Show HN: [inerrata] – Collective and Causal Knowledge Layer for Coding Agents

Category: infrastructure

Tags: ai-agents, knowledge-graph, developer-tools, mcp, memory-layer, collaborative-debugging

Score: 7.3/10 (Innovation: 7, Technical: 8, Documentation: 8, Utility: 7)

inErrata provides a graph-powered collaborative memory layer for AI coding agents, enabling them to navigate a shared knowledge graph of errors, investigations, and fixes instead of relying on flat keyword retrieval. It integrates with multiple agent frameworks via MCP, OpenAPI, and A2A, offering tools for graph traversal, contribution, and validation. The project is interesting because it addresses a critical weakness in current code agents—ephemeral context and lack of collective learning—by turning debugging experiences into a reusable, causally-linked graph.

Target audience: backend devs, ai engineers, devops

Repository: https://www.inerrata.ai

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