Best AutoGen Alternative for Multi-Agent LLM Projects (2025)

Looking for the best AutoGen alternative to orchestrate powerful AI agents – without writing a single line of code? While AutoGen (from Microsoft) helps developers build multi-agent LLM workflows, it requires coding expertise, manual deployment, and lacks built-in interfaces or monetization features. Knolli, on the other hand, is a no-code platform built for creators, teams, and businesses to launch AI copilots with branded UIs, memory retention, workflow control, and revenue tools from day one.

AutoGen is built for developers who want to code multi-agent systems from scratch, with maximum flexibility but significant complexity. Knolli is built for creators and businesses who want to deploy AI copilots in minutes, complete with memory, workflow logic, branded interfaces, and monetization – all with no coding required.

• If you’re an advanced developer comfortable with Python and want complete control over every agent interaction, AutoGen can be a powerful framework.
  
• If you’re building a product or content business and value speed, polish, and profit (subscriptions, paid access) out of the box, Knolli is the clear choice.

AutoGen is an open-source framework (developed by Microsoft Research) that enables developers to compose multiple AI agents which converse with each other (and with humans or tools) to accomplish goals. Instead of a single monolithic AI, AutoGen lets you define teams of agents with specialized roles that can communicate in a dialogue to solve complex tasks. It’s essentially a powerful orchestration engine for multi-agent LLM applications, allowing agents to collaborate, use tools, execute code, and even include human feedback in the loop.

AutoGen treats complex AI workflows as conversations among agents. Developers can program agents in Python (using AutoGen’s APIs) and give each agent a role (e.g. an Assistant agent or a UserProxy agent) with its own logic or tools. For example, one agent might generate a plan or write code, while another simulates a user or evaluates the output, and they pass messages back and forth automatically. AutoGen handles the message routing and scheduling for these dialogues, so the agents continue exchanging information until the task is complete.

Because AutoGen is a code-centric framework, it offers a lot of customization and extensibility for developers. You can integrate external tools or APIs as functions that agents can call (using OpenAI’s function-calling paradigm), or even have an agent generate Python code and execute it to use tools. AutoGen supports connecting to various LLM providers (OpenAI, Azure, local models, etc.) and allows advanced patterns like asynchronously spawning new sub-conversations or tasks based on conversation context. It also provides mechanisms for memory (maintaining conversation history and integrating knowledge bases for retrieval) and for human-in-the-loop configurations when needed.

In short, AutoGen is like a multi-agent command center for LLMs – but you, as the developer, have to build and configure every part of that command center with code. It’s powerful for those with the skills and time to harness it.

• Multi-Agent Conversation Orchestration – AutoGen’s core capability is coordinating multiple agents via conversations. You can define agents with distinct roles (e.g., an AI assistant and a user proxy) that exchange messages turn-by-turn to work toward a solution. The framework automates message passing, so agents can invoke each other flexibly without hard-coding a rigid sequence. This dialog-driven approach aligns with how LLMs naturally operate and makes complex workflows easier to conceptualize.
  
  
• Tool Use & Code Execution – Agents in AutoGen can call external tools or functions as part of their reasoning. AutoGen has first-class support for OpenAI function calling and can execute code that an agent generates on the fly. For example, an AssistantAgent might output a Python code block (to perform a calculation or web request), and a UserProxyAgent can automatically run that code and return results back into the conversation. This built-in tool integration means agents aren’t limited to just chatting – they can take actions in the outside world.
  
  
• Memory and Context Management – AutoGen maintains an in-memory message history by default, enabling agents to remember prior exchanges in a conversation. For longer or persistent sessions, it provides utilities for summarizing or compressing context (e.g. integrating with text compression libraries). Developers can also hook up vector databases or other knowledge bases so that an agent can retrieve information it needs beyond the immediate conversation. In essence, you can equip AutoGen agents with long-term memory or reference knowledge, although setting that up requires additional coding and integration.
  
