![\"10](\"https:\/\/www.kdnuggets.com\/wp-content\/uploads\/kdn-10-agentic-ai-frameworks-you-should-know-in-2026.png\"\/)
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#\u00a0<\/span>Introduction<\/h2>\n\u00a0Agentic AI frameworks are not simply wrappers round a big language mannequin (LLM) and some instruments. The higher choices now assist builders handle issues like state, reminiscence, device utilization, evaluations, and deployment with out having to construct all the things from scratch. TBH, there isn’t a single finest framework for each challenge. Some frameworks offer you express management over agent workflows, whereas others allow you to ship a working prototype with a lot much less code. I’ve spent quite a lot of time researching the newest agentic AI frameworks, studying GitHub discussions and Reddit threads, and I’ve personally labored with a number of of them as effectively. All of that effort helped me slender the checklist right down to 10 agentic AI frameworks that I feel each AI developer ought to pay attention to in 2026. So, let’s get began.<\/p>\n
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#\u00a0<\/span>1. LangGraph (~36k \u2b50)<\/h2>\n\u00a0LangGraph continues to be the most effective choices once you want full management over how an agent works. It fashions functions as graphs of states and transitions, so you may construct workflows that department, loop, pause for overview, get well after failures, and resume from saved checkpoints. That makes it particularly helpful for long-running brokers, customer-support programs, analysis assistants, coding workflows, and operations instruments the place the agent can’t merely “strive once more” from the start. The primary purpose to decide on LangGraph will not be that it makes brokers extra autonomous. It’s that it makes them extra inspectable. You determine the place the mannequin can act freely, the place logic have to be deterministic, the place instruments want approval, and what state ought to persist between runs. Builders recurrently reward that stage of management, however it comes with an actual studying curve. LangGraph is often not the quickest path to a demo however is the higher route when the workflow must survive manufacturing complexity.<\/p>\n
Finest for: Advanced state machines, long-running workflows, and human-in-the-loop brokers<\/p>\n
\u00a0<\/p>\n
#\u00a0<\/span>2. CrewAI (~55k \u2b50)<\/h2>\n\u00a0CrewAI stays standard as a result of its psychological mannequin is simple to grasp. You outline brokers with roles, give them duties, and manage them right into a crew. For instance, you may create a researcher, analyst, author, and reviewer, then allow them to work by a structured course of. This makes CrewAI helpful for shortly constructing multi-agent workflows for analysis, reporting, enterprise automation, and inside operations. It’s particularly good when every function has a transparent goal and the workflow is simple to clarify to non-technical stakeholders. The primary draw back is that role-based multi-agent programs can turn into extra difficult than needed. You continue to must validate outputs, management device entry, and ensure brokers are usually not repeating work. CrewAI is a good place to begin for role-based collaboration, however not each multi-step job wants a full crew.<\/p>\n
Finest for: quick role-based multi-agent prototypes<\/p>\n
\u00a0<\/p>\n
#\u00a0<\/span>3. OpenAI Brokers SDK (~27k \u2b50)<\/h2>\n\u00a0The OpenAI Brokers SDK is among the cleanest frameworks for builders who wish to construct tool-using brokers with out beginning with a big orchestration framework. Its predominant constructing blocks are brokers, instruments, handoffs, guardrails, periods, human approval, and tracing. It’s a good choice once you wish to start with one targeted agent and solely add specialists when there’s a actual purpose to take action. Handoffs make it simpler to route work between brokers, whereas periods and tracing allow you to perceive how the system behaves over time. Regardless of the OpenAI identify, the SDK helps different mannequin suppliers too. Customers usually like its comparatively small API floor and easy developer expertise. The limitation is that it’s much less opinionated about sturdy workflow design than LangGraph, and it’ll really feel most pure for groups already utilizing OpenAI APIs.<\/p>\n
Finest for: light-weight, clear tool-using brokers with clear handoffs, and OpenAI-oriented functions<\/p>\n
\u00a0<\/p>\n
#\u00a0<\/span>4. Google ADK (~20k \u2b50)<\/h2>\n\u00a0Google’s Agent Growth Equipment (ADK) has turn into a significant framework to observe in 2026. It’s a code-first toolkit for outlining brokers, instruments, periods, reminiscence, evaluations, multi-agent patterns, and deployment workflows. It additionally features a native improvement UI, which makes it simpler to examine and check an agent earlier than pushing it right into a cloud setting. ADK makes probably the most sense for groups already utilizing Gemini, Vertex AI, Google Cloud Run, or different Google enterprise providers. However it isn’t restricted to easy Gemini demos. It additionally provides assist for agent-as-workflow patterns, device authentication, analysis, callbacks, asynchronous execution, and Mannequin Context Protocol (MCP) integrations. Group suggestions is constructive concerning the tempo of improvement and the all-in-one lifecycle strategy. The primary warning is that the framework is shifting shortly, so groups ought to pin variations, check upgrades fastidiously, and keep away from tightly coupling enterprise logic to APIs that will nonetheless evolve.<\/p>\n
Finest for: groups constructing brokers round Gemini, Vertex AI, and Google Cloud<\/p>\n
\u00a0<\/p>\n
#\u00a0<\/span>5. PydanticAI (~18k \u2b50)<\/h2>\n\u00a0PydanticAI is among the strongest decisions for Python builders who care about sort security, validated device inputs, and structured outputs. It brings the identical developer expertise that made Pydantic and FastAPI standard into agent improvement. As a substitute of hoping an agent returns legitimate JSON, you may outline schemas, validate outputs, and make the agent work with typed Python objects. That is worthwhile for actual functions equivalent to support-ticket creation, structured analysis experiences, database updates, API payloads, or monetary and operational workflows. PydanticAI is much less targeted on role-playing multi-agent groups and extra targeted on dependable software program engineering. Group suggestions typically highlights that typed objects and validation make failures simpler to identify and repair. It’s a sturdy match when a mistaken area, invalid device argument, or malformed output may cause issues downstream. The trade-off is that it isn’t probably the most pure choice for groups on the lookout for elaborate role-playing multi-agent demos.<\/p>\n
Finest for: Python groups that want structured outputs, typed instruments, and stronger reliability<\/p>\n
\u00a0<\/p>\n
