Alternatives
Products that do what Deepchecks LLM Evaluation does
Validate, monitor, and safeguard LLM-based apps
- 1
- 2
- 3
- 4
Trace LLM requests + costs with OpenTelemetry monitoring
Oct 2025
- 5
- 6
- 7
- 8
- 9
- 10
- 11
- 12
- 13
- 14
- 15SB
*Motivation* Hi hackers, I'm Asif. I know we dislike premature standardization, but hear me out. LLM Application development is extremely iterative, more so than most other types of application development. We need a process that allows us to iterate faster. LLM Development is highly iterative due to the activities that come with regular software development, as well as the need to make the LLM Application accurate and reduce hallucination. To improve hallucination, we need to trial and error various combinations of LLM models, prompt templates (e.g., few-shot, chain-of-thought), prompt…
2024 · github.com
- 16LO
Hey HN, Ola and Karthik here. We are working on Langtrace(https://github.com/Scale3-Labs/langtrace), an open source, open telemetry based SDK and monitoring/evaluations client for LLM based applications. The SDK generates OTEL standard spans and traces for popular LLMs like OpenAI, Anthropic and Cohere, popular frameworks like Langchain and LlamaIndex and vectorDBs like ChromaDB and Pinecone. The LLM monitoring/evaluations space has seen a number of products off late, both open source and closed source ones. But, a couple of things we have observed are: lack of…
2024
- 17HL
At testup.io we have been working for a while to bring artificial intelligence to the field of test automation. Just a few years ago, the primary challenge laid in accurately identifying UI elements following minor structural changes, such as updates to IDs or paths. The emergence of Large Language Models (LLMs) raised the bar for what it meant to be smart. Now, we anticipate the robot to do lots of things autonomously, such as retry in cases of unresponsiveness or handle minor error reports. A more challenging, but soon expected feature, would involve the test robot navigating your web shop…
2024 · github.com
- 18AC
Hi HN, we're Ashpreet, Eli and Yash and we're excited to share Phidata: a collection of AI Apps built with open-source tools. While helping teams build AI products, we built templates for spinning up LLM Apps quickly. Today we're open-sourcing our templates for building: - RAG LLM Apps - Autonomous LLM Apps - Multimodal LLM Apps - Data Engineering LLM Apps Templates are built with FastApi for serving, Streamlit for prototyping, PgVector for vectors and PosgreSQL for storage. Run them locally using docker and in production on AWS - with 1 command. - Github:…
2023 · github.com
- 19LF
I've been building agentic apps for some large Fortune 500 companies (T-Mobile, Twilio, etc.) and developed a mental model that serves as a practical guide in building agentic apps: separate the high-level agent specific logic from low-level platform capabilities. I call it the L-MM: the Logical Mental Model for LLM applications. This mental model has not only been tremendously helpful in building agents but also helping customers think about the development process - so when I am done with a consulting engagement they can move faster across the stack and enable engineers and platform teams…
2025
- 20LO
Hey HN! I built Lumina – an open-source observability platform for AI/LLM applications. Self-host it in 5 minutes with Docker Compose, all features included. The Problem: I've been building LLM apps for the past year, and I kept running into the same issues: - LLM responses would randomly change after prompt tweaks, breaking things - Costs would spike unexpectedly (turns out a bug was hitting GPT-4 instead of 3.5) - No easy way to compare "before vs after" when testing prompt changes - Existing tools were either too expensive or missing features in free tiers What I Built: Lumina is…
Jan 2026 · github.com
- 21IB
I was overspending on GPT-4o. It was really hard to compare different models I could switch to, so I built this LLM comparison tool. It shows leaderboards, pricing, and performance data across 100+ LLMs (including all major providers and open-source models). Key features: - Live pricing comparisons - Benchmark Scores (MMLU, HumanEval, GPQA, etc.) - Context length vs cost analysis - Speed/throughput tests across providers - Quality vs price visualizations - Open source (all data verifiable) Try it out: https://llmstats.com I'd like to know your opinion :) Tech stack: Next.js,…
2025 · llm-stats.com
- 22LO
2024 · github.com
- 23DU
2023 · github.com
- 24HW
Hello everyone! I’m thrilled to announce the latest feature from Mutahunter.ai, the ultimate tool for finding and fixing weaknesses in your code. We’ve designed Mutahunter to leverage mutation testing powered by advanced LLMs, helping you uncover vulnerabilities and enhance your code quality effortlessly. Introducing our newest feature: Detailed Mutation Testing Reports! After running our mutation tests, Mutahunter now generates comprehensive reports that clearly summarize: • Vulnerable code gaps • Test case gaps These reports significantly reduce the cognitive load on developers by…
2024 · github.com
Ranked by how close each launch is in meaning, then by votes. Refine with a description →