If you want to build agents with Portia AI you can try our SDK for free on Github. Stars welcome!
There are a lot of AI Agent builders out there considering the term ‘AI Agent SDKs’ indexed at 0 on Google Trends only 6 months ago.
At first glance, it’s hard to tell them apart and figure out which will be right for your use case. Everyone uses the same words to describe what they’ve created in an attempt to make the products seem ‘right’ for as many people as possible.
After a few weeks of researching the market at Portia AI, I realised it’d be useful to write something high level about the different Agent Builders out there and how you can choose between them.
For when you want to automate something quickly and without writing much code
In this bucket are n8n, MindStudio and now CrewAI. These products are focussed on business users as well as engineers. They’re designed to be easy to use and can be set up using visual workflows instead of code. They trade off simplicity against control so they’re less flexible and you don’t have the same fine grained options as you would get with a code first product. Great for prototyping and trying things out, or if you’ve got a very simple production use case. if you were building a KYC agent for a bank you would want something where you could build in more oversight.
- MindStudio is designed for getting AI agents up and running using templates and without code. It’s useful when you want to move quickly and is integrated with the major LLM providers. It also has built in tools you can use to equip your agents without much effort. It’s most useful for straightforward AI workflows you can prototype in a few minutes, for example an agent that summarises what it sees on the page.
- n8n feels more like a Zapier. It’s most powerful when used for connecting things up to automate your existing business process. The visual builder is the entry point though it can be extended with code. It supports a very large library of LLMs, databases, APIs and tools out of the box which means you don’t need to build your own integrations for your current processes. It’s most useful for the kind of simple automations you would have used Zapier for previously, for example, send me an email when someone submits our lead form and add a record to our CRM. lt’s not well set up for long running or complex autonomous tasks which need multi step reasoning or memory management.
- Crew AI used to be known for their developer SDK product but they’ve now built tooling for no-code workflows too. They combine a visual, no and low code interface with a developer experience. You can create multi-agent systems, using a lead AI to figure out what’s needed and in what order, then delegate to its ‘crew’ of specialist agents. This could be unpredictable as you’re relying on the lead agent to keep everything under control. They’ve now introduced the concept of ‘flows’ to help offset that. You can define the exact steps the agents need to take and in what order using conditional logic, loops and state management. You lose some of the autonomy and flexibility but it increases the reliability and makes your agents easier to audit. I’m interested to see how they prioritise changes to the no code experience and solutions driven approach against building out the developer product. Their website now emphasises the former.
For when you’re looking for more control over your agents for critical use cases
These are opinionated libraries or SDKs for programmers to use to build their agents. They’re code first frameworks without a no code option. It’s more work to get started than the out of the box and modular solutions but you’ll gain fine grained control and transparency. If you’re building multi-agent systems then this is the level of oversight and control you’ll want. If you’re not then you could give one of the solutions above a try first before graduating onto one of these SDKs if they don’t give oversight you need.
- LangChain / LangGraph: LangChain gives you building blocks, think prompts, memory, and tool wrappers, so you can roll your own agent from scratch. It’s one of the most popular tools at this level of abstraction as it supports basically every model, tool, and database you might want to use. LangChain uses an architecture that lets you chain LLM calls and let the model decide what to do next. It’s very adaptable but as with any AI agent, dialling up autonomy means you lose some of the control. It can be hard to debug where things went off the rails. LangGraph, is built on top of LangChain, introduces a more declarative, stateful graph architecture. You define the flow of agent steps explicitly, gaining better visibility and control. It also supports human checkpoints, so you can pause for manual approval. It’s open source and free to try.
- Portia AI: Also open source and developer focussed. It’s our product and we built it to be a production‑grade agent orchestration framework. It’s more opinionated than the other tools mentioned in this section as we build in how wt think a production agent should be put together. You can go back and forth with a planning agent to create a structured plan. It then breaks it into multi‑step workflows and the plan is then immutable. The execution agents then carry out the plan. You can also add in ‘escalation points’ out of the box, where the agent needs to check with a human if certain conditions are hit. There’s also built-in authentication, integrated tools like Slack, GitHub, Zendesk, Google and 1000+ tools you can access via MCP servers. It’s aimed at industries where predictability, auditability, and security matter and where you want guidance on how a production grade agent should work. It’s higher level than Lang Graph and Pydantic.
