dakora 0.2.0

Python SDK for Dakora AI Control Plane

Dakora Python SDK

Centralized prompt management with versioning, execution tracking, and analytics for production LLM applications.

Installation

pip install dakora

Quick Start

from dakora import Dakora

dakora = Dakora(api_key="dk_proj_...")

# Render versioned prompts
result = await dakora.prompts.render("greeting", {"name": "Alice"})
print(result.text)  # "Hello Alice! How can I help you today?"

# Use with agents (automatic tracking)
message = result.to_message()
response = await agent.run(message)

# Query execution history & analytics
executions = await dakora.executions.list(
    agent_id="support-bot"
)

All executions automatically tracked when using with dakora-agents.

Why Dakora?

• Centralized Prompts - Version control, A/B testing, instant updates without code deploys
• Automatic Tracking - Tokens, cost, latency tracked via OpenTelemetry
• Template Linkage - Know exactly which prompts are used in production
• Built-in Analytics - Query execution history by agent, session, template, cost

Template Management

# List prompts
prompts = await dakora.prompts.list(project_id="proj-123")

# Render with inputs
result = await dakora.prompts.render(
    prompt_id="support-response",
    inputs={"ticket": "...", "user": "Alice"}
)

# Version and metadata included
print(result.version)   # "2.1.0"
print(result.prompt_id) # "support-response"

# Convert to agent message (auto-tracking)
message = result.to_message()

Execution Analytics

# Filter by agent, session, template
executions = await dakora.executions.list(
    agent_id="support-bot",      # Specific agent
    session_id="session-789",    # User conversation
    prompt_id="greeting"         # Template used
)

# Calculate costs
total_cost = sum(e["cost_usd"] for e in executions)

# Get full details
execution = await dakora.executions.get(execution_id="trace-456")
print(execution["conversation_history"])
print(execution["templates_used"])

Agent Integration

Automatic execution tracking with Microsoft Agent Framework:

pip install dakora[maf]

from dakora_agents.maf import DakoraIntegration

middleware = DakoraIntegration.setup(dakora)
client = AzureOpenAIChatClient(..., middleware=[middleware])

# Every agent call automatically tracked:
# - Tokens (input/output)
# - Cost ($)
# - Latency (ms)
# - Template linkage
# - Conversation history

Advanced Usage

Pagination:

result = await dakora.executions.list(
    limit=50,
    offset=100,
    include_metadata=True
)

print(f"Showing {len(result['executions'])} of {result['total']}")

Singleton pattern:

# config.py
from dakora import Dakora
dakora = Dakora()  # Uses DAKORA_API_KEY env var

# anywhere.py
from myapp.config import dakora
result = await dakora.prompts.render("greeting", {"name": "Alice"})

Environment variables:

DAKORA_API_KEY=dk_proj_...
DAKORA_BASE_URL=http://localhost:8000  # Optional

FastAPI example:

from fastapi import FastAPI
from dakora import Dakora

dakora = Dakora()
app = FastAPI()

@app.get("/greet/{name}")
async def greet(name: str):
    result = await dakora.prompts.render("greeting", {"name": name})
    return {"message": result.text}

Development

cd packages/client-python
pip install -e .