How does this compare to CLAUDE.md and other Rules you can put in markdown?
Seems a bit similar to spec driven development. How does it differ?
I started building an app with similar goals but with the very different approach. I work on my own coding agent, https://github.com/brainless/nocodo, where I have been trying to build a provenance based engine that will generate or modify prompts to point to the decisions that a team has made. That work is in the branch: feature/praxis_agent_runtime
While working on this I figured what if I build a proxy for coding agents - Claude Code, opencode, Codex, etc. support a proxy. This proxy would edit prompts and tool_calls and feed context from an internal index it will maintain. That index will contain git logs, GitHub/JIRA/etc tickets/epics, PRD or other documents, tech stack setup.
It is just an idea and may not work but working at the proxy layer means this can be deployed at a team level, needs no MCP install and can re-shape prompts for everyone depending on the project. Wild idea perhaps.
Author here! Coding agents kept reworking decisions we'd already settled - reviving an approach we ruled out in an ADR, redoing something a requirement already pinned. The context was in the repo; the agent had no current view of it.
Lore serves your team's durable knowledge - requirements, decisions, designs, roadmaps, prompts - all as typed Markdown, read-only to Claude Code / Cursor over MCP, so the agent cites your decisions instead of contradicting them.
The bet: retrieval is deterministic. No embeddings, no vector store, no model call to pick what's relevant — same query, same bytes, same result, offline. It's not a RAG competitor; it composes — recall fuzzily, then verify the EXACT, CURRENT decision in Lore (it declines the ones you've superseded). Runs in CI (rac validate / rac gate) too.
pipx install rac-core
rac quickstart
claude mcp add lore -- rac mcp
What it isn't: a search index, a memory layer, or an AI feature — the engine makes no LLM calls, no telemetry unless you opt in. Early, and my corpus is small, so I'd like to hear where deterministic grounding breaks down for you vs. where fuzzy recall is enough.(Lore is the product; the engine under it is RAC — Requirements as Code, the `rac` package.) Apache-2.0, typed.
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1. Write ADRs (or get agents to write them)
2. Commit ADRs to git
3. Mention ADRs in AGENTS.md