AI · alternatives · 2026
24 alternatives to Currai
Observability & A/B Testing for LLM Apps
Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes.
- 1
Automated Penetration Testing for AI-Built Apps
2021 · replay.io · its alternatives →
- 2
OpenTelemetry-native observability for agents and LLMs
Aug 2026 · openobserve.ai · its alternatives →
- 3
Hey HN, I just built an experimental VSCode extension called LLM Debugger. It’s a proof-of-concept that lets a large language model take charge of debugging. Instead of only looking at the static code, the LLM also gets to see the live runtime state—actual variable values, function calls, branch decisions, and more. The idea is to give it enough context to help diagnose issues faster and even generate synthetic data from running programs. Here’s what it does: * Active Debugging: It integrates with Node.js debug sessions to gather runtime info (like variable states and stack traces). *…
2025 · github.com · its alternatives →
- 4
Langfuse▲771Open source tracing and analytics for LLM applications
2023 · langfuse.com · its alternatives →
- 5
traceAI▲273Open-source LLM tracing that speaks GenAI, not HTTP.
Apr 2026 · github.com · its alternatives →
- 6
Hi HN! We’re Adrien and Kanav. We met at our previous job, where we spent about a third of our lives combating a constant firehose of bugs. In the hope of reducing this pain for others in the future, we’re working on automating debugging. We’re currently working on a platform that ingests logs and then automatically reproduces, root causes and ultimately fixes production bugs as they happen. You can see some of our work on this here - https://news.ycombinator.com/item?id=39528087 As we were building the root-cause phase of our automated debugger, we realized that we developed…
2024 · github.com · its alternatives →
- 7
Hey HN, Nir, Gal and Tomer here. We’re open-sourcing a set of extensions we’ve built on top of OpenTelemetry that provide visibility into LLM applications - whether it be prompts, vector DBs and more. Here’s the repo: https://github.com/traceloop/openllmetry. There’s already a decent number of tools for LLM observability, some open-source and some not. But what we found was missing for all of them is that they were closed-protocol by design, vendor-locking you to use their observability platform or their proprietary framework for running your LLMs. It’s still early in the…
2023 · github.com · its alternatives →
- 8
I built this out of curiosity about what Claude Code was actually sending to the API. Turns out, watching your tokens tick up in real-time is oddly satisfying. Sherlock sits between your LLM tools and the API, showing you every request with a live dashboard, and auto-saved copies of every prompt as markdown and json.
Jan 2026 · github.com · its alternatives →
- 9
Validate, monitor, and safeguard LLM-based apps
2023 · deepchecks.com · its alternatives →
- 10
- 11
If you've built any web-based app in the last 15 years, you probably used something like Datadog, New Relic, Sentry, etc. to monitor and trace your app, right? Why should it be different when the app you're building happens to be using LLMs? So today we're open-sourcing OpenLLMetry-JS. It's an open protocol and SDK, based on OpenTelemetry, that provides traces and metrics for LLM JS/TS applications and can be connected to any of the 15+ tools that already support OpenTelemetry. Here's the repo: https://github.com/traceloop/openllmetry-js A few months ago we launched…
2024 · github.com · its alternatives →
- 12
Hi HN! We’re the team behind Fiddler, and we recently built an Agent Cache feature. It works by capturing an LLM request/response pair and, once caching is enabled, serving subsequent matching calls locally so duplicate requests never hit the provider. It’s meant to reduce three pains that show up together in agent development: token cost, feedback-loop latency, and output non-determinism. We’d love to hear your thoughts.
Aug 2026 · telerik.com · its alternatives →
- 13
Hi HN, I'm the CEO at https://replay.io. We've been building a time travel debugger for web apps for several years now (previous HN post: https://news.ycombinator.com/item?id=28539247) and are combining our tech with AI to automate the debugging process. AIs are really good at writing code but really bad at debugging -- it's amazing to use Claude to prompt an app into existence, and pretty frustrating when that app doesn't work right and Claude is all thumbs fixing the problem. The basic reason for this is a lack of context. People can use devtools to understand…
2025 · nut.new · its alternatives →
- 14
Breadcrumb▲163Open-source LLM tracing for agent visibility
Mar 2026 · breadcrumb.sh · its alternatives →
- 15
Hey all, I've been working on this side project to get a Cypress.io-like experience, but for Ruby developers. It's plug-n-play with Capybara system tests, with the following features: - visualize assertions/commands as they happen - view all API requests, errors, and logs in the timeline - pause/continue on any step - rewind through history with a recorded video This is brand new, so looking for people to start trying it out and leave feedback.