#\u00a0<\/span>6. smolagents (~28k \u2b50)<\/h2>\n\u00a0smolagents is Hugging Face’s light-weight framework for brokers that suppose in code. As a substitute of forcing each motion into a big JSON object, it permits fashions to generate compact Python code that may name instruments, mix outputs, and resolve duties in a versatile approach. The core agent logic is deliberately sufficiently small to examine, which makes smolagents helpful for experimentation, analysis initiatives, native fashions, and builders who wish to perceive the agent loop as a substitute of adopting a big platform instantly. Customers just like the readability and composability of its code-first strategy. However the identical characteristic creates threat: executing model-generated code requires severe sandboxing, strict permissions, fastidiously designed instruments, and clear boundaries round file, community, and shell entry. It’s glorious for studying and prototyping, however manufacturing use ought to start with safety design moderately than including safety later.<\/p>\n
Finest for: light-weight code brokers, native experimentation, and clear agent loops<\/p>\n
\u00a0<\/p>\n
#\u00a0<\/span>7. Mastra (~25k \u2b50)<\/h2>\n\u00a0Mastra is among the most fascinating TypeScript-first frameworks on this checklist. It provides full-stack groups brokers, workflows, reminiscence, MCP assist, retrieval-augmented era (RAG), evaluations, observability, and integrations with React, Subsequent.js, and Node.js functions. It makes a helpful distinction between brokers and workflows. Use brokers when the mannequin wants flexibility to determine what to do. Use workflows once you want predictable, pre-defined steps. This can be a sensible strategy for groups constructing manufacturing net functions the place you want each AI flexibility and dependable software logic. Mastra is a robust choice for TypeScript groups that need one framework for backend agent logic and frontend product improvement. It’s shifting shortly, although, so manufacturing groups needs to be cautious with model upgrades and package-lock hygiene. That’s particularly necessary in any fast-growing JavaScript ecosystem with a big dependency tree.<\/p>\n
Finest for: TypeScript, Subsequent.js, React, and full-stack agent functions<\/p>\n
\u00a0<\/p>\n
#\u00a0<\/span>8. Microsoft Agent Framework (~12k \u2b50)<\/h2>\n\u00a0Microsoft Agent Framework is the framework to observe for enterprise groups working throughout Python and .NET. It brings collectively concepts beforehand unfold throughout AutoGen and Semantic Kernel, with assist for brokers, multi-agent workflows, periods, middleware, telemetry, graph-based orchestration, and enterprise integrations. The attraction is not only Microsoft branding. It’s the concentrate on predictable software program engineering practices: express orchestration, observability, middleware, sort security, Azure integrations, and governance-friendly deployment paths. That makes it a robust match for inside enterprise brokers, Microsoft 365-connected assistants, Azure-hosted workflows, and organizations that have already got .NET experience. It’s newer than the long-established Python-first frameworks, so its ecosystem continues to be rising. That’s the predominant purpose to deal with it as a strategic platform selection moderately than a default selection for each small prototype. However for Microsoft retailers, it could turn into probably the most logical successor to constructing separate AutoGen and Semantic Kernel stacks.<\/p>\n
Finest for: .NET, Azure, Microsoft environments, and enterprise workflows<\/p>\n
\u00a0<\/p>\n
#\u00a0<\/span>9. Strands Brokers (~6.3k \u2b50)<\/h2>\n\u00a0Strands Brokers takes a model-driven strategy. Slightly than requiring builders to outline each step in a workflow prematurely, it lets the mannequin purpose about which instruments to make use of and methods to proceed. The framework is designed to work from easy conversational assistants by to extra autonomous workflows, whereas supporting a number of mannequin suppliers and MCP instruments. This makes Strands enticing for builders who need much less framework ceremony than graph-based orchestration instruments. It may be a very good match for Amazon Internet Companies (AWS) and Amazon Bedrock customers, however it isn’t restricted to AWS-only deployments. The trade-off is management. A model-driven strategy is handy when the duty is open-ended, however builders want sturdy device boundaries, validation, and approval steps when brokers could make necessary actions. Group discussions additionally present that groups need extra lifecycle management and stronger multi-agent hooks, which is value contemplating earlier than utilizing it for extremely regulated workflows.<\/p>\n
Finest for: light-weight model-driven brokers, particularly in AWS-friendly environments<\/p>\n
\u00a0<\/p>\n
#\u00a0<\/span>10. LlamaIndex Workflows (~400 \u2b50)<\/h2>\n\u00a0LlamaIndex is finest identified for retrieval and knowledge functions, however its Workflows framework deserves consideration for agentic programs. It makes use of an event-driven mannequin wherein workflow steps obtain occasions, carry out work, and emit new occasions. That makes it simpler to precise branching, loops, parallel duties, asynchronous duties, and multi-stage analysis pipelines. It’s notably worthwhile when the troublesome a part of an agent is not only deciding what device to name. It’s discovering, extracting, organizing, and grounding solutions in the best knowledge. That makes LlamaIndex Workflows a pure match for enterprise search, doc evaluation, RAG functions, data assistants, and multi-step analysis programs. The neighborhood typically sees LlamaIndex as stronger for retrieval and doc workflows than for general-purpose agent orchestration. That’s not a weak spot. It merely means you must select it when the principle problem is giving an agent the best knowledge, not constructing an advanced state machine.<\/p>\n
Finest for: Doc-heavy brokers, RAG programs, enterprise data bases, and knowledge pipelines<\/p>\n
\u00a0<\/p>\n
#\u00a0<\/span>Wrapping Up<\/h2>\n\u00a0The most effective framework will not be the one with probably the most hype or GitHub stars. It is the one that really matches what you want, like management, state administration, validation, observability, and power entry. Simply take time to have a look at the choices and decide what works to your workflow and long-term objectives. The agentic AI house is altering quick, so these frameworks may also hold evolving. For now, these are a few of the strongest choices in 2026.\u00a0\u00a0<\/p>\n
Kanwal Mehreen is a machine studying engineer and a technical author with a profound ardour for knowledge science and the intersection of AI with drugs. She co-authored the e-book “Maximizing Productiveness with ChatGPT”. As a Google Technology Scholar 2022 for APAC, she champions variety and educational excellence. She’s additionally acknowledged as a Teradata Range in Tech Scholar, Mitacs Globalink Analysis Scholar, and Harvard WeCode Scholar. Kanwal is an ardent advocate for change, having based FEMCodes to empower ladies in STEM fields.<\/p>\n<\/p><\/div>\n
