YantrikDB – persistent memory for AI agents
Bundled 7MB embedder means zero network calls or model downloads for agent memory.
A portable AI-assisted development workflow. Brainstorm, plan, implement, review, and learn — each cycle makes the next one easier.
Structured AI workflow beats Every's plugin, but Cursor and Continue solve this already.
Individual developers and small teams using AI coding agents (Amp, Claude Code)
Every Compound Engineering guide · Cursor · Continue IDE
So, with the help of Amp Code CLI, I've built my own take on the compound engineering workflow. I tried to keep it agnostic to project stacks and as efficient as possible, so the context window could be used in the best way. I also wanted it to be extendable (for example, just drop your own subagents for review that are specific to your project). I also wanted to be easy to set up and update, so I made a simple CLI tool that keeps track of files in the `.agents` directory, updates when new versions are found in the repository, and displays a diff in the terminal before overwriting any customisations.
I feel this matches well with my personal preferences when working with AI agents, but I would love to have feedback from more people.
Bundled 7MB embedder means zero network calls or model downloads for agent memory.
Portable .agents spec keeps skills separate from specific IDE plugins.
Louvain clustering for agent memory, but AI agent frameworks are already saturated.
MCP monetization platform launching March 2026—good timing, but no live data or paying users yet.
The describe → plan → act split is an elegant, accessibility-inspired way to give LLM agents actionable UI context: annotate with data-ai-* attributes or use the Marker component, call describe(), send it to a planner, then client.act() executes DOM instructions. It's a clever middle layer that turns messy DOM state into structured inputs for server-side planning, though adoption will hinge on robust selector semantics and out-of-the-box integrations with popular LLMs and automation backends.
Deterministic capture + replay for LLM agents is a practical, under-served problem and this repo actually ships a 'golden run' zip with cold‑run verification hashes — that’s the kind of evidence chain auditors want. The focus on portable evidence bundles and stress verification suggests useful forensics and load testing of agent logic, but the release page looks early-stage; I'd like to see integrations (tooling for popular agent frameworks), richer docs, and example pipelines before I'd evangelize it.