Langfuse vs Opik: both are coding tools on Scriptiii AI Tools. Langfuse pricing: Free. Free to use. (reported by the maker) Opik pricing: Free. Free to use. (reported by the maker) Both have 0 upvotes.
Interested in contributing to Langfuse full-time? Langfuse is growing fast (we doubled the team in the last 6 months). Since January 2026 we're part of ClickHouse. We hire remotely, with offices in Berlin, San Francisco, Amsterdam, and New York. See open roles โ
โจ Core Features
AI Observability: Instrument your app and start ingesting traces to Langfuse, thereby tracking LLM calls and other relevant logic in your app such as retrieval, embedding, or agent actions. Inspect and debug complex logs and user sessions. Built on OpenTelemetry. Try the interactive demo to see this in action.
Prompt Management helps you centrally manage, version control, and collaboratively iterate on your prompts. Thanks to strong caching on server and client side, you can iterate on prompts without adding latency to your application. The LLM Playground lets you test prompts and model configurations; when you see a bad result in tracing, you can jump straight to the playground to iterate on it.
Skill Management (beta) brings versioning and labels to agent skills: a SKILL.md with task instructions plus optional supporting files. Promote or roll back versions without changing application code, and link skill versions to the traces they produced.
Evaluations are key to the AI agent development workflow, and Langfuse adapts to your needs. It supports LLM-as-a-judge, code evaluators, user feedback collection, manual labeling, and custom evaluation pipelines via APIs/SDKs. Use Datasets and Experiments to build test sets and benchmarks for pre-deployment testing and continuous improvement.
AI Gateway (beta) gives your organization one endpoint for calls to your model providers. Keep provider credentials in one place, hand out per-user or service account gateway keys, approve models centrally, track AI spend per user, key, and model, and record every call as a Langfuse trace.
Comprehensive API: Langfuse is frequently used to power bespoke AI engineering workflows while using the building blocks provided by Langfuse via the API. An OpenAPI spec, typed SDKs for Python and JS/TS, the Langfuse CLI, and the Langfuse Agent Skill for coding agents are available.
๐ฆ Deploy Langfuse
Langfuse Cloud
Managed deployment by the Langfuse team, generous free tier, no credit card required.
Docker Compose: Run Langfuse on your own machine or a single virtual machine in 5 minutes. The guide also covers configuring Docker log rotation so container logs don't fill up the disk.
# Get a copy of the latest Langfuse repository
git clone --depth=1 https://github.com/langfuse/langfuse.git
cd langfuse
# Run the langfuse docker compose
docker compose up
Kubernetes (Helm): Run Langfuse on a Kubernetes cluster using Helm. This is the preferred production deployment.
[!TIP] Self-hosting Langfuse? Subscribe to the self-hosting update list to get an email about important features and new releases for open source Langfuse โ self-hosting updates only, no marketing.
๐ Integrations
Langfuse is built on OpenTelemetry, so any OpenTelemetry-instrumented application, framework, or collector can send traces to Langfuse.
Instrument your app and start ingesting traces to Langfuse, thereby tracking LLM calls and other relevant logic in your app such as retrieval, embedding, or agent actions. Inspect and debug complex logs and user sessions.
๐ค Agentic installation (recommended)
Install the Langfuse Agent Skill to let your coding agent access all Langfuse features, then ask it to add tracing to your application:
Install the Langfuse Agent Skill from github.com/langfuse/skills
and use it to add tracing to this application with Langfuse
following best practices.
You can also install the skill with the skills CLI (npx skills add langfuse/skills --skill "langfuse") or the Langfuse Cursor plugin. If your agent supports MCP, add the Langfuse Docs MCP server (https://langfuse.com/api/mcp, no authentication) so it can look up current documentation while it works. See the get started guide for details.
Create new API credentials in the project settings
2๏ธโฃ Log your first LLM call
The @observe() decorator makes it easy to trace any Python LLM application. In this quickstart we also use the Langfuse OpenAI integration to automatically capture all model parameters.