\u00a0LangGraph continues to be the most effective choices once you want full management over how an agent works. It fashions functions as graphs of states and transitions, so you may construct workflows that department, loop, pause for overview, get well after failures, and resume from saved checkpoints. That makes it particularly helpful for long-running brokers, customer-support programs, analysis assistants, coding workflows, and operations instruments the place the agent can’t merely “strive once more” from the start. The primary purpose to decide on LangGraph will not be that it makes brokers extra autonomous. It’s that it makes them extra inspectable. You determine the place the mannequin can act freely, the place logic have to be deterministic, the place instruments want approval, and what state ought to persist between runs. Builders recurrently reward that stage of management, however it comes with an actual studying curve. LangGraph is often not the quickest path to a demo however is the higher route when the workflow must survive manufacturing complexity.<\/p>\n
Finest for: Advanced state machines, long-running workflows, and human-in-the-loop brokers<\/p>\n
\u00a0<\/p>\n
#\u00a0<\/span>2. CrewAI (~55k \u2b50)<\/h2>\n\u00a0CrewAI stays standard as a result of its psychological mannequin is simple to grasp. You outline brokers with roles, give them duties, and manage them right into a crew. For instance, you may create a researcher, analyst, author, and reviewer, then allow them to work by a structured course of. This makes CrewAI helpful for shortly constructing multi-agent workflows for analysis, reporting, enterprise automation, and inside operations. It’s particularly good when every function has a transparent goal and the workflow is simple to clarify to non-technical stakeholders. The primary draw back is that role-based multi-agent programs can turn into extra difficult than needed. You continue to must validate outputs, management device entry, and ensure brokers are usually not repeating work. CrewAI is a good place to begin for role-based collaboration, however not each multi-step job wants a full crew.<\/p>\n
Finest for: quick role-based multi-agent prototypes<\/p>\n
\u00a0<\/p>\n
#\u00a0<\/span>3. OpenAI Brokers SDK (~27k \u2b50)<\/h2>\n\u00a0The OpenAI Brokers SDK is among the cleanest frameworks for builders who wish to construct tool-using brokers with out beginning with a big orchestration framework. Its predominant constructing blocks are brokers, instruments, handoffs, guardrails, periods, human approval, and tracing. It’s a good choice once you wish to start with one targeted agent and solely add specialists when there’s a actual purpose to take action. Handoffs make it simpler to route work between brokers, whereas periods and tracing allow you to perceive how the system behaves over time. Regardless of the OpenAI identify, the SDK helps different mannequin suppliers too. Customers usually like its comparatively small API floor and easy developer expertise. The limitation is that it’s much less opinionated about sturdy workflow design than LangGraph, and it’ll really feel most pure for groups already utilizing OpenAI APIs.<\/p>\n
Finest for: light-weight, clear tool-using brokers with clear handoffs, and OpenAI-oriented functions<\/p>\n
\u00a0<\/p>\n
#\u00a0<\/span>4. Google ADK (~20k \u2b50)<\/h2>\n\u00a0Google’s Agent Growth Equipment (ADK) has turn into a significant framework to observe in 2026. It’s a code-first toolkit for outlining brokers, instruments, periods, reminiscence, evaluations, multi-agent patterns, and deployment workflows. It additionally features a native improvement UI, which makes it simpler to examine and check an agent earlier than pushing it right into a cloud setting. ADK makes probably the most sense for groups already utilizing Gemini, Vertex AI, Google Cloud Run, or different Google enterprise providers. However it isn’t restricted to easy Gemini demos. It additionally provides assist for agent-as-workflow patterns, device authentication, analysis, callbacks, asynchronous execution, and Mannequin Context Protocol (MCP) integrations. Group suggestions is constructive concerning the tempo of improvement and the all-in-one lifecycle strategy. The primary warning is that the framework is shifting shortly, so groups ought to pin variations, check upgrades fastidiously, and keep away from tightly coupling enterprise logic to APIs that will nonetheless evolve.<\/p>\n
Finest for: groups constructing brokers round Gemini, Vertex AI, and Google Cloud<\/p>\n
\u00a0<\/p>\n
#\u00a0<\/span>5. PydanticAI (~18k \u2b50)<\/h2>\n\u00a0PydanticAI is among the strongest decisions for Python builders who care about sort security, validated device inputs, and structured outputs. It brings the identical developer expertise that made Pydantic and FastAPI standard into agent improvement. As a substitute of hoping an agent returns legitimate JSON, you may outline schemas, validate outputs, and make the agent work with typed Python objects. That is worthwhile for actual functions equivalent to support-ticket creation, structured analysis experiences, database updates, API payloads, or monetary and operational workflows. PydanticAI is much less targeted on role-playing multi-agent groups and extra targeted on dependable software program engineering. Group suggestions typically highlights that typed objects and validation make failures simpler to identify and repair. It’s a sturdy match when a mistaken area, invalid device argument, or malformed output may cause issues downstream. The trade-off is that it isn’t probably the most pure choice for groups on the lookout for elaborate role-playing multi-agent demos.<\/p>\n
Finest for: Python groups that want structured outputs, typed instruments, and stronger reliability<\/p>\n
\u00a0<\/p>\n
#\u00a0<\/span>6. smolagents (~28k \u2b50)<\/h2>\n\u00a0smolagents is Hugging Face’s light-weight framework for brokers that suppose in code. As a substitute of forcing each motion into a big JSON object, it permits fashions to generate compact Python code that may name instruments, mix outputs, and resolve duties in a versatile approach. The core agent logic is deliberately sufficiently small to examine, which makes smolagents helpful for experimentation, analysis initiatives, native fashions, and builders who wish to perceive the agent loop as a substitute of adopting a big platform instantly. Customers just like the readability and composability of its code-first strategy. However the identical characteristic creates threat: executing model-generated code requires severe sandboxing, strict permissions, fastidiously designed instruments, and clear boundaries round file, community, and shell entry. It’s glorious for studying and prototyping, however manufacturing use ought to start with safety design moderately than including safety later.<\/p>\n
Finest for: light-weight code brokers, native experimentation, and clear agent loops<\/p>\n
\u00a0<\/p>\n
