
monoscope
Monoscope lets you ingest and explore your logs, traces and metrics. We store these in S3 compatible buckets. Query in natural language via LLMs.
Stars: 187

Monoscope is an open-source monitoring and observability platform that uses artificial intelligence to understand and monitor systems automatically. It allows users to ingest and explore logs, traces, and metrics in S3 buckets, query in natural language via LLMs, and create AI agents to detect anomalies. Key capabilities include universal data ingestion, AI-powered understanding, natural language interface, cost-effective storage, and zero configuration. Monoscope is designed to reduce alert fatigue, catch issues before they impact users, and provide visibility across complex systems.
README:
Monoscope lets you ingest and explore your logs, traces and metrics in S3 buckets. Query in natural language via LLMs. Monoscope also let's you create AI agents that run at an interval to automatically detect anomalies in your logs, metrics, and traces. The most important actions and logs and insight are sent as reports to your email every day or week.
Website β’ Discord β’ Twitter β’ Changelog β’ Documentation
π€ AI Anomaly Detection β’ π¬ Natural Language Search β’ β Star Us β’ π€ Contributing
Monoscope automatically detects anomalies in your logs, metrics, and traces using AI β no configuration required.
Monoscope is an open-source observability platform that uses artificial intelligence to understand and monitor your systems automatically. Unlike traditional monitoring tools that require extensive configuration and generate overwhelming alerts, Monoscope learns your system's normal behavior and only alerts you when something is genuinely wrong.
- Universal Data Ingestion: Native support for OpenTelemetry means compatibility with 750+ integrations out of the box
- AI-Powered Understanding: Our LLM engine understands context, not just thresholds
- Natural Language Interface: Query your data in plain English
- Cost-Effective Storage: Store years of data affordably with S3-compatible object storage
- Zero Configuration: Start getting insights immediately without complex setup
# Run with Docker (recommended)
docker run -p 8080:8080 monoscope/monoscope:latest
# Or clone and run locally
git clone https://github.com/monoscope-tech/monoscope.git
cd monoscope
docker-compose up
Visit http://localhost:8080
to access Monoscope. Full installation guide β
Monoscope is built on OpenTelemetry, the industry-standard observability framework. This means you get instant compatibility with 750+ integrations including all major languages, frameworks, and infrastructure components.
- Logs: Application logs, system logs, audit trails
- Metrics: Performance counters, business KPIs, custom metrics
- Traces: Distributed request flows, latency tracking, dependency mapping
# For Python applications
pip install opentelemetry-api opentelemetry-sdk
export OTEL_EXPORTER_OTLP_ENDPOINT="http://localhost:8080"
# For Node.js applications
npm install @opentelemetry/api @opentelemetry/sdk-node
export OTEL_EXPORTER_OTLP_ENDPOINT="http://localhost:8080"
# For Kubernetes clusters
helm install opentelemetry-collector open-telemetry/opentelemetry-collector \
--set config.exporters.otlp.endpoint="monoscope:8080"
Monoscope automatically correlates logs, metrics, and traces from the same service, giving you a complete picture of your system's behavior. No manual correlation or configuration required.
Monoscope's AI engine continuously learns your system's normal behavior patterns and automatically alerts you to genuine issues:
- Context-Aware Detection: Understands that high CPU during deployments is normal, but high CPU at 3 AM is not
- Seasonal Pattern Recognition: Learns daily, weekly, and monthly patterns in your data
- Cross-Signal Correlation: Detects anomalies by analyzing logs, metrics, and traces together
- Noise Reduction: Reduces alert fatigue by 90% compared to threshold-based monitoring
The AI runs continuously in the background, requiring no configuration or training from you.
Query your observability data using plain English instead of complex query languages:
- "Show me all errors in the payment service in the last hour"
- "What caused the spike in response time yesterday at 3 PM?"
- "Which services are consuming the most memory?"
- "Find all database queries taking longer than 1 second"
Monoscope translates your natural language into optimized queries across logs, metrics, and traces, returning relevant results with explanations.
LLM-based engine that understands context and identifies real issues, not just threshold violations |
Search logs and metrics using plain English - no complex query languages required |
Handle millions of events/sec with our custom TimeFusion storage engine |
Store years of data affordably with S3-compatible object storage |
Log Explorer - Main View |
Log Explorer - Detailed View |
Dashboard Analytics |
Monoscope - Open Source Observability |
Monoscope combines high-performance data ingestion with intelligent AI analysis:
