Jaeger

Jaeger (https://www.jaegertracing.io/) is an open-source, distributed tracing platform designed to monitor and troubleshoot microservices-based systems. Jaeger-UI is the web UI for Jaeger, built with React (https://react.dev/) . The current version of React used by Jaeger-UI is v18. This project aims to upgrade Jaeger-UI to React v19, in order to stay up to date with the dependencies. Expected Outcome: A working version of Jaeger-UI upgraded to React v19, with all the features and functionality of the current version. This may include a need to upgrade or replace some other dependencies that may not be compatible with the latest React.

Jaeger UI is built on React. While we are seemingly already on v18.x of React, the upgrade was not done across the board and some other dependencies are still lagging behind, e.g. `"@types/react": "16.8.7"`. It's also blocking upgrades of other dependencies. This project is likely to involve a substantial amount of code contribution, as certain upgrade require fixing the code to use the new APIs, and sometimes we may run into dependencies that are EOL and need to be replaced altogether. - Expected Outcome: Ideal outcome is to have _all_ dependencies upgraded to the latest versions (with the help of @dependabot) and fix all deprecation warnings during the build. But incremental progress towards that goal is also acceptable.

This project will create a Kubernetes deployment for the full stack: the OpenTelemetry Demo application, the Jaeger backend components (Collector, Query), and OpenSearch as a storage backend. The environment will be automatically redeployed weekly to ensure it is always fresh and to solve the problem of data retention. The entire stack will be hosted on a managed Kubernetes cluster (Oracle Kubernetes Engine) within an Oracle Cloud environment generously donated to the project. Expected Outcome: 1. A working, publicly accessible Jaeger demo featuring the OpenTelemetry Demo application, deployed on Oracle Cloud. 2. Fully automated, weekly deployments using the existing Helm- based automation and GitHub Actions. 3. The entire environment defined as "Configuration as Code". 4. A secure deployment following best practices for public-facing services. 5. The UIs for Jaeger, the OTel Demo, the load generator, and OpenSearch Dashboards exposed -publicly via the existing demo.jaegertracing.io URL. 6. Public-facing documentation on the Jaeger website explaining the demo architecture and linking to the automation code.

<p>Distributed tracing is essential for GenAI observability, but observing AI Agents differs fundamentally from observing microservices. While microservice traces focus on network latency and error codes, AI traces focus on the "reasoning process." The current Jaeger UI displays traces in a standard waterfall timeline, making it difficult to distinguish between logical "Tool Calls" and technical operations, and nearly impossible to read multi-paragraph prompts or view generated images. This project aims to transform the Jaeger UI into a first-class GenAI observability tool. When Jaeger detects a trace containing GenAI-specific metadata (following OpenTelemetry semantic conventions), it should automatically adapt its presentation to prioritize the "Agentic Flow."</p><p><br></p><p>Expected Outcome:</p><p><br></p><ul><li>Automatic "GenAI Mode" detection: detect `gen_ai.*` attributes and offer/switch to a specialized visualization mode</li><li>Agentic hierarchy with iconography: use distinct icons to differentiate span types (e.g., "brain" for LLM calls, "wrench" for tool calls, "database" for RAG retrieval)</li><li>Rich-media side panel leveraging ADR-0006 side-panel capability, with Markdown support for prompts and completions, pretty-printed JSON for tool inputs/outputs, and direct rendering of images/audio previews</li><li>Simplified "logical" view: toggle to hide infrastructure-level noise and reveal a clean, high-level reasoning flow</li><li>More compact table view of multiple traces on the Search page</li></ul>

<p>Jaeger is the industry-standard platform for distributed tracing. As microservice architectures grow complex, finding root causes in massive trace data becomes increasingly difficult. While Phase 1 of this initiative established a baseline AI assistant for natural language search, the system currently relies on hard-coded capabilities. This project (Phase 2\) aims to transform the Jaeger AI agent from a static chatbot into an extensible, user-programmable platform. The primary objective is to implement a "Self-Service Skills" framework, architecturally similar to "Claude Code Skills." This will allow end-users to teach the Jaeger AI new debugging workflows (e.g., "Analyze Critical Path" or "Detect N+1 Queries") by simply adding configuration files containing system prompts and logic rules, without needing to recompile the Jaeger binary. The applicant will build this extension within the Jaeger v2 (OpenTelemetry-based) architecture, utilizing **LangChainGo** to orchestrate interactions with Language Models (SLMs/LLMs). This project bridges the gap between generic AI reasoning and domain-specific observability expertise.</p><p><br></p><p>**Expected Outcome:**</p><p>&nbsp;- **Skills Engine Implementation:** An approach compatible with our [BYOA (bring your own agent)](https://docs.google.com/document/d/1qD0OpyRfq-JbO6MCB5gmVxsdPcpPhxz1R_pPnKDdYOg/edit?tab=t.0#heading=h.qgr5ifum0a9m) direction that dynamically discovers, validates, and loads user-defined "Skills" (prompts and tool definitions) from configuration.</p><p>&nbsp;- **Smart Analysis Features:** A polished implementation of Natural Language Search and Contextual Trace Explanation that intelligently leverages these loaded skills.</p><p>&nbsp;- **Local-First Support:** Verified compatibility with local model runners (e.g., Ollama, Llama.cpp) to ensure deterministic performance without sending data to public clouds.</p><p>&nbsp;- **UI Integration:** Enhancements to the Jaeger React UI to expose these AI capabilities and visualize the "reasoning steps" taken by the agent.</p><p>&nbsp;- **Documentation:** A complete guide for users on "How to Author Custom AI Skills for Jaeger."</p><p>- **Learning Opportunities:**</p><p>&nbsp;- **Agentic AI Architecture:** Learn to design stateful AI agents in Go that utilize "Tool Calling" and "Reasoning Loops" rather than simple text generation.</p><p>&nbsp;- **OpenTelemetry Internals:** Gain deep familiarity with the OpenTelemetry Collector architecture, as Jaeger v2 is built directly on top of it.</p><p>&nbsp;- **Cloud-Native Engineering:** Experience contributing to a graduated CNCF project, including navigating code reviews, writing design docs (RFDs), and adhering to open-source best practices.</p><p>&nbsp;- **Full-Stack Development:** Practical experience bridging a complex Go backend with a modern React frontend.</p><p><br></p>