Comparing Platforms for Agentic AI Development
Agentic AI represents the next frontier in AI - autonomous systems that can reason, plan, and act to achieve specific goals. As LLMs have advanced, a new ecosystem of frameworks has emerged to transform these powerful models into practical, task-oriented agents. These frameworks provide the crucial infrastructure for orchestrating AI agents with tools, memory systems, and multi-step workflows.
Selecting the right agentic AI platform is a strategic decision that can significantly impact development velocity, system capabilities, and long-term flexibility. When evaluating potential frameworks, several critical dimensions must be considered:
- Architecture & agent model: How agents are structured and how they coordinate
- Extensibility & customization: The ease of adapting the framework to specific needs
- Integrations & ecosystem: Available connections to models, tools, and services
- Performance & scalability: Efficiency and ability to handle growing workloads
- Maturity & community: Development stage and support resources
- License & cost: Financial and legal considerations for adoption
This comprehensive comparison examines the leading agentic AI frameworks available today, analyzing their strengths, limitations, and optimal use cases. By understanding the distinct approaches each platform takes, we can make an informed decision that aligns with the technical requirements and strategic goals.
LangGraph
LangGraph extends LangChain with a graph-based approach to agent orchestration. It excels in scenarios where explicit workflow control is needed, allowing complex multi-agent interactions with loops and conditional transitions. It’s ideal for deterministic or high-assurance flows but may have performance overhead compared to simpler frameworks.
AWS Strands
AWS Strands provides a model-driven agentic loop with minimal hard-coded logic. It integrates deeply with AWS services while supporting non-AWS models. It’s great for enterprise and cloud-integrated agents, particularly suited for AI agents that automate cloud operations or analyze data from AWS sources.
OpenAI Agents SDK (and Codex)
The OpenAI Agents SDK offers a streamlined, lightweight agent framework. It’s Python-first and minimal, making it ideal for integrating AI agents into existing applications with minimal fuss. The Codex CLI is a specialized agent for coding assistance built on this SDK.
Google ADK (Agent Development Kit)
Google’s ADK is built for multi-agent systems with a modular, hierarchical approach. It supports complex, multi-modal applications with built-in workflow agents and dynamic routing. It’s optimized for Google Cloud but works with other models and environments.
CrewAI
CrewAI models an AI system as a “crew” of agents working together with specialized roles. It provides high-level abstractions for rapid prototyping of agent collaborations, making it excellent for business process automation and multi-step tasks requiring different expertise.
SuperAgent
SuperAgent is a customer-facing AI assistant framework and hosting platform. It focuses on ease of integration and deployment for single-agent assistants that can handle conversations with tool usage and memory. It’s ideal for RAG-based Q&A and simple automations.
MetaGPT
MetaGPT simulates an “AI software company” with predefined roles following Standard Operating Procedures (SOPs). It excels at generating structured outputs from a single prompt, particularly for software design tasks, but is resource-intensive and not suited for real-time interactions.
Microsoft AutoGen (and AutoGen Studio)
AutoGen provides an event-driven multi-agent framework where agents communicate via asynchronous messaging. It supports both conversational agent teams and deterministic workflows, with strong evaluation and deployment capabilities. AutoGen Studio adds a low-code UI for building agent workflows.
Choosing the Right Framework
The ideal platform depends on the project’s requirements:
For complex, custom multi-agent workflows (specialized R&D pipelines, advanced multi-LLM applications): LangGraph or AutoGen offer excellent control, with LangGraph providing explicit graph control and AutoGen offering an asynchronous, event-driven backbone.
For quick development with minimal fuss: OpenAI Agents SDK is lightweight with built-in function-calling and guardrails, ideal for embedding an agent in an existing app. SuperAgent offers a hosted solution for rapid AI assistant integration.
For enterprise-grade systems and business integration: Google ADK excels with rich integration and multimodal support, while AWS Strands offers seamless AWS integration for cloud environments.
For multi-agent teamwork and rapid prototyping: CrewAI provides high-level abstractions for spinning up an “AI team” with minimal code, making it perfect for workflow automation and creative collaboration.