  
• Human-in-the-Loop Flexibility – The framework is designed to include human input whenever necessary. AutoGen’s UserProxyAgent can act as a stand-in for a human user, pausing for real user input at each step or automatically proceeding with a preset behavior. This makes it easy to prototype workflows where a human may approve or adjust an AI’s actions. You can run AutoGen in fully autonomous mode or with a human supervising the agent conversations – it’s configurable based on the use case.
  
  
• Open-Source and Extensible – AutoGen is open-source (MIT license) and highly extensible. Developers can subclass agent classes or register custom response handlers to implement new behaviors. The architecture is modular, with a core messaging layer and higher-level APIs (like AgentChat) on top. This means you can plug in new LLM backends, add custom agent types, or integrate domain-specific tools without modifying the library’s core. AutoGen even introduced an AutoGen Studio (a low-code GUI tool for designing agent workflows) to aid prototyping, though ultimately deploying an AutoGen system will involve coding and technical configuration.

AutoGen is powerful, but many users encounter real-world challenges when trying to go from code to a user-facing AI service. Here’s where AutoGen has notable limitations:

1. No Built-In Interface

AutoGen does not provide any user-facing chat UI or interface for your agents. It’s a backend developer framework, so if you want end-users to interact with your multi-agent system, you must build your own front-end or chat app (web UI, mobile app, etc.). Out of the box, there’s no visual interface – just code libraries. This means significant frontend development effort if you need a polished UI for customers. Essentially, AutoGen assumes you’ll handle the presentation layer yourself, which can add hours or days of work for a team that isn’t primarily front-end focused.

2. Zero Monetization Support

There are no built-in monetization or user management features in AutoGen. You cannot natively charge users for access, set up subscription tiers, or enforce usage limits. If you plan to make money from an AI agent you built with AutoGen, all of that – payments, billing, subscription handling, user authentication/authorization – has to be implemented separately from scratch. AutoGen doesn’t concern itself with revenue models; it’s purely an orchestration framework. So turning your agent into a paid service or product will require integrating payment processors (like Stripe) and building the necessary business logic on your own.

3. Steep Learning Curve and Technical Barrier

AutoGen’s flexibility comes at the cost of complexity. To use it effectively, you need to be comfortable with Python programming and understand concepts like asynchronous agent messaging, tool integration, and custom agent classes. For non-engineers (or even developers unfamiliar with multi-agent paradigms), this is a significant hurdle. Mastering AutoGen for anything beyond simple examples requires learning its abstractions (agents, reply functions, event loops, etc.) and debugging multi-agent interactions – which can be tricky. In other words, AutoGen has a steep learning curve for complex scenarios, and it’s not very approachable for most teams or domain experts who lack a strong software engineering background. Even one tech blogger noted that for most teams or creators, the learning curve of these code-first frameworks is “steep.”

4. Manual Hosting and DevOps Overhead

Using AutoGen in production means managing your own infrastructure. There’s no managed cloud service for AutoGen (no one-click deploy), so you are responsible for deploying the application on a server or cloud platform, setting up any databases or vector stores for memory, and monitoring the system’s uptime and performance. You’ll need to handle scaling (if your usage grows) and ensure reliability. This DevOps work can be non-trivial, especially if you are orchestrating multiple agents that make many API calls – you might need to optimize for latency and cost. In short, AutoGen assumes you have the ability to stand up and maintain the backend on your own. For solo creators or small teams without a dedicated engineer, this operational burden is a major drawback.

5. No Templates or Reusability

AutoGen doesn’t come with pre-built agent templates or industry-specific solutions. Every new AI agent or workflow you create is basically starting from a blank slate. While the AutoGen community and docs provide examples, there are no plug-and-play presets for common use cases (e.g. a Q&A bot, a coding assistant, a marketing advisor). This lack of starter templates means you must design and code the entire agent logic yourself, which slows down development. The ecosystem around AutoGen is still relatively young – there are fewer community-contributed agents or integrations available compared to older frameworks. So, reusability is limited unless you build your own library of components. For someone looking to spin up a standard chatbot or AI assistant quickly, this “no presets” approach can be a roadblock.

