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https://github.com/crewAIInc/crewAI.git
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devin/1749
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lg-upgrade
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@@ -201,6 +201,7 @@
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"observability/arize-phoenix",
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"observability/langfuse",
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"observability/langtrace",
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"observability/maxim",
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"observability/mlflow",
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"observability/openlit",
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"observability/opik",
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152
docs/observability/maxim.mdx
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152
docs/observability/maxim.mdx
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---
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title: Maxim Integration
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description: Start Agent monitoring, evaluation, and observability
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icon: bars-staggered
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---
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# Maxim Integration
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Maxim AI provides comprehensive agent monitoring, evaluation, and observability for your CrewAI applications. With Maxim's one-line integration, you can easily trace and analyse agent interactions, performance metrics, and more.
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## Features: One Line Integration
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- **End-to-End Agent Tracing**: Monitor the complete lifecycle of your agents
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- **Performance Analytics**: Track latency, tokens consumed, and costs
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- **Hyperparameter Monitoring**: View the configuration details of your agent runs
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- **Tool Call Tracking**: Observe when and how agents use their tools
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- **Advanced Visualisation**: Understand agent trajectories through intuitive dashboards
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## Getting Started
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### Prerequisites
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- Python version >=3.10
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- A Maxim account ([sign up here](https://getmaxim.ai/))
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- A CrewAI project
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### Installation
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Install the Maxim SDK via pip:
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```python
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pip install maxim-py>=3.6.2
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```
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Or add it to your `requirements.txt`:
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```
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maxim-py>=3.6.2
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```
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### Basic Setup
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### 1. Set up environment variables
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```python
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### Environment Variables Setup
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# Create a `.env` file in your project root:
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# Maxim API Configuration
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MAXIM_API_KEY=your_api_key_here
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MAXIM_LOG_REPO_ID=your_repo_id_here
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```
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### 2. Import the required packages
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```python
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from crewai import Agent, Task, Crew, Process
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from maxim import Maxim
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from maxim.logger.crewai import instrument_crewai
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```
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### 3. Initialise Maxim with your API key
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```python
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# Initialize Maxim logger
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logger = Maxim().logger()
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# Instrument CrewAI with just one line
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instrument_crewai(logger)
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```
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### 4. Create and run your CrewAI application as usual
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```python
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# Create your agent
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researcher = Agent(
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role='Senior Research Analyst',
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goal='Uncover cutting-edge developments in AI',
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backstory="You are an expert researcher at a tech think tank...",
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verbose=True,
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llm=llm
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)
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# Define the task
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research_task = Task(
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description="Research the latest AI advancements...",
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expected_output="",
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agent=researcher
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)
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# Configure and run the crew
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crew = Crew(
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agents=[researcher],
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tasks=[research_task],
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verbose=True
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)
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try:
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result = crew.kickoff()
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finally:
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maxim.cleanup() # Ensure cleanup happens even if errors occur
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```
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That's it! All your CrewAI agent interactions will now be logged and available in your Maxim dashboard.
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Check this Google Colab Notebook for a quick reference - [Notebook](https://colab.research.google.com/drive/1ZKIZWsmgQQ46n8TH9zLsT1negKkJA6K8?usp=sharing)
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## Viewing Your Traces
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After running your CrewAI application:
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1. Log in to your [Maxim Dashboard](https://getmaxim.ai/dashboard)
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2. Navigate to your repository
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3. View detailed agent traces, including:
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- Agent conversations
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- Tool usage patterns
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- Performance metrics
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- Cost analytics
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## Troubleshooting
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### Common Issues
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- **No traces appearing**: Ensure your API key and repository ID are correc
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- Ensure you've **called `instrument_crewai()`** ***before*** running your crew. This initializes logging hooks correctly.
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- Set `debug=True` in your `instrument_crewai()` call to surface any internal errors:
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```python
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instrument_crewai(logger, debug=True)
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```
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- Configure your agents with `verbose=True` to capture detailed logs:
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```python
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agent = CrewAgent(..., verbose=True)
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```
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- 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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### Support
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If you encounter any issues:
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- Check the [Maxim Documentation](https://getmaxim.ai/docs)
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- Maxim Github [Link](https://github.com/maximhq)
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@@ -11,7 +11,7 @@ dependencies = [
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# Core Dependencies
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"pydantic>=2.4.2",
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"openai>=1.13.3",
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"litellm==1.68.0",
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"litellm==1.72.0",
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"instructor>=1.3.3",
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# Text Processing
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"pdfplumber>=0.11.4",
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14
uv.lock
generated
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uv.lock
generated
@@ -820,7 +820,7 @@ requires-dist = [
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{ name = "json-repair", specifier = ">=0.25.2" },
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{ name = "json5", specifier = ">=0.10.0" },
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{ name = "jsonref", specifier = ">=1.1.0" },
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{ name = "litellm", specifier = "==1.68.0" },
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{ name = "litellm", specifier = "==1.72.0" },
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{ name = "mem0ai", marker = "extra == 'mem0'", specifier = ">=0.1.94" },
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{ name = "onnxruntime", specifier = "==1.22.0" },
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{ name = "openai", specifier = ">=1.13.3" },
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@@ -2245,7 +2245,7 @@ wheels = [
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[[package]]
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name = "litellm"
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version = "1.68.0"
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version = "1.72.0"
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source = { registry = "https://pypi.org/simple" }
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dependencies = [
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{ name = "aiohttp" },
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@@ -2260,9 +2260,9 @@ dependencies = [
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{ name = "tiktoken" },
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{ name = "tokenizers" },
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]
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wheels = [
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]
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[[package]]
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@@ -3123,7 +3123,7 @@ wheels = [
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[[package]]
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name = "openai"
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version = "1.68.2"
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version = "1.78.0"
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source = { registry = "https://pypi.org/simple" }
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dependencies = [
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{ name = "anyio" },
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@@ -3135,9 +3135,9 @@ dependencies = [
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{ name = "tqdm" },
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{ name = "typing-extensions" },
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]
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]
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[[package]]
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