Give Small Teams Prompt and Workflow Memory That Works Across Every AI Tool
The custom GPT that the team built. The carefully-tuned Claude project. The Notion AI macro that finally works. Each lives in one tool. None transfer. MemoryLake gives small teams prompt and workflow memory that flows across every AI tool the team uses.
Give Small Teams Prompt and Workflow Memory That Works Across Every AI Tool
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The problem: small team prompt and workflow investments don't transfer
Three months tuning a custom GPT for the company. Two weeks getting a Claude project right. The team adopts a new AI tool and starts the cycle over. The prompt and workflow craft lives in vendor silos.
How MemoryLake captures cross-tool prompt and workflow memory
Workflow skill memory
Each tuned procedure stored once, callable everywhere.
Prompt pattern memory
Successful framings reusable across tools.
Versioned with audit
Iterate on prompts with full history.
Cross-tool retrieval
ChatGPT, Claude, Notion AI, custom tools unified.
Free forever · No credit card required
How it works for small team prompt and workflow memory
- Connect — Import existing prompts and workflows from each tool.
- Structure — Each becomes typed skill memory.
- Reuse — Every tool retrieves the relevant prompt/workflow.
Before vs. after: small team prompt and workflow memory
| Without MemoryLake | With MemoryLake | |
|---|---|---|
| Prompt investment transfer to new tools | Re-built | Memory follows |
| Workflow consistency across tools | Variable | Memory-driven |
| New team member access to prompts | Manual share | Memory-loaded |
| Audit prompt evolution | Limited | Memory provenance |
Who this is for
Small teams (2-50 people) heavily invested in custom GPTs and Claude projects — where vendor lock-in by prompt investment is becoming real.
Try this use case on your own account
A small bookkeeping firm stores its five calculator tools once as workflow memory, so any model can call them: overtime, commission, mileage, late fees and insurance payouts. Its records and its policies are in memory too, and each tool already applies its own threshold. A model that has just read the policy takes the threshold off before the call, and the tool takes it off again. The answer is exact to the cent and wrong. Take the policies out of the attached memory and it disappears. Tools that take the record id and read the recorded value from memory themselves fix it for both models.
What you will see
- Five tools × three versions stored as facts, each OpenAI tool definition in
fact update --metadata; the app reads them back withfact listand sends them to gpt-4o-mini and claude-haiku-4-5 throughrouter chat --body … --boundary - Bare tools with records + policies attached: 154 of 216 record questions right; mileage and the late fee 0 of 27 each, every time with the threshold taken off twice (
miles=382for a 412-mile log) - The control, same tools with only the records attached: 215 of 216. Parameter descriptions saying "never subtract": 189 of 216, but the late fee is still 0 of 27
- Tools that take the record id: 216 of 216, what-ifs 54 of 54; claude-haiku-4-5 pre-reduces 218 of 373 bare tool calls and gets every record-id answer right. Plus
workflowsandasksubcommands and a local web UI
You need: a MemoryLake API key · the memorylake CLI · Python 3.9+
Run it
git clone https://github.com/memorylake-ai/memorylake-usecases.git cd memorylake-usecases/applied-twice-memory export MEMORYLAKE_API_KEY=sk-… python3 demo.py # or: python3 web/server.py
Watch it run · real, unedited recordings
Frequently asked questions
Import custom GPTs?
Import custom GPTs?
Yes — instructions and knowledge base imports supported.
Privacy?
Privacy?
AES-256 E2E.
Free tier?
Free tier?
Yes — for small teams.