langfuse
fern
langfuse | fern | |
---|---|---|
11 | 29 | |
3,815 | 2,366 | |
32.9% | 3.8% | |
9.9 | 9.9 | |
2 days ago | 6 days ago | |
TypeScript | TypeScript | |
GNU General Public License v3.0 or later | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
langfuse
-
Top Open Source Prompt Engineering Guides & Tools🔧🏗️🚀
Langfuse is an open-source LLM engineering platform that helps teams collaboratively debug, analyze, and iterate on their LLM applications.
- Roast My Docs
-
Show HN: Open-Source LLM Observability and Export to Grafana, Datadog etc.
Congrats on the Show! How’s this different from https://github.com/langfuse/langfuse? The exports seems really interesting
-
RAG observability in 2 lines of code with Llama Index & Langfuse
Thus, we started working on Langfuse.com (GitHub) to establish an open source LLM engineering platform with tightly integrated features for tracing, prompt management, and evaluation. In the beginning we just solved our own and our friends’ problems. Today we are at over 1000 projects which rely on Langfuse, and 2.3k stars on GitHub. You can either self-host Langfuse or use the cloud instance maintained by us.
-
langfuse VS agenta - a user suggested alternative
2 projects | 22 Nov 2023
-
Ask HN: Who is hiring? (November 2023)
- We want to build a tool that is recommended here on HN: you can build a tool you would want to use yourself.
Please see more details here: https://langfuse.com/careers or reach out directly to me: [email protected]
[1] https://github.com/langfuse/langfuse
[2] https://create.t3.gg/
-
How are generative AI companies monitoring their systems in production?
We struggled with this ourselves while building LLM-based products and then open-sourced our observability/monitoring tool [1]. Many use it to track RAG and agents in production, run custom evals on the production traces (focused on hallucination), and track how metrics are different across releases or customers. Feel free to dm if there is something specific you are looking to solve, happy to help.
[1] https://github.com/langfuse/langfuse
-
LLM Analytics 101 - How to Improve your LLM app
Visit us on Discord and Github to engage with our project.
-
Ask HN: Any tools or frameworks to monitor the usage of OpenAI API keys?
Maybe try https://github.com/langfuse/langfuse
It was recently shared on HN
- Show HN: Langfuse – Open-source observability and analytics for LLM apps
fern
-
The Stainless SDK Generator
Lots of these have been popping up lately, they all seem really good.
https://buildwithfern.com/
- Fern: Toolkit to generate SDKs and Docs for your API
-
Ask HN: Who is hiring? (December 2023)
Fern | https://buildwithfern.com | Founding Backend Engineer | $160k + equity | On-site NYC | Full-time
At Fern, we're creating the modern developer experience platform. We work with developer-focused companies to generate SDKs & API documentation. We're looking for a Founding Backend Engineer to help us scale with our users. You'll join a small team (3 of us) and will be a product owner who designs, builds, and ships weekly.
Learn more at https://www.buildwithfern.com/careers
-
Ask HN: Who is hiring? (November 2023)
Fern (YC W23) | Founding Engineer | New York City | $130k-$160k + 0.5-1.0% equity | Full Time | Open Source | https://buildwithfern.com
REST APIs underpin the internet but are still painful to work with. They are often untyped, unstandardized, and out-of-sync across multiple sources of truth. With Fern, we aim to bring great developer experiences to REST APIs.
Our stack is Next.js + Vercel, Express (Node.js) + FastAPI (Python), Postgres DB + Prisma ORM, and AWS CDK. We're open source: https://www.github.com/fern-api/fern
We closed a Seed this year from top-tier US investors, including Y Combinator, Abhinav Asthana (Postman CEO), Arash Ferdowsi (Dropbox co-founder), and Ian McCrystal (Stripe's Head of Docs).
Learn more: https://www.buildwithfern.com/careers
- Fern: Beautiful SDKs and Docs for Your API
-
Show HN: REST Alternative to GraphQL and tRPC
Thank you for your encouraging words and insights!
There are indeed popular DSLs and code to openapi solutions out there. Many of which are easy to plug in to the openapi-stack libraries btw!
I guess I personally always found it frustrating to try to control the generated OpenAPI output using additional tooling and ended up preferring yaml + a visualisation tool as the api design workflow. (e.g. swagger editor)
But something like https://buildwithfern.com, or using zod as substitute for json schema may indeed be worth a try as a step before emitting openapi.
-
Ask HN: Who is hiring? (October 2023)
Fern (YC W23) | Founding Engineer | New York City | $125k-$175k + equity | Full Time | Open Source | https://buildwithfern.com
REST APIs underpin the internet but are still painful to work with. They are often untyped, unstandardized, and out-of-sync across multiple sources of truth. With Fern, we aim to bring great developer experiences to REST APIs.
Our stack is Next.js + Vercel, Express (Node.js) + FastAPI (Python), Postgres DB + Prisma ORM, and AWS CDK.
We closed a Seed this year from top-tier US investors, including Y Combinator, Abhinav Asthana (Postman CEO), Arash Ferdowsi (Dropbox co-founder), and Ian McCrystal (Stripe's Head of Docs).
