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Debug and Evaluate Your Multi-Agent System

Quickly connect your agent-building experience with Okareo to easily debug, evaluate, and optimize your agents.

Key Features​

  • Debugging and RCA Tools: Find issues across agent interactions with root cause analysis (RCA) and message graph checks. Evaluate ongoing interactions for task completion and errors, providing a live view of issues, root causes, and suggested fixes.
  • Multi-Turn Performance Evaluation: Analyze how agents interact, adapt, and achieve goals over multiple turns to ensure reliability and effectiveness.
  • Plug into Your Framework: Integrate with frameworks like CrewAI, Autogen, and others, and run your agents flexibly in your own environment.

CrewAI Integration​

Okareo seamlessly integrates with CrewAI to provide powerful debugging and evaluation capabilities for your multi-agent systems. Okareo Diagram

Setup​

To use Okareo with CrewAI, you'll need to import the CrewAILogger:

from okareo.crewai_logger import CrewAILogger

Usage​

You can use the CrewAILogger in two ways:

  1. As a context manager:
with CrewAILogger(logger_config):
    # Your CrewAI code here
    crew = Crew(
        agents=[agent1, agent2, agent3],
        tasks=[task1, task2, task3],
        # Other Crew parameters
    )
    result = crew.kickoff()
  1. As a standalone object:
crewai_logger = CrewAILogger(logger_config)
crewai_logger.start()
# Your CrewAI code here
crewai_logger.stop()

Autogen Integration​

Okareo also integrates with Autogen to provide powerful debugging and evaluation capabilities for your multi-agent systems. Okareo Diagram

Setup​

To use Okareo with Autogen, you'll need to import the AutogenLogger:

from okareo.autogen_logger import AutogenLogger

Usage​

You can use the AutogenLogger in two ways:

  1. As a context manager:
with autogen_logger:
    # Your Autogen code here
    chat_result_logged = representative.initiate_chat(
        groupchat_manager,
        message=request,
        summary_method="reflection_with_llm",
    )
  1. As a standalone object:
okareo_logger = OkareoLogger(logger_config)
autogen.runtime_logging.start(logger=okareo_logger)
# Your Autogen code goes here...
autogen.runtime_logging.end()

Configuration​

The CrewAILogger and AutogenLogger requires a configuration dictionary. Here are the key options:

  • api_key (required): Your Okareo API key
  • context_token: Unique identifier for the logging context
  • tags: List of tags to associate with the logged data
  • group_name: Name for the group of logged data (default: "crewai-chat-*****")

Example configuration:

logger_config = {
    "api_key": "your_api_key_here",
    "tags": ["testing", "debug"],
    "group_name": "my_first_agent_project"
}

Debugging Features​

With Okareo's CrewAI and Autogen integration, you can:

  1. Visualize agent interactions
  2. Analyze task completion and errors
  3. Perform root cause analysis on issues
  4. Track multi-turn performance