#\u00a0<\/span>7. Mastra (~25k \u2b50)<\/h2>\n\u00a0Mastra is among the most fascinating TypeScript-first frameworks on this checklist. It provides full-stack groups brokers, workflows, reminiscence, MCP assist, retrieval-augmented era (RAG), evaluations, observability, and integrations with React, Subsequent.js, and Node.js functions. It makes a helpful distinction between brokers and workflows. Use brokers when the mannequin wants flexibility to determine what to do. Use workflows once you want predictable, pre-defined steps. This can be a sensible strategy for groups constructing manufacturing net functions the place you want each AI flexibility and dependable software logic. Mastra is a robust choice for TypeScript groups that need one framework for backend agent logic and frontend product improvement. It’s shifting shortly, although, so manufacturing groups needs to be cautious with model upgrades and package-lock hygiene. That’s particularly necessary in any fast-growing JavaScript ecosystem with a big dependency tree.<\/p>\n
Finest for: TypeScript, Subsequent.js, React, and full-stack agent functions<\/p>\n
\u00a0<\/p>\n
#\u00a0<\/span>8. Microsoft Agent Framework (~12k \u2b50)<\/h2>\n\u00a0Microsoft Agent Framework is the framework to observe for enterprise groups working throughout Python and .NET. It brings collectively concepts beforehand unfold throughout AutoGen and Semantic Kernel, with assist for brokers, multi-agent workflows, periods, middleware, telemetry, graph-based orchestration, and enterprise integrations. The attraction is not only Microsoft branding. It’s the concentrate on predictable software program engineering practices: express orchestration, observability, middleware, sort security, Azure integrations, and governance-friendly deployment paths. That makes it a robust match for inside enterprise brokers, Microsoft 365-connected assistants, Azure-hosted workflows, and organizations that have already got .NET experience. It’s newer than the long-established Python-first frameworks, so its ecosystem continues to be rising. That’s the predominant purpose to deal with it as a strategic platform selection moderately than a default selection for each small prototype. However for Microsoft retailers, it could turn into probably the most logical successor to constructing separate AutoGen and Semantic Kernel stacks.<\/p>\n
Finest for: .NET, Azure, Microsoft environments, and enterprise workflows<\/p>\n
\u00a0<\/p>\n
#\u00a0<\/span>9. Strands Brokers (~6.3k \u2b50)<\/h2>\n\u00a0Strands Brokers takes a model-driven strategy. Slightly than requiring builders to outline each step in a workflow prematurely, it lets the mannequin purpose about which instruments to make use of and methods to proceed. The framework is designed to work from easy conversational assistants by to extra autonomous workflows, whereas supporting a number of mannequin suppliers and MCP instruments. This makes Strands enticing for builders who need much less framework ceremony than graph-based orchestration instruments. It may be a very good match for Amazon Internet Companies (AWS) and Amazon Bedrock customers, however it isn’t restricted to AWS-only deployments. The trade-off is management. A model-driven strategy is handy when the duty is open-ended, however builders want sturdy device boundaries, validation, and approval steps when brokers could make necessary actions. Group discussions additionally present that groups need extra lifecycle management and stronger multi-agent hooks, which is value contemplating earlier than utilizing it for extremely regulated workflows.<\/p>\n
Finest for: light-weight model-driven brokers, particularly in AWS-friendly environments<\/p>\n
\u00a0<\/p>\n
#\u00a0<\/span>10. LlamaIndex Workflows (~400 \u2b50)<\/h2>\n\u00a0LlamaIndex is finest identified for retrieval and knowledge functions, however its Workflows framework deserves consideration for agentic programs. It makes use of an event-driven mannequin wherein workflow steps obtain occasions, carry out work, and emit new occasions. That makes it simpler to precise branching, loops, parallel duties, asynchronous duties, and multi-stage analysis pipelines. It’s notably worthwhile when the troublesome a part of an agent is not only deciding what device to name. It’s discovering, extracting, organizing, and grounding solutions in the best knowledge. That makes LlamaIndex Workflows a pure match for enterprise search, doc evaluation, RAG functions, data assistants, and multi-step analysis programs. The neighborhood typically sees LlamaIndex as stronger for retrieval and doc workflows than for general-purpose agent orchestration. That’s not a weak spot. It merely means you must select it when the principle problem is giving an agent the best knowledge, not constructing an advanced state machine.<\/p>\n
Finest for: Doc-heavy brokers, RAG programs, enterprise data bases, and knowledge pipelines<\/p>\n
\u00a0<\/p>\n
#\u00a0<\/span>Wrapping Up<\/h2>\n\u00a0The most effective framework will not be the one with probably the most hype or GitHub stars. It is the one that really matches what you want, like management, state administration, validation, observability, and power entry. Simply take time to have a look at the choices and decide what works to your workflow and long-term objectives. The agentic AI house is altering quick, so these frameworks may also hold evolving. For now, these are a few of the strongest choices in 2026.\u00a0\u00a0<\/p>\n
Kanwal Mehreen is a machine studying engineer and a technical author with a profound ardour for knowledge science and the intersection of AI with drugs. She co-authored the e-book “Maximizing Productiveness with ChatGPT”. As a Google Technology Scholar 2022 for APAC, she champions variety and educational excellence. She’s additionally acknowledged as a Teradata Range in Tech Scholar, Mitacs Globalink Analysis Scholar, and Harvard WeCode Scholar. Kanwal is an ardent advocate for change, having based FEMCodes to empower ladies in STEM fields.<\/p>\n<\/p><\/div>\n
\u00a0The OpenAI Brokers SDK is among the cleanest frameworks for builders who wish to construct tool-using brokers with out beginning with a big orchestration framework. Its predominant constructing blocks are brokers, instruments, handoffs, guardrails, periods, human approval, and tracing. It’s a good choice once you wish to start with one targeted agent and solely add specialists when there’s a actual purpose to take action. Handoffs make it simpler to route work between brokers, whereas periods and tracing allow you to perceive how the system behaves over time. Regardless of the OpenAI identify, the SDK helps different mannequin suppliers too. Customers usually like its comparatively small API floor and easy developer expertise. The limitation is that it’s much less opinionated about sturdy workflow design than LangGraph, and it’ll really feel most pure for groups already utilizing OpenAI APIs.<\/p>\n
Finest for: light-weight, clear tool-using brokers with clear handoffs, and OpenAI-oriented functions<\/p>\n
\u00a0<\/p>\n
