I ran both approaches in parallel for two weeks: a MEMORY.md file for one project, MemoClaw for another. Here’s an honest comparison.

Where local files work fine

Let me be upfront: if you have one agent, one machine, and a small memory file, local files are perfectly adequate. A MEMORY.md under 50 lines, loaded at session start, gets the job done. It’s free, it’s simple, and it works.

The problems start when any of these change.

Problem 1: Multiple agents, one memory

I have a main coding agent and a separate research agent. Both need to know project context. With local files, I had two options: duplicate the file (now they diverge) or share it (now they overwrite each other).

With MemoClaw, both agents use the same wallet. Agent A stores “decided to use tRPC for the API layer”, Agent B recalls it when writing integration code. No file conflicts, no sync logic.

# Research agent stores a finding
memoclaw store "tRPC v11 supports RSC, good fit for our Next.js setup" --tags research

# Coding agent recalls it later
memoclaw recall "what API approach did we pick?"
# → "tRPC v11 supports RSC, good fit for our Next.js setup" (score: 0.87)
# → "decided to use tRPC for the API layer" (score: 0.82)

Problem 2: Search by meaning

My MEMORY.md had 200+ lines after two weeks. Finding something meant grep, which only works if you remember the exact words.

# This finds nothing
grep -i "database" MEMORY.md
# The actual line was: "Postgres on Neon for prod, SQLite for local dev"

Semantic search finds it because it understands that “database” relates to “Postgres on Neon.” This isn’t a minor convenience — it changes how useful memory is.

Problem 3: Token cost

A 200-line MEMORY.md is roughly 3,000 tokens. Loaded every session, that’s 3,000 tokens of context consumed whether you need it or not. After a month, maybe 10% of those lines are relevant to any given session.

With recall, you pull 3-5 relevant memories per query. Maybe 200 tokens. The rest of your context window stays available for actual work.

Problem 4: Cross-device

I work from two machines and occasionally from my phone (via Cursor mobile). The MEMORY.md file is on one machine. I could sync it via git, but then I’m committing memory files and dealing with merge conflicts on personal context.

MemoClaw is an API. It works wherever my wallet works.

When to stick with local files

Honestly? If you have:

• One agent, one machine
• Under 50 memories
• No need for semantic search
• Zero budget

A local file is fine. Don’t over-engineer it.

When to switch

Switch when you notice:

• Your memory file is growing past 100 lines
• You’re grepping and not finding things
• Multiple agents or devices need the same context
• You’re loading kilobytes of context you don’t use

The migration is one command:

memoclaw migrate ./MEMORY.md

It reads the file, splits it into facts, generates embeddings, and stores everything. Takes about 10 seconds for a typical file. You can keep the local file as backup until you trust the setup.

Cost comparison

Local files: $0/month, limited by the problems above.

MemoClaw: 100 free calls to start. After that, $0.005 per store or recall. A realistic daily usage of 20 stores + 30 recalls = $0.25/day. The 17 endpoints that don’t use OpenAI (list, delete, update, tags, export, import, etc.) are free.

For most individual developers, you stay well under $10/month.