2024 · github.com · its alternatives →
- 16
Glassbrain▲102Visual trace replay for AI apps to fix bugs in one click
Apr 2026 · glassbrain.dev · its alternatives →
- 17AT
I recently built a small open-source tool to benchmark different LLM API endpoints — including OpenAI, Claude, and self-hosted models (like llama.cpp). It runs a configurable number of test requests and reports two key metrics: • First-token latency (ms): How long it takes for the first token to appear • Output speed (tokens/sec): Overall output fluency Demo: https://llmapitest.com/ Code: https://github.com/qjr87/llm-api-test The goal is to provide a simple, visual, and reproducible way to evaluate performance across different LLM providers, including…
2025 · llmapitest.com · its alternatives →
- 18
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…
2023 · github.com · its alternatives →
- 19
A simple Python debugger and profiler that generates animated visualizations of program flow, useful for algorithm learning. - CCExtractor/vardbg
2020 · github.com · its alternatives →
- 20
Narev▲7Hey! I want to get your feedback on our A/B testing platform for LLMs. Giving a 100 day Pro trial to anyone who signs up this week. The premise: in 10 minutes compare a bunch of model/parameter/prompt combos to find the best point on the quality-latency-cost tradeoff. Enter your data manually or sync from your tracing platform. ~300 models to select from. Here is a 1 minute Loom: https://www.loom.com/share/39b7cd24166c4e1fafd5e7fbd12a9d4d?...
2025 · narev.ai · its alternatives →
- 21
Hi HN, Hugh and Vince here. LLMonitor helps you record, trace & search your LLM queries and chatbot conversations. You can also capture user feedback on your frontend and correlate it with backend LLM queries then use that to fine-tune your own models. The project started has an internal tool in our previous (failed) AI startup. We’re aware the LLM observability space is very crowded. Apart from being open-source, we differentiate with: - Model-agnostic and minimal lock-in (no MITM of requests). - High focus on DX and dashboard clarity. - Support for complex scenarios: e.g. a chatbot that…
2023 · github.com · its alternatives →
- 22
Auditi▲4I've been building AI agents at work and the hardest part isn't the prompts or orchestration – it's answering "is this agent actually good?" in production. Tracing tells you what happened. But I wanted to know how well it happened. So I built Auditi – it captures your LLM traces and spans and automatically evaluates them with LLM-as-a-judge + human annotation workflows. Two lines to get started: auditi.init(api_key="...") auditi.instrument() # monkey-patches OpenAI/Anthropic/Gemini Every API call is captured with full span trees, token usage, and costs. No code changes to your…
Feb 2026 · github.com · its alternatives →
- 23
Hi Hacker News, As a dev extensively using GPT-4 for coding, I've realized its effectiveness significantly increases with richer context (e.g., code samples, execution state - props to DevinAI for famously console.logging itself). This inspired me to push the idea further and create CaptureFlow. This tool equips your coding LLM with a debugger-level view into your Python apps, via a simple one-line decorator. Such detailed tracing improves LLM coding capabilities and opens new use cases, such as auto-bug fix and test case generation. CaptureFlow-py offers an extensible end-to-end pipeline…
2024 · github.com · its alternatives →
- 24
We are the developers of Phoenix, which we released in April of this year with a goal of bringing LLM observability to the notebook. In the time since, the growth of LLM frameworks and complex agent workflows led us to add support for LLM spans and traces and introduce a simple Eval harness for testing the data from those spans. The latest Traces & Spans release of Phoenix offers: -Out of the box tracing for LlamaIndex and LangChain -Fully local execution, no data sent anywhere, outside of your own LLM calls -Ability to get a common dataframe format across frameworks back to a notebook for…
2023 · github.com · its alternatives →
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