- Ukuhlaziywa kokusebenza okuqhathanisayo phakathi kweLangGraph, AutoGen, CrewAI kanye neLangChain kugxile ekubambezelekeni nasekusetshenzisweni kwamathokheni.
- Umehluko wezakhiwo phakathi kwezinhlelo ezisekelwe kumagrafu, izingxoxo, izindima zokuphatha, kanye nokusebenza okuqondile.
- Ukuhlolwa kwamakhono athuthukile njengokuphathwa kwememori, iphrothokholi ye-MCP, kanye nokuphathwa kwamaphutha ezindaweni zangempela.
- Ukuhlukaniswa kwamathuluzi ngokuya ngobungcweti bawo ekubhaleni amakhodi, ukuphepha kwe-cyber, ezezimali kanye nokwenza izinto ngokuzenzakalela kwewebhu.
Uma wake wazibuza ukuthi imishini isuke kanjani ekuphenduleni imibuzo nje kuphela yaya ekwenzeni izinqumo ezihlakaniphile nasekufezeni izinhlelo eziyinkimbinkimbi, impendulo ilele ezinhlakeni ze-AI ezisebenza kahle. Lawa mapulatifomu awawona nje amalabhulali ekhodi; asebenza njengohlaka oluvumela iModeli Yolimi (LM) ukuthi isebenzisane nomhlaba wangempela, isebenzise amathuluzi, futhi ilungise amaphutha ayo ngaphandle kokudinga isiqondiso esiqhubekayo.
Ukukhetha ithuluzi elifanele kungaba yindaba ebuhlungu ngempela uma ubheka inani elikhulu lezinketho ezitholakala ku-GitHub. Akukhona nje ukuthi iyiphi ethandwa kakhulu, kodwa mayelana nokuqonda ukuthi udinga uhlelo oluqinile nolubikezelwayo noma olunenkululeko yokushintsha nokuthola izindlela ezihlukile lapho kukhona okungahambi kahle. Ngale ndlela, ukungena ohlelweni lwe-ejenti kusho ukulinganisa izinzuzo nezingozi phakathi kwesivinini sempendulo kanye nokujula kokucabanga.
Ukuhlaziywa kokusebenza: Impi yezinhlaka
Ukuze kuqondwe ukuthi ubani ophumelelayo emjahweni, kwenziwe izivivinyo eziningi zokulinganisa ukubambezeleka kanye nokusetshenziswa kwe-token. I-LangGraph ivelele ngokuba yiyona esheshayo emisebenzini eminingi, igcina izikhathi zokulinda ziphansi. Ngakolunye uhlangothi, i-LangChain ivame ukuba nezindleko eziphakeme kakhulu kokubili ngesikhathi kanye nokusetshenziswa kwezinsiza, yize isalokhu iyisici esibalulekile ngenxa yokuguquguquka kwayo.
Uma kukhulunywa ngemisebenzi elula, njengokubiza ithuluzi eliyisisekelo, i-LangChain ne-LangGraph zisebenza cishe njengekhodi yendabuko, zisebenza kahle kakhulu futhi zishesha . Kodwa-ke, i-AutoGen yethula i-overhead encane ngenxa yemvelo yayo yokuxoxa; ngisho nangesinyathelo esilula, ama-ejenti ayo ashintshana imiyalezo , enezela izindleko ezincane eziyisisekelo. Kodwa icala elihlaba umxhwele kakhulu yi-CrewAI, ehlushwa yilokho okubizwa ngokuthi "i-overhead yokuphatha," edla amathokheni afinyelela kathathu kunabaphikisi bayo ngenxa yezinqubo zayo zokuqinisekisa zangaphakathi.
Ezimweni zokuphathwa kwesifundazwe, i-LangGraph ibusa kakhulu ekuzinzeni ngenxa yokwakheka kwayo kwegrafu , ivimbela ukungcoliswa kwedatha phakathi kwezinyathelo. I-LangChain imane nje iyona engabizi kakhulu futhi ilula, kuyilapho i-AutoGen inikeza ibhalansi eqinile, ezibonakalisa iqine kakhulu emaphutheni anengqondo ngokuphatha imiphumela yamathuluzi njengokugeleza kwengxoxo okuqhubekayo.
