Langtrace

Open Source Observability for LLM applications

Open Source & Open Telemetry(OTEL) Observability for LLM applications

Langtrace is an open source observability software which lets you capture, debug and analyze traces and metrics from all your applications that leverages LLM APIs, Vector Databases and LLM based Frameworks.

Open Telemetry Support

The traces generated by Langtrace adhere to Open Telemetry Standards(OTEL). We are developing semantic conventions for the traces generated by this project. You can checkout the current definitions in this repository. Note: This is an ongoing development and we encourage you to get involved and welcome your feedback.

SDK Repositories

Getting Started

Langtrace Cloud ☁️

To use the managed SaaS version of Langtrace, follow the steps below:

Sign up by going to this link.
Create a new Project after signing up. Projects are containers for storing traces and metrics generated by your application. If you have only one application, creating 1 project will do.
Generate an API key by going inside the project.
In your application, install the Langtrace SDK and initialize it with the API key you generated in the step 3.
The code for installing and setting up the SDK is shown below:

If your application is built using typescript/javascript

npm i @langtrase/typescript-sdk

import * as Langtrace from '@langtrase/typescript-sdk' // Must precede any llm module imports
Langtrace.init({ api_key: <your_api_key> })

import * as Langtrace from "@langtrase/typescript-sdk"; // Must precede any llm module imports
LangTrace.init(); // LANGTRACE_API_KEY as an ENVIRONMENT variable

If your application is built using python

pip install langtrace-python-sdk

from langtrace_python_sdk import langtrace
langtrace.init(api_key=<your_api_key>)

from langtrace_python_sdk import langtrace
langtrace.init() # LANGTRACE_API_KEY as an ENVIRONMENT variable

Langtrace self hosted

To run the Langtrace locally, you have to run three services:

Next.js app
Postgres database
Clickhouse database

Important

Checkout our documentation for various deployment options and configurations.

Requirements:

Docker
Docker Compose

The .env file

Feel free to modify the .env file to suit your needs.

Starting the servers

docker compose up

The application will be available at http://localhost:3000.

Take down the setup

To delete containers and volumes

docker compose down -v

-v flag is used to delete volumes

Supported integrations

Langtrace automatically captures traces from the following vendors:

Vendor	Type	Typescript SDK	Python SDK
OpenAI	LLM	✅	✅
Anthropic	LLM	✅	✅
Azure OpenAI	LLM	✅	✅
Cohere	LLM	✅	✅
Groq	LLM	❌	✅
Perplexity	LLM	✅	✅
Langchain	Framework	❌	✅
LlamaIndex	Framework	✅	✅
Langgraph	Framework	❌	✅
DSPy	Framework	❌	✅
CrewAI	Framework	❌	✅
Ollama	Framework	❌	✅
Pinecone	Vector Database	✅	✅
ChromaDB	Vector Database	✅	✅
QDrant	Vector Database	✅	✅
Weaviate	Vector Database	✅	✅
PGVector	Vector Database	✅	✅ (SQLAlchemy)

Langtrace System Architecture

Feature Requests and Issues

To request for features, head over here to start a discussion.
To raise an issue, head over here and create an issue.

Contributions

We welcome contributions to this project. To get started, fork this repository and start developing. To get involved, join our Slack workspace.

Langtrace Star History

Security

To report security vulnerabilites, email us at [email protected]. You can read more on security here.

License

Langtrace application(this repository) is licensed under the AGPL 3.0 License. You can read about this license here.
Langtrace SDKs are licensed under the Apache 2.0 License. You can read about this license here.

Frequently Asked Questions

1. Can I self host and run Langtrace in my own cloud? Yes, you can absolutely do that. Follow the self hosting setup instructions in our documentation.

2. What is the pricing for Langtrace cloud? Currently, we are not charging anything for Langtrace cloud and we are primarily looking for feedback so we can continue to improve the project. We will inform our users when we decide to monetize it.

3. What is the tech stack of Langtrace? Langtrace uses NextJS for the frontend and APIs. It uses PostgresDB as a metadata store and Clickhouse DB for storing spans, metrics, logs and traces.

4. Can I contribute to this project? Absolutely! We love developers and welcome contributions. Get involved early by joining our Discord Community.

5. What skillset is required to contribute to this project? Programming Languages: Typescript and Python. Framework knowledge: NextJS. Database: Postgres and Prisma ORM. Nice to haves: Opentelemetry instrumentation framework, experience with distributed tracing.

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langtrace's Introduction