Apply by emailing [email protected]
-
Show HN: Langfuse – Open-source observability and analytics for LLM apps
Hi HN! Langfuse is OSS observability and analytics for LLM applications (repo: https://github.com/langfuse/langfuse, 2 min demo: https://langfuse.com/video; try it yourself: https://langfuse.com/demo)
Langfuse makes capturing and viewing LLM calls (execution traces) a breeze. On top of this data, you can analyze the quality, cost and latency of LLM apps.
When GPT-4 dropped, we started building LLM apps – a lot of them! [1, 2] But they all suffered from the same issue: it’s hard to assure quality in 100% of cases and even to have a clear view of user behavior. Initially, we logged all prompts/completions to our production database to understand what works and what doesn’t. We soon realized we needed more context, more data and better analytics to sustainably improve our apps. So we started building a homegrown tool.
Our first task was to track and view what is going on in production: what user input is provided, how prompt templates or vector db requests work, and which steps of an LLM chain fail. We built async SDKs and a slick frontend to render chains in a nested way. It’s a good way to look at LLM logic ‘natively’. Then we added some basic analytics to understand token usage and quality over time for the entire project or single users (pre-built dashboards).
Under the hood, we use the T3 stack (Typescript, NextJs, Prisma, tRPC, Tailwind, NextAuth), which allows us to move fast + it means it's easy to contribute to our repo. The SDKs are heavily influenced by the design of the PostHog SDKs [3] for stable implementations of async network requests. It was a surprisingly inconvenient experience to convert OpenAPI specs to boilerplate Python code and we ended up using Fern [4] here. We’re fans of Tailwind + shadcn/ui + tremor.so for speed and flexibility in building tables and dashboards fast.
Our SDKs run fully asynchronously and make network requests in the background. We did our best to reduce any impact on application performance to a minimum. We never block the main execution path.
We've made two engineering decisions we've felt uncertain about: to use a Postgres database and Looker Studio for the analytics MVP. Supabase performs well at our scale and integrates seamlessly into our tech stack. We will need to move to an OLAP database soon and are debating if we need to start batching ingestion and if we can keep using Vercel. Any experience you could share would be helpful!
Integrating Looker Studio got us to first analytics charts in half a day. As it is not open-source and does not work with our UI/UX, we are looking to switch it out for an OSS solution to flexibly generate charts and dashboards. We’ve had a look at Lightdash and would be happy to hear your thoughts.
We’re borrowing our OSS business model from Posthog/Supabase who make it easy to self-host with features reserved for enterprise (no plans yet) and a paid version for managed cloud service. Right now all of our code is available under a permissive license (MIT).
Next, we’re going deep on analytics. For quality specifically, we will build out model-based evaluations and labeling to be able to cluster traces by scores and use cases.
Looking forward to hearing your thoughts and discussion – we’ll be in the comments. Thanks!
[1] https://learn-from-ai.com/
[2] https://www.loom.com/share/5c044ca77be44ff7821967834dd70cba
[3] https://posthog.com/docs/libraries
[4] https://buildwithfern.com/
-
tRPC – Move Fast and Break Nothing. End-to-end typesafe APIs made easy
You can recommend it in what context, from openapi (as they claim https://github.com/fern-api/fern#starting-from-openapi ) or from their ... special ... definition schema?
For those wanting less talk, moar code: https://github.com/fern-api/fern-java/blob/0.4.2-rc3/example... -> https://github.com/fern-api/fern-java/blob/0.4.2-rc3/example...
-
OpenAPI v4 Proposal
I'm one of the builders of an open source project (buildwithfern.com) to improve client codegen. One of the learnings I've had is that the quality of OpenAPI specs varies widely (like really widely). We wrote a linter that suggests improvements to your OpenAPI before you run the code generators and that's been really helpful for generating idiomatic clients.
You can try Fern for free: https://buildwithfern.com
What are some alternatives?
trulens - Evaluation and Tracking for LLM Experiments
openapi-generator - OpenAPI Generator allows generation of API client libraries (SDK generation), server stubs, documentation and configuration automatically given an OpenAPI Spec (v2, v3)
llama_index - LlamaIndex is a data framework for your LLM applications
trpc - 🧙♀️ Move Fast and Break Nothing. End-to-end typesafe APIs made easy.
langchain - 🦜🔗 Build context-aware reasoning applications
openapi-typescript-codegen - NodeJS library that generates Typescript or Javascript clients based on the OpenAPI specification
agenta - The all-in-one LLM developer platform: prompt management, evaluation, human feedback, and deployment all in one place.
speakeasy - Speakeasy CLI - Enterprise developer experience for your API
opentelemetry-instrument-openai-py - OpenTelemetry instrumentation for the OpenAI Python library
electron-trpc - Build type-safe Electron inter-process communication using tRPC
examples - Your one-stop-shop to try Xata out. From packages to apps, whatever you need to get started.
openai-node - The official Node.js / Typescript library for the OpenAI API