Use a client other than Claude Code

The engine speaks the OpenAI API as well as Anthropic’s, so anything that lets you set a base URL works — aider, Continue, LibreChat, the openai and anthropic SDKs, or ten lines of curl.

export OPENAI_BASE_URL=http://127.0.0.1:1919/v1
export OPENAI_API_KEY=local          # unchecked, but most clients demand one

Use whatever GET /v1/models reports as the model name. There is no authentication: any token is accepted, which is why the engine binds loopback (see Reach the panel from another machine).

Why the client matters more than you would expect

Every request carries the harness’s system prompt and tool definitions before any of your work, and that overhead is subtracted from the window on every turn. It is a floor, not a one-off cost.

Claude Code sends roughly 40k tokens of system prompt and tool schemas. A plain chat client sends a few hundred. That is not a small difference — it decides which models are usable at all:

model	window	with a ~40k harness	with a ~2k harness
Qwen3-8B	32k	cannot run	~30k of working room
gpt-oss-20b	128k	~88k	~126k
Qwen3.8-27B	101k	~61k	~99k
Qwen3.6-35B-A3B	212k	~172k	~210k

Qwen3-8B is the striking case. Its 32k window cannot hold Claude Code’s system prompt at all, so the first message fails — yet the same model is perfectly usable from a lighter client with 30k of room to work in. A model that “does not work” may only be failing to fit its harness.

Measure your own client

Do not trust the figures above for a client you actually use — measure it. The engine reports token counts on every response:

curl -s http://127.0.0.1:1919/v1/chat/completions \
  -H 'Content-Type: application/json' \
  -d '{"model":"MODEL","messages":[{"role":"user","content":"hi"}],"max_tokens":1}' \
  | python3 -c 'import json,sys; print(json.load(sys.stdin)["usage"])'

Send hi through your harness and compare its prompt_tokens against that baseline. The difference is what the harness costs you per turn, and it is the number to plan context around.

For a prompt you have in a file, ask the engine directly without generating anything:

curl -s http://127.0.0.1:1919/tokenize \
  -H 'Content-Type: application/json' \
  -d "{\"content\": $(python3 -c 'import json,sys;print(json.dumps(open(sys.argv[1]).read()))' prompt.txt)}" \
  | python3 -c 'import json,sys; print(len(json.load(sys.stdin)["tokens"]), "tokens")'

Pi

Pi is a bring-your-own-key CLI coding agent that is provider-agnostic by design, which makes it the least awkward second harness to put beside Claude Code.

npm install -g --ignore-scripts @earendil-works/pi-coding-agent

Point it at the engine by adding a provider to ~/.pi/agent/models.json. Use whatever GET /v1/models reports as the model id — that is the name you started the model under:

{
  "providers": {
    "lllm3090": {
      "baseUrl": "http://127.0.0.1:1919/v1",
      "api": "openai-completions",
      "apiKey": "local",
      "models": [
        { "id": "Qwen3.8-27B" },
        { "id": "Qwen3.6-35B-A3B" }
      ]
    }
  }
}

Only id is required for a local model; the key is unchecked but most clients insist on one. Add an entry per model you might start — the engine serves one at a time, so the list is what you can switch between, not what runs at once.

Check Pi’s own models documentation if the config schema has moved; this file will drift and theirs will not.

opencode

opencode works the same way and has a fuller agentic feature set, at the cost of a larger configuration surface. Both it and Pi write configuration to disk, which is worth knowing if you value the property that lllm3090 claude has: it sets environment variables for a single subprocess and leaves no trace, so a plain claude elsewhere still reaches Anthropic on your normal account.

Neither tool offers that, so decide deliberately whether you want your default opencode or pi invocation pointing at a local model or a hosted one.

Slots and concurrency

A lighter client changes the slot arithmetic too. With a 40k floor, a 32k slot is useless; with a 2k floor it is a working session. So a small-prompt harness makes higher --parallel counts genuinely useful where an agent harness would need every slot to be large — see Choose a context window and slot count.

Anthropic-protocol clients

Anything speaking Anthropic’s API works against /v1/messages in the same way, which is how Claude Code connects without a proxy. Streaming and tool calls are both supported; see Use Claude Code against a local model.