For generating structured outputs from a single prompt: MetaGPT can automatically create a draft proposal or software prototype from a one-line idea, though it’s resource-intensive and better suited for offline generation than real-time interaction.
The tool need to be matched to the task: use high-level platforms when speed and simplicity are needed, and low-level libraries when customization, control, or complex pipeline integration is required. Often, starting with a quick prototype and later graduating to a more powerful framework for production offers the best path forward.
Comparison Table by Features
After examining each framework individually, a direct feature-by-feature comparison provides invaluable insights for technical decision-makers. The following tables break down how each platform addresses key dimensions, allowing for systematic evaluation based on the specific priorities and requirements. This structured comparison highlights the relative strengths, trade-offs, and distinguishing characteristics across all major aspects of agentic AI frameworks, help identifying which solutions best align with the use cases and technical constraints.
Architecture & Agent Model
The architecture and agent model define how a framework structures and coordinates AI agents. This fundamental aspect determines how agents are created, how they interact, and what patterns of collaboration are supported. We must evaluate whether a framework’s architecture aligns with the intended use cases - from simple single-agent tools to complex multi-agent systems with specialized roles and coordination patterns.
| Framework | Description |
|---|---|
| LangGraph | Graph-based agent workflows (nodes = specialized agents, edges = transitions). Agents operate in a graph/state-machine topology for multi-agent collaboration. |
| AWS Strands | Model-driven agent loop – an LLM directs its own steps (reasoning, tool calls) in a continuous loop until task completion. Minimal hard-coded logic; relies on LLM’s native reasoning for flow control. |
| OpenAI Agents SDK | Lightweight Python-first agent loop with minimal abstractions. One Agent = an LLM with instructions, plus tools, guardrails, etc., run via a built-in loop that handles tool calls and iterates until completion. |
| Google ADK | Modular, hierarchical agents – multi-agent by design. Compose multiple specialized agents in a hierarchy with explicit coordination. Offers high-level workflow agents (Sequential, Parallel, Loop) for deterministic flows, plus LLM-driven dynamic routing for flexible control. |
| CrewAI | “Crew” model – teams of role-based agents. Simplifies multi-agent by defining agents with specialized roles (like a human team) and structured collaboration protocols. |
| SuperAgent | Single-agent microservice oriented. Each “agent” is an AI assistant (LLM-backed) that can handle a conversation/task with tool usage and memory. |
| MetaGPT | Meta-Programming multi-agent – orchestrates multiple GPT-based agents in structured roles to emulate an entire “AI software company”. Uses predefined Standard Operating Procedures (SOPs) to coordinate agents. |
| Microsoft AutoGen | Event-driven multi-agent framework – agents (LLMs, tools, even humans) communicate via asynchronous message passing. Supports both conversational agent teams and deterministic workflows. |
Extensibility & Customization
Extensibility determines how easily developers can adapt and expand a framework to meet specific needs. This includes the ability to define custom tools, integrate new model providers, modify agent behaviors, and create specialized workflows. Frameworks vary from highly code-centric approaches requiring deep programming knowledge to no-code/low-code solutions with configuration interfaces. The right level of extensibility depends on the team’s technical skills and how much customization the use case requires.
