18 Repos
Tools for rendering stack trace data into interactive diagrams to identify performance bottlenecks.
Distinct from Data Visualization: Distinct from Data Visualization: focuses specifically on hierarchical stack-based performance visualization.
Explore 18 awesome GitHub repositories matching data & databases · Hierarchical Performance Visualizers. Refine with filters or upvote what's useful.
Mastra is an orchestration framework designed for building, deploying, and managing autonomous AI agents and multi-agent systems. It provides a comprehensive suite of primitives for creating resilient AI applications, including durable workflow orchestration, event-driven agent loops, and semantic memory management. By integrating these core components, the platform enables developers to build complex, multi-step processes that can reason about goals and execute tasks without manual intervention. The framework distinguishes itself through its focus on observability and secure, isolated execut
Reduces noise and storage costs by dropping specific span types before they reach observability platforms.
Comet LLM is an observability platform and evaluation framework designed for large language model applications and agentic workflows. It functions as a system for tracing, monitoring, and debugging execution flows while providing tools for prompt optimization and the enforcement of AI safety guardrails. The platform distinguishes itself through a combination of model-based scoring and heuristic metrics to quantify output quality and detect hallucinations. It includes a dedicated prompt and agent optimizer with an interactive playground for refining templates and tool configurations. For retri
Captures nested call hierarchies and execution metadata to visualize the chronological flow of complex generative workflows.
FlameGraph is a performance profiling and visualization toolkit designed to identify bottlenecks in software execution. It functions as a processing engine that transforms raw stack trace samples into interactive, hierarchical diagrams. By representing aggregated execution frequency as nested rectangles, the tool allows developers to visualize hot code paths and analyze system behavior across both kernel and user-space environments. The project distinguishes itself through its ability to perform differential profile analysis, which highlights performance regressions or improvements by compari
Generates interactive diagrams from stack trace data to represent code execution frequency.
Elysia is a high-performance TypeScript web framework designed for building type-safe backend services. It provides a modular, plugin-based architecture that allows developers to compose server logic, middleware, and validation schemas into scalable application instances. By leveraging native web standards, the framework ensures portability across diverse JavaScript runtimes, including Node.js, Deno, and various edge computing environments. The framework distinguishes itself through its focus on end-to-end type safety, automatically synchronizing request and response definitions between the s
Wraps code blocks to capture execution time and exceptions as distinct spans within request traces.
Opik is an observability and evaluation platform designed for generative AI applications and agentic workflows. It provides a centralized environment for tracing execution flows, managing prompt templates, and monitoring production performance, allowing teams to gain visibility into complex model interactions and tool usage without requiring manual application code changes. The platform distinguishes itself through its integrated approach to the AI development lifecycle, combining distributed trace instrumentation with automated evaluation frameworks. It supports model-as-a-judge scoring, syn
Organizes individual model calls and execution steps into parent-child relationships to visualize the internal logic of AI applications.
VictoriaMetrics is a high-performance, scalable time series database and observability platform designed for long-term storage and analysis of metric, log, and trace data. It functions as a unified backend for monitoring ecosystems, offering full compatibility with industry-standard protocols and query languages. The system is built to handle massive data volumes through a distributed architecture that supports horizontal scaling and efficient data lifecycle management. The platform distinguishes itself through a storage engine that utilizes consistent hashing for data sharding and log-struct
Ingests and indexes distributed trace spans by mapping resource and span attributes into structured fields for efficient storage and retrieval.
Pyroscope is a continuous profiling platform designed to collect, store, and visualize application performance data. It functions as an application performance management suite that tracks historical resource usage to identify bottlenecks and detect performance regressions over time. The platform distinguishes itself through its use of kernel-level instrumentation and dynamic runtime hooks, which allow for performance monitoring without requiring manual code modifications or application restarts. It employs a sidecar agent architecture to offload telemetry processing, utilizing delta-encoded
Retrieve and display stored profiling metrics through an interactive interface to help developers explore resource consumption and analyze execution patterns without complex queries.
