The memory your agent was missing

RycallWise

Your AI already solved this last Tuesday. Why pay for the same thought again?

RycallWise sits on your machine, between the agent and the model. The first time a task is new, the model works. The second time, local memory replays the technique — about a second, no extra model tokens, nothing uploaded.

100% localMemory stays on the laptop
~1.2s replayVersus a full model call
$0 on a hitRepeats stop the meter

RycallWise does not cache chat transcripts. It stores how a job was done — a playbook — so the next similar job in a different file still matches. That is why a connection-bug fix learned in one repo can replay in the next.

A local memory hit can skip a model call. That is not a promise of a free commercial service. Any subscription or fee will be stated before you buy.

01 · First hit

Ask once. The second ask skips the model.

You prompt the agent as usual. RycallWise looks at local memory first. If it already knows the technique, it answers from disk. If not, the model runs — and the technique is saved for next time.

1Type the task in your coding agent.
2A match returns in about a second. No model call. No bill.
3A miss calls the model, then writes a reusable playbook locally.

localhost · rycallwise memory

> refactor the connection bug in orders.cs

RycallWise: matched playbook “fix-db-connection”
RycallWise: replayed 6 steps from local memory
✔ done in 1.2s — Model Used: none
✔ Tokens saved: ~4,180

02 · Refactors

A playbook, not a copy-paste

The first time you fix a timeout, RycallWise keeps the steps — isolate the client, add retry, log the cause. Next week, a similar bug in a new service still matches. The files changed. The method did not.

1Finish a fix the long way once.
2RycallWise stores the technique, not the customer file path.
3The next similar bug replays those steps in the new file.

rycallwise · playbook

Playbook

fix-db-connection

Learned 14 Aug · used 11 times · 0 tokens on replay

Steps it will replay

1. Find the client factory · 2. Add timeout + retry · 3. Surface a typed error · 4. Log correlation id · 5. Add a regression test

03 · Documents

Read a file without waking the model

CSV, PDF, DOCX, XLSX — a deterministic reader pulls the text. You asked for invoice columns? They come back instantly. The model is not paid to re-read a spreadsheet it already understood last month.

1Point at the file. The local reader opens it.
2You get the extract. Zero LLM tokens.
3Next time the same kind of file arrives, the extract path is already known.

rycallwise · file reader

invoices-april.csv

tokens used

Extracted

42 rows · vendor, amount, due date

04 · Releases

A release checklist that remembers itself

Deployments, incident triage, migrations — the boring work that is dangerous when skipped. RycallWise learns the ordered list and walks it again, so Friday’s release is not a new essay for the model.

1Run a workflow once with the agent watching.
2The checklist is stored as steps, not as a chat dump.
3Next release, those steps run in order from local memory.

rycallwise · workflow

production-release

✔ freeze changelog ✔ run smoke ✔ tag build

→ migrate ○ switch traffic ○ watch error budget

05 · Local by default

Technique only. Paths and names get scrubbed.

Memory lives on the laptop. Technique-only mode stores how to do the work, not the customer paragraph, email, or file path. From chat you can say “forget everything about invoices” and it goes.

1Nothing is uploaded for recall. The index is local.
2Stored items are techniques — playbooks — not raw answers.
3Forget from the same conversation. No dashboard hunt.

rycallwise · privacy

Technique-only · on

Customer text, emails, and paths are scrubbed before save.

> forget everything about invoices
✔ 3 playbooks removed · local only

06 · Proof

Watch the meter run backwards

Every replay shows tokens you did not spend. Finance can see a number. Engineers can see a habit. That is how AI spend stops feeling like a leak.

1Open the local dashboard after a week of real work.
2Hits, misses, and tokens saved sit on one screen.
3Export the trail when someone asks “what did memory do?”

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Give your agents a memory they can keep

Ask for a local trial walkthrough, or tell us how your team repeats the same AI work every week.

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