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gRPC and RPC Frameworks

20 posts · Mar 2010 – Aug 2026

What this trail covers

gRPC continues to evolve as a robust RPC framework, with ongoing development and support for various platforms and languages. This includes advancements in language-specific implementations like Kotlin for Android and Cloud, as well as broader improvements to the gRPC ecosystem. The beta release of gRPC opens the door for its use in production environments, signifying a move towards wider adoption and stability. The Apigee Extension Processor now enables Apigee's API management capabilities to be extended with custom logic, and Gemini Code Assist is now generally available within Apigee API Management, offering AI-assisted API development through natural language prompts, iterative design, duplicate API detection, and enhanced enterprise context.

All Google trails
  1. A unified API for AI model routing- Google Developers BlogIntroduces model routing in Google Cloud API Gateway (Public Preview) to dynamically route OpenAI-compatible requests to Gemini, Claude, or OpenAI OSS-GPT models. Configuration is done via OpenAPI 3.x specifications using the `x-google-api-management` extension, defining backend models and routing rules. Demonstrates a curl example for sending requests to the configured gateway.
  2. Apigee Operator for Kubernetes and GKE Inference Gateway integration for Auth and AI/LLM policies- Google Developers BlogThis post details the integration of the Apigee Operator for Kubernetes with the GKE Inference Gateway. This integration leverages the GCPTrafficExtension resource to enable Apigee to enforce API governance policies (e.g., API keys, quotas, rate limiting, OpenAPI spec validation) on AI inference workloads served through GKE Inference Gateway. It also highlights the GKE Inference Gateway's features for optimized load balancing, dynamic LoRA fine-tuned model serving, and integrated AI safety with Model Armor.
  3. The agentic experience: Is MCP the right tool for your AI future?- Google Developers BlogThis post introduces an open-source example of an MCP server built with Apigee, demonstrating how to secure, scale, and govern AI workloads. It highlights the use of Apigee's API Products for authentication and authorization of AI agents and MCP tools, bridging the gap between managed APIs and AI interactions. The example shows how to expose APIs as tools for AI agents by leveraging API Products, making them 'AI Products' with their own consumers and developers.
  4. Enterprise truth in action: Apigee API hub fueling powerful Developer Portals- Google Developers BlogThis post introduces the Apigee API hub as a central repository for all APIs within an enterprise, serving as the 'source of truth' for governance, consistency, and AI agent integration. It contrasts this with Developer Portals, which act as curated storefronts for API consumers. The post emphasizes how the API hub's comprehensive metadata empowers Developer Portals and is essential for agentic strategies.
  5. Gemini Code Assist in Apigee API Management now generally available- Google Developers BlogIntroduces Gemini Code Assist for Apigee API Management, providing AI-assisted API specification generation and iteration using natural language prompts. Highlights features like AI-generated spec summaries, iterative design, enhanced enterprise context for consistency, duplicate API detection, and enterprise-grade security. Details a streamlined development workflow from creation to implementation, and provides getting started instructions for VS Code users.
  7. Google Cloud announces general availability of APIM Operator for Apigee- Google Developers BlogThe Apigee APIM Operator is now generally available, bringing API Management and API Gateway capabilities to GKE environments. This operator allows for API management using Kubernetes-like YAML configurations, reducing friction for developers and administrators. It supports policy management with RBAC and integrates with Apigee Hybrid instances via traffic extensions. Future work will focus on gRPC and GraphQL support, and addressing resource limitations.
  8. Apigee announces general availability of APIM Extension Processor- Google Developers BlogThis post introduces the General Availability (GA) of the Apigee Extension Processor (version 1.0), a traffic extension that allows Apigee's API management policies to be applied to gRPC streaming traffic. It addresses the challenge of Apigee not natively supporting gRPC streaming in its inline proxy mode. The solution involves using an Application Load Balancer (ALB) as the Policy Enforcement Point (PEP) and the Apigee Extension Processor (acting as a PDP) via a Service Extension. This enables policy enforcement on gRPC bidirectional streaming, Server-Sent Events (SSE), and secure access to Google Cloud services like Bigtable and Vertex AI, all while routing traffic to backends like Cloud Run. The post details the infrastructure and data flow, highlighting how the ALB calls out to Apigee for policy decisions before forwarding the request to the backend, and how responses are handled similarly. A use case is presented for securing and managing gRPC streaming services on Cloud Run with Apigee.