Izakhiwo zangaphakathi kanye nomthelela wazo ku-AI
Isizathu sokuthi i-ejenti iziphathe ngendlela ethile akuyona imodeli ye-AI, kodwa iluphu yangaphakathi yohlaka . Izinhlelo ezisekelwe emishinini yesimo (i-LangGraph) kanye namamodeli okuxoxa (i-AutoGen) zinethuba eliphezulu kakhulu lokushintsha isu lazo uma zihlangabezana nephutha. Esikhundleni sokuyeka, la ma-ejenti azibuza ukuthi enzeni futhi avame ukuthola izindlela ezihlukile, njengokuhlukanisa umsebenzi oyinkimbinkimbi ube yizinyathelo ezincane zesandla.
Ngakolunye uhlangothi, i-CrewAI isebenzisa indlela egxile ohlelweni . Amanxusa ayo anezindima ezichazwe ngokucacile (njengokuthi "Umphenyi" noma "Umbhali"), okubenza babe nesiyalo esikhulu kodwa bangaguquguquki. Uma ithuluzi lehluleka, i-CrewAI izozama ukulungisa ithuluzi noma ilinde ukuthi lisebenze, kunokushiya uhlelo lokuqala ukuze yakhe elisha. Lokhu kuyenza ifaneleke kakhulu ezinqubweni ezihlelekile, kodwa ingafaneleki kangako ezimweni eziphithizelayo.
Ngakolunye uhlangothi, iLangChain isebenzisa i-executor elandelanayo. Nakuba inamandla kakhulu, ukuhamba kwayo kuqondile kakhulu. Uma ukuphathwa kwamaphutha kungalungiswanga kahle, ingamisa inqubo ngokuphelele lapho ihlangabezana nokuhlukile kwe-Python, ngokungafani nabancintisana nayo abaphatha amaphutha njengokubuka nje ukuqondisa ukucabanga okwengeziwe.
Amandla ememori kanye nezinga le-MCP
Inkumbulo yiyo ehlukanisa i-chatbot elula ku-ejenti yangempela. I-LangGraph isebenzisa inkumbulo enezintambo ezivundlile ezivumela ukuthi ilondoloze inqubekela phambili yeseshini futhi iyithole kamuva. I-CrewAI isebenzisa uhlelo olunezingqimba, isebenzisa izitolo ze-vector ezifana ne-ChromaDB kanye nedathabheyisi ye-SQLite ukukhumbula kokubili ukusebenzisana kwakamuva kanye nezintandokazi zesikhathi eside.
Intuthuko ebalulekile embonini yi- Model Context Protocol (MCP) . Le nqubo ivumela ama-ejenti ukuthi axhumane nezizindalwazi zangaphandle nama-API ngaphandle kwesidingo sokudala ukuhlanganiswa ngesandla kwethuluzi ngalinye. Izinhlaka ezifana ne-AutoGen ne-LangGraph sezivele zihlanganisa i-MCP, okwenza kube lula nge-ejenti ukuthi "ixhume futhi idlale" noma iyiphi iseva yedatha ehambisanayo, isuse ukuhlukana kwe-ecosystem.
Izigaba zama-ejenti kanye namacala okusetshenziswa emhlabeni wangempela
Kuye ngenhloso, kunezinhlaka ezikhethekile. Kulabo abafuna ukuzimela okuphelele , i-Auto-GPT iyisilinganiso, ekwazi ukuhlukanisa inhloso ejwayelekile ibe yimisebenzi emincane futhi iyenze yodwa. Kodwa-ke, ezindaweni zokukhiqiza, izinketho eziningi ezilawulwayo njenge-Microsoft's Semantic Kernel ziyathandwa, okuyinto efanele ukusetshenziswa kwebhizinisi futhi ihlangana kalula ne-.NET kanye ne-Python ecosystems.
