AgentGateway is an open-source, AI-native proxy and gateway for routing, observing, and governing traffic to and from AI agents, LLM providers, and MCP servers. Built on the A2A and MCP protocols, it provides a unified gateway for LLM consumption, MCP tool federation, agent-to-agent communication, security, and observability. AgentGateway supports multi-provider LLM routing across OpenAI, Anthropic, Google Gemini, AWS Bedrock, and Azure OpenAI with built-in RBAC, JWT authentication, rate limiting, and OpenTelemetry integration.
AgentGateway publishes 6 APIs on the APIs.io network, including Config API, Debug API, Lifecycle API, and 3 more. Tagged areas include AI Gateway, API Gateway, MCP, LLM, and Agent-to-Agent.
The AgentGateway catalog on APIs.io includes 1 JSON-LD context and 1 Spectral governance ruleset.
AgentGateway’s developer surface includes API reference, code examples, CLI, authentication, changelog, sandbox, developer portal, and 43 more developer resources.
Open Source Surface applies to this provider. This product is open source and we
read its repository directly, so Open Source Surface carries
10 points of the composite. It is scored from what the repository actually
publishes — a security policy, a contribution guide, a release history, a code of conduct — read live from the
provider rather than inferred from our own catalog pointers.
This facet adds; nothing was taken away to make room for it. An open-source project is not excused from
the commercial facets, because exemption would strip it of the points it does earn.
If we have the wrong repository, or this product is not open source, say so on your
provider repo and we
will drop the facet rather than have you publish against it.
Create-or-Update Ergonomics applies to this provider. This API accepts writes, so it
carries 10 points of the composite. It is scored from the published contracts
themselves: whether a caller can create-or-update in one call, whether the write accepts a key the caller already
holds, and whether the response says which branch ran. Without that, every write needs a search-and-branch in
front of it, and the first time that check is skipped a duplicate record is created.
Scored against the observed mean rather than raw — a provider at the catalog average is unchanged by this facet,
not penalised by it.
The six quality facets above are damped to 80 points between them,
because the conditional facet above carries the other
20. That is why each facet's contribution is shown against a damped
maximum: raising a quality facet moves the composite by 80% of its nominal
weight, not 100%. The full arithmetic is at apis.io/rating/.
Improve this rating by publishing the missing artifacts — every area above can be raised, and the full rubric is at apis.io/rating/. Every facet and dimension name above is a link: it opens that measurement's own page — what it means, the exact checks that feed it, how the whole catalog distributes on it, and the providers at the top of it. This rating is computed from github.com/api-evangelist/agentgateway: open an issue to ask a question, or submit a pull request to add artifacts.
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Routes traffic to OpenAI, Anthropic, Google Gemini, AWS Bedrock, and Azure OpenAI through a unified API with model aliasing, failover, and load balancing.
MCP Gateway
Connects LLMs to tools via Model Context Protocol with static and dynamic routing, tool federation, and stateful MCP sessions.
Agent-to-Agent (A2A) Gateway
Enables secure, governed communication between AI agents using the A2A protocol for multi-agent orchestration.
Inference Routing
Intelligently routes requests to self-hosted models based on GPU utilization and request priority.
Security and Authentication
Provides JWT, OAuth2, API key management, CORS, CSRF protection, MCP authentication, and external authorization support.
Traffic Management
Supports request routing and matching, header manipulation, rate limiting, retries, gRPC routing, traffic splitting, and direct responses.
Observability
Integrates with OpenTelemetry for metrics, traces, and access logging with a built-in Admin UI and debugging tools.
Guardrails
Applies prompt guards, content filtering, regex filters, moderation policies, and custom webhooks for AI safety.
Cost Controls
Tracks budget and spend limits per user, team, or application with RBAC-based controls on LLM consumption.
Prompt Enrichment
Supports prompt templates and enrichment for standardizing and augmenting requests before routing to LLM providers.
Route requests across multiple LLM providers with a single API, enabling failover, load balancing, and cost optimization without changing client code.
MCP Tool Federation
Aggregate tools from multiple MCP servers behind a single gateway endpoint, enabling agents to discover and invoke tools from any connected MCP server.
Enterprise AI Governance
Apply organization-wide security policies, rate limits, budget controls, and content filters to all AI agent traffic through a centralized gateway.
REST API to MCP Conversion
Convert existing REST APIs into MCP-native tool endpoints that AI agents can discover and invoke through the Model Context Protocol.
Multi-Agent Orchestration
Enable secure agent-to-agent communication using the A2A protocol, allowing specialized agents to delegate tasks to each other through the gateway.
Observability and Debugging
Collect unified telemetry across all AI agent and LLM interactions to monitor cost, latency, and behavior at scale.
Discovery needs no key. Ratings and market analysis are Pro.
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Kin Score
How this is scored →