Cursor Image Generation
OmniRoute exposes Cursor plan image generation on POST /v1/images/generations through the same provider id as chat: cursor (alias cu).
| Field | Value |
|---|---|
IMAGE_PROVIDERS id |
cursor |
| Format | cursor-agent-image |
| Auth | Same OAuth / API-key connection as chat (provider_connections.provider = "cursor") |
| Models | cursor/auto, cursor/composer-2, cursor/composer-2.5 |
Why the Agent CLI
Section titled “Why the Agent CLI”Cursor chat in OmniRoute uses agent.v1.AgentService/Run (protobuf). That path rejects built-in client tools (shell, write, …). Image generation is a Cursor-native tool executed by the agent CLI against the seat. The image handler therefore spawns agent with a locked prompt and a per-request temp workspace (same shape as community seat bridges), then returns OpenAI-compatible b64_json.
Access restriction (Hard Rules #15 + #17)
Section titled “Access restriction (Hard Rules #15 + #17)”This is the only IMAGE_PROVIDERS format that spawns a child process (the agent
binary). Because POST /v1/images/generations is shared by ~40 other, non-spawning
image providers that remote callers legitimately use, the whole route is not
classified LOCAL_ONLY — instead handleCursorAgentImageGeneration enforces its own
gate using the trusted AUTHZ_HEADER_PEER_LOCALITY verdict the authz pipeline stamps
on every request (from the real TCP peer, never the spoofable Host header): only
loopback and lan callers may reach the spawn; everything else (including a leaked
API key replayed over a public tunnel) gets 403 before any credential lookup or
process spawn happens. See src/server/authz/policies/management.ts for the same
policy applied to the rest of the LOCAL_ONLY tier.
Concurrency gate is module-level (single-instance limitation)
Section titled “Concurrency gate is module-level (single-instance limitation)”CURSOR_IMG_MAX_CONCURRENT is enforced by an in-memory counter/queue scoped to the
Node module instance (open-sse/handlers/imageGeneration/providers/cursorAgentImage.ts).
It correctly limits concurrent agent spawns within one OmniRoute process, but does
not coordinate across multiple processes/instances sharing the same Cursor seat
(e.g. a multi-replica deployment) — each instance enforces its own independent limit.
For a single-instance deployment (the default) this is exact; horizontally scaled
deployments should keep CURSOR_IMG_MAX_CONCURRENT conservative per instance or route
Cursor image traffic to a single instance.
Requirements
Section titled “Requirements”- A connected Cursor account in the dashboard (OAuth or
crsr_…API key). - The Cursor Agent binary available to the OmniRoute process:
- env
CURSOR_AGENT_BIN=/path/to/agent, or ~/.local/bin/agent, orproviderSpecificData.agentBinon the Cursor connection.
- env
Optional tuning:
| Env | Default | Meaning |
|---|---|---|
CURSOR_IMG_TIMEOUT_MS |
210000 |
Per-image wall clock |
CURSOR_IMG_MAX_CONCURRENT |
2 |
Shared-seat concurrency gate |
CURSOR_IMG_MODEL |
(request model / auto) |
Override CLI --model |
Example
Section titled “Example”curl -sS https://<host>/v1/images/generations \ -H "Authorization: Bearer <omni-api-key>" \ -H "Content-Type: application/json" \ -d '{"model":"cursor/auto","prompt":"a lantern in fog","size":"1024x1024"}'Generation typically takes 1–2 minutes. Prefer an internal network path; edge proxies with ~100s timeouts will fail.
LiteLLM
Section titled “LiteLLM”Register an image model with mode: image_generation, api_base: http://omniroute:20128/v1, and model: openai/cursor/auto (or bare cursor/auto depending on your LiteLLM version).
HagiCode
HagiCode is an agentic coding workspace: structured workflows, multi-agent execution, and Hero Dungeon views turn ideas into shipped software.
Turn ideas into polished, usable software with a smarter, faster, and more enjoyable agentic coding workflow.

- SmartStructured workflows turn intent into an executable path from idea to shipped change.
- EfficientMulti-agent workflows keep research, implementation, and review moving in parallel.
- FunHero Dungeon interfaces make long coding sessions visual, collaborative, and rewarding.