Technical writing on data engineering, AI workflows, and the tools we build along the way.
Anthropic's memory_20250818 tool gives your agent a filesystem and read/write operations. Everything else -- search, tagging, expiration, cross-surface access -- is left as an exercise for the developer. Here is what teams building on the primitive are reinventing, and how LoreConvo fits into that picture.
Read morePublished research from a major AI memory provider shows a 91% controlled benchmark collapsing to 49% effective accuracy at 30 days in production. The gap reveals a structural challenge with auto-capture memory systems -- and points to why explicit, structured saves may serve professional agents better than automatic accumulation.
Read moreThere are two ways to give an AI agent persistent memory: build a palace or let the agent take notes for you. The distinction matters more than which tool has the most stars.
Read moreEvery AI session captures how you think -- your business context, your half-formed ideas, your reasoning process. We built LoreConvo to keep that history on your machine, not in a SaaS vendor's database. Here is why the distinction matters.
Read moreWe pointed an OpenAI Codex agent at a project that uses LoreConvo. With no Codex-specific configuration, it registered LoreConvo as an MCP server, called save_session natively, and tagged the session correctly. The mechanism: one .mcp.json file that both Claude Code and Codex read automatically.
Read moreNotion's official Claude MCP integration puts 50 million Notion workspaces one step from your AI agent. LoreDocs takes the opposite approach: a knowledge vault built specifically for AI, not adapted for it. Here is why the direction matters.
Read moreClaude-Mem auto-captures everything and has tens of thousands of happy users. LoreConvo takes a different bet: intentional, project-based organization that scales with complex work. Here is how to tell which one fits your situation.
Read moreLoreConvo and LoreDocs -- persistent session memory and structured knowledge vaults for Claude -- are now live on the official Anthropic plugin marketplace. Here is what they do, how to install them, and why we built them local-first.
Read moreLangGraph is gaining real adoption for agentic AI workflows. But for most teams evaluating it, the question is not how to build a pipeline -- it is whether LangGraph is the right architecture for their problem, and what it actually takes to run in production.
Read moreClaude now remembers things between sessions for free. But built-in memory stores fragments, not sessions. If you work across multiple tools, projects, or surfaces, you need something that travels with you.
Read moreAgentic AI is everywhere in vendor decks, but most teams cannot explain what it actually means for their data operations. This guide cuts through the hype with a practitioner's definition, a real architecture example, and a framework for deciding whether you need outside help.
Read moreOur full-text search was the right call to ship fast. Real data from 217 sessions reveals exactly where it needs help. Honest numbers on why we're moving from FTS5-only to hybrid search.
Read moreYour memory tool's benchmark score means nothing if it can not run in both Claude Code and Claude Cowork. We break down what matters.
Read moreI tried claude-mem, hit the edges of it on a real project, and built something that makes a different set of trade-offs. Here is an honest comparison.
Read moreThree powerful tools for knowledge work. Three different purposes. Here is how to use all three without duplication or confusion.
Read moreYour AI project knowledge scattered across multiple tools is a liability. LoreDocs organizes it in one place, versioned and searchable, so Claude and your team always work from current information.
Read moreEvery Claude session begins with amnesia. LoreConvo auto-save hooks fix that with zero friction for data engineers and AI practitioners.
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