[!TIP]
Not using OpenAI? Visit our documentation to learn how to log other models and frameworks.
pip install langfuse openai
LANGFUSE_SECRET_KEY="sk-lf-..."
LANGFUSE_PUBLIC_KEY="pk-lf-..."
LANGFUSE_BASE_URL="https://cloud.langfuse.com" # ๐ช๐บ EU region
## LANGFUSE_BASE_URL="https://us.cloud.langfuse.com" # ๐บ๐ธ US region
## LANGFUSE_BASE_URL="https://jp.cloud.langfuse.com" # ๐ฏ๐ต Japan region
## LANGFUSE_BASE_URL="https://hipaa.cloud.langfuse.com" # โ๏ธ HIPAA region
from langfuse import observe
from langfuse.openai import openai # OpenAI integration
@observe()
def story():
return openai.chat.completions.create(
model="gpt-4o",
messages=[{"role": "user", "content": "What is Langfuse?"}],
).choices[0].message.content
@observe()
def main():
return story()
main()
3๏ธโฃ See traces in Langfuse
See your language model calls and other application logic in Langfuse.
Our documentation is the best place to start looking for answers. It is comprehensive, and we invest significant time into maintaining it. You can also suggest edits to the docs via GitHub.
Langfuse FAQs where the most common questions are answered.
Use "Ask AI" to get instant answers to your questions.
Support Channels:
Ask any question in our public Q&A on GitHub Discussions. Please include as much detail as possible (e.g. code snippets, screenshots, background information) to help us understand your question.
Open a PR - see CONTRIBUTING.md for details on how to setup a development environment.
๐ฅ License
This repository is MIT licensed, except for the ee folders. See LICENSE and docs for more details.
Dependencies
We deploy this code base in Docker containers. See web/Dockerfile and worker/Dockerfile for how the images are built, including the base images they use.
โญ๏ธ Star History
โค๏ธ Open Source Projects Using Langfuse
Top open-source projects that use the Langfuse Python or JS/TS SDK, ranked by stars (Source):
We take data security and privacy seriously. Please refer to our Security and Privacy page for more information.
Telemetry
By default, Langfuse automatically reports basic usage statistics of self-hosted instances to a centralized server (PostHog).
This helps us to:
Understand how Langfuse is used and improve the most relevant features.
Track overall usage for internal and external (e.g. fundraising) reporting.
The telemetry does not include raw traces, prompts, observations, scores, or dataset contents. We document the exact fields that are collected, where they are sent, and the implementation reference in our telemetry docs.
For Langfuse OSS, you can opt out by setting TELEMETRY_ENABLED=false.
Opik: Open-Source LLM Observability, Evaluation & AI Agent Tracing
Opik is the open-source LLM observability and evaluation platform for AI agent tracing, LLM evaluation, prompt management, and production monitoring. Built by Comet. Apache-2.0 licensed, free to self-host the full platform, with 20,000+ GitHub stars.
Opik covers the full LLM application lifecycle, from the first trace in development to production monitoring, for teams building LLM apps and AI agents. Key offerings include:
AI Agent Tracing & Observability: Deep tracing of LLM calls, conversation logging, and agent activity, with full trace trees for multi-step agents and tool calls.
LLM Evaluation: Datasets, experiments, and LLM-as-a-judge metrics for hallucination detection, moderation, and RAG assessment.
Prompt & Agent Optimization: The Opik Agent Optimizer SDK to improve prompts and agents.
Production-Ready Monitoring: Scalable dashboards and online evaluation rules.
Opik Guardrails: Features to help you implement safe and responsible AI practices.
CI/CD Evaluation: A PyTest integration to test LLM pipelines on every commit.
Key capabilities include:
Development & Tracing:
Track all LLM calls and traces with detailed context during development and in production (Quickstart).