graph LR
A[Your Apps] -->|Logs/Metrics| B[Ingestion API]
B --> C[TimeFusion Engine]
C --> D[S3 Storage]
C --> E[LLM Pipeline]
E --> F[Anomaly Detection]
F --> G[Alerts & Dashboard]
- Language: Built in Haskell for reliability and performance
- Storage: S3-compatible object storage for cost-effective retention
- AI Engine: State-of-the-art LLMs for intelligent analysis
- Scale: Horizontally scalable architecture
Traditional monitoring tools require extensive configuration, generate overwhelming alerts, and still miss critical issues. You spend more time managing your monitoring than actually using it.
Monoscope uses AI to understand your system's behavior, automatically detect anomalies, and provide actionable insights - all without complex configuration.
- DevOps Teams reducing alert fatigue by 90%
- SREs catching issues before they impact users
- Engineering Leaders getting visibility across complex systems
- Startups implementing enterprise-grade observability on a budget
- Features
- Getting Started
- Prerequisites
- Installation
- Development Setup
- Testing
- Useful Links
- Contributing
- License
- π€ AI-Powered Anomaly Detection: Leverages LLMs to automatically identify and alert on unusual patterns
- βοΈ S3-Compatible Storage: Store logs, metrics and traces in any S3-compatible object storage
- π High Performance: Written in Haskell and rust for reliability and performance
- π Real-Time Analytics: Monitor your systems with minimal latency
- π Extensible: Easy to integrate with existing monitoring infrastructure
Prerequisites
Before installing Monoscope, ensure you have the following dependencies:
- Haskell: Install via GHCup
- PostgreSQL with TimescaleDB: For time-series data storage
- LLVM: Required for compilation
- Google Cloud SDK: For GCP integration (if using GCP)
- Install Haskell via GHCup
curl --proto '=https' --tlsv1.2 -sSf https://get-ghcup.haskell.org | sh
- Clone the Repository
git clone https://github.com/monoscope-tech/monoscope.git
cd monoscope
- Install System Dependencies
For macOS:
# Install LLVM
brew install llvm
# Install PostgreSQL with TimescaleDB
brew install postgresql
brew install timescaledb
# Install libpq
brew install libpq
For Linux (Ubuntu/Debian):
# Install LLVM
sudo apt-get install llvm
# Install PostgreSQL and TimescaleDB
# Follow instructions at: https://docs.timescale.com/install/latest/
# Install libpq
sudo apt-get install libpq-dev
- Configure Google Cloud (Optional)
If using Google Cloud integration:
gcloud auth application-default login
- Run Monoscope
stack run
Development Setup
- Create a Docker volume for PostgreSQL data:
docker volume create pgdata
- Run TimescaleDB in Docker:
make timescaledb-docker
- Configure pg_cron extension:
Add the following to your PostgreSQL configuration:
ALTER system SET cron.database_name = 'apitoolkit';
ALTER system SET shared_preload_libraries = 'pg_cron';
Then restart the TimescaleDB Docker container.
Install code formatting and linting tools:
# Code formatter
brew install ormolu
# Linter
brew install hlint
Useful commands:
# Format code
make fmt
# Run linter
make lint
π‘ Tip: For better IDE support, compile Haskell Language Server locally to avoid crashes, especially on macOS. See issue #2391.
To build the service worker:
workbox generateSW workbox-config.js
Running Tests
make test
# OR
stack test --ghc-options=-w
Unit tests don't require a database connection and run much faster. They include doctests and pure function tests.
make test-unit
# OR
stack test apitoolkit-server:unit-tests --ghc-options=-w
make live-test-unit
# OR
stack test apitoolkit-server:unit-tests --ghc-options=-w --file-watch
stack test --test-arguments "--match=SeedingConfig" apitoolkit-server:tests
# OR
stack test --ta "--match=SeedingConfig" apitoolkit-server:tests
- π¬ Discord - Chat with users and contributors
- π Issues - Report bugs or request features
- π¦ Twitter - Follow for updates
- π Blog - Tutorials and case studies
We welcome contributions to Monoscope! Please feel free to:
- Report bugs and request features via GitHub Issues
- Submit pull requests for bug fixes and new features
- Improve documentation and examples
- Share your use cases and feedback
Before contributing, please read our contributing guidelines and ensure your code passes all tests and linting checks.
Monoscope is open source software. Please see the LICENSE file for details.
- [ ] Kubernetes Operator
- [ ] Terraform Provider
- [ ] Mobile App
- [ ] Distributed Tracing Support
- [ ] Custom ML Model Training
See our public roadmap for more details.
Feature | Monoscope | Datadog | Elastic | Prometheus |
---|---|---|---|---|
AI Anomaly Detection | β Built-in | β Add-on | β | β |
Natural Language Search | β | β | β | β |
Cost-Effective Storage | β S3 | β Proprietary | β | β |
No Configuration Alerts | β | β | β | β |
Open Source | β | β | β | β |
For Tasks:
Click tags to check more tools for each tasksFor Jobs:
Alternative AI tools for monoscope
Similar Open Source Tools

