# Isingeniso
Izinhlaka ze-Agentic AI azisekho nje ukugoqa imodeli yolimi enkulu (LLM) namathuluzi ambalwa. Izinketho ezingcono manje zisiza onjiniyela ukuphatha izinto ezifana nesimo, inkumbulo, ukusetshenziswa kwamathuluzi, ukuhlola, nokusebenzisa ngaphandle kokwakha yonke into kusukela ekuqaleni. TBH, alukho uhlaka olulodwa olungcono kakhulu lwawo wonke amaphrojekthi. Ezinye izinhlaka zikunikeza ukulawula okucacile kokugeleza komsebenzi komenzeli, kuyilapho ezinye zikusiza ukuthi uthumele isibonelo esisebenzayo esinekhodi encane kakhulu. Ngichithe isikhathi esiningi ngicwaninga ngezinhlaka zakamuva ze-AI, ngifunda izingxoxo ze-GitHub kanye nemicu ye-Reddit, futhi nami ngisebenze nezimbalwa zazo. Yonke leyo mizamo yangisiza ukuba nginciphise uhlu lwehle lube Izinhlaka eziyi-10 ze-AI engicabanga ukuthi wonke umthuthukisi we-AI kufanele azi ngo-2026. Ngakho-ke, ake siqale.
# 1. I-LangGraph (~36k ⭐)
I-LangGraph kusengenye yezinketho ezinhle kakhulu lapho udinga ukulawula okugcwele kokuthi i-ejenti isebenza kanjani. Imodela izinhlelo zokusebenza njengamagrafu ezifunda noguquko, ukuze ukwazi ukwakha ukugeleza komsebenzi lelo gatsha, iluphu, umise okwesikhashana ukuze ubuyekeze, ululame ngemva kokwehluleka, futhi uqalise kabusha ezindaweni zokuhlola ezilondoloziwe. Lokho kuyenza isebenziseke ikakhulukazi kuma-ejenti asebenza isikhathi eside, amasistimu asekela amakhasimende, abasizi bocwaningo, ukugeleza komsebenzi wokubhala amakhodi, namathuluzi okusebenza lapho umenzeli engeke avele “azame futhi” kwasekuqaleni. Isizathu esiyinhloko sokukhetha i-LangGraph akukhona ukuthi yenza ama-agent azimele. Kuwukuthi kubenza bahloleke ngokwengeziwe. Nguwe onqumayo lapho imodeli ingenza khona ngokukhululeka, lapho ingqondo kufanele inqume, lapho amathuluzi adinga khona ukugunyazwa, nokuthi yisiphi isimo okufanele siqhubeke phakathi kokugijima. Onjiniyela bahlale bencoma lelo zinga lokulawula, kodwa liza nejika lokufunda langempela. I-LangGraph ngokuvamile akuyona indlela eshesha kakhulu eya kudemo kodwa iwumzila ongcono lapho ukugeleza komsebenzi kudinga ukusinda ebunzimeni bokukhiqiza.
Kulungele: Imishini yezwe eyinkimbinkimbi, ukuhamba komsebenzi okuhlala isikhathi eside, nama-agent asebenza ngabantu
# 2. I-CrewAI (~55k ⭐)
I-CrewAI ihlala idumile ngoba imodeli yayo yengqondo kulula ukuyiqonda. Uchaza ama-agent anezindima, ubanike imisebenzi, futhi uwahlele abe yiqembu. Isibonelo, ungakha umcwaningi, umhlaziyi, umbhali, nombuyekezi, bese ubavumela ukuthi basebenze ngenqubo ehlelekile. Lokhu kwenza i-CrewAI ibe usizo ekwakheni ngokushesha ukuhamba komsebenzi kwama-ejenti amaningi okucwaninga, ukubika, ukuzenzela kwebhizinisi, nokusebenza kwangaphakathi. Kuhle kakhulu uma indima ngayinye inenhloso ecacile futhi ukuhamba komsebenzi kulula ukukuchazela ababambiqhaza abangebona abezobuchwepheshe. Okubi okuyinhloko ukuthi izinhlelo zama-ejenti amaningi ezisekelwe indima zingaba yinkimbinkimbi kunesidingo. Usadinga ukuqinisekisa okuphumayo, lawula ukufinyelela kwamathuluzi, futhi uqiniseke ukuthi ama-ejenti awaphindi umsebenzi. I-CrewAI iyisiqalo esihle sokubambisana okusekelwe endimeni, kodwa akuwona wonke umsebenzi wezinyathelo eziningi odinga iqembu eligcwele.
