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Gone are the days when you just wanted to generate Terraform configs or convert YAML into Pulumi. That was helpful in 2022. In 2025, you want actual actions - not just code suggestions. You want to resolve incidents, spin up environments, and trigger jobs - all without hand holding. That’s where AI agentic workflows come in.
Most of what’s written about AI agents today reads like it was written by someone who’s never stared at a broken deployment during a failed Argo rollout at 2AM - because a configmap didn’t propagate.
Most teams experimenting with agents today follow the same pattern: they hook a language model into a Slackbot, wrap it around their runbooks, and expect it to manage incident response or kick off builds. Then they realize they’ve basically built a chatbot that still needs human confirmation for every step.
That’s not enough.
What you need is an agent that can plug directly into your systems - your CI/CD pipelines, your cloud infra, your observability stack - and make decisions based on real state. Not just answer questions. Agents that can:
This list breaks down the top 10 best AI agent frameworks that actually do the job - in real-world DevOps and platform setups. Not proof-of-concepts. Not pitch decks.
Kubiya isn’t just another DevOps bot - it’s a full-blown AI agent framework purpose-built for internal and external platform automation. Think of it as the fastest way to go from “can you restart this job?” to a self-contained, policy-compliant agent that actually does it - without adding another tool to your stack.
At its core, Kubiya is a production-ready agentic framework designed with zero trust principles, built-in policy enforcement, and native integrations for the full software delivery lifecycle. It doesn’t just trigger actions - it manages secrets, enforces RBAC/ABAC rules, supports just-in-time access, and tracks everything with a full audit trail. It scales cleanly via Kubernetes and OpenShift Operators, supports multi-cloud and on-prem, and brings observability out of the box with OTLP and Prometheus hooks.
No wrappers. No duct tape. No extra platforms to glue together.
Kubiya is uniquely positioned for teams looking to deploy real AI agents - not experiments - into production. It’s not a wrapper around runbooks. It’s a self-contained, enterprise-grade framework that supports the full lifecycle of agent development: from initial prototyping to secure, scalable, auditable deployment in production. If you're a platform team trying to reduce toil without increasing risk, this is where you start.
Here’s how Kubiya fits into your existing workflows - without adding friction or complexity.
Developers interact with the agent naturally (via Slack or Teams), and Kubiya handles everything else - from request analysis to secure execution - all within the guardrails of your infrastructure and policies.
Agno isn’t just about triggering workflows - it’s designed to help teams build agents that can reason over time, track what they’ve already done, and adapt their decisions based on what they learn along the way.
At its core, Agno combines three things: a planning system, a memory layer, and tool orchestration. The agent doesn’t just take a prompt and act - it builds a plan, tracks context across steps, and chooses tools dynamically depending on what it finds. Think of it less like a chatbot, and more like a self-guided worker that can manage internal tickets, pull logs, summarize documentation, and escalate when it hits an edge case.
This is especially useful in orgs where decisions aren’t binary and workflows span multiple systems. For example, say a build fails. An Agno agent could investigate the build logs, correlate the failure with recent commits, check if there’s an open incident in PagerDuty or Jira, and take different actions depending on whether the issue is known or novel.
Agno is less about replacing CLI tasks and more about building semi-autonomous agents that can think through problems like a junior SRE or platform engineer might - with some context, some initiative, and a clear idea of when to escalate.
While Agno is focused on building agents that can reason over complex workflows and retain memory across tasks, not every organization needs long-term planning or multi-system orchestration. Sometimes, what’s needed is a more structured, fast-response interface - especially when you’re dealing with customer-facing or high-volume communication channels.
That’s where Botpress comes in.
Botpress is built for one job: designing, deploying, and managing conversational agents that can handle structured interactions - reliably, at scale, and with the kind of flow control most LLM-first tools skip entirely. It’s especially suited for building assistants that interact with users across customer support, internal IT helpdesks, or front-line sales ops.
Unlike frameworks that bolt conversation on top of an agent brain, Botpress starts with the conversation and builds downward - letting you design flows, track user state, manage context, and plug in actions or data from external tools as needed.
Everything is modular. Flows are version-controlled. You can preview, debug, and deploy changes like you would with app code. And most importantly - Botpress gives you deterministic behavior where it matters, even when LLMs are involved.
Botpress is not trying to be an all-purpose agent brain. It's built for fast, structured conversations that plug into workflows and tools you already use - with enough flexibility to introduce LLMs where it adds value, but enough structure to avoid the chaos.
Swarm is a research framework from OpenAI that focuses on enabling multiple AI agents to collaborate in real time - each one assigned a role, objective, or perspective, and coordinating with others to solve complex problems. Think of it as building a team of AI workers, each specializing in something different, and watching how they negotiate, validate, and course-correct as a unit.
