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docs: Add transparency features for prompts and memory systems (#2902)
* 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
This commit is contained in:
Tony Kipkemboi
committed by
João Moura
João Moura
parent
e4e9bf343a
commit
c90272d601
126
docs/observability/agentops.mdx
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126
docs/observability/agentops.mdx
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---
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title: AgentOps Integration
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description: Understanding and logging your agent performance with AgentOps.
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icon: paperclip
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---
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# Introduction
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Observability is a key aspect of developing and deploying conversational AI agents. It allows developers to understand how their agents are performing,
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how their agents are interacting with users, and how their agents use external tools and APIs.
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AgentOps is a product independent of CrewAI that provides a comprehensive observability solution for agents.
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## AgentOps
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[AgentOps](https://agentops.ai/?=crew) provides session replays, metrics, and monitoring for agents.
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At a high level, AgentOps gives you the ability to monitor cost, token usage, latency, agent failures, session-wide statistics, and more.
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For more info, check out the [AgentOps Repo](https://github.com/AgentOps-AI/agentops).
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### Overview
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AgentOps provides monitoring for agents in development and production.
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It provides a dashboard for tracking agent performance, session replays, and custom reporting.
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Additionally, AgentOps provides session drilldowns for viewing Crew agent interactions, LLM calls, and tool usage in real-time.
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This feature is useful for debugging and understanding how agents interact with users as well as other agents.
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### Features
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- **LLM Cost Management and Tracking**: Track spend with foundation model providers.
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- **Replay Analytics**: Watch step-by-step agent execution graphs.
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- **Recursive Thought Detection**: Identify when agents fall into infinite loops.
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- **Custom Reporting**: Create custom analytics on agent performance.
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- **Analytics Dashboard**: Monitor high-level statistics about agents in development and production.
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- **Public Model Testing**: Test your agents against benchmarks and leaderboards.
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- **Custom Tests**: Run your agents against domain-specific tests.
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- **Time Travel Debugging**: Restart your sessions from checkpoints.
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- **Compliance and Security**: Create audit logs and detect potential threats such as profanity and PII leaks.
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- **Prompt Injection Detection**: Identify potential code injection and secret leaks.
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### Using AgentOps
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<Steps>
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<Step title="Create an API Key">
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Create a user API key here: [Create API Key](https://app.agentops.ai/account)
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</Step>
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<Step title="Configure Your Environment">
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Add your API key to your environment variables:
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```bash
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AGENTOPS_API_KEY=<YOUR_AGENTOPS_API_KEY>
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```
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</Step>
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<Step title="Install AgentOps">
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Install AgentOps with:
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```bash
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pip install 'crewai[agentops]'
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```
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or
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```bash
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pip install agentops
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```
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</Step>
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<Step title="Initialize AgentOps">
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Before using `Crew` in your script, include these lines:
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```python
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import agentops
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agentops.init()
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```
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This will initiate an AgentOps session as well as automatically track Crew agents. For further info on how to outfit more complex agentic systems,
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check out the [AgentOps documentation](https://docs.agentops.ai) or join the [Discord](https://discord.gg/j4f3KbeH).
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</Step>
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</Steps>
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### Crew + AgentOps Examples
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<CardGroup cols={3}>
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<Card
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title="Job Posting"
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color="#F3A78B"
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href="https://github.com/joaomdmoura/crewAI-examples/tree/main/job-posting"
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icon="briefcase"
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iconType="solid"
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>
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Example of a Crew agent that generates job posts.
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</Card>
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<Card
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title="Markdown Validator"
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color="#F3A78B"
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href="https://github.com/joaomdmoura/crewAI-examples/tree/main/markdown_validator"
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icon="markdown"
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iconType="solid"
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>
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Example of a Crew agent that validates Markdown files.
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</Card>
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<Card
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title="Instagram Post"
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color="#F3A78B"
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href="https://github.com/joaomdmoura/crewAI-examples/tree/main/instagram_post"
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icon="square-instagram"
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iconType="brands"
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>
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Example of a Crew agent that generates Instagram posts.
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</Card>
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</CardGroup>
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### Further Information
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To get started, create an [AgentOps account](https://agentops.ai/?=crew).
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For feature requests or bug reports, please reach out to the AgentOps team on the [AgentOps Repo](https://github.com/AgentOps-AI/agentops).
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#### Extra links
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<a href="https://twitter.com/agentopsai/">🐦 Twitter</a>
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<span> • </span>
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<a href="https://discord.gg/JHPt4C7r">📢 Discord</a>
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<span> • </span>
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<a href="https://app.agentops.ai/?=crew">🖇️ AgentOps Dashboard</a>
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<span> • </span>
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<a href="https://docs.agentops.ai/introduction">📙 Documentation</a>
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