6. No Team Collaboration Features

AutoGen is essentially a Python library you run in your environment – it’s not a multi-user platform. There are no built-in collaboration or team management features. If a team of people wants to work on the same AI agent project, they have to share code via Git or other dev workflows; AutoGen itself doesn’t provide roles, permissions, or cloud workspaces for multiple contributors. Likewise, if you wanted to let colleagues (non-developers) tweak the agent’s knowledge base or prompts, there’s no simple UI for that – it would require them to edit code or config files. This single-user design makes it less suitable for non-technical stakeholders to be involved in refining the AI. In contrast, businesses often prefer tools where project managers, content experts, or multiple team members can collaborate on an AI agent’s development and monitor its performance. AutoGen doesn’t cater to that out-of-the-box.

Knolli is built specifically to address these pain points. It’s a platform designed for speed, ease of use, and monetization. You don’t need to be a developer – you just bring your expertise or content, and Knolli turns it into an intelligent, branded AI copilot. Here’s how Knolli closes the gaps left by AutoGen:

1. A Branded UI Out-of-the-Box

Forget spending time building a user interface. Knolli gives you a beautiful, responsive chat UI instantly. From day one, your AI agent comes with a ready-to-use web interface that you can customize to match your brand – adjust colors, fonts, and tone, and even host it on your own custom domain for a fully branded experience. This means when you create an AI copilot with Knolli, end-users can immediately interact with it through a polished UI that feels like your product, without you writing any frontend code.

2. Monetization is Built-In

Knolli includes revenue tools by default – it was built with creators and businesses in mind. You can easily add paywalls, subscription plans, pay-per-use pricing, or free trials for your AI agent’s usage. All of this is powered by Stripe integration, so you can start charging users from day one without building a payment system yourself. Set up multiple pricing tiers, limit access based on subscription level, and manage user accounts and billing all within Knolli. In short, if you want to earn from your AI agent, Knolli has you covered out-of-the-box (whereas with AutoGen you’d be stitching together payment APIs on your own).

3. It’s All No-Code

One of Knolli’s biggest strengths is that it requires no coding at all to create and deploy your AI agent. The platform provides a visual, guided interface for everything. You can upload content (PDFs, FAQs, documents), write some instructions or example Q&A pairs for your bot, and click launch – Knolli handles the rest. You don’t need to know Python or YAML; even complex multi-step workflows can be configured through drag-and-drop or form inputs. This dramatically lowers the barrier to entry. Subject matter experts, coaches, marketers – anyone can create an AI copilot representing their knowledge without technical help. And for advanced users who do want to tweak things, Knolli allows custom prompt engineering and offers API access, but those are optional. The core experience is truly no-code and quick.

4. Start With Templates

Unlike AutoGen, where every project starts from scratch, Knolli offers a library of ready-to-use templates to kick-start your agent building. These templates are pre-configured archetypes for common use cases – for example, an “AI Tutor” template for educational bots, a “Sales Assistant” template for answering product questions, a “Therapy Coach” template, a “Product Expert” bot, and more. By starting with a template, you get a working baseline in minutes and can then customize it with your content or branding. This saves tons of time and gives non-developers a blueprint to follow. Whether you need a customer support chatbot or a personal finance advisor bot, there’s likely a Knolli template to build on, so you’re never staring at a blank page.

