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Migrate docs from MkDocs to Mintlify (#1423)
* add new mintlify docs * add favicon.svg * minor edits * add github stats
This commit is contained in:
Tony Kipkemboi
@@ -1,86 +0,0 @@
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---
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title: Agent Monitoring with AgentOps
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description: Understanding and logging your agent performance with AgentOps.
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---
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# Intro
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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, how their agents are interacting with users, and how their agents use external tools and APIs. 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. 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. 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. 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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1. **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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2. **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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3. **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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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, check out the [AgentOps documentation](https://docs.agentops.ai) or join the [Discord](https://discord.gg/j4f3KbeH).
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### Crew + AgentOps Examples
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- [Job Posting](https://github.com/joaomdmoura/crewAI-examples/tree/main/job-posting)
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- [Markdown Validator](https://github.com/joaomdmoura/crewAI-examples/tree/main/markdown_validator)
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- [Instagram Post](https://github.com/joaomdmoura/crewAI-examples/tree/main/instagram_post)
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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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@@ -1,94 +0,0 @@
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---
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title: Initial Support to Bring Your Own Prompts in CrewAI
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description: Enhancing customization and internationalization by allowing users to bring their own prompts in CrewAI.
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---
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# Initial Support to Bring Your Own Prompts in CrewAI
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CrewAI now supports the ability to bring your own prompts, enabling extensive customization and internationalization. This feature allows users to tailor the inner workings of their agents to better suit specific needs, including support for multiple languages.
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## Internationalization and Customization Support
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### Custom Prompts with `prompt_file`
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The `prompt_file` attribute facilitates full customization of the agent prompts, enhancing the global usability of CrewAI. Users can specify their prompt templates, ensuring that the agents communicate in a manner that aligns with specific project requirements or language preferences.
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#### Example of a Custom Prompt File
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The custom prompts can be defined in a JSON file, similar to the example provided [here](https://github.com/joaomdmoura/crewAI/blob/main/src/crewai/translations/en.json).
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### Supported Languages
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CrewAI's custom prompt support includes internationalization, allowing prompts to be written in different languages. This is particularly useful for global teams or projects that require multilingual support.
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## How to Use the `prompt_file` Attribute
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To utilize the `prompt_file` attribute, include it in your crew definition. Below is an example demonstrating how to set up agents and tasks with custom prompts.
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### Example
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```python
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import os
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from crewai import Agent, Task, Crew
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# Define your agents
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researcher = Agent(
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role="Researcher",
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goal="Make the best research and analysis on content about AI and AI agents",
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backstory="You're an expert researcher, specialized in technology, software engineering, AI and startups. You work as a freelancer and is now working on doing research and analysis for a new customer.",
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allow_delegation=False,
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)
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writer = Agent(
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role="Senior Writer",
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goal="Write the best content about AI and AI agents.",
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backstory="You're a senior writer, specialized in technology, software engineering, AI and startups. You work as a freelancer and are now working on writing content for a new customer.",
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allow_delegation=False,
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)
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# Define your tasks
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tasks = [
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Task(
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description="Say Hi",
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expected_output="The word: Hi",
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agent=researcher,
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)
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]
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# Instantiate your crew with custom prompts
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crew = Crew(
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agents=[researcher],
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tasks=tasks,
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prompt_file="prompt.json", # Path to your custom prompt file
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)
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# Get your crew to work!
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crew.kickoff()
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```
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## Advanced Customization Features
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### `language` Attribute
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In addition to `prompt_file`, the `language` attribute can be used to specify the language for the agent's prompts. This ensures that the prompts are generated in the desired language, further enhancing the internationalization capabilities of CrewAI.
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### Creating Custom Prompt Files
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Custom prompt files should be structured in JSON format and include all necessary prompt templates. Below is a simplified example of a prompt JSON file:
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```json
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{
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"system": "You are a system template.",
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"prompt": "Here is your prompt template.",
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"response": "Here is your response template."
