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This project proudly follows and maintains the Semantic Conventions with the OpenTelemetry community, consistently updating to align with the latest standards in Observability.

• 📈 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 (Python, TypeScript, Go) to send traces and metrics to your existing observability tools.

• 🛡️ 11 Built-in Evaluation Types: Automated LLM-as-a-Judge evaluation with hallucination, bias, toxicity, safety, instruction following, completeness, conciseness, sensitivity, relevance, coherence, and faithfulness detection. Context-aware evaluation that treats provided context as the source of truth.

• ⚙️ Rule Engine: Define conditional rules with AND/OR logic to match runtime trace attributes and dynamically retrieve contexts, prompts, and evaluation configs. SDK support across Python, TypeScript, and Go.

• 💲 Cost Tracking for Custom and Fine-Tuned Models: Tailor cost estimations for specific models using custom pricing files for precise budgeting.

• 🐛 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.

• 🚀 Fleet Hub for OpAMP Management: Centrally manage and monitor OpenTelemetry Collectors across your infrastructure using the OpAMP (Open Agent Management Protocol) with secure TLS communication.

flowchart TB;
    subgraph " "
        direction LR;
        subgraph " "
            direction LR;
            OpenLIT_SDK[OpenLIT SDK] -->|Sends Traces & Metrics| OTC[OpenTelemetry Collector];
            OTC -->|Stores Data| ClickHouseDB[ClickHouse];
        end
        subgraph " "
            direction RL;
            OpenLIT_UI[OpenLIT] -->|Pulls Data| ClickHouseDB;
        end
    end

1. Git Clone OpenLIT Repository

   Open your command line or terminal and run:

   git clone git@github.com:openlit/openlit.git

2. Self-host using Docker

   Deploy and run OpenLIT with the following command:

   docker compose up -d

For instructions on installing in Kubernetes using Helm, refer to the Kubernetes Helm installation guide.

Open your command line or terminal and run:

pip install openlit

For instructions on using the TypeScript SDK, visit the TypeScript SDK Installation guide.

Integrate OpenLIT into your AI applications by adding the following lines to your code.

import openlit

openlit.init()

Configure the telemetry data destination as follows:

💡 Info: If the otlp_endpoint or OTEL_EXPORTER_OTLP_ENDPOINT is not provided, the OpenLIT SDK will output traces directly to your console, which is recommended during the development phase.

import openlit

openlit.init(
  otlp_endpoint="http://127.0.0.1:4318", 
)

import openlit

openlit.init()

export OTEL_EXPORTER_OTLP_ENDPOINT = "http://127.0.0.1:4318"

With the Observability data now being collected and sent to OpenLIT, the next step is to visualize and analyze this data to get insights into your AI application's performance, behavior, and identify areas of improvement.

Just head over to OpenLIT at 127.0.0.1:3000 on your browser to start exploring. You can login using the default credentials:

• Email: user@openlit.io
• Password: openlituser

OpenLIT auto-instruments 50+ LLM providers, AI frameworks, and vector databases with a single line of code. Each integration produces OpenTelemetry-native traces and metrics. Click any card to view the integration docs.

LLM Providers