monoscope
Monoscope is an open-source monitoring and observability platform that uses artificial intelligence to understand and monitor systems automatically. It allows users to ingest and explore logs, traces, and metrics in S3 buckets, query in natural language via LLMs, and create AI agents to detect anomalies. Key capabilities include universal data ingestion, AI-powered understanding, natural language interface, cost-effective storage, and zero configuration. Monoscope is designed to reduce alert fatigue, catch issues before they impact users, and provide visibility across complex systems.

Archon
Archon is an AI meta-agent designed to autonomously build, refine, and optimize other AI agents. It serves as a practical tool for developers and an educational framework showcasing the evolution of agentic systems. Through iterative development, Archon demonstrates the power of planning, feedback loops, and domain-specific knowledge in creating robust AI agents.

llamafarm
LlamaFarm is a comprehensive AI framework that empowers users to build powerful AI applications locally, with full control over costs and deployment options. It provides modular components for RAG systems, vector databases, model management, prompt engineering, and fine-tuning. Users can create differentiated AI products without needing extensive ML expertise, using simple CLI commands and YAML configs. The framework supports local-first development, production-ready components, strategy-based configuration, and deployment anywhere from laptops to the cloud.

Hacx-GPT
Hacx GPT is a cutting-edge AI tool developed by BlackTechX, inspired by WormGPT, designed to push the boundaries of natural language processing. It is an advanced broken AI model that facilitates seamless and powerful interactions, allowing users to ask questions and perform various tasks. The tool has been rigorously tested on platforms like Kali Linux, Termux, and Ubuntu, offering powerful AI conversations and the ability to do anything the user wants. Users can easily install and run Hacx GPT on their preferred platform to explore its vast capabilities.

gemini-cli
Gemini CLI is an open-source AI agent that provides lightweight access to Gemini, offering powerful capabilities like code understanding, generation, automation, integration, and advanced features. It is designed for developers who prefer working in the command line and offers extensibility through MCP support. The tool integrates directly into GitHub workflows and offers various authentication options for individual developers, enterprise teams, and production workloads. With features like code querying, editing, app generation, debugging, and GitHub integration, Gemini CLI aims to streamline development workflows and enhance productivity.

lyraios
LYRAIOS (LLM-based Your Reliable AI Operating System) is an advanced AI assistant platform built with FastAPI and Streamlit, designed to serve as an operating system for AI applications. It offers core features such as AI process management, memory system, and I/O system. The platform includes built-in tools like Calculator, Web Search, Financial Analysis, File Management, and Research Tools. It also provides specialized assistant teams for Python and research tasks. LYRAIOS is built on a technical architecture comprising FastAPI backend, Streamlit frontend, Vector Database, PostgreSQL storage, and Docker support. It offers features like knowledge management, process control, and security & access control. The roadmap includes enhancements in core platform, AI process management, memory system, tools & integrations, security & access control, open protocol architecture, multi-agent collaboration, and cross-platform support.