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}
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```
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### Benefits of Custom Prompts
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- **Enhanced Flexibility**: Tailor agent communication to specific project needs.
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- **Improved Usability**: Supports multiple languages, making it suitable for global projects.
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- **Consistency**: Ensures uniform prompt structures across different agents and tasks.
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By incorporating these updates, CrewAI provides users with the ability to fully customize and internationalize their agent prompts, making the platform more versatile and user-friendly.
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@@ -1,90 +0,0 @@
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---
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title: Customizing Agents in CrewAI
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description: A comprehensive guide to tailoring agents for specific roles, tasks, and advanced customizations within the CrewAI framework.
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---
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## Customizable Attributes
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Crafting an efficient CrewAI team hinges on the ability to dynamically tailor your AI agents to meet the unique requirements of any project. This section covers the foundational attributes you can customize.
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### Key Attributes for Customization
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- **Role**: Specifies the agent's job within the crew, such as 'Analyst' or 'Customer Service Rep'.
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- **Goal**: Defines what the agent aims to achieve, in alignment with its role and the overarching objectives of the crew.
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- **Backstory**: Provides depth to the agent's persona, enriching its motivations and engagements within the crew.
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- **Tools** *(Optional)*: Represents the capabilities or methods the agent uses to perform tasks, from simple functions to intricate integrations.
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- **Cache** *(Optional)*: Determines whether the agent should use a cache for tool usage.
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- **Max RPM**: Sets the maximum number of requests per minute (`max_rpm`). This attribute is optional and can be set to `None` for no limit, allowing for unlimited queries to external services if needed.
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- **Verbose** *(Optional)*: Enables detailed logging of an agent's actions, useful for debugging and optimization. Specifically, it provides insights into agent execution processes, aiding in the optimization of performance.
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- **Allow Delegation** *(Optional)*: `allow_delegation` controls whether the agent is allowed to delegate tasks to other agents. This attribute is now set to `False` by default.
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- **Max Iter** *(Optional)*: The `max_iter` attribute allows users to define the maximum number of iterations an agent can perform for a single task, preventing infinite loops or excessively long executions. The default value is set to 25, providing a balance between thoroughness and efficiency.
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- **Max Execution Time** *(Optional)*: `max_execution_time` Sets the maximum execution time for an agent to complete a task.
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- **System Template** *(Optional)*: `system_template` defines the system format for the agent.
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- **Prompt Template** *(Optional)*: `prompt_template` defines the prompt format for the agent.
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- **Response Template** *(Optional)*: `response_template` defines the response format for the agent.
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- **Use System Prompt** *(Optional)*: `use_system_prompt` controls whether the agent will use a system prompt for task execution. Agents can now operate without system prompts.
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- **Respect Context Window**: `respect_context_window` renames the sliding context window attribute and enables it by default to maintain context size.
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- **Max Retry Limit**: `max_retry_limit` defines the maximum number of retries for an agent to execute a task when an error occurs.
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## Advanced Customization Options
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Beyond the basic attributes, CrewAI allows for deeper customization to enhance an agent's behavior and capabilities significantly.
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### Language Model Customization
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Agents can be customized with specific language models (`llm`) and function-calling language models (`function_calling_llm`), offering advanced control over their processing and decision-making abilities. It's important to note that setting the `function_calling_llm` allows for overriding the default crew function-calling language model, providing a greater degree of customization.
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## Performance and Debugging Settings
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Adjusting an agent's performance and monitoring its operations are crucial for efficient task execution.
|
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|
||||
### Verbose Mode and RPM Limit
|
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- **Verbose Mode**: Enables detailed logging of an agent's actions, useful for debugging and optimization. Specifically, it provides insights into agent execution processes, aiding in the optimization of performance.
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- **RPM Limit**: Sets the maximum number of requests per minute (`max_rpm`). This attribute is optional and can be set to `None` for no limit, allowing for unlimited queries to external services if needed.
