Langfuse
Open source tracing, evaluation and prompt management for LLM apps
1/3Langfuse is an open source engineering platform for teams building LLM applications and agents, covering observability and evaluation over the same production data. Traces are hierarchical, so an agent run shows its LLM calls, tool invocations and retrieval steps in the shape they actually happened, and evaluation runs over them through LLM-as-a-judge, heuristic functions or human annotation queues. It also handles prompt management with versioning and one-click rollbacks, a playground for comparing models side by side on real inputs, experiments over test cases, and cost and latency dashboards with alerts. It is MIT licensed with over 100 integrations, and self-hosting is free.
- Hierarchical traces across LLM calls, tools and retrieval
- Evaluation by LLM-as-a-judge, heuristics or human review
- Prompt management with versions, deploys and rollbacks
- Playground compares models side by side on production inputs
- Experiments define test cases and compare the results
- Annotation queues for collaborative human review
- Cost and latency dashboards with automated alerts
- 100+ integrations, including LangChain and OpenTelemetry
Based on what people have said
Every answer links back to the mentions it came fromHow difficult is it to integrate Langfuse into an existing project?
Several users report that adding observability or tracing with Langfuse requires only a small amount of code, specifically mentioning one or two lines.
1 mention · r/LangChain · Sep 2026
What are the benefits of using Langfuse for LLM application observability?
Users describe the platform as helpful for improving chat interfaces and providing visibility into agent activity, including full prompt traces and evaluation scoring. It is noted for its ability to track tokens, which helps in identifying budget consumption, and for offering a hierarchical organization structure for projects.
4 mentions · r/aipromptprogramming · @MMIA18EMV · @NielsRogge · r/foss · Aug 2026 – Sep 2026
Is Langfuse suitable for self-hosting?
Yes, the software is open source and can be self-hosted, with some users inquiring about hosting it on various VPS platforms.
3 mentions · r/AI_Agents · r/hermesagent · @konig0000 · Aug 2026 – Sep 2026
How does Langfuse compare to other observability tools regarding cost?
While some users find Langfuse useful for cost estimation, others have moved away from managed observability platforms like LangSmith due to unpredictable pricing, opting to build their own solutions instead.
2 mentions · r/AI_Agents · r/AI_Agents · Sep 2026
These summarise counts and dates only. Read the quotes themselves under what people are saying.
What people are saying about Langfuse
74% neutral“langfuse is open source and does prompt versioning but you have to self host it and own the infra and our platform team is kinda small so they dont love that idea. portkey has a prompt library thing too but it feels more like an engineering tool than something a non technical person could just go use on their own without help.”
“langfuse is open source selfhostable, like strongest direct replacement, tracing and eval primitives are genuinely good, your own maintenance”
“41. https://github.com/langfuse/langfuse — LLM observability; clients pay for this”
“I’m operating a terabyte-scale Clickhouse - but only because I left Langfuse running for a few months on a MacBook :)But seriously Clickhouse does love disk space.”
“Running it all from a Rancher cluster, with Open router and Langfuse.”
“If you're already on Langfuse/Braintrust, are you tagging outcomes (success/fail categories) or mostly using free-form logs? imo found that to be the swtich from "debugging" to acc being able to measure improvements over time”
“langfuse or braintrust for that half.”
“If you want a hosted tracing UI on top, Langfuse and LangSmith both do step-by-step traces of LLM and tool calls. I'd still keep the conversation and outcome tables in your own database, because that's where you answer the business questions.”
“pick one tracing stack (LangSmith / Langfuse / whatever) and start logging every run with inputs, tool calls, final answer, latency, cost. then write 20-50 golden tasks and score them (even rough LLM-as-judge + a few hard asserts).”
“For example, Langfuse is an amazing open-source LLM observability tool.”
“**How it compares.** Not an observability platform and doesn't replace one: LangSmith or Langfuse watch the system; this signs off that it didn't get worse than the version you approved. Comparison page: [https://digline.dev/comparison/?ref=reddit](https://digline.dev/comparison/?ref=reddit)”
“To answer your question about evaluations sadly, it’s still an engineering territory, tools like langfuse and Langsmith are pretty useful if you’d like to see what happens once prompt goes till you get the response.”
“I also attend a lot of workshops/seminars/training sessions/take the online learning courses from AI analytics tools. Braintrust, Latitude, Langfuse, etc”
“Recently bolted on a Dell 7050 that has Langfuse & Contextforge running and am testing combos of locals to see what runs smooth on the workstation - Chainlit is fun to run too. My brain hurts trying to sort out where it all fits, I need to map out topology , visually.”
“We work with livekit and langfuse. I also work on building and monitoring AI infrastructure.”
“Another thing that helps is making sure trace ID is properly passed at each abstraction layer, and whether you use something like Sentry, Langfuse, or just Grafana; you can quickly build up the whole flow during dev/testing/staging since you don’t need to omit PII at this stage.”
“@boristane 1) https://pydantic.dev/logfire 2) https://langfuse.com/ 3) Langsmith”
“* Langfuse is the only one with a real organization → project hierarchy; Laminar has workspaces with three roles.”
“I mean, for LLMs like via OpenRouter it's fairly easy (even Langfuse can estimate costs), but I also want to control costs on Apify (and it has a bunch of different actors with different cost per operations/volume/compute) - so , so far definitely no, but would really like to”
“📊 Visualize all agent activity in Datadog, Grafana, or Langfuse with OpenTelemetry.”
“Langfuse, Langsmith, Helicone, and just dumping tool calls to a Postgres table have all solved observability for agents already. Nobody serious was flying blind, you just were”
“real problem is real though, no clue which tool ate my budget on some dumb retry loop, but Helicone and Langfuse already do token tracking pretty well if that's what you need :)”
“and langfuse is open source and self hostable - did you consider running that yourself instead of rolling your own, or was your custom work already too far along by then?”
“Now it won't even respond to a Langfuse logfile chunk when my local system took down Tailscale. So even as a backup for when my local system has an issue it doesn't help.”
“We looked at LangSmith and LangFuse and several others. We used LangSmith for a while but we built a lot around it, but things went kind of ballistic on pricing which introduced too much unpredictability into the platform's operating costs, so we decided to roll our own.”