5. Host and Scale Without DevOps

Knolli is a fully hosted cloud platform, which means all the backend infrastructure is handled for you. You don’t have to deploy servers, set up databases, or worry about scaling – Knolli’s team takes care of the technical hosting environment. When you publish your AI copilot, it’s instantly live on a secure URL (or your custom domain) and ready for users. If your bot suddenly gets popular and hundreds of users start interacting, Knolli automatically scales to meet the demand. There’s no need to debug server issues or monitor uptime – it’s all managed. This is a huge relief for small teams who don’t have dedicated DevOps engineers. Essentially, Knolli lets you focus on your AI’s content and behavior, while it handles the heavy lifting of deployment and maintenance.

6. Private, Secure, Enterprise-Ready

Knolli is built with privacy and enterprise security in mind. It offers features like automatic PII (personally identifiable information) stripping from conversations, so sensitive data isn’t accidentally stored. It also supports custom Single Sign-On (SSO) integration and strict access controls for your agents, which is ideal for businesses that need to control who can use the AI (for example, only employees or paying clients). All data on Knolli is encrypted and stays under your control – your knowledge base isn’t used to train outside models, addressing data ownership concerns. In short, Knolli provides an enterprise-grade environment out-of-the-box, which would take substantial effort to configure with an open-source solution. Crucially, this includes team and collaboration features: you can invite team members to your Knolli workspace and manage their roles and permissions, something not possible with a code-only framework. Knolli’s built-in team management means your whole organization can safely work together on AI projects (with appropriate oversight), a feature most other AI platforms lack.

Choose AutoGen if:

• You’re an engineer who is comfortable with a developer-focused, code-centric approach. In other words, you don’t mind writing Python code, configuring agent classes, and managing infrastructure to get your AI system running.
  
  
• Your project absolutely requires multiple AI agents collaborating in a very custom or experimental way that isn’t supported by higher-level platforms. AutoGen offers maximum flexibility for complex, unique agent behaviors.
  
  
• You need fine-grained control over every aspect of the agent workflow and are willing to trade ease-of-use for that control. For example, you want to integrate obscure tools, or optimize the messaging logic in ways that only code can.
  
  
• You enjoy (or at least don’t mind) building the surrounding pieces yourself – such as creating your own UI, setting up databases for memory, and writing any needed business logic for your use case. Essentially, you’re okay being the developer, product manager, and DevOps engineer all in one to get your AI application to end-users.
  
  

Choose Knolli if:

• You want to launch AI agents quickly without coding. Knolli shines if you prefer a plug-and-play solution where you can focus on your content and expertise rather than technical details. In Knolli, you can create a branded AI assistant in a graphical interface and have it live the same day.
  
  
• You plan to monetize your AI or build a business around it. Knolli makes it straightforward to charge for access (subscriptions, etc.) and manage customers. If you’re a consultant, coach, or creator looking to productize your knowledge via an AI chatbot, Knolli provides the storefront and payment system out-of-the-box.
  
  
• You need to deliver a polished, user-facing solution fast. For a startup or team on a tight timeline, Knolli lets you ship an AI copilot to users now, not months from now. The UI, hosting, and scaling are handled, so you can iterate on content and see user feedback immediately.
  
  
• Collaboration and ease of maintenance are important. If you have multiple team members or non-technical stakeholders who want to contribute (e.g., updating the knowledge base, reviewing conversation logs), Knolli’s collaborative platform is built for that scenario. With AutoGen, those colleagues would be left out of the loop unless they learn to code. With Knolli, everyone can participate through a friendly interface.

To illustrate the difference, here are a few scenarios where Knolli enabled success that would be harder to achieve with a code-only approach like AutoGen:

Building a Monetizable Coaching Agent

A professional business coach wanted to create a 24/7 coaching assistant that could serve clients between live sessions. Using Knolli, she uploaded her training materials and past session notes to form the AI’s knowledge base. She configured the copilot to generate session summaries, recall past advice (so it wouldn’t repeat itself), and offer personalized tips to users. Thanks to Knolli’s no-code setup, her coaching bot was up and running in 2 days. Even better, she monetized it by offering it as a subscription service on her website. Within a week, paying users had signed up for monthly access to this AI coach – providing a new stream of income that would have required significant custom development with AutoGen.

