In short. An AI agent framework gives an LLM the tools, memory, and control loop to complete multi-step tasks autonomously. Self-hosting one keeps task data on your own infrastructure instead of a SaaS platform. For most developers starting out, LangGraph or CrewAI are the strongest entry points \u2014 LangGraph for production-grade control, CrewAI for the fastest path to a working prototype.<\/p>\n\n\n\n
agency-agents is a fast-growing, MIT-licensed collection of 200+ specialized AI agent persona definitions \u2014 each one a markdown file describing a role, workflow, and deliverables (a frontend specialist, a security reviewer, a growth marketer, and so on). It’s worth being precise about what this actually is: it is not a Python framework or a service you deploy. Installing it means copying the persona files into the config directory of an AI coding tool you already use (Claude Code, Cursor, Codex, and others), after which you can activate any persona in your existing sessions. There’s no server, no Docker container, and nothing to host on a VPS \u2014 the multi-agent behavior happens inside the coding tool itself. Its popularity (100k+ GitHub stars) makes it worth knowing, but pair it with an actual orchestration framework below if you need a standalone, deployable multi-agent system.<\/p>\n\n\n\n
OpenClaw is an open-source, MIT-licensed personal AI assistant that connects to messaging platforms you already use \u2014 WhatsApp, Telegram, Slack, Discord, Signal, and dozens more \u2014 and runs with persistent memory and a skills system for extending what it can do. It has grown extremely fast (multiple sources put it in the 300,000+ GitHub star range) and is genuinely self-hostable via Docker or npm. Contabo fit: a Cloud VPS 8 or a Cloud VDS S if you’re also running local model inference alongside it.<\/p>\n\n\n\n
Built by the LangChain team, LangGraph models an agent’s workflow as a directed graph \u2014 nodes for LLM calls, tool use, or human input, with edges controlling the flow between them, including conditional branching and loops. It’s widely regarded as the most production-grade option here, with built-in state checkpointing, streaming, and human-in-the-loop support for long-running workflows. Written in Python with TypeScript support, fully model-agnostic.<\/p>\n\n\n\n
Microsoft’s original multi-agent framework models agents as participants in a conversation \u2014 a group chat where multiple agents debate, refine, and hand off work to each other, with support for code execution in a sandboxed environment. Worth knowing before you commit to it: Microsoft has moved AutoGen into maintenance mode, consolidating its architecture into the broader Microsoft Agent Framework (which unifies AutoGen and Semantic Kernel, GA as of April 2026). AutoGen and its community-maintained fork, AG2, remain functional and are still the right choice for existing v0.2-style deployments and academic research on conversational multi-agent patterns \u2014 but new Microsoft-stack projects should start on Microsoft Agent Framework instead. Contabo fit: a Cloud VDS M gives comfortable headroom for heavier, multi-round group-chat runs.<\/p>\n\n\n\n
CrewAI models multi-agent collaboration as a “crew”: each agent gets a role, a goal, and a backstory, then a set of tasks to complete together. It consistently gets cited as the fastest framework to go from zero to a working multi-agent prototype, with a large and active community. The trade-off, per developer feedback, is less fine-grained control and observability than LangGraph once a project moves from prototype toward production scale.<\/p>\n\n\n\n
Flowise is an Apache-2.0-licensed, drag-and-drop visual builder sitting on top of the LangChain library \u2014 you assemble an agent from nodes (a model, a prompt, a document loader, a tool) on a canvas rather than writing the orchestration code by hand. It supports MCP connectivity for hooking up external tools and data sources. Self-hosted via Docker, it’s genuinely lightweight for small flows (1\u20132 GB RAM), though heavier document-processing workloads want more headroom. Contabo fit: a Cloud VPS 6 covers typical usage comfortably.<\/p>\n\n\n\n
Dify is a full LLM application platform \u2014 visual workflow editor, a production RAG pipeline, agent capabilities, and support for 100+ model providers, all in one self-hosted stack. It’s the heaviest tool on this list to run: Docker Compose brings up an API server, a Celery background worker, the web frontend, PostgreSQL, Redis, a vector store (Weaviate by default), a code sandbox, and an nginx proxy, needing roughly 4 GB of RAM as a baseline. Contabo fit: a Cloud VPS 8 (24 GB RAM) gives real headroom if you’re also running local embedding models or heavier concurrent workflows alongside the base platform.<\/p>\n\n\n\n
n8n is a mature (seven-plus years in development), low-code workflow automation platform with 400+ integrations and dedicated AI nodes for wiring LLM calls into a larger automation \u2014 pulling a lead from a CRM, scoring it with an LLM, and posting the result to Slack, all in one flow. If the AI step is one part of a bigger operational workflow rather than the whole point, n8n fits better than a pure agent framework. Contabo fit: a Cloud VPS 6.<\/p>\n\n\n\n