This is not a drop-in tool like Botpress or Kubiya - it’s a lower-level experiment into how agents interact, share memory, and resolve ambiguity through discussion. But it’s a critical concept for teams building autonomous systems that need to reason across boundaries: security + dev + infra, or data + analytics + ops.
Each agent in Swarm can take inputs, reason independently, suggest actions, and provide critiques on other agents’ proposals. The idea is that instead of relying on one large model to do everything, you can split responsibilities and have agents debate or vote on solutions - like a committee, but faster and with better attention to detail.
Swarm is still raw - it’s a GitHub repo, not a SaaS product - but it offers a valuable glimpse into what’s coming next. As teams start building more collaborative agents and distributing responsibilities across domains, frameworks like Swarm could form the backbone of more complex decision-making systems.
Rasa has been around longer than most of the LLM ecosystem - and it shows. It’s built from the ground up to support AI agents in production environments where teams need full control over training data, behavior, and model decision paths. While many newer frameworks rely heavily on proprietary LLMs and APIs, Rasa is designed for teams that want to self-host, fine-tune, and manage their stack on their own terms.
At its core, Rasa combines intent recognition, dialogue state tracking, response generation, and integration management - all wrapped in an open architecture that can scale from simple chat flows to deeply contextual, multi-turn assistants with external tool access.
In 2024, Rasa introduced Rasa Pro and Rasa Studio, which bring in LLM integration, native vector store search, and hybrid NLU pipelines - allowing developers to combine structured intent models with retrieval and generative components while still keeping full transparency.
This makes it a strong fit for teams building domain-specific assistants where behavior needs to be explainable, auditable, and tightly coupled to backend systems.
Rasa isn’t trying to compete with the latest LLM experiment. It’s built for production. It gives teams control, structure, and reliability - whether you’re running on a private cloud, need full GDPR compliance, or simply want an AI assistant that behaves the same today as it did last week.
CrewAI is a Python framework for orchestrating multiple AI agents to work as a “crew,” where each agent plays a specific role and contributes to solving a broader task. It’s heavily inspired by how real teams operate: you’ve got a planner, an executor, a researcher, maybe even a critic - and each agent has a job, memory, and access to tools.
Where many agent frameworks focus on single-threaded execution (prompt in, action out), CrewAI is built around collaboration. You define the crew structure, assign roles, wire in tools (like APIs, files, databases), and let the agents coordinate their work - through structured messages, task handoffs, and feedback loops.
It’s still early in maturity, but the mental model it promotes - distributed responsibility, chain of reasoning, and iterative refinement - makes it ideal for workflows that can’t be handled by a single pass through an LLM.
CrewAI is still a developer-first tool - it’s not wrapped in dashboards or turnkey SaaS features. But if you’re building agents that need to reason in parts, iterate, and refine together, it gives you a strong foundation to build with. The orchestration is lightweight, the mental model is familiar, and the results feel more human - not because the agents are smarter, but because they’re working together like a real team would.
AutoGen, built and maintained by Microsoft, is one of the most advanced frameworks for setting up conversational multi-agent workflows - agents that talk to each other, reflect, delegate, retry tasks, and even pull in human input when needed. It’s not just about defining roles; it’s about building interaction loops and letting agents run end-to-end workflows in collaboration.
What sets AutoGen apart is how much it handles for you. You define agents, their roles, tools, and behaviors, and AutoGen builds the runtime that lets them communicate via structured messaging, reasoning steps, and escalation logic. Agents can retry tasks on failure, reroute steps to different agents, or hand off control to a human operator - all within a single orchestration layer.
It’s also one of the best-documented frameworks for building real applications like automated coding agents, report generators, troubleshooting agents, or internal copilots that chain multiple LLMs and tools in sequence.
AutoGen is very much a framework - it expects you to build around it - but it gives you advanced tooling out of the box for building LLM-driven systems that don’t fall apart the moment a task fails or needs adjustment. It's more production-minded than many research tools, and more capable than single-agent wrappers pretending to be autonomous.
LlamaIndex isn’t an agent framework in the traditional sense - it’s the infrastructure layer that makes agents useful. At its core, it acts as a connective tissue between large language models and the fragmented, unstructured data inside your organization. Think of it as the retrieval and reasoning backbone behind any serious AI workflow.
Originally branded as GPT Index, LlamaIndex has evolved into a full-blown data orchestration layer. It helps developers ingest, chunk, index, query, and route documents, databases, APIs, logs, spreadsheets, PDFs - whatever you’ve got - so that LLMs and agents can query them intelligently.
Without this layer, most agents are either hallucinating or limited to static tools. With it, they can search internal wikis, correlate runtime logs, summarize policy docs, or walk through multi-part reasoning tasks grounded in live data.
