Skip to main content

Documentation Index

Fetch the complete documentation index at: https://docs.overcut.ai/llms.txt

Use this file to discover all available pages before exploring further.

​
Overview

Workflow memory lets agents store and recall knowledge between runs, building on past context instead of starting from scratch every time. Common examples:
  • Coding conventions discovered during reviews
  • Recurring issues seen across multiple PRs
  • Decisions made in earlier runs
  • Insights captured by retrospective runs

​
How it works

Agents automatically have access to workflow memory. No setup or configuration is required. During a run the agent is aware of existing memories and can:
  1. Read a memory to retrieve its contents
  2. Create a new memory to save something for future runs
  3. Update an existing memory to keep it current
Agents decide on their own when storing or reading a memory is useful. Memories are scoped to the workflow: each workflow maintains its own independent set. Memories can also be created and refined by retrospectives: automated reviews that analyze past runs and turn findings into reusable guidance. See Workflow Self-Improvement for how retrospectives create memories, promote tentative memories, and adjust weights over time.

​
Emphasizing memory in prompts

Because memory works automatically, most workflows need no special instructions. If you want to steer what the agent remembers or looks up, add a hint in your agent step prompt. Example prompt snippets:
Make sure to check your memories for any notes about this repository before starting.
After completing the review, remember any new coding conventions you discovered.
Referencing a specific topic in a prompt (for example “check your memories for the deploy checklist”) gives the agent a precise lookup target.