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chore(deps): update dependency agentgateway/agentgateway to v1.3.0#29

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chore(deps): update dependency agentgateway/agentgateway to v1.3.0#29
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renovate/agentgateway-agentgateway-1.x

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This PR contains the following updates:

Package Update Change
agentgateway/agentgateway minor 1.1.0 → 1.3.0

Release Notes

agentgateway/agentgateway (agentgateway/agentgateway)

v1.3.0

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🎉 Welcome to the 1.3.0 release of the agentgateway project!

This release is a major step forward for LLM, MCP, and agentic traffic. Agentgateway v1.3.0 adds a purpose-built UI, AI cost analysis, virtual models, reusable providers and guardrails, 13 new LLM providers, richer MCP support, and many improvements across traffic policy, TLS, telemetry, and operations.

Artifacts

Docker images are available:

  • cr.agentgateway.dev/agentgateway:v1.3.0
  • cr.agentgateway.dev/controller:v1.3.0

Helm charts are available:

  • cr.agentgateway.dev/charts/agentgateway:v1.3.0
  • cr.agentgateway.dev/charts/agentgateway-crds:v1.3.0

Binaries are available below.

Quick Start

Follow the Kubernetes or Standalone quick start guide to get started.

🌟 New features

New UI for LLM, MCP, and traffic management

Agentgateway now includes a rebuilt UI organized around three native views:

  • LLM: Models, providers, policies, guardrails, costs, virtual API keys, and analytics.
  • MCP: Servers, tools, resources, authentication, and MCP policy configuration.
  • Traffic: Gateway API traffic configuration and policy management.

The UI includes onboarding for LLM, MCP, and API capabilities, model and provider setup, per-model policies, request and response guardrails, and unified logs for LLM, MCP, and A2A calls. For more information, see the Kubernetes UI observability docs and the LLM and MCP docs.

AI cost and token analysis

Agentgateway can now calculate token usage and dollar cost for LLM requests, attribute usage, and surface the data in logs, traces, metrics, agctl, and the UI.

Cost and token data can be grouped by model, provider, user, team, and client tool. This makes it possible to analyze spend, export reports, build chargeback workflows, and apply policy decisions such as budgets, alerts, quotas, or cost-sensitive routing at the gateway.

For more information, see Kubernetes LLM cost tracking, Standalone LLM spending, and the agctl costs reference.

Virtual models

Virtual models let clients send one model name while agentgateway chooses the real backend model at request time. This moves routing policy out of clients and into the gateway.

Supported strategies include:

  • Weighted routing to split traffic across models for A/B testing, migrations, and cost optimization.
  • Failover routing to automatically retry fallback models when a primary model fails or is rate-limited.
  • Conditional routing to select models with CEL expressions based on request attributes such as headers, user tier, or prompt shape.

For more information, see Standalone virtual models, Kubernetes LLM load balancing, Kubernetes LLM failover, and Kubernetes LLM content routing.

Reusable providers and guardrails

Providers and guardrails can now be defined once and referenced across many models. This simplifies large LLM deployments where many incoming model names share provider configuration, credentials, or policy.

Standalone deployments can also declare shared guardrails as top-level resources instead of repeating guardrail configuration on every route. For more information, see Standalone guardrails, Standalone multi-layer guardrails, and Kubernetes guardrails.

New and improved LLM providers

Agentgateway adds 13 new first-class LLM providers, including Mistral, Hugging Face, and Cohere, along with expanded custom provider support for providers without built-in integrations. For more information, see the Standalone LLM provider docs and Kubernetes LLM provider docs.

Additional LLM gateway improvements include:

  • Rerank request and response support across providers.
  • Custom LLM providers for InferencePool backends.
  • More precise per-model matching, with exact matches preferred.
  • Streaming guardrails for streaming requests.
  • Webhook guardrail failureMode support.
  • Per-model LLM authorization.
  • Local LLM TLS and CORS support.
  • Latency and throughput telemetry attributes on LLM requests.
  • Bedrock detect-passthrough support, Application Inference Profile prompt cache support, Anthropic beta-header allowlists, host override support, URL-encoded model IDs, and reasoning-signature replay.
  • Anthropic system messages and extra-high thinking support.
MCP improvements

MCP support now includes Okta as a first-class authentication provider, MCP-aware external auth and external processing, resource subscribe and unsubscribe support, improved multiplexing behavior, and broader protocol compliance fixes.

The UI also includes native MCP policy views for access control, traffic shaping, and mutation policies such as authorization, CORS, JWT, rate limiting, transformations, and external processing. For more information, see the Kubernetes MCP docs, Standalone MCP docs, MCP authentication, and MCP guardrails.

Request handling and extensibility

Traffic policies can now buffer request bodies before forwarding, giving policies and extensions access to full request bodies before backend selection. For more information, see Kubernetes body buffering and Standalone body buffering.

External processing support is also expanded with richer processing-mode configuration, and external processors can return an immediate response from request-body and response-body phases. For more information, see Kubernetes external processing and Standalone external processing.

CEL and agctl

CEL support is expanded with helpers for URL encode/decode, timestamp conversions, bit operations on bytes, raw JWT token access, gRPC response status, expressions in direct responses, and CEL-based retry conditions. For more information, see the CEL reference.

The agctl CLI now includes proxy and controller log commands, version reporting with mismatch checks, route groups in config output, and evicted-backend visibility.

Operations and observability

Agentgateway now exposes proxy timing measurements, a config-synchronization metric, request and connection IDs for troubleshooting, and richer distributed traces with JSON mode, body snapshots, effective gateway and route policies, and raw-output file opening. For more information, see Kubernetes observability, Kubernetes tracing, Standalone metrics, and Standalone traces.

🪲 Notable fixes

  • Fixed TCP route precedence.
  • Fixed Gateway status handling when no listeners are valid.
  • Fixed route-level OIDC cookie handling.
  • Fixed capacity-weighted load balancing.
  • Fixed backend eviction retries.
  • Fixed streaming-completion capture across Bedrock, Messages, and Responses API paths.
  • Fixed credential-location expression behavior.
  • Fixed scheme handling from X-Forwarded-Proto.
  • Improved MCP multiplexing and list behavior.
  • Improved MCP protocol compliance across tools, prompts, and resources.

What's Changed

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✂ Note

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This PR was generated by Mend Renovate. View the repository job log.

@renovate renovate Bot force-pushed the renovate/agentgateway-agentgateway-1.x branch from 9cbcf6c to e06b3ed Compare June 18, 2026 21:33
@renovate renovate Bot changed the title chore(deps): update dependency agentgateway/agentgateway to v1.2.1 chore(deps): update dependency agentgateway/agentgateway to v1.3.0 Jun 18, 2026
@renovate renovate Bot force-pushed the renovate/agentgateway-agentgateway-1.x branch from e06b3ed to 04034b7 Compare June 19, 2026 12:07
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