Alternatives
Products that do what DataGrout Math does
Deterministic numeric tools for AI agents, zero credits
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Hey HN, We’ve been experimenting with how to make AI agents more deterministic, observable, and production-safe, and that led us to build AgentML — an open-source language for defining agent behavior as state machines, not prompt chains. My co-founder posted before but linked to the project website instead of the repo, so resharing here. AgentML lets you describe your agent’s reasoning and actions as a finite-state model (think SCXML for agents). Each state, transition, and tool call is explicit and machine-verifiable. That means you can: - Reproduce any decision path deterministically -…
Nov 2025 · github.com
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2025 · appsource.microsoft.com
- 13WI
At Laminar (https://github.com/lmnr-ai/lmnr) we're building open source AI observability platform in Rust. We obsess over instrumentation DX for our Python and TS SDKs and in this new blog we outline how we made the most seamless way of instrumenting recently released claude agent sdk
Dec 2025 · laminar.sh
- 14MD
We’re excited to share ML-Dev-Bench, a new open-source benchmark that tests AI agents on real-world ML development tasks. Unlike typical coding challenges or Kaggle-style competitions, our benchmark simulates end-to-end ML workflows including: - Dataset handling and preprocessing - Debugging model and code failures - Implementing new model architectures - Fine-tuning and improving existing models With 30 diverse tasks, ML-Dev-Bench evaluates agents across critical stages of ML development. To complement this, we built Calipers, a framework that provides systematic performance evaluation and…
2025 · github.com
- 15OS
We’re building an open-source tool that makes it easy to expose secure, LLM-optimized APIs on top of your structured data—without manually designing endpoints or worrying about compliance. AI agents and LLM-powered applications need structured access to data, but traditional APIs and databases weren’t built with AI workloads in mind. Our tool automatically generates APIs that: - Filter out PII & sensitive data to comply with GDPR, CPRA, SOC 2, and other regulations. - Provide traceability & auditing, so AI apps aren’t black boxes, and security teams stay in control. - Optimize for AI…
2025 · github.com
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Oct 2025 · github.com
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Been working on data sovereignty recently and started this list. Hope you can contribute too.
2025 · github.com
- 18LF
We built a no/low-code tool that lets you spin up MCPs from a single prompt. MCPs give LLMs access to tools, data, and actions—but they’re hard to build and deploy. Our tool abstracts that: describe what you want, and it auto-generates and hosts the necessary components. No UI flows, no manual chaining—just prompt and go. Examples: • Pull email, parse a DocSend, check Reddit, draft reply • Extract data from a niche site + send a Slack alert • Combine tools without writing glue code Live demo: https://www.youtube.com/watch?v=4uCiaQrgfoE Built over a weekend after getting…
2025 · generatemcp.com
- 19FA
Founder here. I built NEO, an AI agent designed specifically for AI and ML engineering workflows, after repeatedly hitting the same wall with existing tools: they work for short, linear tasks, but fall apart once workflows become long-running, stateful, and feedback-driven. In real ML work, you don’t just generate code and move on. You explore data, train models, evaluate results, adjust assumptions, rerun experiments, compare metrics, generate artifacts, and iterate; often over hours or days. Most modern coding agents already go beyond single prompts. They can plan steps, write files, run…
Jan 2026 · marketplace.visualstudio.com
- 20AM
I have built many AI agents, and all frameworks felt so bloated, slow, and unpredictable. Therefore, I hacked together a minimal library that works with JSON/dict/kwargs definitions for each step, allowing you a simpler way to define reproducible agents. It supports concurrency for up to 1000 calls/min, giving you speed and predictability in your workflows. Install pip install flashlearn Input is a list of dictionaries Simply take user inputs, API responses, and calculations from other tools and feed them to FlashLearn. user_inputs = [{"query": "When was python launched?"}]…
2025 · github.com
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Hey HN! After spending way too many nights debugging flaky AI tests, I built SteadyText. It's a simple python library for deterministic llm generations and embeddings. We use it in production for: - Testing our AI features (zero flakes in 3 months) - CLI tools that need consistent outputs - Reproducible documentation examples It's not for creative tasks - this is specifically for when you need AI to be boring and predictable. Think of it as the opposite of ChatGPT. The coolest part? It includes a Postgres extension. You can now do: SELECT steadytext_generate('explain this query: ...'); And…
2025 · steadytext.julep.ai
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Deterministic tests for AI agents — no LLM required
11d ago · github.com
- 23FA
Hey HN, we built an Econ+Finance database to let AI agents do investment research. We spend a lot of tokens to organize macro releases and SEC filings into a clean format, so that your agents have more context to do actual analysis. The problem AI agents are great at data analysis. But they become ineffective if most of their context window is spent on gathering and cleaning data, instead of validating hypotheses. Data in the wild is messy and rarely standardized. Definitions and measurements change over time. This problem is compounded by a fragmented data universe. Point solutions exist…
Jul 2026 · github.com
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A turning point on AI agents in Cybersecurity, shown in two recent research papers from UC Berkeley and Stanford: CyberGym: AI agents discovered 15 zero-days in major open-source software BountyBench: AI agents solved real-world bug bounty tasks worth tens of thousands of dollars This represents a pivotal shift in cybersecurity — AI agents can now autonomously do what only elite human hackers could before. Check out their work: CyberGym: https://www.cybergym.io/ BountyBench: https://bountybench.github.io/
2025 · twitter.com
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