Give your app a memory

Store what your users tell you, put what is known about them in front of your model, and get a plain answer when nothing is known.

Get started Set up your agent Get an API key

Terminal

pip install geniffy        # or: npm install geniffy
export GENIFFY_API_KEY="gnf_live_..."

app.py

from geniffy import Geniffy

mem = Geniffy().space("customer_1042")       # one of your users
source = mem.memories.add("Priya Nair signs the Lumen renewal.")
mem.sources.wait(source.id)                  # learning takes a few seconds
print(mem.context("Who signs the Lumen renewal?"))

Terminal

curl https://api.geniffy.com/v1/context \
  -H "Authorization: Bearer $GENIFFY_API_KEY" \
  -H "X-Geniffy-Space: customer_1042" \
  -H "Content-Type: application/json" \
  -d '{"question": "Who signs the Lumen renewal?"}'

Set up your coding agent

Paste this prompt into Claude Code, Cursor, Codex or any coding agent, inside your project. It installs the SDK, gives each of your users a memory of their own, and puts what is known into your prompts.

Agent Prompt

Add long-term memory to this project with Geniffy (docs: https://docs.geniffy.com). 1. Install the SDK for this project's language: `pip install geniffy` (Python) or `npm install geniffy` (TypeScript/JavaScript). Read the key from the GENIFFY_API_KEY environment variable; never hard-code it, never send it to a browser, and never log it. 2. Create one client for the process and reuse it: `Geniffy()` in Python (or `AsyncGeniffy()` in async code), `new Geniffy()` in TypeScript. 3. Give every end user their own memory. Where this app knows which user a request is for, bind a client to them once: `mem = client.space(f"user_{user_id}")` in Python, `const mem = client.space(`user_${userId}`)` in TypeScript, and use only `mem` below that point. A space is created by the first write to it. Never call memory methods on the unbound client for an end user's data: that writes to the account owner's own memory, where every user would be mixed together. 4. Save what is worth remembering. After a user tells the app something about themselves, their work or their plans, call `mem.memories.add(text)`. To save a whole conversation, pass it as the framework holds it: `mem.memories.add(messages=history)` / `mem.memories.add({ messages: history })`. Files: `memories.add_file(path)` / `memories.addFile(blob, { filename })` (PDF or Word .docx). Web pages: `memories.add(url=...)` / `memories.add({ url })`. 5. Use it before the model answers. Call `mem.context(user_question)` and put the string it returns above the user's message in the prompt. It is never empty: when nothing is known it returns a sentence that tells the model to say so, so pass it through as is and do not add a fallback. For a direct answer instead of context, use `mem.ask(question)`; when its `answer` is null, say nothing is known. 6. Let users be forgotten. Wherever this app deletes a user or their data, also call `client.forget_space(f"user_{user_id}")` / `client.forgetSpace(`user_${userId}`)`. 7. Handle errors: catch `AuthenticationError` (a wrong or revoked key) and `UnreadableError` (a file or link that could not be read; `error.source.error` says why). The SDK already retries busy and network errors. Log the `X-Request-ID` of any failed call: it finds the call on the Requests page. 8. Add one small test that writes a note into a test space, waits with `mem.sources.wait(id)`, checks that `mem.context(...)` mentions it, then erases the test space. Keep the change minimal and match this project's style.