localarena 0.3.0

Live multi-provider model evaluation, scoring, reports, and Elo

LocalArena

Live multi-provider model evaluation, deterministic scoring, standalone HTML reports, and Elo standings.

pip install localarena

LocalArena calls the endpoints you explicitly configure. It does not bundle recorded answers, copy public leaderboard results, download models, or fall back to another provider.

First live score

Once a provider is running and a model is available, run a live scored request without creating a configuration file:

localarena quickstart ollama qwen3:0.6b

This writes privacy-safe localarena-results.json and a standalone localarena-report.html. Model downloads and cold loads can take longer than one minute; LocalArena does not manage them.

The one-minute clock starts after the endpoint is reachable, the selected local model is downloaded and warm, or the hosted account, key, and model access are ready. The command itself makes a real Chat Completions request, scores the live response, and writes both outputs. It never substitutes a cached response.

Verify the installed profiles:

localarena providers

If the console command is not on PATH, use python -m localarena on macOS/Linux or py -m localarena on Windows.

Provider-by-provider first score

llama.cpp

Start a chat-capable GGUF with a stable model alias:

llama-server -m /absolute/path/to/model.gguf --alias local-model --host 127.0.0.1 --port 8080

Windows PowerShell can use a Windows model path:

llama-server -m "C:\models\model.gguf" --alias local-model --host 127.0.0.1 --port 8080

Then run:

localarena models llamacpp --timeout 120
localarena quickstart llamacpp local-model

For another trusted host or an authenticated server, add the complete --base-url URL/v1 and --api-key-env VARIABLE options. The server must use a compatible chat template.

Ollama

Start Ollama first if its application or service is not already running. Pull and warm this small example before starting the one-minute timer:

ollama pull qwen3:0.6b
ollama run qwen3:0.6b "Reply with only READY."
ollama ps

After ollama ps shows the model:

localarena models ollama
localarena quickstart ollama qwen3:0.6b

Ollama's loopback API does not require a credential by default.

LM Studio

Download and load a model with a stable API identifier, then start the server on LocalArena's default port:

lms get ibm/granite-4-micro
lms ls
lms load MODEL_KEY --identifier local-model
lms server start --port 1234

Then run:

localarena models lmstudio
localarena quickstart lmstudio local-model

Authentication is disabled by default. If it is enabled, add --api-key-env LM_API_TOKEN after securely loading that variable.

OpenRouter

Hosted calls send the prompt to OpenRouter and the selected upstream provider. Pricing and account limits apply. Load the key into the current shell without placing it in shell history.

macOS or Linux:

printf 'OpenRouter API key: '
read -r -s OPENROUTER_API_KEY
printf '\n'
export OPENROUTER_API_KEY

Windows PowerShell:

$secret = Read-Host "OpenRouter API key" -AsSecureString
$credential = [System.Net.NetworkCredential]::new("", $secret)
$env:OPENROUTER_API_KEY = $credential.Password
Remove-Variable secret, credential

Run the official free router for a connectivity smoke test:

localarena models openrouter --timeout 120
localarena quickstart openrouter openrouter/free --timeout 120

For reproducible comparisons, replace openrouter/free with a concrete model slug returned by discovery. Anthropic models are supported through concrete Anthropic slugs served by OpenRouter; the direct Anthropic Messages protocol is not a native profile.

OpenAI

Hosted calls send the prompt to OpenAI and may incur usage charges. Load a project key into the current shell without placing it in shell history.

macOS or Linux:

printf 'OpenAI API key: '
read -r -s OPENAI_API_KEY
printf '\n'
export OPENAI_API_KEY

Windows PowerShell:

$secret = Read-Host "OpenAI API key" -AsSecureString
$credential = [System.Net.NetworkCredential]::new("", $secret)
$env:OPENAI_API_KEY = $credential.Password
Remove-Variable secret, credential

Discover the models available to the project, then use an accessible Chat Completions model:

localarena models openai --timeout 120
localarena quickstart openai gpt-4.1-mini-2025-04-14 --timeout 120

If that snapshot is not returned, substitute an accessible Chat Completions model ID from discovery.

Custom compatible endpoint

The custom profile requires an explicit API base URL. A compatible POST /chat/completions endpoint is required; GET /models is optional. Make sure a lazy-loaded model is warm before starting the timer.