LlamaIndex isn’t a chatbot framework. It’s the missing layer between your data and your agents. If you’re serious about building internal copilots that aren’t just wrappers around OpenAI, this is the tool that turns your messy, distributed info into something your agents can actually use.
Once your agents can retrieve and reason over data with tools like LlamaIndex, the next challenge is orchestration - not just chaining steps, but dynamically deciding what to do next based on current state, context, or even failure. That’s where LangGraph fits in.
LangGraph is a graph-based framework built by the LangChain team, designed to orchestrate complex, long-running, multi-step workflows using LLMs and agents - with stateful control. It lets you define applications as graphs, not static chains, where each node is an agent, tool, or action, and the edges are conditional transitions based on runtime logic.
Think of it as the evolution of LangChain’s original sequential chains - but now with loops, retries, branching, memory, and state-aware execution baked in. You can build feedback loops, conditionally route based on tool output, escalate failures, or even allow human input - all without losing track of the current task.
This is especially useful when building production-grade agent systems where execution paths can’t be linear. LangGraph lets you encode system behavior like an SRE writing a runbook - flexible, fault-tolerant, and designed for real-life workflows that rarely follow a single path.
LangGraph doesn’t try to be a GUI builder or a chatbot engine. It’s the glue between your LLMs, agents, tools, and runtime logic - with state tracking and flow control designed for engineers building production-grade AI systems. If you’re moving beyond toy agents and into real workflows, LangGraph is one of the few frameworks that’s ready for that complexity.
If LangGraph is what you’d use to orchestrate agents in a controlled, production-grade environment, AutoGPT sits on the other end of the spectrum - maximum autonomy, minimal structure.
AutoGPT was the first major open-source project to explore fully autonomous agents - LLM-powered systems that could plan, execute, reflect, and iterate over goals without constant human direction. You give it an objective (“research Kubernetes autoscaling strategies and write a summary”), and it breaks the task down, decides what tools to use, spawns subprocesses, writes to disk, and loops until it either finishes… or crashes trying.
Let’s be clear: AutoGPT is not a framework you'd drop into production today. It's a sandbox. A testbed. But it introduced core patterns - planning loops, tool use, reflection, retry logic - that every agent framework since has borrowed from. It’s also a great place to understand how “runaway” agents behave when left without strong constraints or orchestration logic.
The project is still evolving, with a community around AutoGPT-Next trying to make it more modular, memory-aware, and extensible. But even in its raw state, it remains one of the best places to prototype agent autonomy and stress-test how far language models can go on their own.
AutoGPT is not what you’d deploy into a bank, but it’s probably what sparked your interest in agent systems in the first place. It’s still one of the best playgrounds for exploring autonomous reasoning - and a reminder that without constraints, agents get creative - sometimes too creative.
Camel AI is a lightweight, open-source agent framework built for rapid prototyping and experimentation. It’s not trying to be an all-in-one DevOps tool or a multi-agent research lab - it’s designed to help developers spin up task-driven agents quickly, assign them roles, and see how they perform with minimal setup.
Inspired by role-playing patterns, Camel AI lets you define "assistant" and "user" agents with distinct system prompts. From there, you can simulate multi-turn dialogues, assign goals, and let the agents negotiate, iterate, or self-correct. It’s particularly useful if you're testing prompt strategies, interaction styles, or chaining logic between agents - without needing orchestration overhead.
Camel AI isn’t production-grade orchestration - it’s a focused, fast-moving framework to help you test, tweak, and prototype how agents behave in structured interactions. If you're iterating on agent logic or exploring prompt patterns, it’s one of the simplest and cleanest places to start.
N8N isn’t an agent framework in the classic LLM sense - but it’s one of the most underrated no-code/low-code platforms for building AI-powered automation agents that actually do stuff. It’s open source, workflow-based, and extensible enough to let you wire up GPTs, APIs, scripts, and even human-in-the-loop approvals - without writing much backend glue.
Think of it as Zapier for engineers. You define triggers, conditions, and actions using a visual builder, then plug in LLMs like OpenAI or Claude for decision-making. It won’t write code from scratch, but it’ll run it, pass outputs between steps, handle retries, parse JSON, hit APIs, and notify you in Slack - all while logging everything.
N8N is a practical bridge between ops and AI. You won’t build autonomous reasoning agents here - but you will automate 80% of your glue work and make LLMs useful across your internal systems. For teams that want reliability, observability, and repeatability over raw autonomy, N8N is often a better starting point than half-baked agent stacks.
Not every team needs full-blown autonomy. What they need are agents that reduce friction, handle real tasks, and don’t break things quietly.
Pick the right tool for the job. Keep the humans in the loop. Build systems that work at 2AM - not just ones that demo well at 2PM.
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