4 Integrations with Agent2Agent (A2A)
View a list of Agent2Agent (A2A) integrations and software that integrates with Agent2Agent (A2A) below. Compare the best Agent2Agent (A2A) integrations as well as features, ratings, user reviews, and pricing of software that integrates with Agent2Agent (A2A). Here are the current Agent2Agent (A2A) integrations in 2026:
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Model Context Protocol (MCP)
Anthropic
Model Context Protocol (MCP) is an open protocol designed to standardize how applications provide context to large language models (LLMs). It acts as a universal connector, similar to a USB-C port, allowing LLMs to seamlessly integrate with various data sources and tools. MCP supports a client-server architecture, enabling programs (clients) to interact with lightweight servers that expose specific capabilities. With growing pre-built integrations and flexibility to switch between LLM vendors, MCP helps users build complex workflows and AI agents while ensuring secure data management within their infrastructure.Starting Price: Free -
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Agent Development Kit (ADK)
Google
The Agent Development Kit (ADK) is a flexible, open-source framework for building and deploying AI agents. It is tightly integrated with Google’s ecosystem, including Gemini models, and supports popular large language models (LLMs). ADK simplifies the development of both simple and complex AI agents, providing a structured environment for building dynamic workflows and multi-agent systems. With built-in tools for orchestration, deployment, and evaluation, ADK helps developers create scalable, modular AI solutions that can be easily deployed on platforms like Vertex AI or Cloud Run.Starting Price: Free -
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Google’s Agent Payments Protocol (AP2) is an open protocol designed together with over 60 payments, fintech, and tech companies (e.g., Mastercard, PayPal, Adyen, Coinbase, Etsy) to enable secure, agent-led transactions across platforms. It builds on earlier open standards like Agent2Agent (A2A) and the Model Context Protocol (MCP) to ensure that when an AI agent initiates or completes a payment on behalf of a user, three core requirements are met: authorization (proving the user explicitly gave permission for that specific purchase), authenticity (ensuring the agent’s intended purchase matches what the user meant), and accountability (clear audit trails and responsibility in case of errors or fraud). The protocol uses mandates, which are cryptographically signed digital contracts backed by verifiable credentials.
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Redpanda Agentic Data Plane
Redpanda Data
Redpanda is an enterprise data streaming platform designed to make AI agents safe, governed, and effective across all organizational data. Its Agentic Data Plane connects agents to data sources across cloud, on-prem, and hybrid environments without creating risk or chaos. Redpanda unifies live data streams and historical data into a single, queryable layer. Built-in governance ensures every agent action is authorized, logged, and auditable. The platform enables agents to retrieve exactly the data they need with full context. Redpanda records and replays all agent activity for transparency and debugging. It helps enterprises move from experimental AI to production-ready agentic systems.
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