Alternatives
Products that do what MeshAG does
Knowledge for AI Agents
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Excited to share a project I’ve been building for months! Would love to receive honest feedback :) My motivation: AI is clearly going to be the interface for data. But earlier attempts (text-to-SQL, etc.) fell short — they treated it like magic. The space has matured: teams now realize that AI + data needs structure, context, and rules. So I built a product to help teams deliver “chat with data” solutions fast with full control and observability (agent tracing, quality scores, etc) — am I wrong? The product allows you to connect any LLM to any data source with centralized context…
Oct 2025 · github.com
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We built an open sourced coordination layer for AI agents working on the same repository. Detects work duplication and design conflicts early
9d ago · twing.dev
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You might know Cube as an open-source semantic layer (https://github.com/cube-js/cube). Started in 2018, now 19K+ stars, 1000+ releases. We kept hitting the same wall: everyone wants AI analytics, but AI without business context hallucinates. The fix is a semantic layer — a model that defines what "revenue" or "churn" actually means. But building one by hand takes weeks. So we built an AI agent that writes the semantic layer itself, then uses it to answer questions and build dashboards with no hallucinations. Connect your data → agent builds the model in seconds → ask…
Feb 2026 · youtube.com
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We built meta-agent: an open-source library that automatically and continuously improves agent harnesses from production traces. Point it at an existing agent, a stream of unlabeled production traces, and a small labeled holdout set. An LLM judge scores unlabeled production traces as they stream. A proposer reads failed traces and writes one targeted harness update at a time, such as changes to prompts, hooks, tools, or subagents. The update is kept only if it improves holdout accuracy. On tau-bench v3 airline, meta-agent improved holdout accuracy from 67% to 87%. We open-sourced meta-agent.…
Apr 2026 · github.com
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I feel like LLMs can help me understand anything. However, after I get a summary, I can't dive in to parts that I find interesting; can't refer to original source easily and can't control context with chatbots. This is an attempt to solve for a complete knowledge consumption experience with AI . Please give me feedback!
Oct 2025 · kerns.ai
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The goal was to bring down the cost at the context eng. level. We do it with Layout Memoization. Instead of dumping HTML into the context window, we have built a continual learning browser harness (read only for now). We have built an early prototype for you to try out, where you can: 1. Spins up a browser instance 2. Extract any structured or tabular data from anywhere on the open-web 3. And you can do all this at the cost of a vector search Would love to hear your thoughts on this. Thanks for taking the time to read it.
8d ago · makralabs.org
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Hi HN! Sean from MindStudio here. I wanted to share something we've been working on that I think introduces some new ideas into the "AI coding agent" space. Remy is an AI agent that builds full-stack TypeScript apps from a spec written in a new flavor of annotated markdown. The spec has two layers: prose describing what the app does, and annotations that carry the technical precision (data types, edge cases, validation rules, code snippets). The agent then "compiles" this into code: backend methods, typed schemas, frontends, test scenarios, and everything else are derived artifacts of the…
Apr 2026 · remy.msagent.ai
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