- Pydantic AI: Pydantic give you the tools to put a production-grade agent together how you want but it’s complex. It’s built for engineers who have strong opinions about how they want their agent architecture to look. You define agents as Pydantic models with structured input and output types, system prompts, and function‑calling tools. It's model‑agnostic and integrates tightly with Pydantic Logfire for real‑time debugging and observability. Agents are reusable, type‑safe, and can use dependency injection for context/tools. Streaming responses are validated continuously for correctness. You still design the workflow, prompt templates, tool logic, and orchestration yourself. However the framework abstracts away validation, structure, and runtime safety, making it easier to build production‑grade agents in Python.
For when you want to lean into the platforms you already use and wire everything up
These frameworks provide scaffolding for creating AI agents but their main benefit is for if you’re already deep in the Google, OpenAI and Amazon ecosystem and don’t want to add a new provider to your stack.
OpenAI Agent SDK: it gives you scaffolding by creating some lightweight abstractions like the agent loop, tool registration, handoffs to other agents, and tracing. It’s then up to you to create the agents, define the tools and guardrails to put this into production. The built in traceability is super useful and the SDK is interoperable with any LLM under the hood. This is a good option if you want something lightweight and code first for building your first agent.
Google’s ADK (agent developer kit) is another open-source, code-first toolkit. It helps developers build agentic systems that use dynamic reasoning or enforce some structure. It provides different built in agent types (loop, parallel, LLM driven) for you to design your work flows around based on how much control you need. It has built in tools to Google services if you’re automating things within that ecosystem. The memory handling out of the box is also super helpful. You’ll still need to figure out the agent logic, prompts and connection everything up. But it’s flexible and abstracts out some of the more complex areas of agent design.
Amazon Bedrock is another production‑ready agent toolkit. It has a visual builder and modular services like Memory, Gateway, and Code Interpreter. You can connect agents to your APIs, Lambda functions, and knowledge bases that already run on Amazon out of the box. It also now supports memory retention without you having to implement it yourself. Like with the Google ADK, you’ll still need to design the agent logic, orchestration, prompts, and integration flow but Amazon AgentCore helps manage key infrastructure: session isolation, observability, identity, and tool access.
Which one should I pick?
As you can tell from this post, there’s no ‘best’ product out there. You need to figure out the trade-offs you want to make when building your agentic systems. Here are some questions that can help you make that choice:
- Who’s building the agents, developers or another business function?
- How much control do you need over your agent?
- Do you need one agent or many?
- Are you creating something quick to test or a crucial piece of infrastructure?
- Do you have strong opinions about the way agents should be structured or are you looking for guidance?
- How much do you want or need to build yourself?
- How much are you locked into a particular vendor?
New frameworks and tools are coming out all the time so we expect to see these tools start to look more similar over time. Also as we’ve seen with the launch of the MCP standard and A2A emerging, things are likely going to get more interoperable over time, not less as standards are established. The frameworks aren’t mutually exclusive and you can choose the right approach based on where you are in your development lifecycle and your answers to the questions above. The no code tools can deliver quick prototypes but are limited. If you need flexibility and control then the mid-level opinionated frameworks are a strong choice. If you have a big developer team and a very custom set up then starting with the base provider SDKs could give you the fine grained control you need. Good luck!
Top comments (3)
Really insightful breakdown, love how clearly the trade-offs are laid out across no-code tools and developer-first frameworks.
One thing we’ve been exploring (and now enabling) at haveto.com is a different angle entirely:
What if your AI agents didn’t need to live inside apps at all… and could run independently, on-chain as microservices?
No backend. No infra scaling headaches.
Just secure, scalable logic running directly on a blockchain, with full transparency and zero vendor lock-in.
If anyone here is building production-grade agents and wants to explore decentralized execution (Python, JS, LLMs, etc.), feel free to reach out to me directly. Happy to chat, share what we’re doing, or just swap ideas.
Would love to hear your feedback.
Very intresting topic is shared, nowadays most of the India based Augmented Reality Services providers have also developed their AI agent while providing augmented reality services to various clients.
I am interested in this information. Thank you.