| Framework | Description |
|---|---|
| LangGraph | Built on LangChain; highly modular – define custom agents, prompts, transitions. Supports cycles, hierarchies, and fine control over flow. |
| AWS Strands | Flexible: define agent via code with three core pieces – Model (any Bedrock or external LLM), Tools (Python functions or ModelContextProtocol services), and Prompt. Easily add custom tools using a decorator. |
| OpenAI Agents SDK | Very extensible in code: turn any Python function into a tool with a decorator (auto schema via Pydantic). Allows normal Python control flow to chain agents (no new DSL). Custom guardrails for input validation and safety checks run in parallel to agents. |
| Google ADK | Highly extensible, code-first (Python SDK). Supports custom tools (pre-built Search, Code execution, etc., or any Python function). Integrates Model Context Protocol (MCP) tools and even allows using other agent frameworks as tools. |
| CrewAI | Highly customizable roles, memory (short-term, long-term, etc.) and tasks. Built-in support for different memory types to help agents remember context. Flow mechanism allows event-driven or stepwise control when needed. |
| SuperAgent | Customization via a simple YAML/markup or UI – define prompts, knowledge base, and tools for an agent without heavy coding. Can incorporate custom code tools and retrieval strategies. |
| MetaGPT | Less of a general toolkit, more a specialized framework: we can tweak role prompts, add or remove roles, and adjust the workflow SOPs. Provides templates for various documents (user stories, requirement docs, code, tests) as outputs. |
| Microsoft AutoGen | Highly modular: we can create custom agent classes, define custom tools/skills, plug in memory modules, etc. Extensible and reusable – workflows defined in JSON or Python can be exported/imported, shared, and deployed across environments. |
Integrations & Ecosystem
The integration capabilities and ecosystem around an agentic framework determine how seamlessly it connects with models, tools, and existing systems. Strong integrations reduce development time and expand what the agents can accomplish. When evaluating frameworks, consider both breadth (number of pre-built integrations) and depth (how thoroughly they integrate) with the tech stack, as well as the community ecosystem providing examples, plugins, and support.
| Framework | Description |
|---|---|
| LangGraph | Full LangChain ecosystem: access to numerous LLMs, vector DBs, tools, and LangSmith logging. Seamless LangChain integration. |
| AWS Strands | Deep AWS integration: works with Amazon Bedrock models (Claude, Titan, etc.) and also Anthropic, OpenAI, Llama2, Ollama local models. Pre-built toolset (file ops, API calls, AWS SDK calls, etc.). Integrates with AWS Glue, Amazon Q, etc. |
| OpenAI Agents SDK | Supports OpenAI models (GPT-4/3.5) out-of-the-box; can integrate others via API wrappers. Ties into OpenAI ecosystem for eval, monitoring, and potentially future OpenAI plugins/Functions. |
| Google ADK | Built for Google Cloud: optimized for Vertex AI and Gemini LLMs, but also supports external models via LiteLLM (Anthropic, Meta, Mistral, etc.). Plays well with third-party libraries like LangChain, LlamaIndex. |
| CrewAI | Broad LLM support – defaults to OpenAI API, but can connect to local models (via Ollama, LM Studio). Integrates with external systems: has tools for web browsing, code execution, API calls, and can work with vector stores. |
| SuperAgent | Comes with connectors to business apps – e.g. built-in support to integrate with Airtable, Salesforce, etc. Supports external vector stores like Pinecone, Weaviate for knowledge retrieval. |
| MetaGPT | Primarily uses OpenAI GPT-4 (or 3.5) for each role by default. Integrates with code execution environments (to run generated code), and can leverage web search or knowledge bases if built into an agent’s prompt. |
| Microsoft AutoGen | Integrates with many model providers via a pluggable client system (OpenAI, Azure OpenAI, local). Built-in tool integrations include web browsing (Playwright-based web surfer), code execution, and more. |
Performance & Scalability
Performance and scalability impact how efficiently the agent system handles workloads and grows with usage. This includes factors like response time, throughput, resource utilization, and ability to scale horizontally or vertically. Different frameworks make different trade-offs between ease of development, robustness, and raw speed. When evaluating performance, consider both the framework’s overhead and how well it can leverage underlying infrastructure and models for the specific use case.
