MindMemOS – Uhlelo lokusebenza lwememori lwe-AI Agent oluvulekele umphakathi, oluvulwe yi-Huawei Noah's Ark Lab Uyasebenza

I-MindMemOS uhlelo lokusebenza lwememori lwe-AI Agent oluvulekele umphakathi, oluvulwe yi-Huawei Noah's Ark Laboratory, futhi luxazulula inkinga yokuthi i-Agent ikhohlwe konke ngemva kokusetshenziswa. Lolu hlelo luhlukanisa imemori ku-Agent eyodwa luyenze impahla ezimele engasetshenziswa kabusha kuwo wonke amafreyimwokhi, lugcine isimo sakamuva nomlando ophelele wokuziphendukela kwemvelo kusakhiwo esinezilinganiso ezintathu: ibhizinisi, isibaluli nesikhathi. I-MindMemOS ihlela imemori ungaxhunyiwe ku-inthanethi nge-Dreaming, icubungule izimpawu zokulungiswa kwamaphutha abasebenzisi nge-Feedback, futhi iguqule amakhono ngokuqhubekayo isebenzisa imikhondo yokwenziwa kwemisebenzi yangempela nge-Skill Evolution, ngaleyo ndlela yenze imemori nohlelo kuguquke ngokubambisana.

Landela i-WeChat bese uphendula ngokuthi “开源” ukuze ujoyine iqembu lokuxhumana lamaphrojekthi e-AI avulekele umphakathi
Isakhiwo sememori esingu-3D: Hlela imemori usebenzisa izixhumanisi ezintathu zebhizinisi, isibaluli nesikhathi, ulandele umlando wokushintsha kwezibaluli kanye nokuxhumana kwencazelo phakathi kwamabhizinisi.
Ukukhishwa kwememori ngezindlela ezimbili: I-MindVanilla igcina ngokushesha ulwazi lwendawo evulekile ngaphandle kwesifanekiso; imodi ye-MindSchema isetha kusengaphambili imithetho yamabhizinisi nezibaluli ukuze ivumelane nezimo zebhizinisi ezahlukene.
Ukusesha kwe-Compact Search: Imojuli yangaphandle ye-Agentic ihlela imizila yokusesha, kanti ungqimba lwangaphakathi lusebenzisa ukusesha kwemizila eminingi, okuhlanganisa ukubheka emuva amabhizinisi, ukunweba ubudlelwano bezinyathelo eziningi nokulandela umugqa wesikhathi.
Ukuhlelwa ungaxhunyiwe ku-inthanethi nge-Dreaming: Esigabeni esingaxhunyiwe ku-inthanethi, ihlonza ubudlelwano bokuphindaphindeka, bokungqubuzana nobokuziphendukela kwemvelo, ihlanganise okuphindaphindiwe, igcine izinguqulo ezingasasebenzi futhi yengeze amaphethini asezingeni eliphezulu.
Umjikelezo wempendulo we-Feedback: Usekela impendulo ecacile nengacacile; ngemva kokunquma uhlobo lwesiginali, wenza isenzo sokwengeza, sokubuyekeza, sokugcina, sokususa noma sokuqinisa.
I-Skill Evolution: Ikhipha ngokuzenzakalelayo amasu aphumelele namaphethini okuhluleka isebenzisa imikhondo yokwenziwa kwemisebenzi yangempela, ikhiqize uhlelo lokuguqula i-Skill bese iphinda ithuthukise izinguqulo.

