Relay: The Modern Project Board for AI Agents and Humans
Traditional project boards track people. Relay tracks people, AI agents, and the baton of context moving between them.
engineering · product · launches
What we're building, why we're building it, and how to use it.
Traditional project boards track people. Relay tracks people, AI agents, and the baton of context moving between them.
Chat history is not a development lifecycle. Give agents explicit records for plans, decisions, risks, tasks, checkpoints, and handoffs.
More tools do not automatically make an agent more capable. A focused profile gives it the smallest, safest set of MCP capabilities for the task at hand.
A capable coding agent needs more than file search. It needs a safe path from proposed change to verified release.
Before an agent analyzes a dataset, it should prove the data is complete, consistent, and within an explicit contract.
Agent answers are stronger when the evidence behind them has a URL, capture time, excerpt, and a local record.
AI agents need access to files and data, but that does not mean a whole workspace needs to leave the machine. Here is a local-first workflow for finding exactly the context an agent needs.
Giving an agent every tool at once creates ambiguity and context overhead. A dedicated tool search layer lets it discover the right capability, inputs, and safety constraints before acting.
Next.js makes building MCP servers surprisingly straightforward. Here's everything from a blank project to a working MCP server connected to Claude Code — with sequential thinking and Playwright patterns included.
MCP is rapidly becoming the universal connector for AI agents. Here's a practical guide to the integrations that matter most — and how to manage them without juggling a dozen config files.
Your AI agent sends prompts to external LLM APIs. What happens in between determines how secure that data is. Understanding MCP proxy servers vs MCP gateways is the first step to getting it right.
AgenticStore now features 31 free, open source MCP tools. We've introduced state-of-the-art free prompt compression, local process tracking, and native setup for Claude Code CLI.
Sending sensitive info to AI models is a major security risk. Today we're releasing Prompt Firewall — a local proxy that intercepts, sanitizes, and optimizes your prompts using deterministic PII filters and optional local LLMs.
Stateless AI sessions mean losing valuable context every time you close your editor. Our new open-source agent memory layer changes that, giving your local agents persistent, searchable context.
Writing code with AI is easy. Fixing vulnerabilities and managing codebase health is hard. We introduced 11 new DevSecOps tools to bring automation to the full lifecycle.
Overloading your LLM's context window with dozens of tool schemas destroys its reasoning capabilities. The solution isn't larger context windows—it's a GUI manager.
Your AI agent knows everything up to its training date — and nothing after. If you're using it for sentiment analysis, competitive research, live event updates, or anything that requires current data, you're getting stale answers. Here's how to fix it.
Every MCP developer has hit the wall: 'Server disconnected.' Wrong Python, broken PATH, venv that the GUI client can't see. The Docker image we shipped today ends this. Pull once, paste one JSON config, get all five tools.
Today we're publishing AgenticStore MCP Tools v1 — a free, open-source toolkit that gives your AI agents real superpowers. No cloud account. No API keys. Just install and run.