- I-LangChain inikeza indlela ehlelekile neqondile efanelekile yamapayipi e-AI alula kanye ne-prototyping esheshayo.
- I-LangGraph ivumela ukudalwa kwama-ejenti ayinkimbinkimbi, anesimo esiqinile akwazi ukuxhumanisa, ukuhlanganisa, kanye nokusebenzisana kwama-ejenti amaningi.
- Ama-Deep Agents asebenza njengethuluzi elisezingeni eliphezulu elihlanganisa imikhuba emihle yokuphathwa komongo kanye nokuhlelwa kwama-sub-agent.
- Lawa mathuluzi alungele ukuhlanganiswa ngokugcwele, okuvumela abathuthukisi ukuthi bashintshele kuma-chain alula baye kumagrafu ayinkimbinkimbi njengoba izidingo zephrojekthi zikhula.
Ukuzicwilisa ezweni lama-ejenti e-AI kuvame ukuzwakala njengokungena esivunguvungwini samathuluzi nemitapo yolwazi. Uma uke wachitha isikhathi ubheka indawo ye-LangChain, cishe uke waphawula ukuthi indawo ikhule ngale nje "kweziketanga," ukwethula i-LangGraph nama-Deep Agents engxubeni. Kulula ukukhungatheka, kodwa imfihlo ukuthi lezi akuzona ngempela izimbangi; ziyizingqimba ezahlukene zesitaki ezenzelwe ukukunikeza amazinga ahlukahlukene okulawula ukuthi i-LLM yakho iziphatha kanjani.
Kungakhathaliseki ukuthi ungunjiniyela onolwazi noma usanda kuqala ukushintshashintsha ngezeluleko, ukuqonda umehluko phakathi komsebenzi oqondile kanye negrafu ejikelezayo kuyisihluthulelo sokwakha into esebenza ngempela ekukhiqizeni. Esikhundleni sokukhetha ithuluzi ngokungahleliwe, kufanele ugxile ebhalansini phakathi kokuzinqumela kanye nokuhlela — empeleni, ukuthi ufuna kangakanani i-AI ukuthi iyibambe ngokumelene nokuthi ufuna ukubamba izintambo ngokuqinile kangakanani.
Isithombe Esikhulu: Ukuqonda I-Agent Stack
Ukuze uqonde izinto, cabanga ngala mathuluzi amathathu njengezingqimba zephiramidi. Esisekelweni, sineLangGraph njengesikhathi sokusebenza , okuyinjini evumela ukunyakaza okuyinkimbinkimbi, okungekho emgqeni. Phezu kwalokho kukhona iLangChain, uhlaka , oluhlinzeka ngezinto ezibalulekile zokwakha kanye ne-harness encane ye-AI. Okokugcina, i-Deep Agents isebenza njenge-harness , inikeza ulwazi "olufakwe amabhethri" ngemikhuba emihle esethiwe yezinto ezifana nememori kanye nobunjiniyela bomongo.
Lolu hlobo lokuguquguquka lusho ukuthi akudingeki uzibophezele ekukhetheni okukodwa. Ungaqala ngokususa okuphezulu bese wehla uye kugrafu yangokwezifiso uma uqaphela ukuthi i-ejenti yakho idinga ukwenza okuthile okungavamile, njengokubuyela emuva esinyathelweni sangaphambilini ngemuva kokuba umuntu enqabe umkhiqizo wayo.
Ama-Deep Agents: Indlu Enamandla Esezingeni Eliphezulu
I-Deep Agents yenzelwe labo abafuna i -ejenti enekhono ngokushesha . Empeleni iyithuluzi eliyinkimbinkimbi eliphatha ukuphathwa komongo okunzima. Esikhundleni sokuthi wena ngokwakho uthole ukuthi ungayifaka kanjani idatha efanele kumodeli, i-Deep Agents iza nemikhuba emihle eyakhelwe ngaphakathi yobunjiniyela bomongo.
- Ukuhlanganiswa Kwesistimu Yamafayela: Ivumela i-ejenti ukuthi ifunde futhi ibhale idatha ngaphandle kokuvala iwindi lokuqukethwe okulinganiselwe kwe-LLM.
- Ama-ejenti angaphansi: Vumela umenzeli oyinhloko ukuthi anikeze imisebenzi ekhethekile “kubasizi” abancane, avimbele ingxoxo eyinhloko ukuthi ingabi yisiphithiphithi esikhulu.
- Amakhono Nenkumbulo: Inikeza indlela yokulayisha izikripthi ezithile uma kudingeka futhi iqinisekisa ukuthi i-ejenti iyasebenza ngempela ufunda futhi uthuthuke kuzo zonke izindlela ezahlukene.
Uma wakha into efana ne-ejenti ye-Go-To-Market (GTM) edinga ukulandelela izintandokazi zommeli ngamunye kanye nama-akhawunti ocwaningo kuwo wonke umlando we-CRM, i-Deep Agents iyindlela okufanele uhambe ngayo ngoba ikhulisa ukumela kanye nokuzimela.