CrewAI-GUI
CrewAI-GUI is a Node-Based Frontend tool designed to revolutionize AI workflow creation. It empowers users to design complex AI agent interactions through an intuitive drag-and-drop interface, export designs to JSON for modularity and reusability, and supports both GPT-4 API and Ollama for flexible AI backend. The tool ensures cross-platform compatibility, allowing users to create AI workflows on Windows, Linux, or macOS efficiently.

paelladoc
PAELLADOC is an intelligent documentation system that uses AI to analyze code repositories and generate comprehensive technical documentation. It offers a modular architecture with MECE principles, interactive documentation process, key features like Orchestrator and Commands, and a focus on context for successful AI programming. The tool aims to streamline documentation creation, code generation, and product management tasks for software development teams, providing a definitive standard for AI-assisted development documentation.

dotclaude
A sophisticated multi-agent configuration system for Claude Code that provides specialized agents and command templates to accelerate code review, refactoring, security audits, tech-lead-guidance, and UX evaluations. It offers essential commands, directory structure details, agent system overview, command templates, usage patterns, collaboration philosophy, sync management, advanced usage guidelines, and FAQ. The tool aims to streamline development workflows, enhance code quality, and facilitate collaboration between developers and AI agents.

AgC
AgC is an open-core platform designed for deploying, running, and orchestrating AI agents at scale. It treats agents as first-class compute units, providing a modular, observable, cloud-neutral, and production-ready environment. Open Agentic Compute empowers developers and organizations to run agents like cloud-native workloads without lock-in.

RookieAI_yolov8
RookieAI_yolov8 is an open-source project designed for developers and users interested in utilizing YOLOv8 models for object detection tasks. The project provides instructions for setting up the required libraries and Pytorch, as well as guidance on using custom or official YOLOv8 models. Users can easily train their own models and integrate them with the software. The tool offers features for packaging the code, managing model files, and organizing the necessary resources for running the software. It also includes updates and optimizations for better performance and functionality, with a focus on FPS game aimbot functionalities. The project aims to provide a comprehensive solution for object detection tasks using YOLOv8 models.

sim
Sim is a platform that allows users to build and deploy AI agent workflows quickly and easily. It provides cloud-hosted and self-hosted options, along with support for local AI models. Users can set up the application using Docker Compose, Dev Containers, or manual setup with PostgreSQL and pgvector extension. The platform utilizes technologies like Next.js, Bun, PostgreSQL with Drizzle ORM, Better Auth for authentication, Shadcn and Tailwind CSS for UI, Zustand for state management, ReactFlow for flow editor, Fumadocs for documentation, Turborepo for monorepo management, Socket.io for real-time communication, and Trigger.dev for background jobs.

bifrost
Bifrost is a high-performance AI gateway that unifies access to multiple providers through a single OpenAI-compatible API. It offers features like automatic failover, load balancing, semantic caching, and enterprise-grade functionalities. Users can deploy Bifrost in seconds with zero configuration, benefiting from its core infrastructure, advanced features, enterprise and security capabilities, and developer experience. The repository structure is modular, allowing for maximum flexibility. Bifrost is designed for quick setup, easy configuration, and seamless integration with various AI models and tools.

