Products

Openlit

Uses AISkill

Open source Agent Harness Engineering Platform

openlit.io Source★ 871 DiscordAI Models & Agents / Prompts, Evals & LLM Observability

AI Benchmark
Desktop
Mobile
Monthly visits
None yet
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Public revenue
Undisclosed
No public revenue evidence yet
Entry price
Unknown
Freemium model, plan pricing not identified yet
Creators
Aman Agarwal
No social account linkedCo-created with Patcher Lit
Launched
2y ago
2024-04-26 · Est.

Traffic

Monthly visits

No traffic data yet.

Events10 changes

Marketing moves

Launches, search & content, referrals, promotions
X accounts@_typeofnull@openlit_io

  • Launched on Fazier (42 upvotes · 5 comments)Launch

    OpenLIT: Zero code Agent Observability — Continuous feedback for testing, tracing & fixing AI agents

    Oct 30, 2025 · 13mo agoLaunch page

  • Launched on Product HuntLaunch

    Trace LLM requests + costs with OpenTelemetry monitoring

    Oct 10, 2025 · 13mo agoLaunch page

  • Launched on Product HuntLaunch

    Customizable self-hosted dashboards for LLM observability

    Aug 21, 2025 · 15mo agoLaunch page

  • Launched on Product HuntLaunch

    Open source dashboard for AI engineering & LLM data

    Jan 16, 2025 · 22mo agoLaunch page

  • Launched on Fazier (107 upvotes · 9 comments)Launch

    OpenLIT 2.0 — Open Source Platform for AI Engineering

    Nov 25, 2024 · 24mo agoLaunch page

Third-party reviews & case dossiers

Acquisition breakdowns, engineer reviews & open-source metrics
Show HN
2024-04-26Source
Post excerpt:
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
2024-04-27Source
Post excerpt:
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
2024-05-28Source
Post excerpt:
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
2024-06-02Source
Post excerpt:
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
2024-08-14Source
What engineers like:
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
2024-11-25Source
What engineers like:
📈 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.
Best suited for:
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
2025-10-30Source
What engineers like:
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.
Best suited for:
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.

Open Source & Agent Ecosystem

GitHub · MCP / Skill
openlit/openlitSelf-hostableCommercial Open Source (COSS)
Last commit 2026-11-07
GitHub Stars
★ 871
Forks
114
License
See repository
Primary language
Python
COSS Monetization Path:Community Self-Hosted (Open Source · Docker) → Managed Cloud (Freemium)
View cloud pricing →
Browse peer open-source projects →

Agent, MCP & Developer Integration

Agent SkillCLI / SDKAPI
Integration docs
Ecosystem & integrations:
OpenLLMetryGrafanaDatadogNew RelicSlackDiscordPagerDutyOpsgenieChromadbPinecone
Exposed tool capabilities:
Agent MonitoringModel EvaluationPrompt ManagementLLM tracingLLM evaluationPrompt management
Browse developer & CLI tools →

Traffic sources

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Traffic data: Vaneform

Pricing

OSS
The published price is 0 USD.
Unlimited self-hosted usage.
Enterprise
Contact sales
Cloud
Unknown

Trial: None · Enterprise custom tier · Observed 2026-11-09 · Pricing page

Pages & changes

Tracked since 2024-07-26

Key pages

Weekly snapshots
openlit.io/ Snapshot 2026-11-10

Changes

  1. Nov 1 · 14d ago

    Homepage headline (H1) changed to “Open source Agent Harness Engineering Platform”Positioning

    Connect LLMs & GenAI with OpenLIT→Open source Agent Harness Engineering Platform

  2. Nov 1 · 14d ago

    Removed technology: Amazon S3Features

    Amazon S3→—

  3. Nov 1 · 14d ago

    Removed technology: Amazon Web ServicesFeatures

    Amazon Web Services→—

  4. Nov 1 · 14d ago

    Removed technology: Cloudflare Bot ManagementFeatures

    Cloudflare Bot Management→—

  5. Nov 1 · 14d ago

    Adopted technology: Cloudflare Browser InsightsFeatures

    —→Cloudflare Browser Insights

  6. Nov 1 · 14d ago

    Removed technology: HSTSFeatures

    HSTS→—

Questions & evidence

How much does Openlit cost?
OSS: The published price is 0 USD.; Enterprise: Contact sales; Cloud: Unknown. Observed 2026-11-09. Check the source for current billing terms, usage limits and taxes.Source
What is Openlit and what platforms does it support?
Openlit is catalogued under Prompts, Evals & LLM Observability: Open source Agent Harness Engineering Platform. Supported platforms: CLI / SDK, API.
Is Openlit open source?
Openlit has a public repository (https://github.com/openlit/openlit) with its source repository published, recording 871 GitHub stars, and supporting self-hosted deployment.Source

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