Hanzo Console - AI Observability and Prompt Management

from langfuse import Langfuse

lf = Langfuse(
    host="https://console.hanzo.ai",
    public_key=os.environ["LANGFUSE_PUBLIC_KEY"],
    secret_key=os.environ["LANGFUSE_SECRET_KEY"],
)

# Create a trace
trace = lf.trace(name="chat-request", user_id="user_123")

# Log a generation (LLM call)
generation = trace.generation(
    name="chat-completion",
    model="zen-70b",
    input=[{"role": "user", "content": "Hello!"}],
    output="Hi there! How can I help?",
    usage={"input": 5, "output": 8, "unit": "TOKENS"},
    metadata={"temperature": 0.7},
)

# Score the trace
trace.score(name="quality", value=0.95, comment="Accurate and concise")

# Flush before exit
lf.flush()

from hanzo import Hanzo
from hanzo.console import observe

client = Hanzo()  # uses HANZO_API_KEY from env

@observe()
def chat(message: str) -> str:
    """Automatically traced: input, output, latency, cost."""
    response = client.chat.completions.create(
        model="zen-70b",
        messages=[{"role": "user", "content": message}],
    )
    return response.choices[0].message.content

result = chat("What is Hanzo?")

from langfuse.openai import openai

# Drop-in replacement: all OpenAI calls auto-traced
openai.base_url = "https://api.hanzo.ai/v1"
openai.api_key = os.environ["HANZO_API_KEY"]

response = openai.chat.completions.create(
    model="zen-70b",
    messages=[{"role": "user", "content": "Hello!"}],
    langfuse_trace_id="trace_abc",  # optional: link to existing trace
)
# Trace automatically sent to console.hanzo.ai

# Create trace
curl -X POST https://console.hanzo.ai/api/public/traces \
  -H "Content-Type: application/json" \
  -u "${LANGFUSE_PUBLIC_KEY}:${LANGFUSE_SECRET_KEY}" \
  -d '{
    "name": "api-request",
    "userId": "user_123",
    "input": {"message": "Hello"},
    "output": {"response": "Hi there!"}
  }'

# Get trace
curl https://console.hanzo.ai/api/public/traces/{trace_id} \
  -u "${LANGFUSE_PUBLIC_KEY}:${LANGFUSE_SECRET_KEY}"

from langfuse import Langfuse

lf = Langfuse(host="https://console.hanzo.ai")

# Get latest production prompt
prompt = lf.get_prompt("chat-system-prompt")
compiled = prompt.compile(user_name="Alice")

# Use in chat completion
response = client.chat.completions.create(
    model="zen-70b",
    messages=[
        {"role": "system", "content": compiled},
        {"role": "user", "content": "Hello!"},
    ],
    langfuse_prompt=prompt,  # links trace to prompt version
)

# Create evaluation dataset
dataset = lf.create_dataset(name="chat-eval-v1")

# Add items
lf.create_dataset_item(
    dataset_name="chat-eval-v1",
    input={"messages": [{"role": "user", "content": "What is Hanzo?"}]},
    expected_output="Hanzo is an AI infrastructure company...",
)

# Run evaluation
for item in lf.get_dataset("chat-eval-v1").items:
    response = client.chat.completions.create(
        model="zen-70b",
        messages=item.input["messages"],
    )

    # Score the result
    item.link(
        trace_id=response.langfuse_trace_id,
        run_name="zen-70b-eval-v1",
    )

import { MCPServer, Tool } from '@hanzo/mcp'

const consoleTools: Tool[] = [
  {
    name: 'console_get_trace',
    description: 'Get trace details for debugging an LLM call',
    parameters: {
      trace_id: { type: 'string', required: true }
    },
    async execute({ trace_id }) {
      const res = await fetch(
        `https://console.hanzo.ai/api/public/traces/${trace_id}`,
        { headers: { 'Authorization': `Bearer ${process.env.HANZO_API_KEY}` } }
      )
      return await res.json()
    }
  },
  {
    name: 'console_get_prompt',
    description: 'Fetch a managed prompt by name and label',
    parameters: {
      name: { type: 'string', required: true },
      label: { type: 'string', default: 'production' }
    },
    async execute({ name, label }) {
      const res = await fetch(
        `https://console.hanzo.ai/api/public/prompts/${name}?label=${label}`,
        { headers: { 'Authorization': `Bearer ${process.env.HANZO_API_KEY}` } }
      )
      return await res.json()
    }
  },
  {
    name: 'console_cost_summary',
    description: 'Get cost breakdown by model and time period',
    parameters: {
      period: { type: 'string', enum: ['day', 'week', 'month'], default: 'week' }
    },
    async execute({ period }) {
      const res = await fetch(
        `https://console.hanzo.ai/api/public/metrics/cost?period=${period}`,
        { headers: { 'Authorization': `Bearer ${process.env.HANZO_API_KEY}` } }
      )
      return await res.json()
    }
  }
]