Best 5 Frameworks To Build Multi-Agent AI Applications
Blog post from Stream
The article provides an overview of building AI agents using memory, knowledgebases, tools, and reasoning, and highlights the use of command line interfaces and agent UIs for interaction. AI agents, powered by large language models (LLMs), automate tasks like online product ordering and restaurant reservations. The text explores various frameworks such as Agno, OpenAI Swarm, CrewAI, Autogen, and LangGraph that facilitate the development of these agents. These frameworks offer features like built-in memory, custom tool integration, and streamlined deployment processes, significantly reducing engineering challenges and accelerating development. The article also discusses the enterprise applications of multi-agent systems in areas such as call analytics, travel management, and conversational banking, while addressing limitations like cost, quality, latency, and safety concerns. Detailed examples illustrate the implementation of AI agents using Python and various frameworks, emphasizing the ease of creating both basic and advanced multi-agent systems.
| Trend | Post Mentions | Total Month Mentions | Posts | Companies | MoM |
|---|---|---|---|---|---|
| AI Agents | 22 | 4,711 | 786 | 221 | +28% |
| LLM | 11 | 5,048 | 855 | 225 | +5% |
| Multi-agent systems | 4 | 338 | 121 | 62 | +27% |
| AI Coding Assistant | 2 | 1,030 | 241 | 100 | -2% |
| Real-time | 2 | 5,379 | 1,225 | 279 | -24% |
| AI Model Fine-tuning | 1 | 470 | 151 | 72 | -14% |
| Vector Search | 1 | 1,541 | 318 | 153 | -17% |
Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.