TryAii

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

from tryaii 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

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

Optional extras for provider integrations:

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

Quick Start

from tryaii 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 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 route "Write a Python function to merge sorted arrays" --quality=5 --cost=1
tryaii eval prompts.json --output results/my-run --quality=5 --cost=1 --speed=1
tryaii models --provider anthropic        # add --json for machine-readable output
tryaii benchmarks --json
tryaii setup                               # download the embedding model + warm centroids
tryaii cachelint request.json              # pre-flight prompt-cache analysis (warn-only)

The cachelint engine also runs inside the clients: pass cache_lint="warn" to DREClient/AsyncDREClient/OpenRouterIntegration (or set TRYAII_CACHE_LINT=warn) to lint every outgoing chat request and verify cache predictions against the response usage — warn-only and fail-open.

| models | --provider <name>, --json | | benchmarks | --json | | setup / regenerate | --model <name> |

Global flags: --no-banner (or TRYAII_NO_BANNER=1), NO_COLOR=1, -v/--verbose, -V/--version. All flags work in any position and match the npm CLI.

Eval over a dataset

# Balanced run into a named folder
tryaii 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 eval prompts.json --output results/budget --max-price=0.10 --output-tokens=2000
tryaii 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 import Router
from tryaii.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 import Router
from tryaii.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[openai]

License

Apache 2.0