Kulungele: ama-prototypes anama-ejenti amaningi asheshayo asekelwe endimeni
# 3. I-OpenAI Agents SDK (~27k ⭐)
I I-OpenAI Agents SDK ingenye yezinhlaka ezihlanzekile zonjiniyela abafuna ukwakha ama-ejenti asebenzisa amathuluzi ngaphandle kokuqala ngohlaka olukhulu lwe-orchestration. Amabhulokhi ayo amakhulu okwakha ama-ejenti, amathuluzi, ama-handoffs, ama- guardrail, amaseshini, ukugunyazwa komuntu, nokulandela umkhondo. Kuyinketho enhle uma ufuna ukuqala nge-ejenti eyodwa egxilile futhi wengeze ochwepheshe kuphela uma kunesizathu sangempela sokwenza kanjalo. Ama-Handoffs akwenza kube lula ukuhambisa umsebenzi phakathi kwama-ejenti, kuyilapho amaseshini nokulandelela kukusiza uqonde indlela isistimu eziphatha ngayo ngokuhamba kwesikhathi. Ngaphandle kwegama le-OpenAI, i-SDK isekela nabanye abahlinzeki bemodeli. Abasebenzisi ngokuvamile bathanda indawo yayo encane ye-API kanye nolwazi oluqondile lukanjiniyela. Umkhawulo wukuthi ayinambono omncane mayelana nesakhiwo sokugeleza komsebenzi esihlala isikhathi eside kune-LangGraph, futhi izozizwa ingokwemvelo emaqenjini asevele asebenzisa i-OpenAI APIs.
Kulungele: ama-ejenti asebenzisa amathuluzi angasindi, ahlanzekile anezandla ezihlanzekile, nezinhlelo zokusebenza ezigxile ku-OpenAI
# 4. I-Google ADK (~20k ⭐)
Ikhithi yokuthuthukisa yomenzeli we-Google (ADK) isibe uhlaka olukhulu okufanele lubukwe ngo-2026. Iyikhithi yamathuluzi ekhodi yokuqala yokuchaza ama-ejenti, amathuluzi, izikhathi, inkumbulo, ukuhlola, amaphethini ama-ejenti amaningi, nokugeleza komsebenzi wokuthunyelwa. Kuphinde kuhlanganise ne-UI yokuthuthukisa yasendaweni, eyenza kube lula ukuhlola nokuhlola umenzeli ngaphambi kokuyiphushela endaweni yamafu. I-ADK yenza umqondo omkhulu emaqenjini asevele asebenzisa i-Gemini, i-Vertex AI, i-Google Cloud Run, noma ezinye izinsiza zebhizinisi ze-Google. Kodwa ayikhawulelwe kumademo weGemini alula. Iphinde inikeze ukwesekwa kwamaphethini okugeleza kwe-ejenti, ukufakazela ubuqiniso kwamathuluzi, ukuhlola, ukuphinda ushayele, ukubulawa okuvumelanayo, kanye nokuhlanganiswa kwe-Model Context Protocol (MCP). Impendulo yomphakathi ilungile mayelana nesivinini sentuthuko kanye nendlela yokuphila konke-konke. Isexwayiso esikhulu ukuthi uhlaka luhamba ngokushesha, ngakho-ke amaqembu kufanele aphine izinguqulo, ahlole ukuthuthukiswa ngokucophelela, futhi agweme ukuhlanganisa ngokuqinile ingqondo yebhizinisi kuma-API angase aguquke.
