AI Detection & Response Python-based SDK for monitoring agent workflows, language model execution, tool calls, and relevant data sources.
Features
- Automatic tracing: Works with any language model provider (OpenAI, Anthropic, custom)
- Zero code changes: Just initialize and go
- Comprehensive telemetry: Tracks tools, models, prompts, reasoning
- Simple API: 2 lines of code
Supported Frameworks
- OpenAI SDK
- Anthropic SDK
- Google (Gemini) SDK
- AWS Bedrock SDK
- Azure Foundry SDK
- LiteLLM SDK
- LangChain
Installation
pip install evoke-sdk
Quick Start
import evoke
# Initialize once at startup
evoke.init(api_key="evoke_pk_your_api_key")
# All LLM calls are now automatically traced!
from openai import OpenAI
client = OpenAI()
response = client.chat.completions.create(
model="gpt-4",
messages=[{"role": "user", "content": "Hello!"}]
)
What Gets Tracked
- Models: Which models were used (gpt-4, claude-3, etc.)
- Prompts & Responses: Full input and output data
- Tools: Which tools/functions were called
- Data Sources: Which vector stores/retrievers were accessed
- Tokens: Input/output/cached token counts
- Timing: Duration and timestamps
- Errors: Exceptions and stack traces
Optional: Explicit Tracing
Use the @guard decorator for high-level workflows:
@evoke.guard(name="customer_support_agent")
def handle_customer_query(query: str):
# Your agent logic
return agent.run(query)
Optional: Add Custom Context
Link custom business values to track custom events and create custom detections:
@evoke.guard(name="process_order")
def process_order(order_id: str, user_id: str):
evoke.add_context(
user_id=user_id,
order_id=order_id,
environment="production"
)
return process()
Flush Before Exit
Ensure all data is sent before your app exits:
# At the end of your script
evoke.flush()
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