langchain-wzrd 0.1.0

WZRD velocity-aware model selection for LangChain.

langchain-wzrd

WZRD velocity-aware model selection for LangChain.

Every call picks the best model based on live adoption velocity from WZRD — HuggingFace downloads, GitHub stars, OpenRouter inference, and ArtificialAnalysis benchmarks, scored every 300 seconds.

Install

pip install langchain-wzrd

Quick start

from langchain_wzrd import ChatWZRD

llm = ChatWZRD(task="code", openai_api_key="sk-or-...")
response = llm.invoke("Write a Python sort function")
print(response.content)

The model is selected per call. If Qwen3.5-9B is accelerating right now, that's what you get. If Llama-70B takes the lead tomorrow, you get that instead.

Earn CCM while routing

llm = ChatWZRD(
    task="code",
    openai_api_key="sk-or-...",
    wzrd_report=True,
    wzrd_keypair="~/.config/solana/id.json",
)

Every routed call auto-reports to WZRD in a background thread. Your agent earns CCM for contributing signal quality. No manual step needed.

Options

Parameter	Default	Description
task	"general"	"code", "chat", "reasoning", "general"
fallback	"meta-llama/llama-3.3-70b-instruct"	Model if WZRD is unavailable
candidates	None	Allowlist of model names to consider
temperature	0.7	Passed to underlying model
max_tokens	None	Passed to underlying model
wzrd_report	False	Auto-report routing decisions to earn CCM
wzrd_keypair	None	Solana keypair path for agent auth

How it works

1. wzrd.pick_details(task) queries the live signal API
2. Best model is selected by trend, confidence, and task fit
3. ChatOpenAI is instantiated with that model via OpenRouter
4. Response is returned — caller sees a normal LangChain ChatModel

No custom scoring. No duplicate routing logic. Thin wrapper around wzrd-client.