Unauthenticated loopback:

localarena models custom --base-url http://127.0.0.1:9000/v1
localarena quickstart custom model-id --base-url http://127.0.0.1:9000/v1

Skip the first command when the endpoint does not implement model discovery.

For a Bearer-authenticated HTTPS endpoint, load the key without placing it in shell history.

macOS or Linux:

printf 'Custom endpoint API key: '
read -r -s LOCALARENA_CUSTOM_API_KEY
printf '\n'
export LOCALARENA_CUSTOM_API_KEY
localarena quickstart custom model-id --base-url https://inference.example.com/v1 --api-key-env LOCALARENA_CUSTOM_API_KEY

Windows PowerShell:

$secret = Read-Host "Custom endpoint API key" -AsSecureString
$credential = [System.Net.NetworkCredential]::new("", $secret)
$env:LOCALARENA_CUSTOM_API_KEY = $credential.Password
Remove-Variable secret, credential
localarena quickstart custom model-id --base-url https://inference.example.com/v1 --api-key-env LOCALARENA_CUSTOM_API_KEY

Use HTTPS whenever a credential crosses the machine boundary. Reusable configurations can read non-Bearer sensitive headers through headers_env.

Expected result

A passing quickstart prints:

[1/1] MODEL_ID × arithmetic-smoke-test: ok

 1. MODEL_ID: score=1.000 elo=1000.0 errors=0
Results: localarena-results.json
Report: localarena-report.html

score=0.000 errors=0 means the live request completed but missed the fixed scorer. errors=1 is a provider or scoring failure and returns exit status 2. The JSON and report omit prompts, answers, evaluator details, and detailed errors by default. Add --include-content only when that content is safe to store.

Reusable command-line evaluations

Create a JSON configuration with one or more model targets and prompt tasks, then run the complete Cartesian product:

{
  "name": "Local arithmetic",
  "models": [
    {
      "name": "qwen-small",
      "provider": "ollama",
      "model": "qwen3:0.6b",
      "parameters": {
        "max_tokens": 128
      }
    }
  ],
  "tasks": [
    {
      "id": "arithmetic",
      "prompt": "Reply with exactly 42.",
      "evaluator": {
        "type": "contains",
        "expected": "42"
      }
    }
  ]
}

localarena run evaluation.json \
  --output results.json \
  --report report.html

Built-in profiles:

• llamacpp — http://127.0.0.1:8080/v1
• ollama — http://127.0.0.1:11434/v1
• lmstudio — http://127.0.0.1:1234/v1
• openrouter — OPENROUTER_API_KEY
• openai — OPENAI_API_KEY
• custom — an explicit compatible base URL

localarena providers
localarena models ollama
localarena quickstart ollama qwen3:0.6b
localarena report results.json --output report.html

Config files use api_key_env and headers_env; literal API keys and sensitive literal headers are rejected. Prompt and answer content is excluded from results by default. Use --include-content only when it is safe to retain that content.

Local and hosted model entries can share one evaluation. Each keeps its own provider, model ID, base URL, runtime credential, request policy, and generation parameters. Exact, containment, regular-expression, JSON, numeric, and live model-judge evaluators are built in.

Python API

from localarena import (
    EvaluationRunner,
    NumericMatch,
    ModelTarget,
    PromptTask,
    create_provider,
    write_html_report,
)

target = ModelTarget(
    name="local-model",
    provider=create_provider("ollama"),
    model="qwen3:0.6b",
    max_tokens=64,
    temperature=0,
)
task = PromptTask.from_text(
    "arithmetic",
    "Return only the result of 6 multiplied by 7.",
    evaluator=NumericMatch(42),
)

run = EvaluationRunner(
    [target],
    [task],
    max_concurrency=2,
).run(name="Live evaluation")

print(run.to_json())
write_html_report(run, "report.html")

Deterministic evaluators include exact, containment, regular-expression, JSON, and numeric scoring. ModelJudge supports live rubric scoring with another configured target. Generation failures and scoring failures remain individual result rows instead of aborting the run.

Elo arena

The lower-level portable arena remains available:

from localarena import Arena, Result

arena = Arena(["model-a", "model-b"])
arena.record("model-a", "model-b", Result.LEFT)

for row in arena.standings():
    print(row.rank, row.name, row.rating)

Arena and evaluation snapshots are JSON-safe. The npm package implements the same arena and evaluation run contracts.