| Framework | Description |
|---|---|
| LangGraph | Emphasizes resilience over raw speed; some boilerplate overhead. Suitable for complex flows but can be slower (CrewAI reports ~5.7× slower vs CrewAI in tests). |
| AWS Strands | Designed for production on AWS – scales in cloud or local. Emphasizes quick development (agents built in days vs months) by exploiting advanced LLM capabilities. |
| OpenAI Agents SDK | Optimized for developer productivity over heavy parallelism – good async support (runner can stream results) but primarily single-agent loops. Should perform similar to direct API usage with slight overhead for tools/guardrails. |
| Google ADK | Designed for production scalability (Google uses it internally for Customer Engagement Suite). Can deploy on containers and scale on cloud. Likely efficient with parallel/async workflows and streaming I/O. |
| CrewAI | Emphasizes ease and speed without sacrificing control. Its creators claim it outperforms some lower-level frameworks (with benchmarks vs LangGraph showing faster execution and higher accuracy in certain QA and coding tasks). |
| SuperAgent | Optimized for production deployment – can handle concurrent API calls, and scale multiple agent instances. Not focused on heavy parallel multi-agent computation, but rather on reliable tool use and memory per agent. |
| MetaGPT | Being a heavy multi-agent conversation, it can be resource-intensive and slow (multiple sequential GPT-4 calls). It’s optimized via structured prompts rather than system optimizations. |
| Microsoft AutoGen | With the v0.4 rewrite, AutoGen is optimized for robustness and scale – asynchronous architecture allows parallel operations and more complex coordination without blocking. |
Maturity & Community
The maturity and community support around a framework indicate its stability, longevity, and the resources available to developers. Established frameworks with active communities provide better documentation, more examples, faster bug fixes, and a wealth of shared knowledge. Newer frameworks might offer cutting-edge features but come with more unknowns. Consider both the technical maturity of the codebase and the health of the community when making adoption decisions.
| Framework | Description |
|---|---|
| LangGraph | Newer (2024) but backed by LangChain team; growing adoption via LangChain user base. Community support via LangChain forums/GitHub. |
| AWS Strands | Brand new (May 2025). Open-sourced by AWS with early interest from Accenture, Meta, etc. Backed by AWS with internal teams using it (Q Developer, AWS Glue) as proof of maturity. |
| OpenAI Agents SDK | Launched 2025, quickly popular (10k+ stars). Backed by OpenAI – active community on GitHub and likely official support evolving. Frequent updates and open development on GitHub. |
| Google ADK | Introduced at Google Cloud Next 2025 – relatively new open source (April 2025) but battle-tested internally (powers Google’s Agentspace product). Rapid growth (~8k stars) and backed by Google; active community expected via Google forums and GitHub. |
| CrewAI | Very popular in 2024–2025 (31k+ GitHub stars), indicating a vibrant community. Created by an open-source startup, with a growing user base (over 100k developers took courses on it). |
| SuperAgent | Backed by Y Combinator (W24) and active development. Moderate community (5k+ stars on GitHub). Used in early-stage products and startups for AI assistants. |
| MetaGPT | Emerged mid-2023 from an open-source project that went viral (55k+ stars). Active research backing (ICLR 2025 paper) by DeepWisdom. Large community interest, but real-world adoption is mostly experimental. |
| Microsoft AutoGen | Started in 2023 as a Microsoft Research project, now at version 0.4 (major redesign incorporating community feedback). Active development by MSR (regular blog updates). |
License/Cost
Licensing and cost factors can significantly impact adoption decisions, especially for enterprise deployments. This includes not just the framework’s license itself, but also associated costs like model API usage, infrastructure requirements, and potential enterprise support. Open-source frameworks offer flexibility but may require more internal expertise, while commercial solutions might provide better support but at higher cost. Understanding the total cost of ownership helps align the choice with budget constraints.
| Framework | Description |
|---|---|
| LangGraph | Open source (LangChain license, e.g. MIT); free to use. |
| AWS Strands | Open source (likely AWS Apache 2.0 license); requires AWS account for Bedrock models (paid per use). |
| OpenAI Agents SDK | Open source (MIT); free to use, but OpenAI API calls incur costs. |
| Google ADK | Open source (Apache 2.0); free toolkit (pay for GCP services if used). |
| CrewAI | Open source (MIT). Free to use; hosted enterprise version or support may be offered by CrewAI Inc. |
| SuperAgent | Open source core (MIT); Hosted service has a freemium model. Self-hosting is free; using the official cloud may incur subscription fees. |
| MetaGPT | Open source (MIT). Free to use; requires API keys for OpenAI etc. MGX (MetaGPT X) appears to be a commercial offering layered on it. |
| Microsoft AutoGen | Open source (code under MIT; content under CC-BY-4.0). Free to use; can deploy on any infrastructure (Azure integrations optional). |