- agent-loop: raise repetition-detection floor to 180 chars and clear thinking
replay anchors when collapsing a detected loop.
- providers/google: ignore empty text parts, retain terminal thoughtSignatures,
and stop function-call signatures clobbering the prior block.
- autolearn: capture goal-mode at the turn boundary; harden managed-skill writes
against hard-links/symlinks (O_NOFOLLOW + nlink); refuse minting managed skills
whose name an authored skill already claims.
- eager tasks: thread agentKind through the session so a custom top-level agentId
still gets always-mode delegation; split Eager Tasks prompt into hard vs soft.
- title-generator: race the online title model against a local tiny-model fallback.
- eager-todo: keep the soft reminder aligned with the todo init schema.
- mcp/stdio: keep close() detaching the read loop instead of awaiting it.
- stream loop: fix collapsing and tool-call thought-signature handling.
- learn (mnemopi): `rememberScoped` returns undefined when the retain failed
(closed DB / disk error). The tool ignored it and reported "Lesson stored"
(and could still mint a skill), silently losing the lesson. Mirror
`mnemopiBackend.save` and fail loudly when no id is returned.
- manage_skill: enforce the action/field contract in the schema via a cross-field
refine (create/update require description+body; delete needs only name) instead
of relying solely on a runtime throw in execute. Kept as a refine, not a
discriminated union, so the wire schema stays a single root object — both
strict structured-output mode and the Anthropic tool-schema builder require
that.
Addresses review threads on PR #2542 (threads 19, 14).
The `learn` tool previously required a `hindsight`/`mnemopi` backend. It now
also works when `memory.backend` is `local` (the file-based rollout backend):
lessons append to a `learned.md` under the project's memory root, kept separate
from the consolidation artifacts so a consolidation pass never clobbers them,
and are injected into future sessions alongside the memory summary.
- memories: `saveLearnedLesson` (newest-first, deduped, count- and per-field
size-capped, secret-redacted, injection-neutralized) with per-path write
serialization; `buildMemoryToolDeveloperInstructions` reads `learned.md` and
shares one injection budget with the summary; `redactSecrets` extended with
GitHub/npm/Slack/Google token prefixes.
- local backend: implements `save()`; status reports `writable: true`.
- learn tool: `local` execute branch; `createIf`/`isToolAllowed`/auto-include
and the standing guidance extended to `local`; local saves tier as a `write`
approval.
- read-path prompt: renders the learned-lessons block when present.
- Lessons are injection-neutralized and secret-redacted on BOTH write and read
(they render unescaped into the system prompt).
Also moves the auto-learn CHANGELOG entry out of the released [15.12.6] section
(a cherry-pick artifact) back under [Unreleased] and notes the local backend.
Tests: local storage (format, dedup, cap, redaction incl. provider/delimiter-
split tokens, concurrency), read-back (with/without summary, off-gating, raw
hand-edited file), tool gating + write-approval tiering.
Add a default-off "auto-learn" loop. When `autolearn.enabled` is set, after the
agent stops a session controller nudges it to capture reusable lessons: durable
facts go to long-term memory and repeatable procedures become "managed skills" —
SKILL.md files written to an isolated ~/.omp/agent/managed-skills directory that is
discovered and surfaced like authored skills but never overwrites them.
Two tools back this:
- `manage_skill` — create/update/delete managed skills.
- `learn` — record a lesson, optionally minting/enhancing a managed skill in the
same call (requires a hindsight/mnemopi memory backend).
The nudge is passive by default (a hidden reminder rides the next turn);
`autolearn.autoContinue` instead auto-runs one capture turn at stop, and
`autolearn.minToolCalls` (default 5) gates trivial turns. Plan/goal-mode turns and
subagents are never nudged, and the controller re-checks the live setting at fire
time so a mid-session opt-out takes effect.
Isolation & precedence: managed skills are a separate lowest-priority discovery
provider, so an authored skill of the same name wins across every provider and
custom directory regardless of third-party toggles; a disabled higher-priority
authored skill can never hide a managed one, and managed never masks an enabled
authored skill. Managed names and descriptions are sanitized on both write and
read (control/format chars, angle brackets, and Markdown fences) before they render
into the system prompt, and the SKILL.md byte cap is enforced on the final
serialized file.
Default off → zero footprint when disabled.
can1357