HyperDX is an OpenTelemetry observability platform that provides centralized log management, distributed tracing, and a self-hosted monitoring stack. It functions as a unified system for collecting, indexing, and visualizing logs, metrics, and traces from cloud and container environments. The platform distinguishes itself with specialized tooling for large language model monitoring and session replay, allowing user interactions in the browser to be linked to backend telemetry. It employs schema-less JSON parsing to index structured logs dynamically and uses source maps to resolve minified sta
Connects database table columns to span identifiers and status codes to visualize distributed traces.
OpenLLMetry is an OpenTelemetry-based observability framework and instrumentation library for generative AI applications. It provides toolsets for tracing and monitoring large language model workflows, capturing telemetry from model providers, agent frameworks, and vector databases using standardized semantic conventions. The project distinguishes itself by providing a specialized evaluation and experimentation suite that associates user feedback and prompt version hashes with specific execution traces. It includes a system for tracking model reasoning paths and enforcing security guardrails
Organizes nested AI workloads and agentic tasks into a tree of spans to visualize execution flow.
This project is a structured tracing framework for Rust that serves as an async-aware instrumentation library and telemetry data collector. It provides a structured logging facade and the tools necessary to record, filter, and route event-based diagnostic data from both standard applications and embedded systems. The framework distinguishes itself through a core implementation that supports bare-metal and no-standard-library environments without requiring a dynamic memory allocator. It specifically handles the complexities of asynchronous workflows by propagating diagnostic contexts across fu
Verifies if a span is active to avoid performing expensive computations when logging is disabled.
Hotspot ist eine grafische Benutzeroberfläche zur Analyse und Visualisierung von Performance-Daten, die mit dem Linux-Tool perf erfasst wurden. Es fungiert als Performance-Profiling-Visualisierer und Assembly-Level-Profiler, der Performance-Kosten auf spezifische Instruktionen abbildet, die mit dem ursprünglichen Quellcode synchronisiert sind. Das Projekt zeichnet sich durch einen Remote-Symbol-Resolver aus, der Performance-Daten von eingebetteten Zielen auf lokale Host-Debug-Symbole und Sysroots abbildet. Es enthält zudem ein spezialisiertes Off-CPU-Analysetool, das entwickelt wurde, um Thread-Wartezeiten und I/O-Blöcke mithilfe von Kernel-Scheduler-Tracepoints zu identifizieren. Das Tool deckt ein breites Spektrum an Performance-Analysefunktionen ab, einschließlich CPU-Profiling, Low-Level-Code-Inspektion und zeitlinienbasierter Datenfilterung. Es bietet Visualisierungsmethoden wie Flame-Graphs und Call-Graphs, um Systemengpässe und inlinte Funktionen zu identifizieren. Das System unterstützt die Datenaufzeichnung durch das Starten von Profiling-Tools für neue Anwendungen oder das Anhängen an bestehende Prozesse und ermöglicht den Export analysierter Performance-Profile in portable Formate für den Austausch zwischen Maschinen.
Provides interactive flame graphs and call graphs to visualize hierarchical stack trace data and identify performance bottlenecks.
Grafana Tempo is a high-scale distributed tracing backend and columnar trace database. It serves as an observability data store that persists and queries spans and traces using OpenTelemetry standards, allowing for the analysis of request flows across microservices. The system distinguishes itself by using an object-store based backend with columnar Parquet storage. This architecture enables efficient attribute searching and large-scale data retrieval through dedicated attribute columnization and block-based data partitioning. It includes a specialized TraceQL query engine for filtering trace
Enables searching for traces based on names, durations, and status codes using boolean logic.
orpc is a contract-first API development framework for TypeScript that starts with a shared contract definition and generates type-safe clients and servers from that single source of truth. It guarantees end-to-end type safety, meaning inputs, outputs, errors, and streaming data are all checked at compile time across the client–server boundary. What distinguishes orpc from typical RPC frameworks is its ability to export contracts as OpenAPI specifications, to optimize server-side rendering by calling API handlers directly inside the server process, and to support real‑time bidirectional commu
Adds custom attributes, events, and naming to spans automatically created for each middleware execution.