#\u00a0<\/span>4. Google ADK (~20k \u2b50)<\/h2>\n\u00a0Google’s Agent Growth Equipment (ADK) has turn into a significant framework to observe in 2026. It’s a code-first toolkit for outlining brokers, instruments, periods, reminiscence, evaluations, multi-agent patterns, and deployment workflows. It additionally features a native improvement UI, which makes it simpler to examine and check an agent earlier than pushing it right into a cloud setting. ADK makes probably the most sense for groups already utilizing Gemini, Vertex AI, Google Cloud Run, or different Google enterprise providers. However it isn’t restricted to easy Gemini demos. It additionally provides assist for agent-as-workflow patterns, device authentication, analysis, callbacks, asynchronous execution, and Mannequin Context Protocol (MCP) integrations. Group suggestions is constructive concerning the tempo of improvement and the all-in-one lifecycle strategy. The primary warning is that the framework is shifting shortly, so groups ought to pin variations, check upgrades fastidiously, and keep away from tightly coupling enterprise logic to APIs that will nonetheless evolve.<\/p>\n
Finest for: groups constructing brokers round Gemini, Vertex AI, and Google Cloud<\/p>\n
\u00a0<\/p>\n
#\u00a0<\/span>5. PydanticAI (~18k \u2b50)<\/h2>\n\u00a0PydanticAI is among the strongest decisions for Python builders who care about sort security, validated device inputs, and structured outputs. It brings the identical developer expertise that made Pydantic and FastAPI standard into agent improvement. As a substitute of hoping an agent returns legitimate JSON, you may outline schemas, validate outputs, and make the agent work with typed Python objects. That is worthwhile for actual functions equivalent to support-ticket creation, structured analysis experiences, database updates, API payloads, or monetary and operational workflows. PydanticAI is much less targeted on role-playing multi-agent groups and extra targeted on dependable software program engineering. Group suggestions typically highlights that typed objects and validation make failures simpler to identify and repair. It’s a sturdy match when a mistaken area, invalid device argument, or malformed output may cause issues downstream. The trade-off is that it isn’t probably the most pure choice for groups on the lookout for elaborate role-playing multi-agent demos.<\/p>\n
Finest for: Python groups that want structured outputs, typed instruments, and stronger reliability<\/p>\n
\u00a0<\/p>\n
#\u00a0<\/span>6. smolagents (~28k \u2b50)<\/h2>\n\u00a0smolagents is Hugging Face’s light-weight framework for brokers that suppose in code. As a substitute of forcing each motion into a big JSON object, it permits fashions to generate compact Python code that may name instruments, mix outputs, and resolve duties in a versatile approach. The core agent logic is deliberately sufficiently small to examine, which makes smolagents helpful for experimentation, analysis initiatives, native fashions, and builders who wish to perceive the agent loop as a substitute of adopting a big platform instantly. Customers just like the readability and composability of its code-first strategy. However the identical characteristic creates threat: executing model-generated code requires severe sandboxing, strict permissions, fastidiously designed instruments, and clear boundaries round file, community, and shell entry. It’s glorious for studying and prototyping, however manufacturing use ought to start with safety design moderately than including safety later.<\/p>\n
Finest for: light-weight code brokers, native experimentation, and clear agent loops<\/p>\n
\u00a0<\/p>\n
#\u00a0<\/span>7. Mastra (~25k \u2b50)<\/h2>\n\u00a0Mastra is among the most fascinating TypeScript-first frameworks on this checklist. It provides full-stack groups brokers, workflows, reminiscence, MCP assist, retrieval-augmented era (RAG), evaluations, observability, and integrations with React, Subsequent.js, and Node.js functions. It makes a helpful distinction between brokers and workflows. Use brokers when the mannequin wants flexibility to determine what to do. Use workflows once you want predictable, pre-defined steps. This can be a sensible strategy for groups constructing manufacturing net functions the place you want each AI flexibility and dependable software logic. Mastra is a robust choice for TypeScript groups that need one framework for backend agent logic and frontend product improvement. It’s shifting shortly, although, so manufacturing groups needs to be cautious with model upgrades and package-lock hygiene. That’s particularly necessary in any fast-growing JavaScript ecosystem with a big dependency tree.<\/p>\n
Finest for: TypeScript, Subsequent.js, React, and full-stack agent functions<\/p>\n
\u00a0<\/p>\n
#\u00a0<\/span>8. Microsoft Agent Framework (~12k \u2b50)<\/h2>\n\u00a0Microsoft Agent Framework is the framework to observe for enterprise groups working throughout Python and .NET. It brings collectively concepts beforehand unfold throughout AutoGen and Semantic Kernel, with assist for brokers, multi-agent workflows, periods, middleware, telemetry, graph-based orchestration, and enterprise integrations. The attraction is not only Microsoft branding. It’s the concentrate on predictable software program engineering practices: express orchestration, observability, middleware, sort security, Azure integrations, and governance-friendly deployment paths. That makes it a robust match for inside enterprise brokers, Microsoft 365-connected assistants, Azure-hosted workflows, and organizations that have already got .NET experience. It’s newer than the long-established Python-first frameworks, so its ecosystem continues to be rising. That’s the predominant purpose to deal with it as a strategic platform selection moderately than a default selection for each small prototype. However for Microsoft retailers, it could turn into probably the most logical successor to constructing separate AutoGen and Semantic Kernel stacks.<\/p>\n
Finest for: .NET, Azure, Microsoft environments, and enterprise workflows<\/p>\n
\u00a0<\/p>\n
#\u00a0<\/span>9. Strands Brokers (~6.3k \u2b50)<\/h2>\n\u00a0Strands Brokers takes a model-driven strategy. Slightly than requiring builders to outline each step in a workflow prematurely, it lets the mannequin purpose about which instruments to make use of and methods to proceed. The framework is designed to work from easy conversational assistants by to extra autonomous workflows, whereas supporting a number of mannequin suppliers and MCP instruments. This makes Strands enticing for builders who need much less framework ceremony than graph-based orchestration instruments. It may be a very good match for Amazon Internet Companies (AWS) and Amazon Bedrock customers, however it isn’t restricted to AWS-only deployments. The trade-off is management. A model-driven strategy is handy when the duty is open-ended, however builders want sturdy device boundaries, validation, and approval steps when brokers could make necessary actions. Group discussions additionally present that groups need extra lifecycle management and stronger multi-agent hooks, which is value contemplating earlier than utilizing it for extremely regulated workflows.<\/p>\n
