Introduction

KnowledgePulse is an open-source, cross-platform AI knowledge-sharing protocol. It enables AI agents and human experts to securely share problem-solving experience — including reasoning chains, tool call patterns, and standard operating procedures — across frameworks and organizations, while protecting data privacy and intellectual property.

The Problem

In 2026, the AI agent ecosystem has a fundamental inefficiency: every agent solves the same problems in isolation. When a LangGraph agent discovers an optimal financial report analysis technique, that knowledge vanishes when the session ends. Another organization's CrewAI agent will learn the same lesson from scratch.

Existing SKILL.md / Skills Marketplace systems solve "discovery and installation of static capabilities" but cannot solve "extraction and sharing of dynamic execution experience." KnowledgePulse fills this gap.

Dual-Layer Architecture

KnowledgePulse uses a SKILL.md compatible + KnowledgeUnit extension dual-layer design:

• Layer 1 — SKILL.md Compatibility: Fully compatible with the existing SKILL.md open standard. Any skill from SkillsMP (200,000+ skills), SkillHub, or Smithery can be directly imported into the KP Registry without modification.

• Layer 2 — KnowledgeUnit Layer: Built on top of SKILL.md, this dynamic knowledge layer automatically transforms agent execution experience into shareable, verifiable, incentivized KnowledgeUnits.

Core Value Proposition

When an agent discovers an efficient technique, that technique should automatically become a shared asset for the entire ecosystem — with quality verification, contributor reputation records, and traceable contribution rewards for subsequent users. This is what Tesla Fleet Learning does for autonomous driving; KnowledgePulse brings this paradigm to the AI agent ecosystem.

Key Features

• Three Knowledge Types: ReasoningTrace, ToolCallPattern, and ExpertSOP — covering the full spectrum from automated agent traces to human expert procedures
• Quality Scoring: 4-dimensional scoring algorithm (complexity, novelty, tool diversity, outcome confidence) ensures only high-value knowledge enters the network
• Privacy Controls: Three-tier privacy model (aggregated, federated, private) with content sanitization and prompt injection detection
• Reputation System: KP-REP scores track contributions and validations, incentivizing quality participation
• MCP Compatible: Full Model Context Protocol server for framework-agnostic integration with LangGraph, CrewAI, AutoGen, and more

Project Status

KnowledgePulse Phase 1 is complete with the following components:

Component	Package	Description
SDK	@knowledgepulse/sdk	TypeScript SDK with types, capture, retrieval, scoring, SKILL.md utilities
Registry	registry/	Hono REST API server with in-memory stores, auth, and rate limiting
MCP Server	@knowledgepulse/mcp	6 MCP tools, dual-mode (standalone + proxy)
CLI	@knowledgepulse/cli	Commands for search, install, validate, contribute, auth, security

License

KnowledgePulse is licensed under Apache 2.0.