I asked if a local-first Markdown memory server existed. You gave me ~20 suggestions. Here's what I found after going through all of them.

A week ago I posted here asking if something specific existed: a fully local agent memory system that stores memories as readable files — not a database, not a cloud service, just files I can open, read, and edit. A lot of you responded with suggestions. I went through all of them. Here's the breakdown. --- **First, some of these aren't memory systems** Before the actual comparison, worth flagging a few things that kept coming up but are different tools: ChromaDB is a vector database — you build memory systems on top of it, it isn't one. qmd is a document search engine with no write pipeline and no memory lifecycle. ContextKeep does context compression. LCM preserves session context, not persistent memory across sessions. Not criticisms — all useful tools. Just not what I was asking about. --- **The established options** **mem0** is the market leader and earns it — mature ecosystem, SDKs in multiple languages, graph-based memory with entity extraction, proven at production scale. If cloud infrastructure isn't a concern, this is probably the right answer for most people. The tradeoffs: it defaults to OpenAI, leans toward their hosted service, and stores everything in a database. Your agent's memory is largely opaque and off your machine. **Hindsight** was the most common suggestion in my thread and the recommendation is usually fair. Knowledge graph, entity resolution, handles contradictory memories. Solid project. But it needs Postgres and a vector DB to run, and the storage is SQL — you can't just read what your agent knows without querying it. **Zep** has the longest track record in this space with real enterprise deployments behind it. Multi-modal memory, structured knowledge extraction. Similar infrastructure requirements to Hindsight, cloud-first by default. **Honcho** takes a more academic angle — continual learning, stateful architecture. More research-grade than production-ready right now, and the AGPL license plus cloud dependency limit how freely you can use it. --- **OpenClaw-specific options** **memory-lancedb-pro** is the strongest memory plugin in the OpenClaw ecosystem — hybrid retrieval, a proper decay model, actively maintained. If you're already on OpenClaw this is likely your pick. Not a standalone server though. **GBrain** is similar — MCP-first, decent OpenClaw integration, but not particularly useful outside that ecosystem. --- **The most interesting new entrant** **mnem** is the most technically ambitious newcomer. Rust, single binary, bundled embedder — no Python, no Ollama, no external dependencies. The pitch is "git for agent memories": branch your memory state, diff it, merge across agents, revert bad writes. GraphRAG for retrieval. Benchmarks well against mem0. Two weeks old, so test coverage is thin. The other limitation for my use case: it uses content-addressed graph nodes rather than readable files, so you still can't open the vault and see what's inside. Genuinely worth watching though. --- **The gap I was originally asking about** After going through everything, here's what I couldn't find in a single package: fully local + human-readable file storage + smart deduplication + importance decay + standalone server with no infrastructure requirements. Some projects have two or three of these. Nobody had all of them — which is why I posted the original thread. The dedup matters because agents write redundant memories constantly without it. The decay matters because old context shouldn't be weighted the same as recent context. The readable files matter if you want to know what your agent actually thinks it knows and correct it when it's wrong. And fully local matters both for cost and for privacy — if your agent is accumulating knowledge about your projects and decisions, that data probably shouldn't be leaving your machine. --- **What actually fills it: Engram by Obsidian68** github.com/Obsidian68/Engram This wasn't in any of your suggestions — I found it separately while still going through the list. Brand new, almost no stars. But it's the only project that checks all four boxes. Memories land as Markdown files in a folder. You can open it in VS Code, read exactly what the agent knows, edit entries directly, delete things that are wrong — agent sees the changes next session. Full REST API and MCP server. Smart dedup on writes. Importance decay so older memories naturally lose weight over time. Runs entirely on Ollama — no API keys, no external calls, nothing leaves your machine. The privacy aspect is worth emphasizing. Most mature options in this space have your agent's memory either on a hosted service or routing through external APIs. Engram is local by design, not by configuration. Honest caveats: one week old, docs are sparse, no SDK yet, rough edges exist. But for local dev and personal agent setups, it solves exactly what I was looking for and nothing else in the field is doing it the same way. Side note: many different projects are named "Engram" on GitHub. Search specifically for Obsidian68/Engram. --- **Summary** - Production, cloud fine → mem0 - Knowledge graph, entity resolution → Hindsight or Zep - Memory versioning and branching → mnem - OpenClaw specifically → memory-lancedb-pro - Local, readable files, fully private, zero infra → Engram (Obsidian68) Thanks for all the suggestions in the original thread — went through every single one.