Hlanganisa ikhosombe bese ulungisa ukucushwa: cp .env.example .env kanye no-cp config/mindmemos/dev.example.yaml config/mindmemos/dev.yaml, bese usetha i-chat_model_router, i-embed_model_router, i-rerank_model_router kanye ne-API key kumafayela okucushwa.
Qalisa izinsizakalo: Sebenzisa i-make dev ukuze uqalise isitaki esiphelele sezinto ezincike ku-Docker kanye nesevisi ye-FastAPI (imbobo ezenzakalelayo ngu-8000), noma i-make dev-core ukuze uqalise kuphela izinto ezibalulekile ezincike kuzo.
Faka futhi ulungise i-SDK: pip install mindmemos-sdk, bese usebenzisa i-mindmemos auth ukuze ulungise i-base_url, i-api_key kanye ne-user_id.
Engeza futhi useshe imemori: Biza i-client.memory.add() nge-SDK ukuze ubhale izingxoxo noma amaqiniso, bese usebenzisa i-client.memory.search() ukwenza ukusesha.
Thumela impendulo: Biza i-client.memory.feedback() noma i-CLI ethi mindmemos memory feedback ukuze uthumele izimpawu zokulungisa amaphutha ezicacile noma ezingacacile.
Qalisa i-Dreaming: Sebenzisa i-mindmemos memory dreaming noma ubize i-Dreaming API ukuze uqinise imemori ungaxhunyiwe ku-inthanethi.
Bhalisa futhi uguqule i-Skill: Bhalisa i-Skill yasendaweni nge-mindmemos skill register, bese usebenzisa i-mindmemos skill evolve ukuze uqale ukuvela kwenguqulo ngokusekelwe emikhondweni yokwenziwa kwemisebenzi.

Iwebhusayithi esemthethweni: https://mindmemos.cn/
Ikhosombe le-GitHub: https://github.com/mindscale-noah/MindMemOS

Imemori iyaphatheka: Ihlukanisa ungqimba lokuxhuma lwe-Agent, ungqimba lwama-algorithm ememori kanye nongqimba lwesakhiwo sememori, ukuze imemori ibe impahla ezimele yesikhathi eside engasetshenziswa kabusha kuzo zonke izinhlelo.
Ukubuyisa okuhlelekile kuhamba phambili: Ukunemba okuphelele kwe-LoCoMo kufinyelela ku-94.03% (SOTA), kanti i-PersonaMem ifinyelela ku-70.63%, okudlula kakhulu izixazululo ezifana ne-Mem0, MemU, Zep ne-EverOS.
Ikhwalithi yememori lapho okuncane kungcono: I-Dreaming icindezela u-19.4%-23.5% wememori esebenzayo, ngesikhathi esifanayo inyusa ukunemba kwezimpendulo zemibuzo ngamaphesenti afinyelela ku-10.3.
I-Skill iyaziguqula: I-Skill yokuqala engathuthukisiwe iba ngaphansi kwesisekelo esingenayo i-Skill, kodwa i-MindEvolve-Sup inyusa izinga lokuphumelela kwemisebenzi lisuka ku-51.3% liye ku-57.2%, okufakazela ukubaluleka kokuguqula ulwazi lube yimithetho.
Ukuziphendukela okuqhutshwa yimpendulo: Indlela ye-Feedback ibuyisela izimpawu zokulungiswa kwamaphutha abasebenzisi ekukhishweni kwememori nasemasu okusesha.
Izindlela eziningi zokuxhuma: Isekela ukufakwa endaweni (Docker + FastAPI), i-API esebenza efwini, i-Python SDK, i-CLI kanye ne-plugin ye-OpenClaw.

Umsizi we-AI owenziwe ngezifiso: Ugcina izintandokazi zabasebenzisi, isizinda samaphrojekthi kanye nolwazi lwamaphutha adlule phakathi kwezingxoxo, ukuze kungadingeki ukuqeqesha i-Agent kabusha njalo.
Ipulatifomu yokubambisana kwama-Agent amaningi: Ilabhulali yememori efanayo inikezela ukwabelana nokusetshenziswa kabusha phakathi kwama-Agent ahlukene afana ne-OpenClaw, Hermes, Claude Code ne-OpenHands.
Ukuklama ama-algorithm okuzenzakalelayo (LLM4AD): Ihlela ulwazi olusebenzayo namathiphu okugwema amaphutha ekusesheni ama-algorithm lube imemori enezendlalelo ezintathu, engaseshwa futhi iphinde isetshenziswe.
I-automation eyinkimbinkimbi yemisebenzi yasehhovisi: Isebenzisa i-Skill Evolution ukuze ithuthukise ngokuqhubekayo amasu okwenza imisebenzi ephindaphindwayo efana nokucubungula amaspredishithi nokuhlela amadokhumenti.
Ukwakha iphrofayela yomsebenzisi yesikhathi eside: Ilandelela indlela izibaluli zabasebenzisi ezishintsha ngayo ngokuhamba kwesikhathi, isekele izincomo ezenziwe ngezifiso nokuhlela komphakathi.