I’ve been running three different AI coding assistants this week — Claude Code for refactoring, Codex for prototyping, Cursor for frontend work. Want to know the biggest headache? Not the API costs. Not the context limits. It’s the fact that none of them remembers what the others learned about my codebase.

Claude Code figures out my project structure in a 30-minute session. Then I switch to Codex for a quick prototype, and it asks me the same questions Claude already answered. Every. Single. Time.

So when I saw EverOS trending on GitHub (9,207 stars as of this morning, and climbing fast), I had to dig in. And the pitch is simple but ambitious: a portable memory layer for any AI agent. Conversations become Markdown files. Markdown files get vector-indexed. And every agent you run can pull from the same shared memory store.

But I’m skeptical of “portable” claims — most of them break the second you switch tools. So I tested it. Here’s what actually happened.

What Is EverOS?

Look, EverOS isn’t an agent. It doesn’t chat with you, write code, or browse the web. It’s a memory backend — a local service your agents talk to when they need to remember past interactions.

The architecture is what caught my attention:

So that first layer is the killer feature for me. Markdown-native memory. Every chat, every agent session, every debug history — saved as plain .md files on your disk. No proprietary formats. And no vendor lock-in either. If EverOS disappeared tomorrow, I still have every memory as readable Markdown files.

So I installed it on my Ryzen 9 workstation running Ubuntu 24.04 — cloned the repo, ran the setup script, and had it serving in about 90 seconds. And the default config spins up a local API on localhost:9741 and starts logging everything your agents send to it.

How It Works Under the Hood

Now, EverOS uses OpenRouter for LLM calls (meaning you bring your own API key for whatever model you want — Claude, GPT-4o, Gemini, whatever). It also taps DeepInfra for its embedding and reranking pipeline, which handles the semantic search part.

Here’s the flow I saw when I connected Claude Code to it:

1. Claude finishes a task and writes a summary to the EverOS API
2. EverOS saves it as a Markdown file in ~/.everos/memories/
3. The same file gets embedded into LanceDB’s vector store
4. Next session, Codex asks EverOS: “what do we know about this project?”
5. EverOS returns the top-3 relevant memory fragments

I tested this exact loop. Set up Claude Code to log its understanding of my project (a Django monorepo with about 40 apps). Then I opened Codex in a separate terminal and asked “Hey, what’s the project structure here?” Codex, via the EverOS MCP plugin, pulled Claude’s previous notes and answered correctly — without re-reading a single file.

That moment is when I stopped being a skeptic.

The Good Stuff

Memory Actually Persists Across Tools

And this was the main test — it passed. I went Claude Code → Codex → Cursor, all inside different terminal sessions. Each one could recall what the previous one learned. The memories are time-stamped and tagged by agent type, so you can filter by “things Claude learned” vs “things Codex prototyped.”

Markdown-Native = No Lock-In

Honestly, I cannot overstate how valuable this is. I opened ~/.everos/memories/ and found clean Markdown files with titles like claude-code-2026-06-27-project-structure.md. Human-readable. Greppable. Editable. I could cat them, git them, pipe them into another tool. Every AI memory system should work like this.

API-First Design

EverOS exposes a clean REST API that any tool can hit. But let’s be direct: the MCP (Model Context Protocol) plugin means Claude Code, Cursor, and any MCP-compatible agent can connect out of the box — same integration pattern as how Agent Skills adds structured workflows to these tools. For everything else, there’s a Python SDK and a direct HTTP endpoint.

Where It Falls Short

Setup Requires Some Tinkering

Sure, the “90-second install” I mentioned works on a clean Linux machine. But I also tried it on macOS (M3 MacBook Air) and hit a dependency issue with the LanceDB native bindings — had to install the Rust toolchain to compile them. The docs cover this, but it’s not frictionless.

No Multi-User Support Yet

EverOS is designed for a single user on a single machine. If you want a team-shared memory store (say, your whole engineering team’s agents sharing context), you’re out of luck. The repo’s issues show this is on the roadmap, but it’s not here today.

The Embedding Pipeline Is Opinionated

But you’re locked into DeepInfra for embeddings and reranking unless you dig into the source and swap it out. The vector store is LanceDB by default — no Pinecone, no Chroma, no Qdrant. For a tool that sells itself on portability, the embedding pipeline itself isn’t very portable.

EverOS vs Alternatives

And I spent an afternoon comparing these four. EverOS wins on portability — it’s the only one that stores memories as files you can actually read. Mem0 and Zep are more mature for team use, but neither gives you the “just open a folder and read the files” experience. Letta is powerful but locked into its own agent ecosystem.

Who Actually Needs This

So you should install EverOS right now if:

• You use 2+ AI coding assistants. The cross-tool memory sharing is genuinely useful.
• You want human-readable memory files. The Markdown-native design means you can review, edit, or delete memories with any text editor.
• You run agents locally. EverOS is designed for local-first workflows.
• You hate vendor lock-in. Your memories are files on your disk. That’s it.

Skip it if:

• You need team-shared memory. Wait for the multi-user feature.
• You want a fully managed service. Go with Zep or Mem0.
• You only use one AI tool. The cross-tool sharing won’t matter to you.

The Bottom Line

Still, EverOS is one of those projects that looks simple on paper but solves a real pain point in practice. I went from “interesting, let me check the GitHub repo” to “this is going in my daily toolkit” in about two hours of testing. That said, the Markdown-native approach is genuinely different from everything else in this space, and the cross-tool memory sharing works better than I expected for a 0.x release.

But here’s the honest part: it’s early. The single-user limitation, the DeepInfra lock-in, the build hiccup on macOS — these are real friction points. If you’re an individual developer running multiple AI tools, install it today. If you’re looking for a team memory solution, check back in a few months.

So here’s my verdict: EverOS gets a strong recommendation for solo developers who want their AI agents to stop amnesiaring every session. It’s not the most polished memory solution out there, but it’s the most portable one — and portable is what I needed.