I've been using Claude Code daily for months. I build with it, debug with it, architect with it. It's genuinely brilliant at writing code.
But every morning, the same ritual: open a new session, wait for Claude to ask what framework I'm using, re-explain my database prefix, re-state that I use Haiku for casual chat and Sonnet for complex reasoning, re-describe the file structure I've explained fifty times before.
After one particularly frustrating session where Claude got my database prefix wrong for the fourth time that week — it's a custom prefix, not the default, and that matters — I decided to fix it.
I built a persistent memory system for Claude Code. I've been using it in production for weeks now. Here's what actually changed.
The Before: Death by a Thousand Re-explanations
My project is a WordPress plugin with about 20 core PHP classes, a React-ish frontend, multiple Ai model integrations, Stripe billing, and five distinct product lines sharing one codebase. It's not a toy project.
Before persistent memory, every Claude Code session started something like this:
Me: Fix the billing bug in the subscription handler.
Claude: I'll look at the codebase. What billing system are you using?
Me: Stripe. The handler is in class-stripe.php.
Claude: Got it. Let me read that file... I see references to
different subscription tiers. Can you explain the tier structure?
Me: [sighs, pastes the tier table for the 30th time]
Context compaction made it worse. I'd be deep in a debugging session, Claude would auto-compact, and suddenly it forgot which files we were looking at. I'd have to re-establish the entire mental model we'd built together over the last hour.
The built-in MEMORY.md helped a little, but 200 lines isn't enough for a complex project. And after compaction, Claude would sometimes ignore it entirely — a well-documented bug that Anthropic still hasn't fully fixed.
I estimated I was spending 15–20 minutes per session just re-establishing context. Multiply that by 5–8 sessions a day, and I was losing over an hour daily to Claude's amnesia.
What I Built
I built CogmemAi — a cloud-based MCP server that gives Claude Code persistent memory with semantic search. The core idea is simple:
- Extract — Ai analyzes conversations and identifies facts worth remembering (architecture decisions, bug fixes, preferences, patterns)
- Store — Each memory gets a semantic embedding for meaning-based retrieval, an importance score, and project scoping
- Surface — At the start of every session, the most relevant memories load automatically based on meaning, importance, and recency
The MCP server itself is a thin HTTP client — no local databases, no vector stores eating RAM, no Docker containers. All the heavy lifting happens server-side. Setup is one command:
npx cogmemai-mcp setup
That installs the server, configures Claude Code, and enables automatic compaction recovery. The whole thing takes about 30 seconds.
The After: Claude Just Knows
The difference was immediate. Here's what a session looks like now.
I type claude in my terminal. CogmemAi loads my project context — the top memories ranked by importance and relevance. Claude sees things like:
- The live database uses a custom prefix, not the default
wp_ - Which Ai model to use for different interaction types — casual vs. complex reasoning
- Changes to one subsystem must be mirrored in the parallel system (they share logic)
- Specific CSS override patterns that require special selectors
- Which encryption wrapper functions to use instead of raw library calls
I don't type any of this. I don't paste a cheat sheet. Claude just knows my project because it remembers the last hundred sessions of working on it together.
When I say "fix the billing bug," Claude already knows we use Stripe, already knows the tier structure, already knows which file to look at. We skip straight to the actual work.
The Compaction Recovery Moment
The real "wow" moment came the first time auto-compaction fired with compaction recovery active.
I was deep in a session — we'd been working on a complex feature for over an hour, context was filling up. Claude auto-compacted. Normally, that's where the pain starts.
Instead, this happened: context was automatically preserved before compaction and seamlessly restored afterward, including my project context plus a summary of the current session.
Claude's next response started with a summary of what we'd been working on and continued right where we left off. No "can you remind me what we were doing?" No lost context. It just worked.
That was the moment I knew this was a game-changer.
What I Didn't Expect
Some things surprised me about having persistent memory:
Compound knowledge. Over days and weeks, Claude's understanding of my project gets deeper. Early memories captured the basics — file structure, tech stack, naming conventions. Later memories captured subtler patterns — which functions are fragile, which CSS selectors need special handling, which API endpoints have rate limits. The accumulated context makes Claude dramatically better at making good decisions without being told.
Bug fix history. When a similar bug shows up, Claude can recall the previous fix. "We fixed a similar issue in class-tasks.php last week — the problem was the recurring task auto-advance logic." That's not something MEMORY.md could capture in 200 lines.
Cross-machine continuity. I work from two machines. Before, each one had its own disconnected MEMORY.md. Now, memories are in the cloud — I pick up on my laptop exactly where I left off on my desktop. Same context, same accumulated knowledge.
The "it remembered" dopamine hit. There's something genuinely satisfying about starting a new session and having Claude reference a decision you made three days ago without being prompted. It feels like working with someone who actually pays attention.
What It Doesn't Do
I want to be honest about the limitations:
- It's not magic. Claude still makes mistakes. Memory gives it better context, but it can still hallucinate or make wrong architectural choices. You still need to review everything.
- It needs network. The memory system is cloud-based. No internet, no memories. If you need offline-first, a local vector database solution may be a better fit.
- Initial population takes time. The first few sessions, Claude is still learning your project. After a week of active use, the memory becomes genuinely useful. After two weeks, it's indispensable.
By the Numbers
After several weeks of daily use on a complex, multi-product codebase:
- 200+ memories accumulated across architecture decisions, bug fixes, patterns, preferences, and session summaries
- ~15 minutes saved per session in re-explanation time
- 5–8 sessions per day = roughly 1–2 hours saved daily
- Zero RAM issues — nothing running locally except a thin HTTP client
- Zero crashes — there's nothing local to crash
The time savings alone justify it. But the real value is qualitative — Claude makes better decisions because it has context. It suggests the right patterns because it's seen my patterns. It avoids mistakes because it remembers the last time we hit that wall.
Should You Try It?
If you use Claude Code for serious development — not one-off scripts, but real projects with architecture, conventions, and accumulated decisions — persistent memory changes the experience fundamentally.
Whether you use CogmemAi, a local vector database solution, or a hand-rolled approach doesn't matter as much as the principle: stop letting your Ai assistant forget everything every session.
The technology exists. The tools are available. The only question is whether you're willing to spend 60 seconds setting one up to save hours every week.
CogmemAi has a free tier with 1,000 memories — enough to see whether persistent memory changes your workflow. Setup is one command: npx cogmemai-mcp setup
I'm Scott, a network and systems engineer with 30+ years in the industry. I built CogmemAi because I couldn't stand re-explaining my codebase every morning. If you try it, I'd love to hear how it goes.
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