Finest for: light-weight model-driven brokers, particularly in AWS-friendly environments<\/p>\n
\u00a0<\/p>\n
#\u00a0<\/span>10. LlamaIndex Workflows (~400 \u2b50)<\/h2>\n\u00a0LlamaIndex is finest identified for retrieval and knowledge functions, however its Workflows framework deserves consideration for agentic programs. It makes use of an event-driven mannequin wherein workflow steps obtain occasions, carry out work, and emit new occasions. That makes it simpler to precise branching, loops, parallel duties, asynchronous duties, and multi-stage analysis pipelines. It’s notably worthwhile when the troublesome a part of an agent is not only deciding what device to name. It’s discovering, extracting, organizing, and grounding solutions in the best knowledge. That makes LlamaIndex Workflows a pure match for enterprise search, doc evaluation, RAG functions, data assistants, and multi-step analysis programs. The neighborhood typically sees LlamaIndex as stronger for retrieval and doc workflows than for general-purpose agent orchestration. That’s not a weak spot. It merely means you must select it when the principle problem is giving an agent the best knowledge, not constructing an advanced state machine.<\/p>\n
Finest for: Doc-heavy brokers, RAG programs, enterprise data bases, and knowledge pipelines<\/p>\n
\u00a0<\/p>\n
#\u00a0<\/span>Wrapping Up<\/h2>\n\u00a0The most effective framework will not be the one with probably the most hype or GitHub stars. It is the one that really matches what you want, like management, state administration, validation, observability, and power entry. Simply take time to have a look at the choices and decide what works to your workflow and long-term objectives. The agentic AI house is altering quick, so these frameworks may also hold evolving. For now, these are a few of the strongest choices in 2026.\u00a0\u00a0<\/p>\n
Kanwal Mehreen is a machine studying engineer and a technical author with a profound ardour for knowledge science and the intersection of AI with drugs. She co-authored the e-book “Maximizing Productiveness with ChatGPT”. As a Google Technology Scholar 2022 for APAC, she champions variety and educational excellence. She’s additionally acknowledged as a Teradata Range in Tech Scholar, Mitacs Globalink Analysis Scholar, and Harvard WeCode Scholar. Kanwal is an ardent advocate for change, having based FEMCodes to empower ladies in STEM fields.<\/p>\n<\/p><\/div>\n
\u00a0PydanticAI is among the strongest decisions for Python builders who care about sort security, validated device inputs, and structured outputs. It brings the identical developer expertise that made Pydantic and FastAPI standard into agent improvement. As a substitute of hoping an agent returns legitimate JSON, you may outline schemas, validate outputs, and make the agent work with typed Python objects. That is worthwhile for actual functions equivalent to support-ticket creation, structured analysis experiences, database updates, API payloads, or monetary and operational workflows. PydanticAI is much less targeted on role-playing multi-agent groups and extra targeted on dependable software program engineering. Group suggestions typically highlights that typed objects and validation make failures simpler to identify and repair. It’s a sturdy match when a mistaken area, invalid device argument, or malformed output may cause issues downstream. The trade-off is that it isn’t probably the most pure choice for groups on the lookout for elaborate role-playing multi-agent demos.<\/p>\n
Finest for: Python groups that want structured outputs, typed instruments, and stronger reliability<\/p>\n
\u00a0<\/p>\n
#\u00a0<\/span>6. smolagents (~28k \u2b50)<\/h2>\n\u00a0smolagents is Hugging Face’s light-weight framework for brokers that suppose in code. As a substitute of forcing each motion into a big JSON object, it permits fashions to generate compact Python code that may name instruments, mix outputs, and resolve duties in a versatile approach. The core agent logic is deliberately sufficiently small to examine, which makes smolagents helpful for experimentation, analysis initiatives, native fashions, and builders who wish to perceive the agent loop as a substitute of adopting a big platform instantly. Customers just like the readability and composability of its code-first strategy. However the identical characteristic creates threat: executing model-generated code requires severe sandboxing, strict permissions, fastidiously designed instruments, and clear boundaries round file, community, and shell entry. It’s glorious for studying and prototyping, however manufacturing use ought to start with safety design moderately than including safety later.<\/p>\n
Finest for: light-weight code brokers, native experimentation, and clear agent loops<\/p>\n
\u00a0<\/p>\n
#\u00a0<\/span>7. Mastra (~25k \u2b50)<\/h2>\n\u00a0Mastra is among the most fascinating TypeScript-first frameworks on this checklist. It provides full-stack groups brokers, workflows, reminiscence, MCP assist, retrieval-augmented era (RAG), evaluations, observability, and integrations with React, Subsequent.js, and Node.js functions. It makes a helpful distinction between brokers and workflows. Use brokers when the mannequin wants flexibility to determine what to do. Use workflows once you want predictable, pre-defined steps. This can be a sensible strategy for groups constructing manufacturing net functions the place you want each AI flexibility and dependable software logic. Mastra is a robust choice for TypeScript groups that need one framework for backend agent logic and frontend product improvement. It’s shifting shortly, although, so manufacturing groups needs to be cautious with model upgrades and package-lock hygiene. That’s particularly necessary in any fast-growing JavaScript ecosystem with a big dependency tree.<\/p>\n
Finest for: TypeScript, Subsequent.js, React, and full-stack agent functions<\/p>\n
\u00a0<\/p>\n
#\u00a0<\/span>8. Microsoft Agent Framework (~12k \u2b50)<\/h2>\n\u00a0Microsoft Agent Framework is the framework to observe for enterprise groups working throughout Python and .NET. It brings collectively concepts beforehand unfold throughout AutoGen and Semantic Kernel, with assist for brokers, multi-agent workflows, periods, middleware, telemetry, graph-based orchestration, and enterprise integrations. The attraction is not only Microsoft branding. It’s the concentrate on predictable software program engineering practices: express orchestration, observability, middleware, sort security, Azure integrations, and governance-friendly deployment paths. That makes it a robust match for inside enterprise brokers, Microsoft 365-connected assistants, Azure-hosted workflows, and organizations that have already got .NET experience. It’s newer than the long-established Python-first frameworks, so its ecosystem continues to be rising. That’s the predominant purpose to deal with it as a strategic platform selection moderately than a default selection for each small prototype. However for Microsoft retailers, it could turn into probably the most logical successor to constructing separate AutoGen and Semantic Kernel stacks.<\/p>\n
