AI Tool

AgentGateway Review

AgentGateway is an open-source HTTP and gRPC gateway that manages traditional application traffic alongside AI-native protocols such as MCP and A2A within a single data plane.

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agents

Why it matters

1Unifies HTTP, gRPC, TCP, MCP, and A2A traffic management.

2Supports 13+ LLM providers including OpenAI, Anthropic, and Gemini.

3Offers AI Cost & Analysis for token and dollar cost attribution.

4Donated to the Linux Foundation and accepted as an Agentic AI Foundation (AAIF) project in 2026.

Specs

API Available

Yes, public API

overview

What is AgentGateway?

AgentGateway is a unified AI-native proxy tool developed by Solo.io (donated to the Linux Foundation) that enables organizations to manage and secure communication for AI agent workloads. It functions as a single data plane for traditional HTTP/gRPC traffic and AI-native protocols like Model Context Protocol (MCP) and Agent-to-Agent (A2A) communication, centralizing routing, security, observability, and governance.

features

Key Features of AgentGateway

AgentGateway provides a comprehensive set of features designed for managing and securing AI agent traffic, alongside traditional application protocols. Its architecture, built in Rust, prioritizes performance and reliability for diverse workloads.

Unified Data Plane: Manages HTTP, gRPC, TCP, Model Context Protocol (MCP), and Agent-to-Agent (A2A) traffic.
LLM Gateway Routing: Routes traffic to multiple LLM providers (e.g., OpenAI, Claude, Gemini, self-hosted models) via a unified OpenAI-compatible API, supporting token budgeting, caching, and failover.
MCP Gateway: Provides a governed front door for agents to discover, authenticate, and use external tools via the Model Context Protocol, including role-based access controls (RBAC) and audit logging.
A2A Bridging: Facilitates secure and consistent communication between agents from different frameworks (e.g., LangChain, CrewAI, ADK) using the A2A protocol.
AI Cost & Analysis: Tracks token and dollar costs for LLM requests, providing attribution in logs, traces, metrics, and the UI.
Enterprise AI Governance: Enforces security policies, prompt guardrails, PII masking, and manages agent identities.
Comprehensive Security: Includes mTLS, OIDC, JWT, API Keys, Basic Auth, External Authorization, Request Authorization (RBAC, CEL), MCP Auth, and PII-shield.
Observability: Offers observable by default tracing on every hop, integrating with OpenTelemetry.

use cases

Who Should Use AgentGateway?

AgentGateway is designed for organizations and developers building and deploying AI agentic systems, particularly those requiring robust governance, security, and observability across heterogeneous AI and traditional application environments. It addresses the complexities of managing diverse AI protocols and LLM interactions at scale.

Enterprises deploying AI Agents: For enforcing security policies, prompt guardrails, and PII masking across agent interactions and LLM calls.
Developers building Agentic AI applications: For routing agent-to-agent invocations between frameworks like LangChain, CrewAI, and ADK, and managing Model Context Protocol (MCP) servers.
Organizations utilizing multiple LLM providers: For routing to OpenAI, Claude, Gemini, or self-hosted LLM models with credentialing, failover, and cost control.
Cloud-native environments: For running north–south or east–west service traffic through a single binary, integrating with Kubernetes deployments.

how to use

How to Use AgentGateway

AgentGateway can be deployed as a standalone binary or within Kubernetes environments. Initial setup involves configuring the gateway to manage desired traffic types and integrating with LLM providers or agent frameworks.

1Download and install the AgentGateway binary or deploy via Helm charts to a Kubernetes cluster.
2Configure LLM providers by defining virtual models and guardrails for routing and policy enforcement.
3Set up MCP gateway configurations to manage access and authentication for external tools used by agents.
4Define A2A bridging rules for secure communication between different agent frameworks.
5Implement security policies using mTLS, OIDC, JWT, or external authorization services.
6Monitor traffic, costs, and agent behavior through integrated observability features and the dedicated UI.

pricing

AgentGateway Pricing & Plans

AgentGateway is an open-source project, with its core functionality available for free. Paid enterprise offerings, which include additional support and features, are available through cloud marketplaces such as AWS Marketplace, Microsoft Azure Marketplace, and Google Cloud Marketplace. Specific pricing for these enterprise distributions is not publicly detailed and typically involves usage-based or subscription models tailored to organizational needs.

Open-Source Core: Free to use and deploy.
Enterprise Distributions: Available via AWS Marketplace, Azure Marketplace, and Google Cloud Marketplace (pricing varies by provider and usage).

Pros

+Unified data plane for traditional HTTP/gRPC and AI-native protocols (MCP, A2A).
+Comprehensive LLM gateway with routing, caching, failover, and cost attribution for 13+ providers.
+Robust security features including mTLS, OIDC, PII masking, and role-based access controls.
+Detailed observability with tracing on every hop and OpenTelemetry integration.
+Open-source project under the Linux Foundation, fostering neutral governance and reducing vendor lock-in.
+Built in Rust for high performance and reliability in managing diverse traffic types.

Cons

−Relative youth of the project (launched April 2026, though Linux Foundation donation was earlier) may imply evolving documentation.
−Initial setup documentation can be perceived as overwhelming for new users.
−Specific pricing for enterprise cloud marketplace offerings is not publicly transparent.
−Requires familiarity with cloud-native concepts and potentially Kubernetes for full utilization.

Similar Tools

AgentGateway vs Competitors

AgentGateway differentiates itself from traditional API gateways by offering native, out-of-the-box support for AI-specific protocols and workloads, providing a unified data plane for both AI and conventional traffic.

Kong Gateway↗

A highly extensible, open-source API Gateway that provides routing, load balancing, authentication, and traffic management for microservices and APIs.

Kong Gateway is a robust general-purpose API gateway for HTTP and gRPC traffic. While it can be extended with custom plugins to handle AI-specific routing or protocols, it lacks Agentgateway's out-of-the-box support for AI-native protocols like MCP/A2A and dedicated LLM gateway features.

Apache APISIXOn Stork

A dynamic, real-time, high-performance API gateway that provides rich traffic management features, security, and observability for APIs and microservices.

Apache APISIX offers similar general-purpose API gateway capabilities to Kong for HTTP and gRPC. Like Kong, it would require custom development or plugins to replicate Agentgateway's specialized handling of AI-native protocols (MCP/A2A) and dedicated LLM routing features.

Envoy ProxyOn Stork

A high-performance open-source edge and service proxy designed for cloud-native applications, offering advanced load balancing, traffic management, and observability features.

Envoy is a foundational proxy that provides extreme flexibility for building custom gateways. However, it requires significant configuration and development effort to achieve the 'unified data plane' and out-of-the-box AI-native protocol support that Agentgateway offers. It's more of a building block than a ready-to-use solution for AI traffic.

See every agentgateway alternative, compared→