Openlit
含 AI面向開發者和生產團隊的開源可自託管平台，用於追蹤、評估和保護 AI 智慧體，並管理提示詞、金鑰、成本及 GPU 監控。
openlit.io 源碼★ 871 DiscordAI 模型與智慧體 / 提示詞、評測與可觀測
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- 首次發布
- 2 年前
- 2024-04-26 · 估算
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行銷動作
發布、搜尋與內容、引薦、促銷在 Fazier 發布（42 票 · 5 評論）發布
OpenLIT: Zero code Agent Observability — Continuous feedback for testing, tracing & fixing AI agents
2025 年 10 月 30 日 · 13 個月前發布頁
在 Product Hunt 發布發布
Trace LLM requests + costs with OpenTelemetry monitoring
2025 年 10 月 10 日 · 13 個月前發布頁
在 Product Hunt 發布發布
Customizable self-hosted dashboards for LLM observability
2025 年 8 月 21 日 · 15 個月前發布頁
在 Fazier 發布（107 票 · 9 評論）發布
OpenLIT 2.0 — Open Source Platform for AI Engineering
2024 年 11 月 25 日 · 24 個月前發布頁
第三方評測與案例拆解
客戶來源管道拆解、工程師評測與開源檔案Show HN
2024-04-26查看原文
- 創作者原帖摘錄：
- Hey HN, we're super excited to share something we've been working on: OpenLIT. After an engaging preview that some of you might recall, we are now proudly announcing our first stable release! *What's OpenLIT?* Simply put, OpenLIT is an open-source tool designed to make monitoring your Large Language Model (LLM) applications straightforward. It’s built on Ope
Show HN
2024-04-27查看原文
- 創作者原帖摘錄：
- Hey Everyone! I am super excited to share something we've (me and my friend) been working on: OpenLIT. After an engaging preview that some of you might recall, we are now proudly announcing our first stable release! *What's OpenLIT?* Simply put, OpenLIT is an open-source tool designed to make monitoring your Large Language Model (LLM) applications straightfo
Show HN
2024-05-28查看原文
- 創作者原帖摘錄：
- Hey folks! I'm thrilled to let you all know about something my friend and I have been working on at [OpenLIT]( https://github.com/openlit/openlit )! For those of you working with LLMs, we’ve got some pretty cool updates. We’ve successfully integrated Prometheus and Jaeger—yes, the go-to observability tools to fully support your LLM application observability.
Show HN
2024-06-02查看原文
- 創作者原帖摘錄：
- Hello everyone, I’m the maintainer of OpenLIT, and like many of you, I’ve always struggled with debugging LLM responses on Ollama and GPT4All—it's possible but with significant effort tbh. So, taking advantage of my role as maintainer, I tackled the problem head-on to make it simpler. I've developed an OpenTelemetry-native integration within OpenLIT aimed at
Fazier
2024-08-14查看原文
- 核心亮點評測：
- Advanced Monitoring of LLM and VectorDB Performance World's First OpenTelemetry-Native Collector for GPU Monitoring Cost Tracking for Custom and Fine-Tuned Models OpenTelemetry-Native & Vendor-Neutral SDKs allowing you to send data to any OpenTelemetry endpoint
Fazier
2024-11-25查看原文
- 核心亮點評測：
- 📈 Analytics Dashboard: Monitor your AI application’s health and performance with detailed dashboards that track metrics, costs, and user interactions, providing a clear view of overall efficiency. 🔌 OpenTelemetry-native Observability SDKs: Vendor-neutral SDKs to send traces and metrics to your existing observability tools. 💲 Cost Tracking for Custom and Fine-Tuned Models: Tailor cost estimations for specific models using custom pricing files for precise budgeting. 🔒 Guardrails: Implement security measures and input validation to protect against malicious injection attacks. 🧪 Evaluations: Assess LLM responses to ensure accuracy and relevance. 🔑 API Keys and Secrets Management: Securely handle your API keys and secrets centrally, avoiding insecure practices. 🐛 Exceptions Monitoring Dashboard: Quickly spot and resolve issues by tracking common exceptions and errors with a dedicated monitoring dashboard. 💭 Prompt Management: Manage and version prompts using Prompt Hub for consistent and easy access across applications. 🔑 API Keys and Secrets Management: Securely handle your API keys and secrets centrally, avoiding insecure practices. 🎮 Experiment with different LLMs: Use OpenGround to explore, test, and compare various LLMs side by side.
