enroute
Unified LLM routing with first-class traces, environments, and benchmarks.
enroute is for builders putting AI into products. Start with an OpenRouter-style multi-provider router. Keep going with the thing OpenRouter does not give you: a single Trace object shared by production traffic and RL-style environments — so you can understand prompts, build datasets, run benchmarks, and eventually autoroute to the best model for your data.
flowchart LR
App[Your app] --> Client[Enroute]
Env[Environment] --> Client
Client --> Trace[Trace]
Trace --> Dataset[Dataset]
Dataset --> Bench[Benchmark]
Install
pip install enroute
# optional OpenTelemetry exporter
pip install "enroute[otel]"
Quickstart
Set ENROUTE_API_KEY, then use the client like the OpenAI SDK:
export ENROUTE_API_KEY=enroute-...
from enroute import Enroute, Message
client = Enroute()
response = client.chat(
model="openai/gpt-4o-mini",
messages=[Message(role="user", content="Hello from enroute")],
models=["anthropic/claude-sonnet-4"], # optional fallbacks
)
print(response.text)
client.close()
# Traces → .enroute/traces.jsonl
Optional BYOK (pass your own upstream keys explicitly):
import os
from enroute import Enroute
client = Enroute(providers={"openai": os.environ["OPENAI_API_KEY"]})
Four pillars
| Pillar | What you get |
|---|---|
| Router | Sync/async chat + streaming, fallbacks, retries, cost accounting, model catalog |
| Tracing | JSONL / SQLite / OTel sinks, redaction, sampling, late labels, attempt history |
| Environments | Tasks + tools + scorers → scored traces (the RL harness) |
| Benchmarks | Environment × models → markdown/JSON report with win rates |
Environments in 30 seconds
from enroute import Environment, TaskData
env = Environment(name="support-triage", version="0.1.0")
@env.tool
def lookup_order(order_id: str) -> dict:
"""Look up an order."""
return {"status": "shipped"}
@env.scorer(weight=1.0)
def ok(rollout) -> float:
return 1.0 if "shipped" in (rollout.response.text or "").lower() else 0.0
@env.tasks
def tasks():
yield TaskData(task_id="1", input="Where is order A?")
rollout = env.rollout(next(env.iter_tasks()), client, model="openai/gpt-4o-mini")
print(rollout.trace.outcome)
Docs
Full documentation: concept pages, guides, and generated API reference.
- Trace schema stability:
schemas/trace.v1.json - Routing walkthrough:
examples/routing/(also under docs → Guides)
Development
uv sync --group dev --group docs
uv run pytest -m "not live"
uv run ruff check .
uv run mypy src/enroute
uv run mkdocs build --strict
License
Apache-2.0
1 maintainer
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