The Tool Desk
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First identify what the agent is forgetting
“Memory” can mean three different things, and each calls for a different fix:
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- Conversation or session context: messages and tool activity from an earlier interaction. A later run may not have that history.
- Project knowledge: stable facts such as architecture, conventions, and the commands used to test the code. These belong in maintained project instructions or documentation.
- Run state: temporary progress, such as which files were changed, what remains to do, or the last error. This is a checkpoint to restore, not lasting project knowledge.
Reproduce the problem and inspect the run environment: does each job get a fresh runner, container, workspace, or agent sandbox? Then check whether the workflow explicitly restores the files the next run needs. The OpenAI Agents SDK documentation notes that a fresh, empty sandbox starts with empty memory; persistence requires preserving and reusing the relevant memory directory, session state, or snapshot (OpenAI Agents SDK: Agent memory).
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Choose storage by what must survive
No single storage mechanism is right for every kind of context. Compare the retention and recovery needs, who must share the information, whether changes should be reviewed, and who is allowed to write data that a trusted run will later consume.
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| Storage | Best fit | Important trade-off |
|---|---|---|
| Workflow artifact | Run outputs such as logs or test results, or files passed between jobs. | Artifacts are outputs and handoffs, not a substitute for dependency caching or a general long-term memory store. Deleting the workflow run also deletes its artifacts. See GitHub’s workflow artifacts documentation. |
| GitHub Actions cache | Reusable files or short-lived, potentially branch-local state. | Caches can be evicted and are not a durable record. They are also not signed or verified; restrict writes and never store secrets. See GitHub’s dependency caching reference. |
| Repository instructions or documentation | Stable, reviewable project facts that should travel with the code. | The agent or workflow must read the files, and maintainers must keep them accurate. VS Code advises verifying repository memory and moving stable guidance into project documentation or custom instructions (VS Code: Use memory with agents in VS Code). |
| Dedicated memory branch | Durable memory history kept separately from the main project files. | The workflow must explicitly read and update it; writes should be controlled and the contents checked against current code. GitHub Agentic Workflows describes this and related approaches in its MemoryOps patterns. |
| Issue or pull-request comment | Context for continuing a particular review or follow-up. | It remains attached to that issue or pull request, rather than acting as a general project-wide knowledge store. See GitHub Agentic Workflows MemoryOps. |
| Agent sandbox memory | Lessons or state reused across runs of a sandbox agent. | A later run needs the configured memory directory, session state, or snapshot restored. A new empty sandbox has no earlier memory. See the OpenAI Agents SDK memory guide. |
Set up memory in two layers
Keep stable guidance with the project
Add concise, reviewed instructions to the repository’s existing agent-instructions or documentation location. Cover only information that is useful across tasks: the project’s layout, important conventions, and the commands that actually validate a change. The workflow or agent must be configured to read those files; merely committing them does not guarantee an agent will use them.
Keep this guidance factual and easy to correct. If the code changes, update the instructions. VS Code’s guidance emphasizes verifying repository memory and moving stable information into project documentation or custom instructions rather than trusting stale notes (VS Code memory documentation).
Save a compact checkpoint for work in progress
For an interrupted task, persist only what a later run needs to resume: the task identifier or scope, completed work, remaining steps, relevant files, and the last useful error or test result. Choose an artifact for a handoff tied to a workflow run, or another persistence mechanism when the state must outlive that run. Make the next run explicitly restore and read the checkpoint.
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Keep checkpoints separate from permanent project guidance. A checkpoint can become obsolete as soon as the task or code changes; preserve enough context to resume, but do not treat it as an authoritative description of the project.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.If you use a GitHub Actions cache, make misses safe
A cache is useful when reuse is a convenience, not a prerequisite for correctness. GitHub’s documentation accessed on October 5, 2026 says Actions cache entries are removed after more than seven days without access and documents a default 10 GB cache limit per repository. When that limit is reached, least-recently-used entries are evicted. GitHub says owners may be able to increase the limit; it documents a configurable maximum of up to 10 TB for repositories owned by users, which should not be generalized to organization or enterprise repositories. These are GitHub Actions platform limits and may change. Check the current cache documentation before relying on them.
- Handle a cache miss: the workflow should be able to regenerate or recover the needed state rather than fail because an entry has expired or been evicted.
- Protect cache writes: limit which workflows can save data that a trusted workflow later restores. Do not let untrusted changes seed state that trusted runs will execute.
- Do not cache secrets: GitHub warns that cache contents are not signed or verified and that any workflow run able to read a cache may extract its contents. Restored files can also affect files later executed.
That warning is why cache security is part of the memory design, not an afterthought. Consult GitHub’s dependency caching reference for its current cache behavior and security guidance.
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Verify that memory really survives a new run
- Record the expected behavior: decide whether the next run should regain project conventions, resume a particular task, or both.
- Start a genuinely fresh run: use the same clean-run conditions that exposed the forgetting, rather than relying on files left in a reused workspace.
- Check restoration and reading: confirm the workflow restores the intended file or state and that the agent is instructed to read it.
- Check for stale or unsafe content: compare saved facts with the current code, remove obsolete notes, and verify that only trusted workflows can write state consumed by trusted runs.
- Test recovery without saved state: for caches and other fallible persistence, confirm the workflow can regenerate or safely restart when the state is missing.
Do not infer that persistence improves task completion just because a file exists. Verify that a later run actually reads useful, current context; no general success-rate or cost improvement is established by the platform documentation cited here.
Where GitHub Agentic Workflows fit
GitHub Agentic Workflows documents a Cache Memory option and MemoryOps patterns, including memory associated with repository workflows and issues or pull requests. These are GitHub-specific implementation approaches, not universal CI features. GitHub describes Agentic Workflows as “in public preview and subject to change,” so check its current overview and the Cache Memory reference before adopting them.
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