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* docs: Fix major memory system documentation issues - Remove misleading deprecation warnings, fix confusing comments, clearly separate three memory approaches, provide accurate examples that match implementation * fix: Correct broken image paths in README - Update crewai_logo.png and asset.png paths to point to docs/images/ directory instead of docs/ directly * docs: Add system prompt transparency and customization guide - Add 'Understanding Default System Instructions' section to address black-box concerns - Document what CrewAI automatically injects into prompts - Provide code examples to inspect complete system prompts - Show 3 methods to override default instructions - Include observability integration examples with Langfuse - Add best practices for production prompt management * docs: Fix implementation accuracy issues in memory documentation - Fix Ollama embedding URL parameter and remove unsupported Cohere input_type parameter * docs: Reference observability docs instead of showing specific tool examples * docs: Reorganize knowledge documentation for better developer experience - Move quickstart examples right after overview for immediate hands-on experience - Create logical learning progression: basics → configuration → advanced → troubleshooting - Add comprehensive agent vs crew knowledge guide with working examples - Consolidate debugging and troubleshooting in dedicated section - Organize best practices by topic in accordion format - Improve content flow from simple concepts to advanced features - Ensure all examples are grounded in actual codebase implementation * docs: enhance custom LLM documentation with comprehensive examples and accurate imports * docs: reorganize observability tools into dedicated section with comprehensive overview and improved navigation * docs: rename how-to section to learn and add comprehensive overview page * docs: finalize documentation reorganization and update navigation labels * docs: enhance README with comprehensive badges, navigation links, and getting started video
118 lines
4.6 KiB
Plaintext
118 lines
4.6 KiB
Plaintext
---
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title: "Overview"
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description: "Monitor, evaluate, and optimize your CrewAI agents with comprehensive observability tools"
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icon: "face-smile"
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---
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## Observability for CrewAI
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Observability is crucial for understanding how your CrewAI agents perform, identifying bottlenecks, and ensuring reliable operation in production environments. This section covers various tools and platforms that provide monitoring, evaluation, and optimization capabilities for your agent workflows.
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## Why Observability Matters
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- **Performance Monitoring**: Track agent execution times, token usage, and resource consumption
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- **Quality Assurance**: Evaluate output quality and consistency across different scenarios
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- **Debugging**: Identify and resolve issues in agent behavior and task execution
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- **Cost Management**: Monitor LLM API usage and associated costs
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- **Continuous Improvement**: Gather insights to optimize agent performance over time
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## Available Observability Tools
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### Monitoring & Tracing Platforms
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<CardGroup cols={2}>
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<Card title="AgentOps" icon="paperclip" href="/observability/agentops">
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Session replays, metrics, and monitoring for agent development and production.
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</Card>
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<Card title="OpenLIT" icon="magnifying-glass-chart" href="/observability/openlit">
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OpenTelemetry-native monitoring with cost tracking and performance analytics.
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</Card>
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<Card title="MLflow" icon="bars-staggered" href="/observability/mlflow">
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Machine learning lifecycle management with tracing and evaluation capabilities.
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</Card>
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<Card title="Langfuse" icon="link" href="/observability/langfuse">
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LLM engineering platform with detailed tracing and analytics.
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</Card>
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<Card title="Langtrace" icon="chart-line" href="/observability/langtrace">
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Open-source observability for LLMs and agent frameworks.
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</Card>
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<Card title="Arize Phoenix" icon="meteor" href="/observability/arize-phoenix">
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AI observability platform for monitoring and troubleshooting.
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</Card>
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<Card title="Portkey" icon="key" href="/observability/portkey">
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AI gateway with comprehensive monitoring and reliability features.
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</Card>
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<Card title="Opik" icon="meteor" href="/observability/opik">
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Debug, evaluate, and monitor LLM applications with comprehensive tracing.
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</Card>
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<Card title="Weave" icon="network-wired" href="/observability/weave">
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Weights & Biases platform for tracking and evaluating AI applications.
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</Card>
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</CardGroup>
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### Evaluation & Quality Assurance
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<CardGroup cols={2}>
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<Card title="Patronus AI" icon="shield-check" href="/observability/patronus-evaluation">
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Comprehensive evaluation platform for LLM outputs and agent behaviors.
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</Card>
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</CardGroup>
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## Key Observability Metrics
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### Performance Metrics
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- **Execution Time**: How long agents take to complete tasks
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- **Token Usage**: Input/output tokens consumed by LLM calls
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- **API Latency**: Response times from external services
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- **Success Rate**: Percentage of successfully completed tasks
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### Quality Metrics
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- **Output Accuracy**: Correctness of agent responses
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- **Consistency**: Reliability across similar inputs
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- **Relevance**: How well outputs match expected results
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- **Safety**: Compliance with content policies and guidelines
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### Cost Metrics
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- **API Costs**: Expenses from LLM provider usage
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- **Resource Utilization**: Compute and memory consumption
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- **Cost per Task**: Economic efficiency of agent operations
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- **Budget Tracking**: Monitoring against spending limits
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## Getting Started
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1. **Choose Your Tools**: Select observability platforms that match your needs
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2. **Instrument Your Code**: Add monitoring to your CrewAI applications
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3. **Set Up Dashboards**: Configure visualizations for key metrics
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4. **Define Alerts**: Create notifications for important events
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5. **Establish Baselines**: Measure initial performance for comparison
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6. **Iterate and Improve**: Use insights to optimize your agents
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## Best Practices
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### Development Phase
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- Use detailed tracing to understand agent behavior
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- Implement evaluation metrics early in development
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- Monitor resource usage during testing
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- Set up automated quality checks
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### Production Phase
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- Implement comprehensive monitoring and alerting
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- Track performance trends over time
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- Monitor for anomalies and degradation
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- Maintain cost visibility and control
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### Continuous Improvement
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- Regular performance reviews and optimization
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- A/B testing of different agent configurations
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- Feedback loops for quality improvement
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- Documentation of lessons learned
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Choose the observability tools that best fit your use case, infrastructure, and monitoring requirements to ensure your CrewAI agents perform reliably and efficiently. |