Kulungele: ama-ejenti wokwakha amaqembu azungeze i-Gemini, i-Vertex AI, ne-Google Cloud
# 5. I-PydanticAI (~18k ⭐)
I-PydanticAI ingenye yezinketho eziqine kakhulu zonjiniyela be-Python abakhathalela uhlobo lokuphepha, okokufaka kwamathuluzi aqinisekisiwe, nemiphumela ehlelekile. Iletha umuzwa ofanayo wonjiniyela owenze i-Pydantic ne-FastAPI yaduma ekuthuthukisweni komenzeli. Esikhundleni sokuthemba ukuthi umenzeli uzobuyisa i-JSON evumelekile, ungachaza izikimu, uqinisekise okuphumayo, futhi wenze umenzeli asebenze ngezinto ezithayiphiwe zePython. Lokhu kubalulekile ezinhlelweni zokusebenza zangempela ezifana nokudalwa kwamathikithi okusekela, imibiko yocwaningo ehlelekile, izibuyekezo zesizindalwazi, ukulayishwa kwe-API, noma ukuhamba komsebenzi wezezimali nokusebenza. I-PydanticAI ayigxilile kakhulu ekudlaleni indima yama-ejenti amaningi futhi igxile kakhulu kubunjiniyela besofthiwe obuthembekile. Impendulo yomphakathi ivamise ukugqamisa ukuthi izinto ezithayiphiwe nokuqinisekisa kwenza ukwehluleka ukukubona nokukulungisa kube lula. Ilingana ngokuqinile uma inkambu engalungile, i-agumenti yethuluzi engavumelekile, noma okukhiphayo okungalungile kungabangela izinkinga ezansi nomfula. Ukuhwebelana ukuthi akuyona inketho engokwemvelo kakhulu emaqenjini afuna amademo adlala indima eningi enama-ejenti amaningi.
Kulungele: Amaqembu ePython adinga okuphumayo okuhlelekile, amathuluzi athayiphiwe, nokuthembeka okuqinile
# 6. ama-smolagents (~28k ⭐)
ama-smolagents wuhlaka lweHugging Face olulula lwama-ejenti acabanga ngekhodi. Esikhundleni sokuphoqa zonke izenzo zibe into enkulu ye-JSON, ivumela amamodeli ukuthi akhiqize ikhodi ye-Python ehlangene engabiza amathuluzi, ihlanganise okuphumayo, futhi ixazulule imisebenzi ngendlela eguquguqukayo. I-logic ye-agent eyinhloko incane ngamabomu ngokwanele ukuthi ingahlolwa, okwenza ama-smolagents abe usizo ekuhloleni, amaphrojekthi ocwaningo, amamodeli endawo, nonjiniyela abafuna ukuqonda iluphu ye-ejenti esikhundleni sokwamukela inkundla enkulu ngokushesha. Abasebenzisi bathanda ukucaca nokuvumelana kwendlela yayo yokuqala yekhodi. Kodwa isici esifanayo sidala ubungozi: ukusebenzisa ikhodi ekhiqizwe imodeli kudinga i-sandboxing ebucayi, izimvume eziqinile, amathuluzi aklanywe ngokucophelela, kanye nemingcele ecacile ezungeze ifayela, inethiwekhi, nokufinyelela kwegobolondo. Kuhle kakhulu ekufundeni nasekufanekiseni, kodwa ukusetshenziswa kokukhiqiza kufanele kuqale ngedizayini yezokuphepha kunokwengeza ukuvikeleka kamuva.
