Thanks for exploring persistent memory for SkillOpt. Memory can be valuable if retrieval measurably improves training, but the current design is not safe to merge yet.

The primary blocker is consent and data handling: merely having a global MEM0_API_KEY causes the trainer to upload up to 4,000 characters of the full skill plus reflection patch summaries to a third-party hosted service. There is no explicit SkillOpt opt-in, redaction, or user-facing disclosure. A key present for another application must not silently enable data export.

Other required changes:

• Add an explicit config flag/backend selection in addition to the key, defaulting off.
• Redact secrets and sensitive paths/content before any upload, and document exactly what leaves the machine.
• Use a stable project-specific namespace; the current env/config-name user ID can mix unrelated projects.
• Avoid two synchronous network writes per training step; use bounded timeouts plus batching/async behavior.
• Add the optional dependency/extra and configuration schema/documentation.
• Validate non-dict or null patches instead of throwing and then swallowing the error.
• Add mocked tests for disabled, enabled, failure, redaction, namespace, and patch-validation paths.
• Integrate retrieval into the training loop and provide evidence that it improves quality or utility. At present the trainer only writes; it never calls the retrieval APIs, so this adds privacy/cost/latency without a training benefit.

The CLA check is also still incomplete. Please rework this in the original PR if you would like to continue; that preserves your authorship while allowing a safe review. We can re-review once explicit consent, privacy controls, tests, and a real retrieval benefit are present.

Hi @jrauch713-svg — following up with a concise reminder of what is still required before this can be considered:

• Explicit opt-in: today a global MEM0_API_KEY silently enables upload. Uploading must be selected explicitly, not inferred from a key that may exist for another application.
• Redaction of sensitive content before it leaves the process.
• Stable project-specific namespacing for stored data.
• A real retrieval benefit: the current integration is write-only and never reads memory back into training.
• Focused tests covering disabled/enabled behavior, redaction, namespacing, malformed patches, and service failures.
• Completion of the CLA, which is still pending.

The branch is also conflicting. Do you plan to continue with this direction? If so, please let us know; we are glad to help keep the scope reviewable.

Not at all — we are not asking you to stop, and you did nothing wrong by exploring this direction. We think a Mem0 integration could be valuable for SkillOpt.

Our review only means that the current implementation needs some iteration before it can be merged. The main concerns are that it can upload skill/reflection content without an explicit SkillOpt opt-in, and that it currently writes memories without retrieving and using them to improve training.

If you would like to continue, we would be happy to help break the work into smaller, clearer steps:

1. Complete the CLA by replying to the policy bot with the option that correctly applies to you.
2. Add an explicit, disabled-by-default Mem0 configuration and redact sensitive content before upload.
3. Add project-specific namespacing and focused tests.
4. Add retrieval back into the training loop so the integration provides a measurable benefit.

There is no pressure to do everything at once. Please let us know if you would like to continue, and we can help review each step.

Yes — please continue! We would be very happy to work with you on this, and your instinct to narrow the scope is exactly right. We also appreciate that you identified the write-only design as the central issue.

Our preference would be to keep one small retrieval path in this PR, because that is what turns the integration from external logging into a useful memory loop. It does not need to become a large system. A reviewable minimum would be:

1. An explicit Mem0 setting that is disabled by default — the presence of MEM0_API_KEY alone must not enable uploads.
2. Redaction before any outbound request, plus a stable project-specific namespace.
3. One bounded retrieval call before reflection, with the retrieved context actually included in the reflection input.
4. Mocked tests showing that disabled mode sends nothing, redaction/namespacing work, retrieved context reaches reflection, and service failures degrade gracefully.

You do not need to solve batching, async writes, sophisticated ranking, or run a large benchmark study in this iteration. A deterministic end-to-end test and a small opt-in smoke example are enough for us to review the correctness of the first useful loop; broader performance evaluation can be follow-up work.

Please take the time you need, complete the CLA using the option that applies to you, and rebase onto current main when convenient. Feel free to push an incremental version or ask questions before everything is finished — we are glad to help, especially for your first contribution here. Thank you for continuing!

@microsoft-github-policy-service agree

Hi @Yif-Yang — pushed. The branch is rebased onto current main and the four items from your reviewable minimum are in.

1. Explicit opt-in. Uploading now requires train.mem0_enabled: true. A MEM0_API_KEY present for another application no longer enables anything — a disabled run doesn't even read the key. All mem0_* resolution is in skillopt/memory/settings.py.

2. Redaction + namespacing. skillopt/memory/redaction.py is a single choke point; no payload reaches the client without passing through it. It strips vendor keys, bearer/basic tokens, JWTs, private keys, key = value secret assignments, the project root, and /home/<user> prefixes. Credentials are scrubbed before paths are collapsed, so a key embedded in a home path can't survive. Relative paths and filenames are preserved so stored memories stay useful.

Namespaces are skillopt:<env>:<sha256-prefix-of-project-root> — stable across runs of one project, distinct across projects, and the raw path is hashed rather than sent.

I mirrored the secret patterns from skillopt_sleep/staging.py rather than importing them, since pyproject.toml keeps that package decoupled with zero research dependency. Happy to change that if you'd rather share the code.

3. One bounded retrieval call before reflection. hook_pre_reflect fetches relevant history and appends it to the reflection input. I deliberately assigned it to a separate reflect_context variable rather than reusing step_buffer_context, because that same variable also feeds the autonomous learning-rate decision and the rewrite prompt — those keep seeing the unaugmented context.

4. Mocked tests — tests/test_mem0_memory.py, 15 cases, no network. Covers: a bare MEM0_API_KEY not enabling uploads, explicit opt-in writing, redaction of secrets and home paths, the cap applied after redaction, namespace stability and project separation, retrieval reaching the actual reflection prompt (verified end-to-end by capturing the user message handed to the optimizer), retrieval being independently disableable, graceful degradation on service failure, timeout bounding, and malformed-patch filtering.

Also in this push:

• Every call is wall-clock bounded by mem0_timeout_seconds (default 5s), so an unreachable service costs one bounded pause instead of blocking a step.
• Malformed patches (None, strings, non-lists) are filtered rather than raised inside a swallowing try.
• mem0 optional extra declared, deliberately outside all.
• configs/features/mem0_memory.yaml as a documented opt-in example, following the soft_gate.yaml convention.
• docs/reference/config.md documents every key plus exactly what leaves the machine.

Two things I hit while rebasing that are worth flagging:

• The original diff showed ~20 changed lines in trainer.py that weren't real changes — a LF→CRLF conversion of the configure_qwen_chat block. That's also what caused the conflict with the newer configure_cursor_exec block. The actual integration is 12 lines; it's cleaned up now.
• The mem0_* keys needed mapping into _FLATTEN_MAP under train.. As flat top-level keys they were silently dropped for structured configs — which is every config in the repo — so the feature would have appeared inert with no error. Regression test added for that.

Full suite: 485 passed, 6 skipped, 0 failed. ruff check clean on all new files.

One honest caveat: all tests are mocked — nothing has been run against the live Mem0 service yet. If you'd like a real smoke run before merging, say so and I'll do it.

Left out per your guidance: batching, async writes, ranking, and benchmark evaluation.

Thanks again for the detailed review and for breaking it into steps — it made this much easier to approach.