Finest for: .NET, Azure, Microsoft environments, and enterprise workflows<\/p>\n
\u00a0<\/p>\n
#\u00a0<\/span>9. Strands Brokers (~6.3k \u2b50)<\/h2>\n\u00a0Strands Brokers takes a model-driven strategy. Slightly than requiring builders to outline each step in a workflow prematurely, it lets the mannequin purpose about which instruments to make use of and methods to proceed. The framework is designed to work from easy conversational assistants by to extra autonomous workflows, whereas supporting a number of mannequin suppliers and MCP instruments. This makes Strands enticing for builders who need much less framework ceremony than graph-based orchestration instruments. It may be a very good match for Amazon Internet Companies (AWS) and Amazon Bedrock customers, however it isn’t restricted to AWS-only deployments. The trade-off is management. A model-driven strategy is handy when the duty is open-ended, however builders want sturdy device boundaries, validation, and approval steps when brokers could make necessary actions. Group discussions additionally present that groups need extra lifecycle management and stronger multi-agent hooks, which is value contemplating earlier than utilizing it for extremely regulated workflows.<\/p>\n
Finest for: light-weight model-driven brokers, particularly in AWS-friendly environments<\/p>\n
\u00a0<\/p>\n
#\u00a0<\/span>10. LlamaIndex Workflows (~400 \u2b50)<\/h2>\n\u00a0LlamaIndex is finest identified for retrieval and knowledge functions, however its Workflows framework deserves consideration for agentic programs. It makes use of an event-driven mannequin wherein workflow steps obtain occasions, carry out work, and emit new occasions. That makes it simpler to precise branching, loops, parallel duties, asynchronous duties, and multi-stage analysis pipelines. It’s notably worthwhile when the troublesome a part of an agent is not only deciding what device to name. It’s discovering, extracting, organizing, and grounding solutions in the best knowledge. That makes LlamaIndex Workflows a pure match for enterprise search, doc evaluation, RAG functions, data assistants, and multi-step analysis programs. The neighborhood typically sees LlamaIndex as stronger for retrieval and doc workflows than for general-purpose agent orchestration. That’s not a weak spot. It merely means you must select it when the principle problem is giving an agent the best knowledge, not constructing an advanced state machine.<\/p>\n
Finest for: Doc-heavy brokers, RAG programs, enterprise data bases, and knowledge pipelines<\/p>\n
\u00a0<\/p>\n
#\u00a0<\/span>Wrapping Up<\/h2>\n\u00a0The most effective framework will not be the one with probably the most hype or GitHub stars. It is the one that really matches what you want, like management, state administration, validation, observability, and power entry. Simply take time to have a look at the choices and decide what works to your workflow and long-term objectives. The agentic AI house is altering quick, so these frameworks may also hold evolving. For now, these are a few of the strongest choices in 2026.\u00a0\u00a0<\/p>\n
Kanwal Mehreen is a machine studying engineer and a technical author with a profound ardour for knowledge science and the intersection of AI with drugs. She co-authored the e-book “Maximizing Productiveness with ChatGPT”. As a Google Technology Scholar 2022 for APAC, she champions variety and educational excellence. She’s additionally acknowledged as a Teradata Range in Tech Scholar, Mitacs Globalink Analysis Scholar, and Harvard WeCode Scholar. Kanwal is an ardent advocate for change, having based FEMCodes to empower ladies in STEM fields.<\/p>\n<\/p><\/div>\n
\u00a0Mastra is among the most fascinating TypeScript-first frameworks on this checklist. It provides full-stack groups brokers, workflows, reminiscence, MCP assist, retrieval-augmented era (RAG), evaluations, observability, and integrations with React, Subsequent.js, and Node.js functions. It makes a helpful distinction between brokers and workflows. Use brokers when the mannequin wants flexibility to determine what to do. Use workflows once you want predictable, pre-defined steps. This can be a sensible strategy for groups constructing manufacturing net functions the place you want each AI flexibility and dependable software logic. Mastra is a robust choice for TypeScript groups that need one framework for backend agent logic and frontend product improvement. It’s shifting shortly, although, so manufacturing groups needs to be cautious with model upgrades and package-lock hygiene. That’s particularly necessary in any fast-growing JavaScript ecosystem with a big dependency tree.<\/p>\n
Finest for: TypeScript, Subsequent.js, React, and full-stack agent functions<\/p>\n
\u00a0<\/p>\n
#\u00a0<\/span>8. Microsoft Agent Framework (~12k \u2b50)<\/h2>\n\u00a0Microsoft Agent Framework is the framework to observe for enterprise groups working throughout Python and .NET. It brings collectively concepts beforehand unfold throughout AutoGen and Semantic Kernel, with assist for brokers, multi-agent workflows, periods, middleware, telemetry, graph-based orchestration, and enterprise integrations. The attraction is not only Microsoft branding. It’s the concentrate on predictable software program engineering practices: express orchestration, observability, middleware, sort security, Azure integrations, and governance-friendly deployment paths. That makes it a robust match for inside enterprise brokers, Microsoft 365-connected assistants, Azure-hosted workflows, and organizations that have already got .NET experience. It’s newer than the long-established Python-first frameworks, so its ecosystem continues to be rising. That’s the predominant purpose to deal with it as a strategic platform selection moderately than a default selection for each small prototype. However for Microsoft retailers, it could turn into probably the most logical successor to constructing separate AutoGen and Semantic Kernel stacks.<\/p>\n
Finest for: .NET, Azure, Microsoft environments, and enterprise workflows<\/p>\n
\u00a0<\/p>\n
#\u00a0<\/span>9. Strands Brokers (~6.3k \u2b50)<\/h2>\n\u00a0Strands Brokers takes a model-driven strategy. Slightly than requiring builders to outline each step in a workflow prematurely, it lets the mannequin purpose about which instruments to make use of and methods to proceed. The framework is designed to work from easy conversational assistants by to extra autonomous workflows, whereas supporting a number of mannequin suppliers and MCP instruments. This makes Strands enticing for builders who need much less framework ceremony than graph-based orchestration instruments. It may be a very good match for Amazon Internet Companies (AWS) and Amazon Bedrock customers, however it isn’t restricted to AWS-only deployments. The trade-off is management. A model-driven strategy is handy when the duty is open-ended, however builders want sturdy device boundaries, validation, and approval steps when brokers could make necessary actions. Group discussions additionally present that groups need extra lifecycle management and stronger multi-agent hooks, which is value contemplating earlier than utilizing it for extremely regulated workflows.<\/p>\n
