Do these 3 things before closing this tab:
1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitchesA CI agent does not automatically carry its conversation or workspace into the next run. To make it remember useful project context, persist the right files and explicitly restore and read them later. Keep durable project guidance in reviewed, version-controlled documentation; use a cache or artifact only for the kind of temporary state it is designed to retain.
The title suggests a specific first-person fix, but no CI provider, agent, repository, or tested configuration is identified here. The practical guidance below explains how to diagnose and address the underlying problem without claiming an unverified implementation.
First identify what the agent forgot
“Memory” can mean three different things, and each requires a different fix:
- Conversation or session context: prior messages and the agent’s immediate reasoning. A new session may not include this history.
- Durable project knowledge: architecture, conventions, commands, and other facts that should remain useful across tasks. Store these in maintained project instructions or documentation.
- Run-to-run workflow state: progress on a particular task, such as completed steps, the current position, or the last error. Persist a concise checkpoint that a later run can restore.
OpenAI’s Agents SDK documentation notes that a fresh, empty sandbox starts with empty memory. A new run therefore cannot be assumed to know what an earlier run saw; the workflow must preserve and reuse the relevant memory directory, session state, or snapshot. See OpenAI Agents SDK: Agent memory.
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Check whether the next run can access the previous state
Before changing the agent’s prompt, inspect the workflow lifecycle. Determine whether each run starts on a fresh runner, container, sandbox, or workspace, and whether any saved files are restored into the new environment. A file written near the end of one run helps only if a later run can retrieve it and the agent actually reads it.
- Reproduce the failure and record what is missing: conversation history, project conventions, or task progress.
- Inspect the workflow’s job and sandbox setup to see which files survive between runs and which are recreated.
- Trace any save-and-restore steps. Confirm that the saved path is correct, that the later run can access it, and that the agent is instructed to load it.
- Run a follow-up task that depends on one specific saved fact or checkpoint. Confirm that the agent retrieved the expected content rather than guessing.
Choose persistence by the kind of information
GitHub Actions artifacts, caches, repository files, and agent memory directories are not interchangeable. Pick based on the information’s purpose, desired retention, sharing scope, and how safely it can be written and restored.
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| Mechanism | Best fit | Limits and considerations |
|---|---|---|
| Workflow artifact | Outputs from a run, such as logs or test results, or files passed between jobs. | It is not a dependency cache or a general long-term memory store. Artifacts are tied to a workflow run; deleting the run also deletes its artifacts. See GitHub Docs: Workflow artifacts. |
| Actions cache or Cache Memory | Reusable files or short-lived, potentially branch-local state. | Caches can be evicted, and their contents are not signed or verified. Design for a cache miss, restrict writes, and do not store secrets. See GitHub Docs: Dependency caching reference and GitHub Agentic Workflows: Cache Memory. |
| Repository documentation or a dedicated memory branch | Durable, reviewable facts such as architecture, commands, and coding conventions. | The workflow or agent must read and maintain the files; stale guidance must be checked against current code. See Visual Studio Code: Use memory with agents in VS Code and GitHub Agentic Workflows: MemoryOps. |
| Issue or pull-request comment memory | Context needed for follow-up work tied to a particular issue or review. | That context is attached to the issue or pull request, not a universal project-wide store. See GitHub Agentic Workflows: MemoryOps. |
| Agent sandbox memory directory | Lessons intended for later runs using the same sandbox-agent setup. | Preserve and reuse the configured memory directory, session state, or snapshot. A new, empty sandbox starts without memory. See OpenAI Agents SDK: Agent memory. |
Put stable project knowledge under review
Save information that should remain true beyond a single task in a source-controlled instruction or documentation file. Useful entries include where key components live, how to run the relevant tests, and repository-specific conventions. Keep it concise enough for an agent to use and specific enough to verify.
Do not treat memory as automatically authoritative. Visual Studio Code advises verifying repository memory and moving stable guidance into project documentation or custom instructions. Review saved facts against current code, correct outdated directions, and remove information that no longer applies.
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Make task checkpoints small and recoverable
For work that may span runs, persist only what the next run needs to resume: the task, completed work, current position, relevant files, next action, and any unresolved error. Keep transient checkpoints separate from stable project guidance, so a one-off status does not become a misleading project rule.
An artifact can pass a checkpoint or output between jobs, while a cache may be suitable for reusable or short-lived state. In either case, the next run must restore the data and be told how to interpret it. Because persistence can fail or expire, the workflow should still have a recovery path: regenerate what is reproducible, or stop and report that required state is unavailable rather than silently proceeding as if it had been restored.
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Account for GitHub Actions cache limits and security
As documented by GitHub on October 5, 2026, an Actions cache entry can be removed after more than seven days without access. The default per-repository cache limit is 10 GB; GitHub says owners may be able to increase it, and additional usage can incur cost. GitHub documents a configurable maximum of up to 10 TB for repositories owned by users; that figure should not be generalized to organization or enterprise repositories, whose limits depend on their settings. These are GitHub Actions platform limits, not guarantees that a particular cache will remain available. Check the current dependency caching documentation before relying on them.
GitHub warns: “Cache contents are not signed or verified, and any workflow run that can read a cache may extract its contents.” Treat restored cache files as untrusted input, especially if a workflow later executes them. Avoid putting credentials or other secrets in a cache, and limit which workflows can write data that trusted workflows later restore.
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Verify memory across runs
A persistence setup is not proven by the fact that one run wrote a file. Test the full handoff with a later run, and check both retrieval and freshness:
- Confirm the later run starts with the expected restored file or session state.
- Ask it to use a concrete saved fact or resume a known checkpoint, then verify the result against the repository.
- Simulate missing or expired state and check that the workflow recovers or stops clearly.
- Review whether the agent can overwrite memory and whether that write is available to workflows that should not control it.
- Remove or correct a stale memory entry and confirm that later runs no longer follow it.
GitHub Agentic Workflows is documented as a public preview and subject to change; its available memory patterns and behavior may evolve. See GitHub Docs: About GitHub Agentic Workflows.
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