tensemem

Memory that knows its tenses: embedded, deterministic state memory for AI agents

A new way for AI to search for data

View on GitHub

5GitHub stars

Python

What XERJ indexed

Snapshot

Whole repository

The whole repository at commit dc240b1b77 (2026-10-21).

Selected work

Shubham
@shubhsting

“[low-risk] Add StorageBackend protocol”. 7 files, 226 lines in tests, src/tensemem.

Licence

The repository

Apache-2.0

Checked by a person at the pinned commit.

The XERJ corpus

Apache-2.0, with credit

The author agreed by email on 2026-10-23. Same terms as the repository. Answers name the repo, the commit and the file:line.

in licence reviewnot in the hub yetsuggested use: adapt with attribution

Example questions

Where does tensemem handle “[low-risk] Add StorageBackend protocol”?
What does src/tensemem do in tensemem?

The prompt

Paste it into your AI agent. It clones tensemem/tensemem at the pinned commit and indexes it on your machine.

Search the GitHub repository tensemem/tensemem with XERJ (https://hub.xerj.org/corpus/tensemem--tensemem).
Run these in a shell, in order, and check each result before the next:
1. xerj --help  (success: it prints usage). If xerj is missing: curl -fsSL https://xerj.org/get | sh
2. git clone https://github.com/tensemem/tensemem tensemem && git -C tensemem checkout dc240b1b7752850a43671916b86fbab56a940f25
3. Start a local node and leave it running: xerj --insecure --data-dir ./xerj-data
4. xerj autoindex ./tensemem --url http://127.0.0.1:9200 --prefix tensemem --progress plain
5. xerj autoindex map --url http://127.0.0.1:9200
Then answer my question from the indexed files and cite the ax_path of every hit. Query syntax: https://xerj.org/llms.txt
Licence: Apache-2.0. Keep the copyright notice and credit the authors.

Shubham AI, prompts and agents corpora

Selected work and consent: the XERJ registry.