Use the right owner for each layer

InferCrane does not need to replace every component in an inference or agent stack. It provides the stable model endpoint, durable lifecycle, routing policy, and operational evidence. A user-managed gateway can translate provider protocols, and a sandbox can execute agent tools while calling that endpoint like any other application.

Connect a user-managed LiteLLM gateway

Keep LiteLLM’s provider configuration and credentials in LiteLLM. Connect its OpenAI-compatible surface to InferCrane in observe-only mode first:

infercrane connect https://litellm.internal.example/v1 \
  --as company-models \
  --type litellm \
  --model coder

infercrane doctor company-models

After qualification, transfer routing ownership explicitly:

infercrane adopt promote company-models --ownership traffic-managed

InferCrane does not bundle, fork, install, or license LiteLLM. It owns the logical endpoint and operational evidence; LiteLLM remains responsible for protocol translation and its upstreams.

Add managed APIs without changing application code

An authenticated OpenRouter or OpenAI-compatible API can be a first-class endpoint binding. Stage it as a fallback while the application continues sending model="coder-production":

infercrane endpoint bind coder-production \
  --name managed-fallback \
  --target managed-coder \
  --ownership traffic-managed \
  --external-adapter openai-compatible-external \
  --secret-reference SECRET_REFERENCE_ID \
  --request-limit 1000 \
  --cost-limit-usd 25.00 \
  --max-request-cost-usd 0.10 \
  --acknowledge-external-data \
  --enable-external

infercrane endpoint plan coder-production \
  --policy primary-fallback \
  --bindings self-hosted,managed-fallback

The plan remains a candidate until evaluated and promoted. InferCrane never forwards its public API key to the provider and never stores the provider key in binding configuration.

Use OpenRouter as deployment overflow

OpenRouter can also be optional deployment-level emergency capacity, not an implicit default. Configure it with a secret reference, explicit model mapping, privacy acknowledgement, and hard request and cost reservation ceilings. InferCrane selects it before sending bytes and never silently duplicates or shadows a request.

export OPENROUTER_API_KEY='...'
infercrane secret create openrouter --from-env OPENROUTER_API_KEY --output json

infercrane target add openrouter-qwen \
  --provider openrouter \
  --url https://openrouter.ai/api/v1 \
  --upstream-model qwen/qwen3-8b

Complete the bounded policy using Governed external capacity. Real OpenRouter billing qualification is still deferred; InferCrane does not fabricate price evidence.

Call InferCrane from an agent sandbox

The sandbox is the application execution environment. Create it with the sandbox provider, then issue a short-lived credential restricted to exactly one InferCrane endpoint:

infercrane sandbox connect \
  --provider e2b \
  --external-id sandbox-01JCGW \
  --external-revision template-v3 \
  --endpoint coder-production \
  --ttl 30m

Inject the shown-once credential through the sandbox provider’s secret mechanism, then use an ordinary OpenAI client inside the sandbox:

import os
from openai import OpenAI

client = OpenAI(
    base_url=os.environ["INFERCRANE_URL"].rstrip("/") + "/v1",
    api_key=os.environ["INFERCRANE_API_KEY"],
)

response = client.responses.create(
    model="coder-production",
    input="Summarize the test failure in /workspace/test.log.",
)
print(response.output_text)

import OpenAI from "openai";

const client = new OpenAI({
  baseURL: `${process.env.INFERCRANE_URL}/v1`,
  apiKey: process.env.INFERCRANE_API_KEY,
});

const response = await client.responses.create({
  model: "coder-production",
  input: "Summarize the test failure in /workspace/test.log.",
});
console.log(response.output_text);

Use the sandbox provider’s secret injection and network allow-list controls. InferCrane’s issued token cannot enumerate or invoke another endpoint alias and cannot access the control API.

infercrane sandbox list
infercrane sandbox rotate SANDBOX_REFERENCE_ID
infercrane sandbox revoke SANDBOX_REFERENCE_ID --yes

InferCrane does not create, pause, snapshot, isolate, or delete sandboxes, and it does not record sandbox commands, files, prompts, or outputs. E2B, Modal, Kubernetes, and similar names describe the external execution owner, not an InferCrane sandbox runtime. Real provider secret injection and isolation remain provider-specific qualification.

Bring a trained artifact into the release path

Keep training data and execution in MLflow, Kubeflow, SkyPilot, or your existing pipeline. Hand InferCrane a signed content-free artifact identity bound to one immutable candidate revision:

infercrane training keygen --file training-handoff.key

infercrane training sign coder-runtime REVISION_ID \
  --provider mlflow \
  --run run-2026-08-14-42 \
  --repository mlflow://registry/coder/42 \
  --immutable-revision 42 \
  --digest sha256:aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa \
  --key training-handoff.key \
  --file coder-42.handoff.json

infercrane training verify coder-42.handoff.json
infercrane training attach coder-runtime coder-42.handoff.json

Attachment does not promote the revision. Continue through benchmark, Replay, semantic quality evidence, and Release Guard. See Training artifact handoffs.

Why the boundary is useful

Change inference providers without rebuilding the agent sandbox.
Change sandbox vendors without changing the logical model endpoint.
Keep model rollout evidence separate from tool-execution security policy.
Move externally trained artifacts into a guarded release without moving training data or keys.
Avoid turning InferCrane into a gateway fork, workflow engine, or sandbox isolation runtime.

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REFERENCE

PROJECT

Compose gateways and agent sandboxes

Use the right owner for each layer

Connect a user-managed LiteLLM gateway

Add managed APIs without changing application code

Use OpenRouter as deployment overflow

Call InferCrane from an agent sandbox

Bring a trained artifact into the release path

Why the boundary is useful

​Use the right owner for each layer

​Connect a user-managed LiteLLM gateway

​Add managed APIs without changing application code

​Use OpenRouter as deployment overflow

​Call InferCrane from an agent sandbox

​Bring a trained artifact into the release path

​Why the boundary is useful

Use the right owner for each layer

Connect a user-managed LiteLLM gateway

Add managed APIs without changing application code

Use OpenRouter as deployment overflow

Call InferCrane from an agent sandbox

Bring a trained artifact into the release path

Why the boundary is useful