In-cluster sidecar for canvas.flow-master.ai. Brokers POST /internal/canvas-llm/chat to the configured upstream LLM provider so the in-app Command Assistant can synthesise fluent natural-language replies when no deterministic tool matches.

Why a Node sidecar (not a Python one)

This was a Python httpx proxy that hand-wrote OpenAI and Anthropic calls. PR canvas-frontend#6 swapped it for a Node sidecar that depends on @earendil-works/pi-ai — the same unified provider client @earendil-works/pi-coding-agent uses. Why:

It is the OSS-first move the canvas user asked for ("you could use a fork pi coding agent for that agent and then integrate it because pi coding agent is very clean and small while being efficient and useful").
pi-ai normalises streaming completions across OpenAI (completions + responses), Anthropic, Google Gemini, Google Vertex, Amazon Bedrock, Azure OpenAI, Mistral, GitHub Copilot. Canvas inherits all of them.
A future move to pi-ai's tool-calling shape (so the agent can hit real EA2 endpoints, not just our deterministic regex matcher) becomes mechanical instead of a rewrite.

Wire-compatible

The HTTP contract is the same: POST /internal/canvas-llm/chat with {messages, max_tokens} returns {content, provider}. The frontend's src/lib/llmClient.ts does not change.

Configuration

Set one of: ANTHROPIC_API_KEY, OPENAI_API_KEY, GEMINI_API_KEY. Optional: ANTHROPIC_MODEL, OPENAI_MODEL, GEMINI_MODEL, PORT (8080).

If no key is set, every request returns 503 so the frontend's deterministic-tool fallback fires gracefully.

Run

cd llm-proxy
npm install
ANTHROPIC_API_KEY=… node server.mjs

Container:

docker build -f Dockerfile.node -t canvas-llm-proxy .
docker run -e ANTHROPIC_API_KEY=… -p 8080:8080 canvas-llm-proxy

Legacy

The previous Python sidecar (main.py, requirements.txt, Dockerfile) is retained for one release so deployers can roll back. It will be removed once the Node sidecar is in production.