Kulungele: I-ejenti yekhodi engasindi, ukuhlola kwasendaweni, namalophu e-ejenti asobala
# 7. I-Mastra (~25k ⭐)
I-Mastra ingenye yezinhlaka ezithakazelisa kakhulu ze-TypeScript-first kulolu hlu. Inikeza ama-ejenti wamaqembu esitaki esigcwele, ukugeleza komsebenzi, inkumbulo, ukusekelwa kwe-MCP, isizukulwane esithuthukisiwe sokubuyisa (i-RAG), ukuhlaziya, ukubonakala, nokuhlanganiswa nezinhlelo zokusebenza ze-React, Next.js, ne-Node.js. Kwenza umehluko owusizo phakathi kwama-ejenti nokuhamba komsebenzi. Sebenzisa ama-ejenti lapho imodeli idinga ukuguquguquka ukuze unqume ukuthi yenzeni. Sebenzisa ukugeleza komsebenzi uma udinga izinyathelo ezibikezelwe, ezichazwe ngaphambilini. Lena indlela esebenzayo yamaqembu akha izinhlelo zokusebenza zewebhu zokukhiqiza lapho udinga kokubili ukuguquguquka kwe-AI kanye nengqondo yesicelo ethembekile. I-Mastra iyinketho eqinile yamaqembu e-TypeScript afuna uhlaka olulodwa lwe-logic ye-backend nokuthuthukiswa komkhiqizo ongaphambili. Ihamba ngokushesha, nokho, ngakho-ke amaqembu akhiqizayo kufanele aqaphele ukuthuthukiswa kwenguqulo kanye nenhlanzeko yokukhiya iphakheji. Lokho kubaluleke kakhulu kunoma iyiphi i-JavaScript ecosystem ekhula ngokushesha enesihlahla esikhulu sokuncika.
Kulungele: I-TypeScript, Next.js, React, kanye nezinhlelo zokusebenza ze-ejenti ezigcwele isitaki
# 8. I-Microsoft Agent Framework (~12k ⭐)
I-Microsoft Agent Framework wuhlaka lokubuka amaqembu ebhizinisi asebenza kuyo yonke iPython ne-.NET. Ihlanganisa imibono eyasakazwa ngaphambilini kuyo yonke i-AutoGen kanye ne-Semantic Kernel, ngokusekelwa kwama-ejenti, ukuhamba komsebenzi kwama-ejenti amaningi, izikhathi, i-middleware, i-telemetry, i-orchestration esekelwe kumagrafu, nokuhlanganiswa kwebhizinisi. Okukhangayo akukhona nje ukufaka uphawu lweMicrosoft. Kugxilwa ezinkambisweni zobunjiniyela be-software obungabikezelwa: i-orchestration ecacile, ukubonwa, i-middleware, ukuphepha kohlobo, ukuhlanganiswa kwe-Azure, nezindlela zokusebenzisa ezilungele ukubusa. Lokho kuyenza ifaneleke ngokuqinile kuma-ejenti ebhizinisi angaphakathi, abasizi abaxhumene ne-Microsoft 365, ukuhamba komsebenzi okusingathwa yi-Azure, nezinhlangano esezivele zinabo ubuchwepheshe be-.NET. Yintsha kunezinhlaka ezasungulwa kudala ze-Python-first, ngakho-ke i-ecosystem yayo isakhula. Leso isizathu esiyinhloko sokuyiphatha njengokukhethwa kweplathifomu yamasu esikhundleni sokukhetha okuzenzakalelayo kwawo wonke ama-prototype amancane. Kodwa ezitolo ze-Microsoft, kungase kube umlandeli onengqondo kakhulu wokwakha izitaki ezihlukene ze-AutoGen kanye ne-Semantic Kernel.
Kulungele: .NET, Azure, Microsoft environments, kanye nokugeleza komsebenzi kwebhizinisi
# 9. Ama-Strands Agents (~6.3k ⭐)
Ama-Strands Agents ithatha indlela eqhutshwa yimodeli. Kunokuba ifune onjiniyela ukuthi bachaze zonke izinyathelo zokugeleza komsebenzi kusenesikhathi, ivumela imodeli ukuthi icabange ngokuthi imaphi amathuluzi okufanele asetshenziswe nokuthi kufanele uqhubeke kanjani. Uhlaka luklanyelwe ukusebenza kusukela kubasizi bengxoxo abalula kuye ekugelezeni komsebenzi okuzimele, kuyilapho lusekela abahlinzeki abangamamodeli amaningi namathuluzi e-MCP. Lokhu kwenza ama-Strands ahehe konjiniyela abafuna umcimbi omncane wohlaka kunamathuluzi e-orchestration asuselwa kugrafu. Kungaba ukulingana okuhle ikakhulukazi kubasebenzisi be-Amazon Web Services (AWS) kanye nabasebenzisi be-Amazon Bedrock, kodwa akukhawulelwe ekusetshenzisweni kwe-AWS kuphela. Ukuhwebelana kuwukulawula. Indlela eqhutshwa yimodeli ilungile uma umsebenzi usuvuliwe, kodwa onjiniyela badinga imingcele eqinile yamathuluzi, ukuqinisekiswa, nezinyathelo zokugunyaza lapho ama-ejenti engenza izenzo ezibalulekile. Izingxoxo zomphakathi ziphinde zibonise ukuthi amaqembu afuna ukulawula okwengeziwe komjikelezo wempilo kanye namahhuku ama-ejenti amaningi aqinile, okufanele kucatshangelwe ngaphambi kokuyisebenzisela ukuhamba komsebenzi okulawulwa kakhulu.