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|
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### Maximum Iterations for Task Execution
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The `max_iter` attribute allows users to define the maximum number of iterations an agent can perform for a single task, preventing infinite loops or excessively long executions. The default value is set to 25, providing a balance between thoroughness and efficiency. Once the agent approaches this number, it will try its best to give a good answer.
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## Customizing Agents and Tools
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Agents are customized by defining their attributes and tools during initialization. Tools are critical for an agent's functionality, enabling them to perform specialized tasks. The `tools` attribute should be an array of tools the agent can utilize, and it's initialized as an empty list by default. Tools can be added or modified post-agent initialization to adapt to new requirements.
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|
||||
```shell
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pip install 'crewai[tools]'
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```
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|
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### Example: Assigning Tools to an Agent
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```python
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import os
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from crewai import Agent
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from crewai_tools import SerperDevTool
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|
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# Set API keys for tool initialization
|
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os.environ["OPENAI_API_KEY"] = "Your Key"
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os.environ["SERPER_API_KEY"] = "Your Key"
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|
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# Initialize a search tool
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search_tool = SerperDevTool()
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|
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# Initialize the agent with advanced options
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agent = Agent(
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role='Research Analyst',
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goal='Provide up-to-date market analysis',
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backstory='An expert analyst with a keen eye for market trends.',
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tools=[search_tool],
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memory=True, # Enable memory
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verbose=True,
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max_rpm=None, # No limit on requests per minute
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max_iter=25, # Default value for maximum iterations
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)
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```
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|
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## Delegation and Autonomy
|
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Controlling an agent's ability to delegate tasks or ask questions is vital for tailoring its autonomy and collaborative dynamics within the CrewAI framework. By default, the `allow_delegation` attribute is now set to `False`, disabling agents to seek assistance or delegate tasks as needed. This default behavior can be changed to promote collaborative problem-solving and efficiency within the CrewAI ecosystem. If needed, delegation can be enabled to suit specific operational requirements.
|
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|
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### Example: Disabling Delegation for an Agent
|
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```python
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agent = Agent(
|
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role='Content Writer',
|
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goal='Write engaging content on market trends',
|
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backstory='A seasoned writer with expertise in market analysis.',
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allow_delegation=True # Enabling delegation
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)
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```
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## Conclusion
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Customizing agents in CrewAI by setting their roles, goals, backstories, and tools, alongside advanced options like language model customization, memory, performance settings, and delegation preferences, equips a nuanced and capable AI team ready for complex challenges.
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@@ -1,36 +0,0 @@
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---
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title: Forcing Tool Output as Result
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description: Learn how to force tool output as the result in an Agent's task in CrewAI.
|
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---
|
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|
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## Introduction
|
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In CrewAI, you can force the output of a tool as the result of an agent's task. This feature is useful when you want to ensure that the tool output is captured and returned as the task result, avoiding any agent modification during the task execution.
|
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|
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## Forcing Tool Output as Result
|
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To force the tool output as the result of an agent's task, you need to set the `result_as_answer` parameter to `True` when adding a tool to the agent. This parameter ensures that the tool output is captured and returned as the task result, without any modifications by the agent.
|
||||
|
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Here's an example of how to force the tool output as the result of an agent's task:
|
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|
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```python
|
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# ...
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||||
from crewai.agent import Agent
|
||||
from my_tool import MyCustomTool
|
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|
||||
# Create a coding agent with the custom tool
|
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coding_agent = Agent(
|
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role="Data Scientist",
|
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goal="Produce amazing reports on AI",
|
||||
backstory="You work with data and AI",
|
||||
tools=[MyCustomTool(result_as_answer=True)],
|
||||
)
|
||||
|
||||
# Assuming the tool's execution and result population occurs within the system
|
||||
task_result = coding_agent.execute_task(task)
|
||||
```
|
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|
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## Workflow in Action
|
||||
|
||||
1. **Task Execution**: The agent executes the task using the tool provided.
