TryAii-DRE

Embedding-based AI model router. Understands your prompt semantically and routes to the best model based on benchmarks, cost, speed, and quality.

from tryaii_dre import DREClient, Router

router = Router()
result = router.route("Write a Python function to merge sorted arrays")

print(result.best_model)     # "gpt-5.2"
print(result.best_reasoning) # "Quality: 0.94 on [HumanEval (93%), SWE-bench (87%)]"

client = DREClient(api_key="sk-or-...")
response = client.chat("Write a quicksort implementation")
print(response.content)

Install

pip install tryaii-dre

The base install includes local embeddings via sentence-transformers - no API keys needed.

Optional extras for provider integrations:

pip install tryaii-dre[openrouter]  # Route & call models via OpenRouter (adds httpx)
pip install tryaii-dre[openai]      # Use OpenAI embeddings instead of local (adds openai)
pip install tryaii-dre[redis]       # Redis client for planned distributed cache (not yet implemented)
pip install tryaii-dre[all]         # All optional integrations

Quick Start

from tryaii_dre import Router, Priorities

router = Router()

# Route with default balanced priorities
result = router.route("Explain quantum entanglement simply")
print(result.best_model)

# Quality-first (ignore cost)
result = router.route(
    "Debug this memory leak in my Node.js app",
    priorities=Priorities(quality=5, cost=1, speed=2),
)

# Budget mode
result = router.route(
    "Summarize this email",
    priorities=Priorities.budget(),
)

CLI

Installing the package adds a tryaii-dre command (same surface as the Node SDK). It opens with an animated blue→red banner, then runs your command. The banner prints to stderr and auto-suppresses when output is piped, so --json stays clean.

tryaii-dre route "Write a Python function to merge sorted arrays" --quality=5 --cost=1
tryaii-dre eval prompts.json --output results/my-run --quality=5 --cost=1 --speed=1
tryaii-dre models --provider anthropic        # add --json for machine-readable output
tryaii-dre benchmarks --json
tryaii-dre setup                               # download the embedding model + warm centroids

Global flags: --no-banner (or TRYAII_NO_BANNER=1), NO_COLOR=1, -v/--verbose.

Eval over a dataset

# Balanced run into a named folder
tryaii-dre eval prompts.json --output results/my-run --quality=5 --cost=1 --speed=1

# Budget-aware: --max-price is the total budget for the whole dataset
tryaii-dre eval prompts.json --output results/budget --max-price=0.10 --output-tokens=2000
tryaii-dre eval prompts.json --output results/budget-fit --max-price=0.10 --output-tokens=2000 --budget-mode=fit-output

The input can be an array of strings or objects with prompt, optional id, and optional category. In budgeted eval, quality/cost/speed priority flags are ignored: price is the hard constraint, and the optimizer maximizes model quality within that price. --budget-mode=fit-output lowers the fixed output token estimate when the requested length cannot fit the total budget. The command writes results.jsonl, summary.json, and index.html.

OpenRouter Integration

from tryaii_dre import Router
from tryaii_dre.integrations import OpenRouterIntegration

router = Router()
openrouter = OpenRouterIntegration(router, api_key="sk-or-...")

response = openrouter.chat("Write a quicksort implementation")
print(response.model_used)  # Auto-selected best model
print(response.content)     # Actual response

OpenAI Embeddings

from tryaii_dre import Router
from tryaii_dre.embeddings import OpenAIEmbeddingProvider

router = Router(
    embedding_provider=OpenAIEmbeddingProvider(),
)

result = router.route("Summarize this architecture decision")
print(result.best_model)

Install the OpenAI client first:

pip install tryaii-dre[openai]

License

Apache 2.0