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Build the gateway as a small HTTP boundary around a separate agent-turn orchestrator: Axum accepts the request, Tokio runs the work, a provider adapter translates model traffic, and a stable event format carries progress to the client. For a server-pushed response, stream those events over Server-Sent Events (SSE). The key design work is not just connecting the pieces; it is defining tool permissions, backpressure, timeouts, and cancellation before the gateway is exposed to real traffic.
Choose the gateway contract before writing handlers
Decide what clients send, what they receive, and how a turn ends before tying the implementation to a model provider. An example route is POST /v1/agent/stream; that is a design choice, not a required standard. Axum provides routing, request extractors, typed JSON handling, and response construction. Its project describes it as an HTTP routing and request-handling library focused on ergonomics and modularity: Axum project documentation.
Define a gateway-owned event vocabulary—such as turn started, text delta, tool requested, tool completed, error, and turn finished—and map provider-specific messages into it. That separation lets clients depend on your contract rather than a provider’s exact stream format. Keep provider adapters responsible for translating requests, parsing chunks, and representing tool calls. A Rust crate example translates Anthropic Messages requests and responses, including SSE and tool calls, to and from an OpenAI-compatible upstream; it demonstrates an adapter approach, not complete equivalence between protocols: adapter example.
The Tool Desk
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Keep the route handler short. It should validate the incoming request, create a bounded Tokio channel, launch the turn orchestrator, and return a streaming response backed by the channel receiver. The orchestrator calls the provider, converts stream chunks to gateway events, and, if the model requests an authorized tool, executes it and sends the result back into the model loop.
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Organize code by responsibility
A practical starting layout is gateway for routes and request/response types, agent for orchestration, streaming for event definitions and SSE conversion, and state for persistence. Put provider-specific wire handling behind separate adapters if more than one upstream is supported.
Common dependencies in this design include Axum, Tokio, Reqwest, Serde, Tower and tower-http, tracing, tokio-stream, and tokio-util; Redis is relevant only when shared state or cross-instance fan-out is needed. Treat dependency lists in tutorials as examples: choose crate versions and features compatible with the Axum release you actually use. Axum’s README identifies its released branch as 0.8.x, its main branch as work toward 0.9, and lists MSRV 1.80: Axum README.
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Keep reusable dependencies in application state
Store the reusable HTTP client, provider configuration, and state backend in shared application state rather than constructing them for every request. Keep credentials out of source code and redact them from debug output, errors, logs, and traces. A secret wrapper with a redacted Debug representation helps prevent accidental disclosure.
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Stream events with a transport suited to the turn
SSE is a natural fit when the server pushes progress and generated content to a client over HTTP. Map the receiver side of the Tokio channel into an SSE response, and define the event names and payload schema as part of the gateway contract. Account for the fact that SSE keeps a connection open: proxy and load-balancer idle timeouts must accommodate it, and the server needs a clear policy for clients that stop reading or disconnect.
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Use WebSockets as a candidate when the client needs ongoing messages in both directions on the same connection. If clients submit a request for each turn and only need server-to-client updates while that turn runs, SSE is simpler. This is a transport decision based on interaction shape, not a universal performance rule.
Bound work and cancel it when clients leave
A bounded channel prevents an unresponsive reader from causing an unlimited event backlog. When its buffer fills, sends wait or fail according to the API and policy you choose; that makes slow consumption visible as backpressure. A dropped receiver can also be used as a signal to stop the turn, but cancellation must be wired through provider requests and child tool tasks rather than assumed to happen automatically.
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- Set explicit limits for concurrent requests and channel capacity.
- Use separate, deliberate timeouts for the overall request, upstream model calls, and tool execution.
- Propagate disconnect cancellation to provider work and spawned tools; avoid orphaned work after a client goes away.
- Return structured errors for provider and tool failures, and define whether an error terminates the stream or is represented as a gateway event.
Tool execution is an authorization boundary, especially for tools with external side effects. Decide which tools a caller or agent may invoke, validate arguments, and constrain execution duration and privileges. A model’s request to use a tool is not itself permission to run it.
Choose state and fan-out for the deployment
A single-process gateway can keep state in memory and use Tokio channels without adding a network dependency. For multiple instances that need shared conversation state or cross-instance pub/sub fan-out, Redis is one option; it adds an operational dependency and should be introduced for a real deployment requirement, not by default. The implementation tutorial uses Redis with conversation-state TTL and pub/sub: SitePoint’s Rust gateway tutorial.
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Make readiness reflect dependencies the instance needs to serve traffic. If Redis is required for a particular deployment, readiness should account for its availability; liveness should answer the separate question of whether the process itself is functioning.
Add security, middleware, and operational visibility
An upstream model API key is not client authentication. If the gateway is reachable beyond a trusted local environment, authenticate callers and authorize what they may do. Axum works with Tower and tower-http middleware for concerns such as timeouts and tracing; apply middleware and service limits deliberately rather than treating a working route as production-ready.
- Keep secrets in environment-based or dedicated secret management, not source code.
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- Set rate and concurrency limits, and restrict tool permissions and execution time.
- Expose health and readiness checks with dependency-aware behavior.
- Trace each turn and measure time-to-first-token, stream throughput, tool latency, upstream round trips, and failures.
A broader gateway project may cover additional concerns such as provider routing, MCP and A2A-related features, authentication, authorization, rate limits, TLS, and observability. Those are scope comparisons, not requirements for a custom Axum service; documented provider support can vary by capability and may be native, translated, estimated, provider-dependent, or unavailable: agentgateway documentation.
Interpret published performance figures cautiously
SitePoint’s 2026 tutorial reports approximately 0.8 ms gateway-internal P50 time-to-first-byte, 4.5 ms P99 latency at 1,000 concurrent connections, a maximum of 12,000 sustained SSE connections, and about 18 MB of memory at 1,000 connections. These are figures reported by that tutorial for a four-core, 8 GB machine using a mock LLM; its gateway-internal timings exclude end-to-end network hops. The tutorial notes that hardware, operating system, and kernel tuning affect the results. Treat them as setup-specific reported measurements, not a general Rust-versus-other-language comparison, and reproduce any figures that matter on your target environment: SitePoint tutorial and benchmark context.
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