Kulungele: ama-ejenti ashayelwa amamodeli angasindi, ikakhulukazi ezindaweni ezinobungani be-AWS
# 10. I-LlamaIndex Workflows (~400 ⭐)
I-LlamaIndex yaziwa kakhulu ngokubuyisa kanye nezicelo zedatha, kodwa yayo Ukuhamba komsebenzi uhlaka ludinga ukunakwa kwezinhlelo ze-ejenti. Isebenzisa imodeli eqhutshwa umcimbi lapho izinyathelo zokuhamba komsebenzi zithola khona imicimbi, zenza umsebenzi, futhi zikhiphe imicimbi emisha. Lokho kwenza kube lula ukuveza i-branching, amalophu, imisebenzi ehambisanayo, imisebenzi engavumelaniyo, namapayipi ocwaningo lwezigaba eziningi. Kubaluleke kakhulu lapho ingxenye enzima ye-ejenti ingagcini nje ngokunquma ukuthi iliphi ithuluzi okufanele lishayelwe. Iwukuthola, ukukhipha, ukuhlela, kanye nokusekela izimpendulo kudatha efanele. Lokho kwenza i-LlamaIndex Workflows ifaneleke ngokwemvelo ekusesheni ibhizinisi, ukuhlaziya amadokhumenti, izinhlelo zokusebenza ze-RAG, abasizi bolwazi, nezinhlelo zocwaningo lwezinyathelo eziningi. Umphakathi uvamise ukubona i-LlamaIndex inamandla okubuyisa nokugeleza komsebenzi wamadokhumenti kune-orchestration yenhloso evamile yomenzeli. Lokho akubona ubuthakathaka. Kusho nje ukuthi kufanele uyikhethe lapho inselele enkulu inika umenzeli idatha efanele, hhayi ukwakha umshini wezwe oyinkimbinkimbi.
Kulungele: Ama-ejenti asindayo amadokhumenti, amasistimu e-RAG, izisekelo zolwazi lwebhizinisi, namapayipi edatha
# Esonga
Uhlaka olungcono kakhulu akulona olunezinkanyezi ze-hype noma ze-GitHub. Yilona elifanelana nalokho okudingayo, njengokulawula, ukuphathwa kombuso, ukuqinisekiswa, ukubonakala, nokufinyelela kwamathuluzi. Vele uzinike isikhathi sokubheka izinketho bese ukhetha lokho okusebenzela ukuhamba kwakho komsebenzi kanye nemigomo yesikhathi eside. Isikhala se-AI se-agent sishintsha ngokushesha, ngakho-ke lezi zinhlaka nazo zizoqhubeka nokuvela. Okwamanje, lezi ezinye zezinketho eziqine kakhulu ngo-2026.
Kanwal Mehreen ungunjiniyela wokufunda ngomshini kanye nombhali wezobuchwepheshe onothando olunzulu lwesayensi yedatha kanye nokuhlangana kwe-AI nemithi. Ubhale ngokuhlanganyela i-ebook ethi “Ukukhulisa Ukukhiqiza Nge-ChatGPT”. Njenge-Google Generation Scholar 2022 ye-APAC, ulwela ukuhlukahluka kanye nokwenza kahle ezifundweni. Uphinde aqashelwe njengeTeradata Diversity in Tech Scholar, Mitacs Globalink Research Scholar, kanye neHarvard WeCode Scholar. U-Kanwal ungummeli oshisekayo woshintsho, njengoba esungule ama-FEMCodes ukuze ahlomise abesifazane emikhakheni ye-STEM.
