--- title: "The Agentic Framework Shortlist" newsletter: "MLOps Community" date: 2025-09-09 source: https://aaif.live/newsletters/mlopscommunity/2025-09-09-the-agentic-framework-shortlist --- # The Agentic Framework Shortlist *10 options every dev should know in 2025* *MLOps Community — Agentic AI Foundation, 2025-09-09* Lots of chatter about Nano Banana, but Mistral hitting €10B [https://www.reuters.com/world/europe/asml-becomes-mistral-ais-top-shareholder-after-leading-latest-funding-round-2025-09-07/] suggests the most creative AI output right now is in the valuation spreadsheets. ## by TOP 10 FRAMEWORKS FOR MULTI-AGENT & AGENTIC AI SYSTEMS IN 2025 by Rohit Ghumare [https://www.linkedin.com/in/rohit-ghumare/] From automating research to orchestrating collaborative AI “crews,” the multi-agent paradigm is redefining artificial intelligence. As businesses and developers look for the best tools to build, scale, and maintain these systems, here are 10 frameworks that lead the way. 1. MOTIA Polyglot backend framework that unifies API, Background Jobs & AI Agents Motia is a backend framework that brings APIs, background jobs, workflows, and AI agents into a single event-driven system. It supports multiple languages (TypeScript, Python, and more), with state management, observability, and deployment built in. * Unified design: APIs, jobs, workflows, and agents run together in one environment. * Language flexibility: Mix Steps written in different languages within the same project. * Observability: Built-in tracing, logging, and data visibility for each workflow. * Event-driven workflows: Processes are connected through events with state handled automatically. Best for: Engineers building scalable event-driven systems who want to avoid juggling multiple frameworks and libraries. Link: https://github.com/MotiaDev/motia 2. Agno Build Multi-Agent Systems with memory, knowledge and reasoning. Agno is Python-first and designed for flexible, memory-rich agentic architectures that integrate with more LLMs and data sources than any other. * Model-Agnostic: Works with 23+ LLM providers, plug-and-play with major vector stores. * Multi-Modal: Supports text, image, audio, video making it truly versatile for agent input/output. * Built-in Memory: From instant recall to long-term agentic knowledge bases, plus web scraping and RAG (retrieval-augmented generation) out-of-the-box. * Transparent Reasoning: Fine-grained traces and reasoning visibility for debugging and auditability. Best for: Rapid prototyping of agents that learn, adapt, access the live web, and coordinate across modalities. Link: https://github.com/agno-agi/agno 3. PYDANTIC AI Type-Safe Agent Systems with Guaranteed Output Reliability From the creators of FastAPI, Pydantic AI brings Python’s renowned data validation to multi-agent LLM workflows. * Structured Validation: Every agent output is guaranteed to match a strict schema, no more JSON market or unexpected surprises. * Async & Modular: Works natively with async Python flows, supporting delegation and graph-based hand-off between multiple agents. Great for: Regulated industries, research assistants, enterprise workflows where data integrity is a must. Link: https://github.com/pydantic/pydantic 4. Xpanderai Enterprise-Ready Agentic Interface Builder Xpanderai is designed to meet large-scale, enterprise “AI workforce” needs. * Agent Graphs: Automatically creates agentic workflows that connect to internal APIs, VPCs, webhooks, automation tasks, and private data. * Gateways & Interfaces: Run AI agents securely inside cloud, on-prem, or hybrid setups, maintaining complete data sovereignty. * Zero-Effort Deployment: Engineers simply describe the integration and goals, Xpanderai handles the rest. Best for: Organizations looking to deploy custom, secure, multi-agent AI “employees” on-premises or at scale.# Link: https://github.com/xpander-ai/xpander.ai 5. LangChain The Generalist’s Modular Chain for LLM Apps LangChain remains the most popular “Swiss Army knife” for LLM-centric agents and pipelines. * Vast Ecosystem: Works with OpenAI, Anthropic, HuggingFace, Ollama, Groq, and many others. * Chain-Oriented: Quickly set up memory, tool usage, and reasoning flows with simple primitives. * Flexible but Manual: Great for both single and multi-agent logic, but multi-agent orchestration requires careful configuration. Best for: Building tool-using LLM agents, document Q&A bots, and all-purpose assistant workflows. Link: https://github.com/langchain-ai/langchain 6. CrewAI Role-Driven Collaboration Framework CrewAI brings agentic teamwork to the foreground. * Role-Based Design: Mimics human teams (researcher, writer, reviewer), assigning each agent a distinct workflow role. * Rapid Deployment: Minimal code to set up new “crews.” * Internal Messaging: Agents communicate, pass results, and self-coordinate under a crew “director.” * Popular Uses: Content generation, research automation, market analysis, and modular agentic teams. Best for: Any scenario where task division and agent collaboration are more natural than single-large-agent approaches. Link: https://github.com/crewAIInc/crewAI 7. AUTOAGENT Zero-Code, Fully Automated Multi-Agent Creation AutoAgent is the revolution for non-programmers: design multi-agent systems with natural language only. * No Coding Needed: Define agents, workflows, and vector databases through plain English. * Native RAG: Agentic retrieval, function-calling, and even self-improving workflows all included. * Universal LLM Support: Out-performs many popular solutions and supports all major LLMs, acting as a self-developing agent OS. Best for: Teams looking for fast deployment without developer bottlenecks, or enterprises seeking open-source alternatives to expensive research agents. Link: https://github.com/HKUDS/AutoAgent Best for: Advanced users building detailed, auditable, or research-grade orchestration of many agent types. Link: https://github.com/langchain-ai/langgraph 8. LANGGRAPH Graph-Based Orchestration for Complex Agent Workflows Going beyond linear chains, LangGraph introduces graph-based, DAG-style agent orchestration. * Explicit Multi-Agent Coordination: Each agent is a node, flows are directed, perfect for intricate processes and transparency. * Open Source (with some proprietary add-ons): Enables custom RAG, specialized agent flows, and structured data movement. 9. MASTRA The Lightweight, Flexible Team Agent Framework Mastra is designed for teams prioritizing speed, experimentation, and memory-efficient agentic workflows. * Plug-in Architecture: Agents pick their own tools, memory, and capabilities. * Fast Setup: Get a testable multi-agent prototype running in minutes. * Community Focused: Rapid development tied to open-source best practices. Best for: Startups or research labs taking ideas from experiment to launch without heavy overhead. Link: https://github.com/mastra-ai/mastra 10. AUTOGEN Microsoft’s Adaptive Multi-Agent Orchestrator AutoGen from Microsoft brings robust, event-driven architecture to collaborative AI agents. * Event Loop Messaging: Multiple agents converse, reflect, and act together on complex workflows. * Enterprise-Grade: Scales effortlessly for big data, process coordination, and research pipelines. * Educational Leader: Heavily used in academic and developer onboarding. Best for: Enterprises or teams building data science, automation, or collaborative research tools, needing adaptive, highly coordinated multi-agent logic. Link: https://github.com/microsoft/autogen Closing Thoughts: Choosing Your Agentic Future The world of agentic AI frameworks is more exciting, and more varied than ever. Whether you’re a solo developer, a product-driven team, or the AI lead at an enterprise, the choice depends on your need for: * Visual vs. code-first workflows * Live web reasoning & multi-modality * Data validation and reliability * No-code accessibility * Enterprise scalability Explore these frameworks. Prototype quickly. * Motia: [https://github.com/MotiaDev/motia] Event-driven orchestration across APIs, jobs, workflows, and agents. * Agno: [https://github.com/agno-agi/agno] Multi-modal, memory-first agentic systems. * Pydantic AI: [https://github.com/pydantic/pydantic] Rigorously type-safe agent workflows. * Xpanderai: [https://github.com/xpander-ai/xpander.ai] Enterprise-scale agent interface building. * LangChain: [https://github.com/langchain-ai/langchain]Modular chains for highly-customizable LLM agents. * CrewAI: [https://github.com/crewAIInc/crewAI]Collaboration-first, role-based agent teams. * AutoAgent: [https://github.com/HKUDS/AutoAgent]No-code agent system creation. * LangGraph: [https://github.com/langchain-ai/langgraph] Directed-graph orchestration for detailed, auditable flows. * Mastra: [https://github.com/mastra-ai/mastra] Lightweight, flexible prototyping. * AutoGen: [https://github.com/microsoft/autogen]Adaptive orchestration for collaborative agents. Interested in partnering with us? Get in touch: partners@mlops.community Thanks for reading. See you in Slack [https://go.mlops.community/slack], YouTube [https://www.youtube.com/channel/UCG6qpjVnBTTT8wLGBygANOQ?view_as=subscriber], and podcast [https://home.mlops.community/public/content/] land. Oh yeah, and we are also on X [https://twitter.com/mlopscommunity] and LinkedIn [https://go.mlops.community/linkedin]. Thanks again to Rohit Ghumare [https://www.linkedin.com/in/rohit-ghumare/] for his contribution. --- Source: https://aaif.live/newsletters/mlopscommunity/2025-09-09-the-agentic-framework-shortlist