Extensive 3rd-party integrations for easy observability: Seamlessly integrate with a growing list of frameworks, supporting many of the largest and most popular ones natively (including recent additions like Google ADK, Autogen, and Flowise AI). (Integrations)
Annotate traces and spans with feedback scores via the Python SDK or the UI.
Leverage Opik Agent Optimizer and Opik Guardrails to continuously improve and secure your LLM applications in production.
Who it's for: ML engineers building LLM-powered agents, AI teams moving from prototype to production, and engineering teams that need open-source, self-hostable observability they can run in their own environment.
Why open source matters here: Opik is Apache-2.0 licensed and free to self-host: the full platform, backend included, not just a client SDK. The repository includes the server backend, web application, tracing, datasets, experiments, evaluations, prompt management, online evaluation, and agent optimization components, all under Apache-2.0. You can run LLM observability inside your own infrastructure with no data leaving your environment and no Enterprise sales conversation required.
[!TIP]
If you are looking for features that Opik doesn't have today, please raise a new Feature request ๐
โก Quick Start
Install the Python SDK and configure it:
pip install opik
opik configure
Wrap any function with the @track decorator to start logging traces:
Every call to my_function is now logged to Opik, including nested calls, so this works for full agent and pipeline traces, not just single LLM calls. See the Quickstart guide for the TypeScript SDK and other setup options.
Connect your coding agent
Let Claude Code, Cursor, VS Code Copilot, Codex or opencode read your traces, score outputs and run evaluations from chat. One command sets it up. It needs uv and no SDK:
uvx opik mcp configure
The badges and the add-mcp fallback below target Opik Cloud; the command above also handles self-hosted deployments. Other MCP clients on Opik Cloud: npx add-mcp https://www.comet.com/opik/api/v1/mcp --name opik-mcp. Details, troubleshooting and FAQ are in the MCP server guide.
๐ How Does Opik Compare?
Opik competes in the LLM observability / AI agent evaluation category alongside LangSmith, Arize (Phoenix and Arize AX), Weights & Biases (Weave), Langfuse, and Braintrust.
Capability
Opik
LangSmith
Phoenix
Arize AX
Weights & Biases (Weave)
Langfuse
Braintrust
Open source
Yes, Apache-2.0 (full platform)
No
Source-available (Elastic License 2.0, not OSI-approved)
No
Open-source SDK/toolkit; self-managed platform requires a commercial license
When teams choose Opik: Opik's full observability, evaluation, and optimization platform is Apache-2.0 licensed and free to self-host. Unlike closed platforms whose self-hosted deployment requires an Enterprise plan, Opik can be deployed without a commercial license, and it's framework-agnostic so it won't lock you into a single agent ecosystem. See the table above for where self-hosting and licensing differ across alternatives.
โ Frequently Asked Questions
Is Opik open source?
Opik is licensed under Apache 2.0. Its server, web application, and core observability and evaluation capabilities can be self-hosted without a commercial license.
Can I self-host Opik?
Yes. Opik can be deployed locally or in your own infrastructure using the documented self-hosting options.
Does Opik support AI agent tracing?
Yes. Opik captures multi-step traces containing LLM calls, tool executions, retrieval steps, and other agent activity.
Deploy Opik in your own environment. Choose between Docker for local setups or Kubernetes for scalability.
Self-Hosting with Docker Compose (for Local Development & Testing)
This is the simplest way to get a local Opik instance running. Note the new ./opik.sh installation script:
On Linux or Mac Environment:
## Clone the Opik repository
git clone https://github.com/comet-ml/opik.git
## Navigate to the repository
cd opik
## Start the Opik platform
./opik.sh
On Windows Environment:
## Clone the Opik repository
git clone https://github.com/comet-ml/opik.git
## Navigate to the repository
cd opik
## Start the Opik platform
powershell -ExecutionPolicy ByPass -c ".\\opik.ps1"
Installation Script Options
The opik.sh and opik.ps1 scripts support the following options:
## Start full Opik suite (default behavior)
./opik.sh
## Start only infrastructure services (databases, caches etc.)
./opik.sh --infra
## Start infrastructure + backend services
./opik.sh --backend
## Enable guardrails with any profile
./opik.sh --guardrails # Guardrails with full Opik suite
./opik.sh --backend --guardrails # Guardrails with infrastructure + backend
## Build the containers from source before starting
./opik.sh --build
## Check that all containers are healthy
./opik.sh --verify
## Stop all containers
./opik.sh --stop
## Stop all containers and remove all Opik data volumes
## WARNING: ALL OPIK DATA WILL BE LOST
./opik.sh --clean
## Show all available options
./opik.sh --help
Use the --help or --info options to troubleshoot issues. Dockerfiles now ensure containers run as non-root users for enhanced security. Once all is up and running, you can now visit localhost:5173 on your browser! For detailed instructions, see the Local Deployment Guide.
Self-Hosting with Kubernetes & Helm (for Scalable Deployments)
For production or larger-scale self-hosted deployments, Opik can be installed on a Kubernetes cluster using our Helm chart. Click the badge for the full Kubernetes Installation Guide using Helm.
๐ป Opik Client SDK
Opik provides a suite of client libraries and a REST API to interact with the Opik server. This includes SDKs for Python and TypeScript, plus first-party OpenTelemetry support: any language with an OpenTelemetry SDK โ including Java, Ruby, and .NET โ can send traces to Opik. For detailed API and SDK references, see the Opik Client Reference Documentation.
Python SDK Quick Start
To get started with the Python SDK:
Install the package:
## install using pip
pip install opik
## or install with uv
uv pip install opik
Configure the python SDK by running the opik configure command, which will prompt you for your Opik server address (for self-hosted instances) or your API key and workspace (for Comet.com):
opik configure
[!TIP]
You can also call opik.configure(use_local=True) from your Python code to configure the SDK to run on a local self-hosted installation, or provide API key and workspace details directly for Comet.com. Refer to the Python SDK documentation for more configuration options.
You are now ready to start logging traces using the Python SDK.
๐ Logging Traces with Integrations
The easiest way to log traces is to use one of our direct integrations. Opik supports a wide array of frameworks, including recent additions like Google ADK, Autogen, AG2, and Flowise AI:
[!TIP]
If the framework you are using is not listed above, feel free to open an issue or submit a PR with the integration.
If you are not using any of the frameworks above, you can also use the track function decorator to log traces:
import opik
opik.configure(use_local=True) # Run locally
@opik.track
def my_llm_function(user_question: str) -> str:
# Your LLM code here
return "Hello"
[!TIP]
The track decorator can be used in conjunction with any of our integrations and can also be used to track nested function calls.
๐งโโ๏ธ LLM as a Judge metrics
The Python Opik SDK includes a number of LLM as a judge metrics to help you evaluate your LLM application. Learn more about it in the metrics documentation.
To use them, simply import the relevant metric and use the score function:
from opik.evaluation.metrics import Hallucination
metric = Hallucination()
score = metric.score(
input="What is the capital of France?",
output="Paris",
context=["France is a country in Europe."]
)
print(score)
Opik also includes a number of pre-built heuristic metrics as well as the ability to create your own. Learn more about it in the metrics documentation.
๐ Evaluating your LLM Applications
Opik allows you to evaluate your LLM application during development through Datasets and Experiments. The Opik Dashboard offers enhanced charts for experiments and better handling of large traces. You can also run evaluations as part of your CI/CD pipeline using our PyTest integration.
โญ Star Us on GitHub
If you find Opik useful, please consider giving us a star! Your support helps us grow our community and continue improving the product.
Both listings report Free. Free to use. Pricing is reported by the maker and is not independently verified.
Which has more upvotes, Langfuse or Opik?
Both have 0 upvotes.
What does each maker say Langfuse and Opik are for?
Langfuse: Open-source LLM engineering platform for tracing, evaluation and prompt management. Opik: Comet open-source platform for tracing, evaluating and monitoring LLM apps and agents.
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