- 適用人群：
- Real-time Application Monitoring: Use the Analytics Dashboard to monitor performance metrics and exceptions in real-time, allowing developers to quickly identify and resolve performance issues or bugs in AI applications. Secure Development Practices: Implement Guardrails and centralized API Keys and Secrets Management to ensure secure code and configuration practices, reducing the risk of vulnerabilities such as injection attacks. Cost-effective Model Training: Utilize Cost Tracking for Custom Models to develop cost-efficient AI models by accurately estimating training expenses, helping teams stay within budget constraints. Continuous Improvement of AI Outputs: Incorporate Evaluations to consistently assess LLM outputs, enabling developers to iteratively refine model responses for accuracy and relevance in user applications. Prototype and Test AI Solutions: Use OpenGround to rapidly prototype, test, and compare different large language models, helping developers find the best fit for their specific application needs through experimentation.
Fazier
2025-10-30查看原文
- 核心亮點評測：
- 1. Zero-Code Instrumentation OpenLIT enables observability without needing any code changes. It automatically instruments LLMs, AI agents, vector databases, and GPUs without manual setup. This helps teams get instant visibility into their AI pipelines with minimal effort. 2. OpenTelemetry-Native Built fully on the OpenTelemetry standard, making it easy to integrate with existing monitoring tools like Grafana, Prometheus, Elastic, and New Relic. Ensures consistency and compatibility across various observability platforms. 3. End-to-End Tracing Provides detailed traces for every AI request, including model calls, retrieval steps, and tool usage in agent workflows. Helps identify slow or failing components and optimize performance. 4. Metrics and Cost Tracking Tracks latency, throughput, token usage, and costs per model, request, or environment. Enables detailed cost analysis and optimization for multi-model or multi-provider setups. 5. Hallucination and Quality Detection Detects and flags potential hallucinations or low-quality responses. Offers simple evaluation metrics to assess response reliability. 6. Prompt Management and Experiment Tracking Stores and versions prompts for consistent experimentation. Allows for the comparison of model outputs, helping teams refine prompts and maintain quality control. 7. Self-Hosted and Privacy-Focused Being open-source, OpenLIT can be fully self-hosted. Keeps all observability data within your infrastructure, ensuring security and compliance. 8. Dashboards and Visualization Includes ready-to-use dashboards for latency, cost, token usage, GPU performance, and error tracking.
- 適用人群：
- 1. Debugging AI Applications Trace entire LLM and agent workflows to pinpoint the exact cause of poor or incorrect responses. Identify whether errors originate from the model, the retrieval step, or the external tool. 2. Cost Optimization Monitor token usage and costs across models and environments. Compare different LLMs to find the best balance between accuracy, latency, and price. 3. Performance Monitoring Observe response times and throughput to ensure stable and efficient AI services. Track GPU usage for locally hosted or fine-tuned models to optimize hardware utilization. 4. Regression Testing and Version Control Track prompt versions and compare new model responses with previous ones. Detect performance regressions before deployment. 5. Monitoring AI Agents and Tools Understand how often agents invoke different tools and how each tool impacts latency and reliability. Spot slow or error-prone tools quickly. 6. Compliance and Security Use the self-hosted setup to ensure all telemetry data remains within your secure infrastructure. Ideal for industries with strict data protection requirements, such as finance or healthcare. 7. Setting AI Service-Level Objectives (SLOs) Define measurable goals such as latency limits, cost per request, or acceptable hallucination rates. Integrate alerts into existing observability systems for proactive monitoring. 8. Onboarding and Knowledge Sharing Use OpenLIT traces and dashboards as learning tools for new engineers. Helps them understand real AI workflows and system interactions without needing deep system access.
開源倉庫與 Agent 生態
GitHub 開源與智慧體接入
最近提交 2026-11-07
- GitHub 星標
- ★ 871
- 分支數 (Forks)
- 114
- 開源協議
- 見倉庫說明
- 主要語言
- Python
開源商業化路徑：社群版免費自部署（開源協議 · Docker） → 官方免維運雲託管（免費增值）
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Unlimited self-hosted usage.
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試用：未提供 · 設企業客製化檔 · 觀察於 2026-11-09 · 原定價頁
頁面與變化
開始記錄於 2024-07-26
關鍵頁面
每週快照openlit.io/ 快照 2026-11-10
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問題與證據
- Openlit 的價格是多少？
- OSS: The published price is 0 USD.; Enterprise: 聯繫銷售; Cloud: 未知. 觀察於 2026-11-09。 當前計費條件、用量限制與稅費以來源頁面為准。來源
- Openlit 是什麼產品，支援哪些平台？
- Openlit 歸屬「提示詞、評測與可觀測」市場領域：OpenLIT 是面向開發者和生產團隊的開源可自託管平台，用於追蹤、評估和保護 AI 智慧體，並管理提示詞、金鑰、成本及 GPU 監控。。 支援平台包括：CLI 與 SDK、開放 API。
- Openlit 是開源專案嗎？採用什麼協議？
- Openlit 擁有公開程式碼倉庫（https://github.com/openlit/openlit），已公開原始碼倉庫，記錄有 871 個 GitHub Stars，並支援自託管部署。來源
同分類研究對象
AI 模型與智慧體 / 提示詞、評測與可觀測
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