|
||||
2. **Tool Output**: The tool generates the output, which is captured as the task result.
|
||||
3. **Agent Interaction**: The agent may reflect and take learnings from the tool but the output is not modified.
|
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4. **Result Return**: The tool output is returned as the task result without any modifications.
|
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@@ -1,163 +0,0 @@
|
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---
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title: Connect CrewAI to LLMs
|
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description: Comprehensive guide on integrating CrewAI with various Large Language Models (LLMs) using LiteLLM, including supported providers and configuration options.
|
||||
---
|
||||
|
||||
## Connect CrewAI to LLMs
|
||||
|
||||
CrewAI uses LiteLLM to connect to a wide variety of Language Models (LLMs). This integration provides extensive versatility, allowing you to use models from numerous providers with a simple, unified interface.
|
||||
|
||||
!!! note "Default LLM"
|
||||
By default, CrewAI uses the `gpt-4o-mini` model. This is determined by the `OPENAI_MODEL_NAME` environment variable, which defaults to "gpt-4o-mini" if not set. You can easily configure your agents to use a different model or provider as described in this guide.
|
||||
|
||||
## Supported Providers
|
||||
|
||||
LiteLLM supports a wide range of providers, including but not limited to:
|
||||
|
||||
- OpenAI
|
||||
- Anthropic
|
||||
- Google (Vertex AI, Gemini)
|
||||
- Azure OpenAI
|
||||
- AWS (Bedrock, SageMaker)
|
||||
- Cohere
|
||||
- Hugging Face
|
||||
- Ollama
|
||||
- Mistral AI
|
||||
- Replicate
|
||||
- Together AI
|
||||
- AI21
|
||||
- Cloudflare Workers AI
|
||||
- DeepInfra
|
||||
- Groq
|
||||
- And many more!
|
||||
|
||||
For a complete and up-to-date list of supported providers, please refer to the [LiteLLM Providers documentation](https://docs.litellm.ai/docs/providers).
|
||||
|
||||
## Changing the LLM
|
||||
|
||||
To use a different LLM with your CrewAI agents, you have several options:
|
||||
|
||||
### 1. Using a String Identifier
|
||||
|
||||
Pass the model name as a string when initializing the agent:
|
||||
|
||||
```python
|
||||
from crewai import Agent
|
||||
|
||||
# Using OpenAI's GPT-4
|
||||
openai_agent = Agent(
|
||||
role='OpenAI Expert',
|
||||
goal='Provide insights using GPT-4',
|
||||
backstory="An AI assistant powered by OpenAI's latest model.",
|
||||
llm='gpt-4'
|
||||
)
|
||||
|
||||
# Using Anthropic's Claude
|
||||
claude_agent = Agent(
|
||||
role='Anthropic Expert',
|
||||
goal='Analyze data using Claude',
|
||||
backstory="An AI assistant leveraging Anthropic's language model.",
|
||||
llm='claude-2'
|
||||
)
|
||||
```
|
||||
|
||||
### 2. Using the LLM Class
|
||||
|
||||
For more detailed configuration, use the LLM class:
|
||||
|
||||
```python
|
||||
from crewai import Agent, LLM
|
||||
|
||||
llm = LLM(
|
||||
model="gpt-4",
|
||||
temperature=0.7,
|
||||
base_url="https://api.openai.com/v1",
|
||||
api_key="your-api-key-here"
|
||||
)
|
||||
|
||||
agent = Agent(
|
||||
role='Customized LLM Expert',
|
||||
goal='Provide tailored responses',
|
||||
backstory="An AI assistant with custom LLM settings.",
|
||||
llm=llm
|
||||
)
|
||||
```
|
||||
|
||||
## Configuration Options
|
||||
|
||||
When configuring an LLM for your agent, you have access to a wide range of parameters:
|
||||
|
||||
| Parameter | Type | Description |
|
||||
|-----------|------|-------------|
|
||||
| `model` | str | The name of the model to use (e.g., "gpt-4", "claude-2") |
|
||||
| `temperature` | float | Controls randomness in output (0.0 to 1.0) |
|
||||
| `max_tokens` | int | Maximum number of tokens to generate |
|
||||
| `top_p` | float | Controls diversity of output (0.0 to 1.0) |
|
||||
| `frequency_penalty` | float | Penalizes new tokens based on their frequency in the text so far |
|
||||
| `presence_penalty` | float | Penalizes new tokens based on their presence in the text so far |
|
||||
| `stop` | str, List[str] | Sequence(s) to stop generation |
|
||||
| `base_url` | str | The base URL for the API endpoint |
|
||||
| `api_key` | str | Your API key for authentication |
|
||||
|
||||
For a complete list of parameters and their descriptions, refer to the LLM class documentation.
