Multi-Agent

The A2A (Agent2Agent) protocol lets you connect a CrewAI agent to a LangGraph agent — or any other compliant framework — over a standard HTTP interface, with no custom glue code. Setup takes about 15 minutes once your CrewAI environment is running. What Is the A2A Protocol? The A2A (Agent2Agent) protocol is an open HTTP-based standard that defines how AI agents from different frameworks discover each other, exchange tasks, and stream results — without requiring framework-specific integration code. Originally developed by Google and donated to the Linux Foundation in early 2026, A2A is now a vendor-neutral specification backed by Anthropic, Microsoft, Salesforce, and over 50 other organizations. Think of it as the HTTP of multi-agent systems: just as HTTP lets any browser talk to any web server regardless of their underlying technology, A2A lets any compliant agent talk to any other. The protocol uses JSON-RPC 2.0 over HTTPS, supports server-sent events for streaming, and mandates an /.well-known/agent.json discovery endpoint so agents can advertise their capabilities. CrewAI adopted A2A as a first-class feature in version 0.80, making it possible to delegate tasks from a CrewAI crew to a LangGraph graph, a Semantic Kernel agent, or a custom Python service — all with a single configuration block. For teams building composite AI systems in 2026, A2A removes the biggest integration pain point: the need to write and maintain bespoke adapter layers every time you add a new agent framework. ...

AG2 (formerly Microsoft AutoGen, now maintained by the ag2ai community) is a Python framework for building multi-agent AI systems where multiple LLM-powered agents collaborate, debate, and execute tasks autonomously. The v0.4 rewrite introduced an async-first, event-driven architecture that makes AG2 one of the most capable frameworks for complex conversational agent pipelines in 2026. What Is AG2 (AutoGen v0.4) and Why It Matters in 2026 AG2 is an open-source Python framework that enables developers to build networks of LLM-powered agents that communicate with each other through structured message passing to solve complex tasks collaboratively. Originally released as Microsoft AutoGen, the project transitioned to the independent ag2ai organization in November 2024 with over 54,000 GitHub stars and millions of cumulative downloads. The v0.4 release was a complete architectural redesign — not an incremental update — focused on async-first execution, improved code quality, robustness, and scalability for production workloads. In 2026, AG2 powers document review pipelines at enterprise scale, code generation workflows in CI/CD systems, and research automation for data teams. The framework supports Python 3.10 through 3.13 and integrates with OpenAI, Anthropic, Google Gemini, Alibaba DashScope, and local models via Ollama. What makes AG2 distinctive is its conversation-centric model: agents don’t just call tools — they argue, critique, refine, and reach consensus through structured dialogue, which is fundamentally different from how LangGraph or CrewAI approach orchestration. ...

CrewAI is a Python framework for building multi-agent AI systems where each agent has a defined role, goal, and backstory — and agents collaborate to complete complex tasks. Install it with pip install crewai, define agents and tasks in YAML files, then wire them together with a Python class. As of April 2026, CrewAI has 49k GitHub stars and over 14,800 monthly searches, making it the fastest-growing multi-agent framework available. ...