPI and npm packages, with instrumentation libraries for Flask, Django, FastAPI, and Express. Java and Go SDKs are in active development, scheduled for Q4 2024 release.
2. Can I integrate TraceRoot.AI with existing monitoring tools? Yes, the platform ingests data from OpenTelemetry, Prometheus, and Grafana, and correlates it with traces/logs captured via its SDK. Webhooks and API endpoints allow bidirectional integration with incident management tools like PagerDuty.
3. Is there a free trial available? The Starter plan includes a 7-day free trial with full access to AI chat mode, 100k trace/log ingestions, and basic GitHub integration. No credit card is required for trial activation.
4. How does data security work? All data is encrypted in transit (TLS 1.3) and at rest (AES-256). Enterprise tiers support private VPC deployments, role-based access control (RBAC), and optional air-gapped storage for sensitive environments.
5. What support do you offer? All plans include 24/5 email and chat support, while Pro and higher tiers provide dedicated Slack channels with engineering response SLAs under 2 hours for critical issues.