rkllama
RKLLama is a server and client tool designed for running and interacting with LLM models optimized for Rockchip RK3588(S) and RK3576 platforms. It allows models to run on the NPU, with features such as running models on NPU, partial Ollama API compatibility, pulling models from Huggingface, API REST with documentation, dynamic loading/unloading of models, inference requests with streaming modes, simplified model naming, CPU model auto-detection, and optional debug mode. The tool supports Python 3.8 to 3.12 and has been tested on Orange Pi 5 Pro and Orange Pi 5 Plus with specific OS versions.

farfalle
Farfalle is an open-source AI-powered search engine that allows users to run their own local LLM or utilize the cloud. It provides a tech stack including Next.js for frontend, FastAPI for backend, Tavily for search API, Logfire for logging, and Redis for rate limiting. Users can get started by setting up prerequisites like Docker and Ollama, and obtaining API keys for Tavily, OpenAI, and Groq. The tool supports models like llama3, mistral, and gemma. Users can clone the repository, set environment variables, run containers using Docker Compose, and deploy the backend and frontend using services like Render and Vercel.

enferno
Enferno is a modern Flask framework optimized for AI-assisted development workflows. It combines carefully crafted development patterns, smart Cursor Rules, and modern libraries to enable developers to build sophisticated web applications with unprecedented speed. Enferno's intelligent patterns and contextual guides help create production-ready SAAS applications faster than ever. It includes features like modern stack, authentication, OAuth integration, database support, task queue, frontend components, security measures, Docker readiness, and more.
For similar tasks

qdrant
Qdrant is a vector similarity search engine and vector database. It is written in Rust, which makes it fast and reliable even under high load. Qdrant can be used for a variety of applications, including: * Semantic search * Image search * Product recommendations * Chatbots * Anomaly detection Qdrant offers a variety of features, including: * Payload storage and filtering * Hybrid search with sparse vectors * Vector quantization and on-disk storage * Distributed deployment * Highlighted features such as query planning, payload indexes, SIMD hardware acceleration, async I/O, and write-ahead logging Qdrant is available as a fully managed cloud service or as an open-source software that can be deployed on-premises.

SynapseML
SynapseML (previously known as MMLSpark) is an open-source library that simplifies the creation of massively scalable machine learning (ML) pipelines. It provides simple, composable, and distributed APIs for various machine learning tasks such as text analytics, vision, anomaly detection, and more. Built on Apache Spark, SynapseML allows seamless integration of models into existing workflows. It supports training and evaluation on single-node, multi-node, and resizable clusters, enabling scalability without resource wastage. Compatible with Python, R, Scala, Java, and .NET, SynapseML abstracts over different data sources for easy experimentation. Requires Scala 2.12, Spark 3.4+, and Python 3.8+.

mlx-vlm
MLX-VLM is a package designed for running Vision LLMs on Mac systems using MLX. It provides a convenient way to install and utilize the package for processing large language models related to vision tasks. The tool simplifies the process of running LLMs on Mac computers, offering a seamless experience for users interested in leveraging MLX for vision-related projects.

Java-AI-Book-Code
The Java-AI-Book-Code repository contains code examples for the 2020 edition of 'Practical Artificial Intelligence With Java'. It is a comprehensive update of the previous 2013 edition, featuring new content on deep learning, knowledge graphs, anomaly detection, linked data, genetic algorithms, search algorithms, and more. The repository serves as a valuable resource for Java developers interested in AI applications and provides practical implementations of various AI techniques and algorithms.

Awesome-AI-Data-Guided-Projects
A curated list of data science & AI guided projects to start building your portfolio. The repository contains guided projects covering various topics such as large language models, time series analysis, computer vision, natural language processing (NLP), and data science. Each project provides detailed instructions on how to implement specific tasks using different tools and technologies.

awesome-AIOps
awesome-AIOps is a curated list of academic researches and industrial materials related to Artificial Intelligence for IT Operations (AIOps). It includes resources such as competitions, white papers, blogs, tutorials, benchmarks, tools, companies, academic materials, talks, workshops, papers, and courses covering various aspects of AIOps like anomaly detection, root cause analysis, incident management, microservices, dependency tracing, and more.