Uptrace is an OpenTelemetry-based observability platform designed to collect, store, and analyze distributed traces, metrics, and logs. It functions as a centralized logging backend, a distributed tracing system, and a metrics engine to monitor application performance and system health. The platform is distinguished by AI-powered operational capabilities, allowing users to query telemetry data and manage monitoring dashboards using natural language. It specifically includes specialized monitoring for generative AI pipelines, tracking token usage and response quality for LLM interactions and r
Allows manual definition of span or event clusters using specific attribute fingerprints.
Logfire is an OpenTelemetry observability platform and Python application monitoring tool. It provides a suite of tools for collecting, storing, and querying spans, logs, and metrics to monitor application performance and execution. The platform features a specialized monitor for Pydantic data validation, tracking data flow and validation outcomes in real time. It also includes a telemetry analysis tool that uses standard SQL to query observability data and connect to business intelligence tools. The system provides automatic instrumentation for Python libraries and frameworks, allowing for
Tracks function call lifecycles using nested timed spans to visualize execution hierarchies and identify latency.
Dieses Projekt ist ein umfassender Leitfaden und eine Referenz für Performance-Programmierung in Go, mit Fokus auf Laufzeiteffizienz und Speicheroptimierung. Es bietet eine Sammlung von Patterns und Techniken, die darauf ausgelegt sind, die Ausführungsgeschwindigkeit durch Reduzierung des Garbage-Collection-Overheads und Optimierung der Speichernutzung zu steigern. Die Ressource zeichnet sich durch detaillierte Referenzimplementierungen zur Speicheroptimierung aus, wie etwa Escape-Analyse, Object Pooling und Structure Memory Alignment. Sie bietet spezifische Strategien zur Reduzierung der Binärgröße und zur Verbesserung der CPU-Cache-Effizienz durch Optimierung des Speicherlayouts von Strukturen und die Verwendung von Zero-Allocation-Platzhaltern. Das Projekt deckt ein breites Spektrum an Backend-Engineering-Fähigkeiten ab, einschließlich Concurrency-Management mit Worker Pools und Synchronisations-Primitiven, High-Performance-RPC und HTTP-Routing sowie Strategien für verteiltes Caching. Es enthält zudem Anleitungen zur Observability durch CPU- und Memory-Profiling sowie Quality-Assurance-Patterns für funktionale Unit-Tests und die Generierung von Mock-Objekten. Der Inhalt ist als Serie von Tutorials, Architekturbeispielen und Benchmarking-Leitfäden strukturiert, um Entwicklern bei der Analyse und Behebung von Performance-Engpässen zu helfen.
Provides a utility to transform raw profile files into interactive graphs or call trees for performance analysis.
Agenta is a Prompt Ops lifecycle manager and prompt management platform that decouples prompt engineering from application code. It serves as a centralized system for developing, versioning, and deploying prompt templates and model configurations across different environments. The platform functions as an AI agent orchestrator with a visual interface for building agent workflows and connecting models to external tools. It further acts as an evaluation framework and observability tool, utilizing OpenTelemetry to capture execution traces, monitor latency, and track token costs. The system cove
Provides programmatic access to execution logs and timing data using attribute filters.
OpenCensus-go is an observability instrumentation library designed to capture and export telemetry data from distributed systems. It functions as a framework for application performance monitoring and distributed request tracing, allowing developers to track system health, latency, and the progression of requests across service boundaries. The project distinguishes itself through a modular architecture that decouples data collection from storage. By utilizing a pluggable exporter interface, it enables the transmission of metrics and trace data to a variety of external monitoring and analysis
Records request progression across services by capturing hierarchical timing segments as spans.