|
||||
|
||||
## Connecting to OpenAI-Compatible LLMs
|
||||
|
||||
You can connect to OpenAI-compatible LLMs using either environment variables or by setting specific attributes on the LLM class:
|
||||
|
||||
### Using Environment Variables
|
||||
|
||||
```python
|
||||
import os
|
||||
|
||||
os.environ["OPENAI_API_KEY"] = "your-api-key"
|
||||
os.environ["OPENAI_API_BASE"] = "https://api.your-provider.com/v1"
|
||||
os.environ["OPENAI_MODEL_NAME"] = "your-model-name"
|
||||
```
|
||||
|
||||
### Using LLM Class Attributes
|
||||
|
||||
```python
|
||||
llm = LLM(
|
||||
model="custom-model-name",
|
||||
api_key="your-api-key",
|
||||
base_url="https://api.your-provider.com/v1"
|
||||
)
|
||||
agent = Agent(llm=llm, ...)
|
||||
```
|
||||
|
||||
## Using Local Models with Ollama
|
||||
|
||||
For local models like those provided by Ollama:
|
||||
|
||||
1. [Download and install Ollama](https://ollama.com/download)
|
||||
2. Pull the desired model (e.g., `ollama pull llama2`)
|
||||
3. Configure your agent:
|
||||
|
||||
```python
|
||||
agent = Agent(
|
||||
role='Local AI Expert',
|
||||
goal='Process information using a local model',
|
||||
backstory="An AI assistant running on local hardware.",
|
||||
llm=LLM(model="ollama/llama2", base_url="http://localhost:11434")
|
||||
)
|
||||
```
|
||||
|
||||
## Changing the Base API URL
|
||||
|
||||
You can change the base API URL for any LLM provider by setting the `base_url` parameter:
|
||||
|
||||
```python
|
||||
llm = LLM(
|
||||
model="custom-model-name",
|
||||
base_url="https://api.your-provider.com/v1",
|
||||
api_key="your-api-key"
|
||||
)
|
||||
agent = Agent(llm=llm, ...)
|
||||
```
|
||||
|
||||
This is particularly useful when working with OpenAI-compatible APIs or when you need to specify a different endpoint for your chosen provider.
|
||||
|
||||
## Conclusion
|
||||
|
||||
By leveraging LiteLLM, CrewAI offers seamless integration with a vast array of LLMs. This flexibility allows you to choose the most suitable model for your specific needs, whether you prioritize performance, cost-efficiency, or local deployment. Remember to consult the [LiteLLM documentation](https://docs.litellm.ai/docs/) for the most up-to-date information on supported models and configuration options.
|
||||
@@ -1,64 +0,0 @@
|
||||
---
|
||||
title: CrewAI Agent Monitoring with Langtrace
|
||||
description: How to monitor cost, latency, and performance of CrewAI Agents using Langtrace, an external observability tool.
|
||||
---
|
||||
|
||||
# Langtrace Overview
|
||||
|
||||
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. 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. This integration allows you to log hyperparameters, monitor performance regressions, and establish a process for continuous improvement of your Agents.
