Mcp

Heimdall is an open-source, CPU-only knowledge layer that gives AI coding agents a verified, self-healing memory across every repository you work in. Instead of returning plausible-but-unverified matches, every search result carries a trust verdict — STRONG, WEAK, REBUILT, or STALE — re-checked against your live filesystem at query time. It indexes with tree-sitter and local embeddings, spends zero tokens on memory maintenance, and re-anchors moved files automatically. What is Heimdall and why does it exist? Heimdall is a trust-verified knowledge layer built specifically for AI coding agents. It was released on Hacker News on August 22, 2026, and is published on npm as @ariantdeva/heimdall at version 0.2.1. The project’s core claim is simple: most agent memory tools return results that look right but have not been checked against the actual state of your codebase. Heimdall exists to close that gap. ...

AgentBrain is a local-first, long-term memory tool for AI agents that stores everything as a plain Markdown vault. Instead of locking your agent’s memory into a proprietary database or cloud API, it gives you an append-only Markdown folder you can read, edit, grep, and version with Git. It uses index-first retrieval with CJK-aware BM25 to stay token-efficient, and enforces human-approved consolidation to prevent multi-agent write conflicts. What Is AgentBrain? A Local-First Markdown Memory Vault for AI Agents AgentBrain is an open-source Python tool (Python 3.10+) that gives AI agents a durable, local-first memory layer stored as plain Markdown files. The core idea is simple: an agent’s long-term knowledge should live in a human-readable folder on your own machine, not inside a black-box database or a third-party cloud service. ...

An OpenAgent Compat Lab is a deterministic testing environment that verifies an agent endpoint actually conforms to a protocol contract — running real JSON-RPC calls, validating the response against a published schema, and checking authentication before an agent is ever trusted to interoperate. It answers the question “can these two agents talk” with evidence rather than a handshake. As platforms like A2Apex score agents on a 0–100 trust scale using live endpoint tests, and as the A2A protocol gathers more than 50 launch partners, deterministic compatibility testing has become the missing trust layer for agent interoperability. ...

An AI agent architecture is the structural design that lets an LLM observe its environment, reason about a goal, and act on it by calling tools in a repeating loop. In this 20-chapter tutorial you will move from the fundamentals—what an agent is and how the Observe-Reason-Act loop works—through the core building blocks, the five workflow patterns and three agent patterns, hands-on Python code, memory, tool integration, framework comparisons, and production concerns such as evaluation, security, and emerging standards like MCP. By the end you will know exactly when to use a workflow, when to use a full agent, and how to design and ship a reliable agentic system. ...

REVLab is a Windows PE reverse-engineering workflow platform that combines static analysis, packer detection, unpacking, disassembly, decompilation, a dynamic sandbox, network capture, custom pipelines, and MCP/AI-agent integration into a single local-first web application. It turns RE from a pile of disconnected utilities into one repeatable, graph-orchestrated pipeline, and it is a brand-new open-source entrant that differentiates itself through automation and AI-driven analysis rather than raw disassembly power. What Is REVLab? A Windows PE Reverse-Engineering Workflow Platform REVLab is an open-source, local-first platform aimed squarely at Windows Portable Executable (PE) reverse engineering. Where traditional tooling forces an analyst to hop between a debugger, a disassembler, a packer detector, and a network sniffer, REVLab bundles those steps into a single application with one web UI and one orchestrating workflow engine. ...

A knowledge inbox for AI agents and Obsidian is a local-first ingestion pipeline that turns raw inputs — links, PDFs, videos, screenshots, and plain text — into structured Markdown knowledge cards inside your own vault. Instead of dumping every snippet into cloud read-it-later apps, it normalizes all sources through a single pipeline, enriches them with OCR, transcription, and auto-tagging, then stores everything as human-readable files plus a queryable SQLite index. Your AI agents read the same local store your notes live in. ...

The n8n Governance Kit is a one-click way to deploy a governed n8n instance into your own Railway account and operate it safely with Claude Code. It provisions n8n with zero-secret handling, ships native MCP and Claude skills, and enforces agent-based governance so production workflows stay reliable. This guide explains the architecture, the deploy flow, and how to operate it. What Is an n8n Governance Kit and Why You Need One in 2026 An n8n governance kit is a packaged set of rules, skills, and deployment tooling that lets you run n8n in production without letting automation quality slip. Provisioning n8n is a commodity in 2026 — any developer can spin up an instance. The real product is keeping a non-technical user’s workflows from breaking once they are live. ...

GitHub Copilot code review has reached a major milestone with the general availability of agent skills and MCP (Model Context Protocol) server support, announced on July 29, 2026. These features transform Copilot code review from a static analysis tool into an extensible platform that understands your team’s coding standards, integrates with your existing toolchain, and delivers context-aware feedback on every pull request. What’s New — Agent Skills and MCP Go GA On July 29, 2026, GitHub announced that agent skills and MCP server support for Copilot code review are now generally available for all Copilot Pro, Pro+, Business, and Enterprise users. This marks the culmination of a public preview that began on June 2, 2026, and represents GitHub’s most significant investment in code review intelligence to date. ...

The open-source AI developer tools ecosystem in 2026 has matured to the point where every proprietary AI service — from GitHub Copilot to Pinecone to ChatGPT — has a viable, production-ready open-source alternative. This directory covers 18+ categories of open-source AI devtools, including coding agents, local inference engines, agent frameworks, vector databases, RAG pipelines, evaluation platforms, and observability tools, with maturity badges and direct comparisons to the closed-source tools they replace. ...

What is Mem-Port and Why Portable Agentic Memory Matters Mem-Port is an open-source MCP (Model Context Protocol) memory server that gives AI coding agents persistent, portable long-term memory using an embedded SurrealDB database. Unlike traditional memory solutions that lock agent context to a single machine or tool, Mem-Port lets you carry your AI agent’s entire memory — facts, episodes, skills, and entity relationships — between machines, copilots, and teams as a single portable JSON bundle. Think of it as a thumb drive for your AI’s brain. ...