  9. Now in Developer Preview: Enhancing Chat with the Google Chat API- Google Developers BlogThe Google Chat API has been updated to allow Chat apps to create spaces and memberships using their own application identity, rather than on behalf of a user. This enables more autonomous and sophisticated Chat app functionalities, such as automatically creating incident management spaces when an issue is detected and subsequently adding relevant personnel. New OAuth scopes for `chat.app.spaces.create`, `chat.app.spaces`, `chat.app.delete`, and `chat.app.memberships` have been introduced to facilitate this capability.
  10. Google Chat APIs now generally available to all Workspace developers- Google Developers BlogThe Google Chat API, previously in Developer Preview for creating spaces and adding members, is now generally available to all Workspace developers. This includes APIs for managing messages, reactions, and attachments. Additionally, a new feature for importing user data into Google Chat is available in Developer Preview, allowing for the migration of existing messages, attachments, reactions, and memberships using "import mode" spaces that preserve historical timestamps and context.
  11. Announcing gRPC Kotlin 1.0 for Android and Cloud- Google Developers BlogThis post announces the stable release of gRPC Kotlin 1.0, providing a native Kotlin API for gRPC on Android and Cloud platforms. It highlights the benefits of using Kotlin with gRPC, such as improved developer productivity and type safety, and details the features and improvements included in this stable release.
  12. Discontinuing support for JSON-RPC and global HTTP batch endpoints- Google Developers BlogThis post announces the discontinuation of support for JSON-RPC and global HTTP batch endpoints for Google APIs. It advises developers to migrate to gRPC for new integrations and to utilize the client libraries for existing integrations. The deprecation is scheduled to take effect on July 13, 2021, with a grace period for migration. This change aims to streamline API access and promote the use of more performant and feature-rich communication protocols.
  13. Developing bots for Hangouts Chat- Google Developers BlogThis post introduces the ability for developers to build bots for Hangouts Chat. It details how to use the Hangouts Chat API to create bots that can interact with users and perform automated tasks within the chat environment. The post covers the basic concepts of bot development, including handling messages, responding to events, and integrating with other services. It highlights the use of webhooks and the API's event-driven nature to facilitate bot functionality.
  14. Using field masks with update requests to Google APIs- Google Developers BlogThis post introduces the concept and implementation of field masks for update requests in Google APIs. It explains how field masks allow for partial updates by specifying which fields should be modified, thereby improving efficiency and reducing bandwidth usage. The post provides examples of how to use field masks with common Google APIs and discusses the benefits for developers in terms of control and performance.
  15. Using field masks with Google APIs for partial response- Google Developers BlogThis post introduces and explains the concept of field masks in Google APIs. It details how field masks enable clients to request only specific fields from a resource, thereby reducing the amount of data transferred over the network. The post provides examples of how to use field masks with the Google APIs Client Library for Python and explains the benefits, such as improved performance and reduced bandwidth consumption, particularly for APIs that return large resources.
  16. gRPC releases Beta, opening door for use in production environments- Google Developers BlogThis post announces the beta release of gRPC, marking a significant milestone towards its production readiness. It highlights the framework's readiness for production environments and its potential for wider adoption.
  17. Introducing gRPC, a new open source HTTP/2 RPC Framework- Google Developers BlogThis post announces the introduction of gRPC, a new open-source RPC framework built on HTTP/2. It highlights gRPC's advantages over traditional REST APIs, including its performance, efficiency, and support for features like streaming and bidirectional communication. The post details the core components of gRPC, such as Protocol Buffers for defining service interfaces and messages, and the underlying HTTP/2 transport. It also provides guidance on getting started with gRPC for various programming languages.
  18. Making APIs Faster: Introducing Partial Response and Partial Update- Google Developers BlogIntroduced Partial Response and Partial Update features for APIs. Partial Response allows clients to specify which fields they want to receive in an API response, reducing payload size and improving performance. Partial Update allows clients to send only the fields they wish to modify in an API request, further optimizing bandwidth and processing.