Internal Marketing Assistant for Onboarding

A growing B2B startup used Knolli to build an internal marketing assistant to help onboard new hires. They collected all their past campaign briefs, style guides, and marketing SOPs and fed them into Knolli as the knowledge base. With that, they launched an AI copilot that new marketing team members could chat with to learn company processes, get quick answers about branding guidelines, and even receive step-by-step workflow help. The result: new hires could ramp up in hours instead of weeks. The assistant was embedded directly in the team’s Notion workspace for convenience. Had they tried to use AutoGen, the team would have needed an engineer to set up the agent and integrate it with Notion, plus build an interface. Knolli’s turnkey solution saved them significant development time and let the marketing team (not just the engineers) own and update the assistant’s content as things evolved.

Selling Niche Legal Knowledge

A legal consultant recognized that a lot of her niche expertise (in, say, data privacy law) could be packaged as an AI Q&A service for junior lawyers and clients. She used Knolli to upload a repository of 500+ frequently asked questions, contract clauses with explanations, and regulatory guides that she had created over the years. In Knolli, she configured an AI legal advisor that could reference this trove of knowledge to answer questions or explain contract language on demand. Because Knolli handles paywalls, she set the bot up as a paid service – users could subscribe monthly to get unlimited legal Q&A help. In short order, she had a paid AI advisor live on her site, generating passive income. Creating something similar with AutoGen would have required hiring a developer to implement the interface, payment system, and ensure the large knowledge base was searchable – but Knolli provided all those capabilities out-of-box, allowing a subject matter expert to go to market solo.

“AutoGen was powerful, but I couldn’t figure out how to build a user-facing app around it. Knolli solved that problem for me – I had a working chatbot with a UI and payment system on day one, without coding.”
— Alex R., Indie Founder

“I went from zero to three custom AI bots in 4 days. It’s like Webflow for AI copilots – drag, drop, and done. Knolli made the whole process easy and even fun.”
— Nisha M., Online Course Creator

For creators and teams seeking an AutoGen alternative that delivers no-code setup, multi-agent workflow control, long-term memory integration, instant monetization, a polished branded UI, and enterprise-ready collaboration features, Knolli is the clear winner. It provides all the advanced capabilities you need to build user-facing AI agents – without the headaches of coding or managing infrastructure.

AutoGen is a brilliant framework for specialized developer-driven projects, but if your goal is to launch a branded, monetizable AI copilot that can engage users 24/7, Knolli will get you there faster and with a lot less friction. In 2025, Knolli stands out as the best AutoGen alternative for turning knowledge and AI into real-world products.

Try Knolli today to build your own AI copilot in minutes and see the difference for yourself.

What is the best AutoGen alternative?

Knolli is the best AutoGen alternative if you want a no-code AI agent builder with built-in monetization and an instant, branded UI. It includes everything AutoGen lacks for non-developers—visual setup, templates, payments, and team features—all in one platform.

Does Knolli require coding?

No. You can launch AI agents on Knolli without writing any code. Just upload content, set your prompts, and go live. For advanced users, APIs and prompt customization are available—but optional.

Can I monetize my Knolli AI agent?

Yes. Knolli supports subscriptions, one-time payments, usage-based billing, and free trials—all powered by Stripe. No extra tools needed. Just set your price, and the platform handles the rest.

Is Knolli secure for enterprise or consulting use?

Absolutely. Knolli includes SSO, access controls, PII stripping, and full data privacy. Your content stays secure and is never used to train external models—ideal for confidential projects and enterprise teams.

 Does AutoGen come with templates or a UI?

No. AutoGen is a code-first framework with no built-in UI or templates. You’ll need to build everything from scratch. Knolli, on the other hand, gives you ready-to-use templates and a fully-hosted frontend, so you can launch faster.