# Openclaw Plugin > LycheeMem OpenClaw Plugin - Skill: `lycheemem/openclaw-plugin` (Agent Skill, multi-file: 9 files) - Install (CLI): `npx skillmds@latest add lycheemem/openclaw-plugin` - Raw SKILL.md: https://api.skillmd.com/api/skills/lycheemem/openclaw-plugin/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: lycheemem (https://skillmd.com/u/lycheemem) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/lycheemem/openclaw-plugin --- # LycheeMem OpenClaw Plugin ## Purpose This plugin is a thin adapter between OpenClaw and LycheeMem. It does not replace `memory-core`, does not claim `plugins.slots.memory`, and does not duplicate LycheeMem algorithms. Collaboration model with OpenClaw: - OpenClaw remains the host for the main reasoning loop, current-turn coordination, workspace guidance, and short-range conversational continuity. - LycheeMem serves as the external structured long-term memory layer for cross-session recall, historical project background, entity relationships, and reusable procedures. - Treat OpenClaw memory and LycheeMem as complementary layers, not competing memory owners. Default plugin tool exposure: - `lychee_memory_smart_search` (primary recall path, default lean response) - `lychee_memory_search` (developer raw retrieval) - `lychee_memory_append_turn` - `lychee_memory_consolidate` ## Use It For - Historical facts the user mentioned earlier - Long-running project context across sessions - Entity and relationship recall - Reusing procedural skills or workflows from prior work - Compressing verbose retrieval results into a shorter `background_context` when needed ## Do Not Use It For - Workspace rules already covered by `MEMORY.md` or `memory/*.md` - Stable preferences already maintained in `memory-core` - Replacing OpenClaw's built-in memory owner - Re-answering the current turn with a second memory system when OpenClaw already has enough local context ## Coordination Rules - Prefer OpenClaw's built-in memory and workspace context for same-session continuity, immediate local preferences, and repository-bound instructions. - Prefer LycheeMem when the user is asking for longer-horizon context such as "上次这个项目怎么做的", "这个项目长期背景是什么", or "之前沉淀过哪些规则/关系/流程". - Do not perform duplicate recall for the same question by calling both OpenClaw memory search and LycheeMem retrieval in the same turn unless the user explicitly wants a comparison. - When LycheeMem returns a useful `background_context`, treat it as supplemental long-term context injected into OpenClaw's reasoning loop, not as a replacement for host memory. - When OpenClaw already has enough local context to answer well, avoid unnecessary LycheeMem calls. ## Trigger Guidance - Prefer `lychee_memory_smart_search` for recall questions such as "上次怎么处理的", "用户之前提过什么", "这个项目长期背景是什么". Treat it as the default recall path. - Let `lychee_memory_smart_search` use minimal output by default so the agent mainly receives synthesized `background_context` instead of verbose retrieval details. - Use `lychee_memory_search` only during development or debugging when you explicitly want the raw retrieval payload. - When this plugin runs inside OpenClaw with host lifecycle integration enabled, assume the host usually mirrors natural-language user and assistant turns into LycheeMem automatically. - In that host-integrated mode, do not manually call `lychee_memory_append_turn` from the model during normal operation, because it would duplicate the host-managed transcript mirror. - If host lifecycle integration is unavailable, disabled, or you are debugging a non-standard flow, call `lychee_memory_append_turn` manually after each completed dialogue turn so the transcript can later be consolidated. - Do not append raw tool invocations, tool arguments, tool outputs, scratchpad text, or other orchestration-only traces unless the user explicitly wants those artifacts stored as memory. - Do not call OpenClaw `memory-core` search and `lychee_memory_search` for the same recall problem in the same turn. - When this plugin runs inside OpenClaw with host lifecycle integration enabled, assume `/new`, `/reset`, and `/stop` boundaries may trigger `lychee_memory_consolidate` automatically with `background=true`. - Important long-term signals such as explicit memory requests, defaults, stable preferences, rules, and project standards may also trigger proactive background consolidation before the next reset boundary. - Use `lychee_memory_consolidate` manually at wrap-up when host automation is unavailable, disabled, or you are debugging explicit persistence behavior. - Even in host-integrated mode, it is acceptable for the model to call `lychee_memory_consolidate` when it intentionally wants to persist important new long-term knowledge early. The model should still avoid manual `lychee_memory_append_turn` in that case. ## Recommended Pattern The intended pattern is: 1. let OpenClaw evaluate whether its local memory, workspace instructions, and current-turn context are already sufficient 2. if longer-horizon recall is needed, call `lychee_memory_smart_search` with its default lean response 3. inject the returned `background_context` into the main reasoning context as supplemental long-term memory 4. answer in OpenClaw's normal reasoning loop 5. let the host lifecycle adapter mirror the natural-language user turn and assistant turn automatically when available; otherwise call `lychee_memory_append_turn` manually using the same `session_id` 6. do not append tool-call metadata or raw tool outputs by default 7. let host lifecycle boundaries trigger background consolidation when available, and allow proactive consolidation when the turn clearly introduces durable long-term knowledge; otherwise call `lychee_memory_consolidate` manually only if new memory-worthy information appeared in the mirrored natural-language turns 8. in host-integrated mode, if you choose to call `lychee_memory_consolidate` manually, do not precede it with extra model-driven `lychee_memory_append_turn` calls unless you are explicitly debugging transcript mirroring Developer debugging path: 1. call `lychee_memory_search` 2. inspect the raw retrieval payload This keeps OpenClaw in charge of the main reasoning loop while LycheeMem stays focused on long-term structured memory retrieval and persistence.