Finest for: light-weight model-driven brokers, particularly in AWS-friendly environments<\/p>\n
\u00a0<\/p>\n
#\u00a0<\/span>10. LlamaIndex Workflows (~400 \u2b50)<\/h2>\n\u00a0LlamaIndex is finest identified for retrieval and knowledge functions, however its Workflows framework deserves consideration for agentic programs. It makes use of an event-driven mannequin wherein workflow steps obtain occasions, carry out work, and emit new occasions. That makes it simpler to precise branching, loops, parallel duties, asynchronous duties, and multi-stage analysis pipelines. It’s notably worthwhile when the troublesome a part of an agent is not only deciding what device to name. It’s discovering, extracting, organizing, and grounding solutions in the best knowledge. That makes LlamaIndex Workflows a pure match for enterprise search, doc evaluation, RAG functions, data assistants, and multi-step analysis programs. The neighborhood typically sees LlamaIndex as stronger for retrieval and doc workflows than for general-purpose agent orchestration. That’s not a weak spot. It merely means you must select it when the principle problem is giving an agent the best knowledge, not constructing an advanced state machine.<\/p>\n
Finest for: Doc-heavy brokers, RAG programs, enterprise data bases, and knowledge pipelines<\/p>\n
\u00a0<\/p>\n
#\u00a0<\/span>Wrapping Up<\/h2>\n\u00a0The most effective framework will not be the one with probably the most hype or GitHub stars. It is the one that really matches what you want, like management, state administration, validation, observability, and power entry. Simply take time to have a look at the choices and decide what works to your workflow and long-term objectives. The agentic AI house is altering quick, so these frameworks may also hold evolving. For now, these are a few of the strongest choices in 2026.\u00a0\u00a0<\/p>\n
Kanwal Mehreen is a machine studying engineer and a technical author with a profound ardour for knowledge science and the intersection of AI with drugs. She co-authored the e-book “Maximizing Productiveness with ChatGPT”. As a Google Technology Scholar 2022 for APAC, she champions variety and educational excellence. She’s additionally acknowledged as a Teradata Range in Tech Scholar, Mitacs Globalink Analysis Scholar, and Harvard WeCode Scholar. Kanwal is an ardent advocate for change, having based FEMCodes to empower ladies in STEM fields.<\/p>\n<\/p><\/div>\n
\u00a0Strands Brokers takes a model-driven strategy. Slightly than requiring builders to outline each step in a workflow prematurely, it lets the mannequin purpose about which instruments to make use of and methods to proceed. The framework is designed to work from easy conversational assistants by to extra autonomous workflows, whereas supporting a number of mannequin suppliers and MCP instruments. This makes Strands enticing for builders who need much less framework ceremony than graph-based orchestration instruments. It may be a very good match for Amazon Internet Companies (AWS) and Amazon Bedrock customers, however it isn’t restricted to AWS-only deployments. The trade-off is management. A model-driven strategy is handy when the duty is open-ended, however builders want sturdy device boundaries, validation, and approval steps when brokers could make necessary actions. Group discussions additionally present that groups need extra lifecycle management and stronger multi-agent hooks, which is value contemplating earlier than utilizing it for extremely regulated workflows.<\/p>\n
Finest for: light-weight model-driven brokers, particularly in AWS-friendly environments<\/p>\n
\u00a0<\/p>\n
#\u00a0<\/span>10. LlamaIndex Workflows (~400 \u2b50)<\/h2>\n\u00a0LlamaIndex is finest identified for retrieval and knowledge functions, however its Workflows framework deserves consideration for agentic programs. It makes use of an event-driven mannequin wherein workflow steps obtain occasions, carry out work, and emit new occasions. That makes it simpler to precise branching, loops, parallel duties, asynchronous duties, and multi-stage analysis pipelines. It’s notably worthwhile when the troublesome a part of an agent is not only deciding what device to name. It’s discovering, extracting, organizing, and grounding solutions in the best knowledge. That makes LlamaIndex Workflows a pure match for enterprise search, doc evaluation, RAG functions, data assistants, and multi-step analysis programs. The neighborhood typically sees LlamaIndex as stronger for retrieval and doc workflows than for general-purpose agent orchestration. That’s not a weak spot. It merely means you must select it when the principle problem is giving an agent the best knowledge, not constructing an advanced state machine.<\/p>\n
Finest for: Doc-heavy brokers, RAG programs, enterprise data bases, and knowledge pipelines<\/p>\n
\u00a0<\/p>\n
#\u00a0<\/span>Wrapping Up<\/h2>\n\u00a0The most effective framework will not be the one with probably the most hype or GitHub stars. It is the one that really matches what you want, like management, state administration, validation, observability, and power entry. Simply take time to have a look at the choices and decide what works to your workflow and long-term objectives. The agentic AI house is altering quick, so these frameworks may also hold evolving. For now, these are a few of the strongest choices in 2026.\u00a0\u00a0<\/p>\n
Kanwal Mehreen is a machine studying engineer and a technical author with a profound ardour for knowledge science and the intersection of AI with drugs. She co-authored the e-book “Maximizing Productiveness with ChatGPT”. As a Google Technology Scholar 2022 for APAC, she champions variety and educational excellence. She’s additionally acknowledged as a Teradata Range in Tech Scholar, Mitacs Globalink Analysis Scholar, and Harvard WeCode Scholar. Kanwal is an ardent advocate for change, having based FEMCodes to empower ladies in STEM fields.<\/p>\n<\/p><\/div>\n
\u00a0The most effective framework will not be the one with probably the most hype or GitHub stars. It is the one that really matches what you want, like management, state administration, validation, observability, and power entry. Simply take time to have a look at the choices and decide what works to your workflow and long-term objectives. The agentic AI house is altering quick, so these frameworks may also hold evolving. For now, these are a few of the strongest choices in 2026.\u00a0\u00a0<\/p>\n
Kanwal Mehreen is a machine studying engineer and a technical author with a profound ardour for knowledge science and the intersection of AI with drugs. She co-authored the e-book “Maximizing Productiveness with ChatGPT”. As a Google Technology Scholar 2022 for APAC, she champions variety and educational excellence. She’s additionally acknowledged as a Teradata Range in Tech Scholar, Mitacs Globalink Analysis Scholar, and Harvard WeCode Scholar. Kanwal is an ardent advocate for change, having based FEMCodes to empower ladies in STEM fields.<\/p>\n<\/p><\/div>\n