AI-Security-and-Privacy-Events
AI-Security-and-Privacy-Events is a curated list of academic events focusing on AI security and privacy. It includes seminars, conferences, workshops, tutorials, special sessions, and covers various topics such as NLP & LLM Security, Privacy and Security in ML, Machine Learning Security, AI System with Confidential Computing, Adversarial Machine Learning, and more.

AI_Spectrum
AI_Spectrum is a versatile machine learning library that provides a wide range of tools and algorithms for building and deploying AI models. It offers a user-friendly interface for data preprocessing, model training, and evaluation. With AI_Spectrum, users can easily experiment with different machine learning techniques and optimize their models for various tasks. The library is designed to be flexible and scalable, making it suitable for both beginners and experienced data scientists.
For similar jobs

lollms-webui
LoLLMs WebUI (Lord of Large Language Multimodal Systems: One tool to rule them all) is a user-friendly interface to access and utilize various LLM (Large Language Models) and other AI models for a wide range of tasks. With over 500 AI expert conditionings across diverse domains and more than 2500 fine tuned models over multiple domains, LoLLMs WebUI provides an immediate resource for any problem, from car repair to coding assistance, legal matters, medical diagnosis, entertainment, and more. The easy-to-use UI with light and dark mode options, integration with GitHub repository, support for different personalities, and features like thumb up/down rating, copy, edit, and remove messages, local database storage, search, export, and delete multiple discussions, make LoLLMs WebUI a powerful and versatile tool.

Azure-Analytics-and-AI-Engagement
The Azure-Analytics-and-AI-Engagement repository provides packaged Industry Scenario DREAM Demos with ARM templates (Containing a demo web application, Power BI reports, Synapse resources, AML Notebooks etc.) that can be deployed in a customerβs subscription using the CAPE tool within a matter of few hours. Partners can also deploy DREAM Demos in their own subscriptions using DPoC.

minio
MinIO is a High Performance Object Storage released under GNU Affero General Public License v3.0. It is API compatible with Amazon S3 cloud storage service. Use MinIO to build high performance infrastructure for machine learning, analytics and application data workloads.

mage-ai
Mage is an open-source data pipeline tool for transforming and integrating data. It offers an easy developer experience, engineering best practices built-in, and data as a first-class citizen. Mage makes it easy to build, preview, and launch data pipelines, and provides observability and scaling capabilities. It supports data integrations, streaming pipelines, and dbt integration.

AiTreasureBox
AiTreasureBox is a versatile AI tool that provides a collection of pre-trained models and algorithms for various machine learning tasks. It simplifies the process of implementing AI solutions by offering ready-to-use components that can be easily integrated into projects. With AiTreasureBox, users can quickly prototype and deploy AI applications without the need for extensive knowledge in machine learning or deep learning. The tool covers a wide range of tasks such as image classification, text generation, sentiment analysis, object detection, and more. It is designed to be user-friendly and accessible to both beginners and experienced developers, making AI development more efficient and accessible to a wider audience.

tidb
TiDB is an open-source distributed SQL database that supports Hybrid Transactional and Analytical Processing (HTAP) workloads. It is MySQL compatible and features horizontal scalability, strong consistency, and high availability.

airbyte
Airbyte is an open-source data integration platform that makes it easy to move data from any source to any destination. With Airbyte, you can build and manage data pipelines without writing any code. Airbyte provides a library of pre-built connectors that make it easy to connect to popular data sources and destinations. You can also create your own connectors using Airbyte's no-code Connector Builder or low-code CDK. Airbyte is used by data engineers and analysts at companies of all sizes to build and manage their data pipelines.

labelbox-python
Labelbox is a data-centric AI platform for enterprises to develop, optimize, and use AI to solve problems and power new products and services. Enterprises use Labelbox to curate data, generate high-quality human feedback data for computer vision and LLMs, evaluate model performance, and automate tasks by combining AI and human-centric workflows. The academic & research community uses Labelbox for cutting-edge AI research.