I-LangChain: Ukwakhiwa Kwezakhiwo Ezihlanganisiwe
I-LangChain iyibhuloho elixhumanisa ama-LLM nomhlaba wangempela. Isendlalelo sokuthatha isinyathelo esenza kube lula kakhulu ukufaka amamodeli ahlukene, izizindalwazi ze-vector, nama-API. Umqondo oyinhloko lapha "uchungechunge" - ukulandelana kwemisebenzi lapho umphumela wesinyathelo esisodwa uba khona okokufaka kwesilandelayo. Lena i- Directed Acyclic Graph (DAG) , okusho ukuthi iyindlela eyodwa engenazo izihibe.
Lolu hlaka luyiphupho lokwenza ama-prototyping ngokushesha . Uma udinga i-bot ephendula umbuzo womsebenzisi, isesha isitolo se-vector, futhi ifingqe umphumela, ungawuqeda ngemizuzu embalwa. Akunambono futhi kuncane , kukunikeza amabhlogo okwakha aluhlaza ukuze uhlanganise i-harness yakho eyenziwe ngokwezifiso. Kuzinhlelo zokusebenza ezibucayi kakhulu, i-LangChain ivame ukuthandwa ngoba unokulawula okucacile kwalokho okufinyelela imodeli esinyathelweni ngasinye.
I-LangGraph: Ingcweti Yobunzima
Uma i-logic ye-ejenti yakho iqala ukubukeka njengeshadi lokugeleza kunokuba ibe uhlu, udinga i-LangGraph. Ngokungafani namaketanga ajwayelekile, i-LangGraph ivumela ukuhamba komsebenzi okujikelezayo . Lokhu kusho ukuthi i-ejenti yakho ingabuyela emuva, izame futhi isinyathelo esihlulekile, noma ihlukanise izindlela eziningi ezifanayo ngokusekelwe ezimweni zesikhathi sangempela.
Enye yezinto ezishintsha kakhulu umdlalo lapha ukuphathwa kwesimo okucacile . Uchaza into yesimo ebuyekezwa njengoba inqubo idlula kuma-node ahlukene. Lokhu kubalulekile ezinqubweni ezinde lapho i-ejenti ingadinga ukuma khona, ilinde umuntu ukuthi anikeze isithupha, bese iqala kabusha lapho igcine khona. Leli khono "lomuntu ngaphakathi kwe-loop" yilona elenza ukukhiqizwa kwe-LangGraph kulungele izimo zokusetshenziswa kwebhizinisi, njenge -AI agents ze-logistics kanye ne-TMS workflows noma amapayipi okusekela amakhasimende ayinkimbinkimbi asetshenziswa yizikhondlakhondla ezifana ne-Uber noma i-Klarna.
Ukuqhathanisa Ukuhamba Komsebenzi: Yikuphi Okufanelana Nephrojekthi Yakho?
Ukukhetha ithuluzi elifanele kuncike ekutheni udinga ukubikezela okungakanani . Uma inqubo yakho iqondile kusukela kokufaka kuya kokukhiphayo, i-LangChain iyona engcono kakhulu. Kulula ukulungisa amaphutha futhi kuyashesha ukuyisebenzisa emisebenzini efana nokufingqa amadokhumenti noma amapayipi ayisisekelo e-RAG. Kodwa-ke, uma i-AI yakho idinga ukwenza njengomlingani weqembu—ukucabanga, ukulungisa amaphutha ayo, nokusebenzisana namanye ama-ejenti— i-LangGraph iyona kuphela ukukhetha kwangempela.
Kulabo abangaqiniseki, umthetho omuhle wesithupha ukuqala ngama-Deep Agents ukuze ubone ukuthi izici ezakhelwe ngaphakathi zikuyisa kude kangakanani. Uma uzithola ulwa ne-harness noma udinga ukufaka ikhodi ye-domain logic ethile kakhulu esimweni somsebenzi, leso yisibonakaliso sakho sokuya eLangGraph . Ngenkathi iLangGraph inomugqa wokufunda oqinile odinga ulwazi olwengeziwe lwe-OOP, ukunemba nokuthembeka ekunikezayo kwezinhlelo eziyinkimbinkimbi akunakulinganiswa.
Ukusebenzisa i-AI Stack yakho
Kungakhathaliseki ukuthi usebenzisa uchungechunge olulula noma igrafu eyinkimbinkimbi, ukuthuthela ekukhiqizeni kudinga okungaphezu nje kokuhlelwa. Yilapho-ke ama-AI Gateways namathuluzi okubuka afana neLangSmith eqala khona ukusebenza. Isango lisebenza njengesendlalelo sokulawula, ukuphatha ukukhawulelwa kwamanani, ukuhlehla, kanye nokulandelela izindleko ukuze ibhili lakho le-API lingaqhumi. Ukuhlanganisa lezi zinhlaka nesango eliqinile kuqinisekisa ukuthi i-ejenti yakho ayigcini nje ngokuhlakanipha encwadini yamanothi, kodwa iphephile futhi iyakhula ezweni langempela.
Ukuzulazula kulezi zingqimba ezintathu kukuvumela ukuthi ulinganisele ukuzimela kokudala kwe-LLM nokuthembeka okuqinile kwekhodi yendabuko. Ngokusebenzisa i-modularity ye-LangChain, ukuguquguquka kwe-LangGraph, kanye namandla akhiwe ngaphambilini e-Deep Agents, ungakha konke kusukela ku-bot elula ye-FAQ kuya kubasebenzi abazimele ngokugcwele abanama-ejenti amaningi abaphatha isimo, abakhumbula umlando, kanye nabalinganisa izinkulungwane zabasebenzisi.