- Izinhlelo Zokubhala Amakhodi: Amathuluzi afana ne-OpenHands (eyayikade i-OpenDevin) kanye ne-Aider aguqula i-terminal ibe yindawo yokuhlela ephindwe kabili.
- Ukuphepha kwe-inthanethi: Izinhlaka ezifana ne-Cybersecurity AI (CAI) zivumela ukusetshenziswa kwe ukuhlolwa kokuphepha ngama-ejenti e-AI ngokuzimele.
- Ezezimali Nezempilo: I-FinRL isebenzisa ukufunda kokuqinisa ekuhwebeni, kuyilapho i-HIA ihlaziya imibiko yezokwelapha ukuze ikhiphe ukuqonda okubalulekile.
- Ukuzenzakalela kweWebhu: Ama-ejenti afana neSkyvern asebenzisa i-computer vision ukuze azulazule kuwebhu futhi agcwalise amafomu ayinkimbinkimbi.
Ezweni langempela, sibone i-CrewAI ikhanya ekudalweni kokuqukethwe , lapho ithimba elihlanganisiwe lama-ejenti liguqula khona isifinyezo sibe yisihloko esiphelele. Ngakolunye uhlangothi, i-LangGraph iyindlela ekhethwayo yabahleli bokuhamba abayinkimbinkimbi abadinga ukubuza ngama-API amaningi endiza namahhotela ngenkathi begcina isimo sokusesha.
Indlela yokukhetha uhlaka olufanele iphrojekthi yakho
Akukho lutho olulula; konke kuncike kulokho okwakhayo. Uma udinga i-prototyping esheshayo, enama-ejenti amaningi, esekelwe ezindimeni , i-CrewAI iyindlela emfushane. Uma ufuna ukulawula okuphelele phezu kwe-logic, ikhono lokuchaza amagrafu ayinkimbinkimbi, kanye nokuzinza kokukhiqiza okuphezulu , i-LangGraph iyindlela enengqondo. Kulabo abadinga ukuxhumana okungenazo izinsiza kanye nokucaca okuphelele kuma-chat log, i-AutoGen iyona ewinayo.
Kubalulekile ukuhlaziya ijika lokufunda . Ngenkathi i-BotPress inikeza isikhombikubona sokuhudula nokulahla esikhangayo ngokubonakalayo kubasebenzisi abangebona ochwepheshe, izinhlaka ezifana ne-LangChain zidinga ukuqonda okuqinile kokuhlela ukuze kuvinjelwe ikhodi ukuthi ingabi engalawuleki futhi ingabi nzima ukuyiphatha. Ukwanda kanye nokuphepha kumele kucatshangelwe , ikakhulukazi lapho kuphathwa idatha ebucayi engaphansi kwemithetho efana ne-GDPR.
Ukukhetha kokugcina kufanele kusekelwe ohlotsheni lomsebenzi. Ama-ejenti awahlulwa uma izinyathelo zishintshashintsha futhi zidinga inkumbulo yesikhathi eside noma izimpendulo endaweni ezungezile. Kodwa-ke, uma umsebenzi ungashintshi futhi ungabikezeleki, ukuhamba komsebenzi okulula noma uhlelo lwe-RAG olulula luzoba lushibhile kakhulu futhi lusheshe , kugwenywe ukubambezeleka kanye nezindleko zokubala ezihlobene nokuhlelwa kwe-ejenti.
Ukuvela kwala mathuluzi kusiholela esikhathini esizayo lapho umehluko phakathi kwesofthiwe ne-ejenti uphela khona, okuvumela ukuguquguquka kanye nomthombo ovulekile ukwenza ukufinyelela kube lula ezenzakalelayo ezihlakaniphile, kuthuthukisa konke kusukela ekuphathweni kwedatha engahlelekile kuya ekuphequluleni kwewebhu okuzenzakalelayo.
