Curious what it's like behind the scenes at Langfuse? Caleb Love Seeling filmed a "Day in the Life" as a Technical Support Engineer on our team. Have a look, and if it looks like your kind of thing, we're hiring!
Langfuse
Software Development
Open Source LLM Engineering Platform, now part of ClickHouse
About us
Langfuse is an open source AI engineering platform. It helps teams collaboratively develop, monitor, evaluate, and debug AI applications. Langfuse can be self-hosted in minutes and is battle-tested and used in production by thousands of users from YC startups to large companies like Khan Academy or Twilio. Langfuse builds on a proven track record of reliability and performance. Developers can trace any Large Language model or framework using our SDKs for Python and JS/TS, our open API or our native integrations (OpenAI, Langchain, Llama-Index, Vercel AI SDK). Beyond tracing, developers use Langfuse Prompt Management, its open APIs, and testing and evaluation pipelines to improve the quality of their applications. Product managers can analyze, evaluate, and debug AI products by accessing detailed metrics on costs, latencies, and user feedback in the Langfuse Dashboard. They can bring humans in the loop by setting up annotation workflows for human labelers to score their application. Langfuse can also be used to monitor security risks through security framework and evaluation pipelines. Langfuse enables non-technical team members to iterate on prompts and model configurations directly within the Langfuse UI or use the Langfuse Playground for fast prompt testing. Langfuse is open source and we are proud to have a fantastic community on GitHub and Discord that provides help and feedback. Do get in touch with us! Langfuse is now part of ClickHouse.
- Website
-
https://langfuse.com
External link for Langfuse
- Industry
- Software Development
- Company size
- 11-50 employees
- Headquarters
- San Francisco
- Type
- Privately Held
- Founded
- 2022
- Specialties
- Langfuse, Large Language Models, Observability, Prompt Management, Evaluations, Testing, Open Source, LLM, AI, Analytics, Open Source, and Artificial Intelligence
Products
![Click here to view Langfuse]()
Langfuse
Product Analytics Software
Langfuse is an open-source LLM engineering platform that helps teams collaboratively debug, analyze, and iterate on their LLM applications.
Locations
-
Primary
Get directions
San Francisco, US
-
Get directions
Berlin, DE
Employees at Langfuse
Updates
-
Overview of what's new in 2026. Very productive year so far, more to come!
This year has been really good for Langfuse so far. Quick summary of largest releases from January through July. Want to see me demo everything on the slide and hear what comes next? Join our community town hall next week (link in comments)
-
-
Memory is one layer of the agent stack observability often can't see. EverOS now speaks OTel natively, so memory operations land in Langfuse: what got stored, what got recalled, how confident that recall was, how conflicting memories got merged, and what it cost.
Memory is the one layer of the agent stack observability can't see. That makes memory a hidden decision layer: what gets stored, what gets recalled, what gets consolidated, and what gets forgotten. Every one of those choices shapes what your agent says next, and none of them show up in a log line. When the agent gets it wrong, you are guessing. We build EverOS, an open-source memory engine for AI agents (Apache-2.0, local-first). As of 1.2.0 it emits OpenTelemetry natively, so those decisions stop being hidden. Our Langfuse integration page went live today: https://lnkd.in/gPM68PDD What that makes visible: • 𝗧𝗵𝗲 𝗺𝗲𝗺𝗼𝗿𝘆 𝗹𝗶𝗳𝗲𝗰𝘆𝗰𝗹𝗲 𝗮𝘀 𝗮 𝘁𝗿𝗮𝗰𝗲 𝘁𝗿𝗲𝗲. One write opens a trace, and every stage becomes a step inside it: boundary detection, extraction, reflection, and storage. Searches get a trace of their own, from the query embedding through recall and ranking. Each