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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
73 lines
2.5 KiB
Plaintext
73 lines
2.5 KiB
Plaintext
---
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title: Langtrace Integration
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description: How to monitor cost, latency, and performance of CrewAI Agents using Langtrace, an external observability tool.
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icon: chart-line
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---
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# Langtrace Overview
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Langtrace is an open-source, external tool that helps you set up observability and evaluations for Large Language Models (LLMs), LLM frameworks, and Vector Databases.
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While not built directly into CrewAI, Langtrace can be used alongside CrewAI to gain deep visibility into the cost, latency, and performance of your CrewAI Agents.
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This integration allows you to log hyperparameters, monitor performance regressions, and establish a process for continuous improvement of your Agents.
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## Setup Instructions
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<Steps>
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<Step title="Sign up for Langtrace">
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Sign up by visiting [https://langtrace.ai/signup](https://langtrace.ai/signup).
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</Step>
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<Step title="Create a project">
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Set the project type to `CrewAI` and generate an API key.
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</Step>
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<Step title="Install Langtrace in your CrewAI project">
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Use the following command:
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```bash
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pip install langtrace-python-sdk
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```
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</Step>
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<Step title="Import Langtrace">
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Import and initialize Langtrace at the beginning of your script, before any CrewAI imports:
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```python
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from langtrace_python_sdk import langtrace
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langtrace.init(api_key='<LANGTRACE_API_KEY>')
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# Now import CrewAI modules
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from crewai import Agent, Task, Crew
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```
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</Step>
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</Steps>
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### Features and Their Application to CrewAI
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1. **LLM Token and Cost Tracking**
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- Monitor the token usage and associated costs for each CrewAI agent interaction.
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2. **Trace Graph for Execution Steps**
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- Visualize the execution flow of your CrewAI tasks, including latency and logs.
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- Useful for identifying bottlenecks in your agent workflows.
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3. **Dataset Curation with Manual Annotation**
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- Create datasets from your CrewAI task outputs for future training or evaluation.
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4. **Prompt Versioning and Management**
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- Keep track of different versions of prompts used in your CrewAI agents.
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- Useful for A/B testing and optimizing agent performance.
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5. **Prompt Playground with Model Comparisons**
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- Test and compare different prompts and models for your CrewAI agents before deployment.
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6. **Testing and Evaluations**
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- Set up automated tests for your CrewAI agents and tasks.
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