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
## Setup Instructions
|
||||
|
||||
1. Sign up for [Langtrace](https://langtrace.ai/) by visiting [https://langtrace.ai/signup](https://langtrace.ai/signup).
|
||||
2. Create a project, set the project type to crewAI & generate an API key.
|
||||
3. Install Langtrace in your CrewAI project using the following commands:
|
||||
|
||||
```bash
|
||||
# Install the SDK
|
||||
pip install langtrace-python-sdk
|
||||
```
|
||||
|
||||
## Using Langtrace with CrewAI
|
||||
|
||||
To integrate Langtrace with your CrewAI project, follow these steps:
|
||||
|
||||
1. Import and initialize Langtrace at the beginning of your script, before any CrewAI imports:
|
||||
|
||||
```python
|
||||
from langtrace_python_sdk import langtrace
|
||||
langtrace.init(api_key='<LANGTRACE_API_KEY>')
|
||||
|
||||
# Now import CrewAI modules
|
||||
from crewai import Agent, Task, Crew
|
||||
```
|
||||
|
||||
### Features and Their Application to CrewAI
|
||||
|
||||
1. **LLM Token and Cost Tracking**
|
||||
|
||||
- Monitor the token usage and associated costs for each CrewAI agent interaction.
|
||||
|
||||
2. **Trace Graph for Execution Steps**
|
||||
|
||||
- Visualize the execution flow of your CrewAI tasks, including latency and logs.
|
||||
- Useful for identifying bottlenecks in your agent workflows.
|
||||
|
||||
3. **Dataset Curation with Manual Annotation**
|
||||
|
||||
- Create datasets from your CrewAI task outputs for future training or evaluation.
|
||||
|
||||
4. **Prompt Versioning and Management**
|
||||
|
||||
- Keep track of different versions of prompts used in your CrewAI agents.
|
||||
- Useful for A/B testing and optimizing agent performance.
|
||||
|
||||
5. **Prompt Playground with Model Comparisons**
|
||||
|
||||
- Test and compare different prompts and models for your CrewAI agents before deployment.
|
||||
|
||||
6. **Testing and Evaluations**
|
||||
- Set up automated tests for your CrewAI agents and tasks.
|
||||
@@ -1,58 +0,0 @@
|
||||
---
|
||||
title: Replay Tasks from Latest Crew Kickoff
|
||||
description: Replay tasks from the latest crew.kickoff(...)
|
||||
|
||||
---
|
||||
|
||||
## Introduction
|
||||
CrewAI provides the ability to replay from a task specified from the latest crew kickoff. This feature is particularly useful when you've finished a kickoff and may want to retry certain tasks or don't need to refetch data over and your agents already have the context saved from the kickoff execution so you just need to replay the tasks you want to.
|
||||
|
||||
## Note:
|
||||
You must run `crew.kickoff()` before you can replay a task. Currently, only the latest kickoff is supported, so if you use `kickoff_for_each`, it will only allow you to replay from the most recent crew run.
|
||||
|
||||
Here's an example of how to replay from a task:
|
||||
|
||||
### Replaying from Specific Task Using the CLI
|
||||
To use the replay feature, follow these steps:
|
||||
|
||||
1. Open your terminal or command prompt.
|
||||
2. Navigate to the directory where your CrewAI project is located.
|
||||
3. Run the following commands:
|
||||
|
||||
To view the latest kickoff task_ids use:
|
||||
```shell
|
||||
crewai log-tasks-outputs
|
||||
```
|
||||
|
||||
Once you have your `task_id` to replay, use:
|
||||
```shell
|
||||
crewai replay -t <task_id>
|
||||
```
|
||||
|
||||
**Note:** Ensure `crewai` is installed and configured correctly in your development environment.
|
||||
|
||||
### Replaying from a Task Programmatically
|
||||
To replay from a task programmatically, use the following steps:
|
||||
|
||||
1. Specify the `task_id` and input parameters for the replay process.
|
||||
2. Execute the replay command within a try-except block to handle potential errors.
|
||||
|
||||
```python
|
||||
def replay():
|
||||
"""
|
||||
Replay the crew execution from a specific task.