step carries its own time and cost, so the price of remembering one thing is itemised rather than arriving as a single number at the end of the month. • 𝗖𝗼𝗻𝗳𝗹𝗶𝗰𝘁𝗶𝗻𝗴 𝗺𝗲𝗺𝗼𝗿𝗶𝗲𝘀 𝗴𝗲𝘁𝘁𝗶𝗻𝗴 𝗿𝗲𝘀𝗼𝗹𝘃𝗲𝗱. When a plan changes or a preference is updated, EverOS Reflection merges the two memories and retires the outdated one, which then stops coming back from search. This normally happens invisibly, and it is the hardest thing to debug when it goes wrong. Now it shows up in the trace, so you can see which conversation changed what your agent believes. • 𝗥𝗲𝗰𝗮𝗹𝗹 𝗰𝗼𝗻𝗳𝗶𝗱𝗲𝗻𝗰𝗲 𝗼𝗻 𝗲𝘃𝗲𝗿𝘆 𝘀𝗲𝗮𝗿𝗰𝗵. Each search returns a score for how good EverOS estimates the recall was. Trend it over time and you catch the queries that quietly come back with nothing useful. There is no wrapper library and no instrumentation code to write. Tracing is one config block, off by default, and content capture is opt-in. Stop guessing why your agent forgot. Check the integration page for the full setup, or replay our recording of a real run into your own Langfuse project, no install required. Thanks to the Langfuse team for collaborating on this! 🙌
-
-
Quarterly Langfuse Town Hall. Join the Langfuse team, maintainers, and community for our quarterly call. Marc Klingen will demo the features we shipped in the last 3 months and share more about our upcoming roadmap. Afterward, we'll have lots of time for Q&A. 𝗪𝗵𝗲𝗻: Wednesday, August 12th, 2026, at 9am PT / 6pm CEST 𝗪𝗵𝗲𝗿𝗲: Virtual on Google Meet Link in comments.
-
-
Next stop on our Europe Tour: 🇬🇧 London End of September, we'll be in town for Open House by ClickHouse and host a hands-on workshop "From 0 to Production", showing you in three hours how to bring an agent to life using Langfuse. Sign up in the comments ⬇️
-
-
Langfuse reposted this
ClickHouse is joining the Open Secure AI Alliance alongside NVIDIA and other industry leaders to help build open tools that keep AI agents secure. When an agent gets compromised, the investigation comes down to the record of what it actually did. That record is the evidence layer of agent security, and it should be open. Langfuse is our open-source, MIT-licensed platform for agent tracing, evaluations, and guardrail monitoring. It works with any model and harness, runs air-gapped on your own infrastructure, and stores every trace in ClickHouse, which is also open-source under Apache 2.0. Agents need open audit trails, and that's the layer we joined this alliance to build. Read more: https://lnkd.in/emjq8Smb
-
-
Meet us at one of our World Tour stops
The Langfuse team is doing a world tour at ClickHouse OpenHouse. We could not be more excited to share some product announcements and meet you all in person! Sign up links in comments
-
-
Meet us in Amsterdam on Sep 1
I'll speak at OpenHouse Amsterdam on Sep 1 about all things Langfuse. Join me there to meet the team and other customers in person; OpenHouse SF was very fun, looking forward to replicate this also across Europe. Register for free, find sign up link in comments.
-
-
Any table in Langfuse is now a chart. Toggle the Observations table into a time series over the exact same query. Same filters, same time range, same data. Pick the metric (count, latency, cost, tokens), the aggregation (sum, average, p95), and a breakdown by model, environment, or name. Found a view worth keeping? "Add to dashboard" saves it as a widget, filters and all. Link to docs in the comments.
-
Meet us at OpenHouse New York on Sep 10 Marc will share what's new and coming in Langfuse + the Ramp team will showcase how they use Langfuse to ship agents at rapid pace Sign up link below in comments
Excited to speak at OpenHouse New York on Sep 10 about all things Langfuse. I'll be joined by the great team at Ramp, who will share their story with Langfuse and how it helps them ship and improve their prod agents at a rapid pace. Join us. It will be fun. Register for free, find sign up link in comments.
-