Documentation Index

Fetch the complete documentation index at: https://docs.crewai.com/llms.txt

Use this file to discover all available pages before exploring further.

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Maxim Overview

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Features

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Prompt Management

• Prompt Playground
• Prompt Versions
• Prompt Comparisons

Create, refine, experiment and deploy your prompts via the playground. Organize of your prompts using folders and versions, experimenting with the real world cases by linking tools and context, and deploying based on custom logic.Easily experiment across models by configuring models and selecting the relevant model from the dropdown at the top of the prompt playground.

As teams build their AI applications, a big part of experimentation is iterating on the prompt structure. In order to collaborate effectively and organize your changes clearly, Maxim allows prompt versioning and comparison runs across versions.

Iterating on Prompts as you evolve your AI application would need experiments across models, prompt structures, etc. In order to compare versions and make informed decisions about changes, the comparison playground allows a side by side view of results.

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Why use Prompt comparison?

Prompt comparison combines multiple single Prompts into one view, enabling a streamlined approach for various workflows:

1. Model comparison: Evaluate the performance of different models on the same Prompt.
2. Prompt optimization: Compare different versions of a Prompt to identify the most effective formulation.
3. Cross-Model consistency: Ensure consistent outputs across various models for the same Prompt.
4. Performance benchmarking: Analyze metrics like latency, cost, and token count across different models and Prompts.

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Observability & Evals

• Agent Tracing
• Analytics + Evals
• Alerting
• Dashboards

Track your agent’s complete lifecycle, including tool calls, agent trajectories, and decision flows effortlessly.

Run detailed evaluations on full traces or individual nodes with support for:

• Multi-step interactions and granular trace analysis
• Session Level Evaluations
• Simulations for real-world testing

Auto Evals on Logs

Evaluate captured logs automatically from the UI based on filters and sampling

Human Evals on Logs

Use human evaluation or rating to assess the quality of your logs and evaluate them.

Node Level Evals

Evaluate any component of your trace or log to gain insights into your agent’s behavior.

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Set thresholds on error, cost, token usage, user feedback, latency and get real-time alerts via Slack or PagerDuty.

Visualize Traces over time, usage metrics, latency & error rates with ease.

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Getting Started

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Prerequisites

• Python version >=3.10
• A Maxim account (sign up here)
• Generate Maxim API Key
• A CrewAI project

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Installation

pip install maxim-py

maxim-py

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Basic Setup

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1. Set up environment variables

### Environment Variables Setup

# Create a `.env` file in your project root:

# Maxim API Configuration
MAXIM_API_KEY=your_api_key_here
MAXIM_LOG_REPO_ID=your_repo_id_here

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2. Import the required packages

from crewai import Agent, Task, Crew, Process
from maxim import Maxim
from maxim.logger.crewai import instrument_crewai

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3. Initialise Maxim with your API key

# Instrument CrewAI with just one line
instrument_crewai(Maxim().logger())

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4. Create and run your CrewAI application as usual

# Create your agent
researcher = Agent(
    role='Senior Research Analyst',
    goal='Uncover cutting-edge developments in AI',
    backstory="You are an expert researcher at a tech think tank...",
    verbose=True,
    llm=llm
)

# Define the task
research_task = Task(
    description="Research the latest AI advancements...",
    expected_output="",
    agent=researcher
)

# Configure and run the crew
crew = Crew(
    agents=[researcher],
    tasks=[research_task],
    verbose=True
)

try:
    result = crew.kickoff()
finally:
    maxim.cleanup()  # Ensure cleanup happens even if errors occur

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Viewing Your Traces

1. Log in to your Maxim Dashboard
2. Navigate to your repository
3. View detailed agent traces, including:
   • Agent conversations
   • Tool usage patterns
   • Performance metrics
   • Cost analytics
   

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Troubleshooting

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Common Issues

• No traces appearing: Ensure your API key and repository ID are correct
• Ensure you’ve called instrument_crewai() before running your crew. This initializes logging hooks correctly.
• Set debug=True in your instrument_crewai() call to surface any internal errors:
  instrument_crewai(logger, debug=True)
  
• Configure your agents with verbose=True to capture detailed logs:
  agent = CrewAgent(..., verbose=True)
  
• Double-check that instrument_crewai() is called before creating or executing agents. This might be obvious, but it’s a common oversight.

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Resources