|
||||
"""
|
||||
task_id = '<task_id>'
|
||||
inputs = {"topic": "CrewAI Training"} # This is optional; you can pass in the inputs you want to replay; otherwise, it uses the previous kickoff's inputs.
|
||||
try:
|
||||
YourCrewName_Crew().crew().replay(task_id=task_id, inputs=inputs)
|
||||
|
||||
except subprocess.CalledProcessError as e:
|
||||
raise Exception(f"An error occurred while replaying the crew: {e}")
|
||||
|
||||
except Exception as e:
|
||||
raise Exception(f"An unexpected error occurred: {e}")
|
||||
```
|
||||
|
||||
## Conclusion
|
||||
With the above enhancements and detailed functionality, replaying specific tasks in CrewAI has been made more efficient and robust. Ensure you follow the commands and steps precisely to make the most of these features.
|
||||
126
docs/how-to/agentops-observability.mdx
Normal file
126
docs/how-to/agentops-observability.mdx
Normal file
@@ -0,0 +1,126 @@
|
||||
---
|
||||
title: Agent Monitoring with AgentOps
|
||||
description: Understanding and logging your agent performance with AgentOps.
|
||||
icon: paperclip
|
||||
---
|
||||
|
||||
# Introduction
|
||||
|
||||
Observability is a key aspect of developing and deploying conversational AI agents. It allows developers to understand how their agents are performing,
|
||||
how their agents are interacting with users, and how their agents use external tools and APIs.
|
||||
AgentOps is a product independent of CrewAI that provides a comprehensive observability solution for agents.
|
||||
|
||||
## AgentOps
|
||||
|
||||
[AgentOps](https://agentops.ai/?=crew) provides session replays, metrics, and monitoring for agents.
|
||||
|
||||
At a high level, AgentOps gives you the ability to monitor cost, token usage, latency, agent failures, session-wide statistics, and more.
|
||||
For more info, check out the [AgentOps Repo](https://github.com/AgentOps-AI/agentops).
|
||||
|
||||
### Overview
|
||||
|
||||
AgentOps provides monitoring for agents in development and production.
|
||||
It provides a dashboard for tracking agent performance, session replays, and custom reporting.
|
||||
|
||||
Additionally, AgentOps provides session drilldowns for viewing Crew agent interactions, LLM calls, and tool usage in real-time.
|
||||
This feature is useful for debugging and understanding how agents interact with users as well as other agents.
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
### Features
|
||||
|
||||
- **LLM Cost Management and Tracking**: Track spend with foundation model providers.
|
||||
- **Replay Analytics**: Watch step-by-step agent execution graphs.
|
||||
- **Recursive Thought Detection**: Identify when agents fall into infinite loops.
|
||||
- **Custom Reporting**: Create custom analytics on agent performance.
|
||||
- **Analytics Dashboard**: Monitor high-level statistics about agents in development and production.
|
||||
- **Public Model Testing**: Test your agents against benchmarks and leaderboards.
|
||||
- **Custom Tests**: Run your agents against domain-specific tests.
|
||||
- **Time Travel Debugging**: Restart your sessions from checkpoints.
|
||||
- **Compliance and Security**: Create audit logs and detect potential threats such as profanity and PII leaks.
|
||||
- **Prompt Injection Detection**: Identify potential code injection and secret leaks.
|
||||
|
||||
### Using AgentOps
|
||||
|
||||
<Steps>
|
||||
<Step title="Create an API Key">
|
||||
Create a user API key here: [Create API Key](https://app.agentops.ai/account)
|
||||
</Step>
|
||||
<Step title="Configure Your Environment">
|
||||
Add your API key to your environment variables:
|
||||
```bash
|
||||
AGENTOPS_API_KEY=<YOUR_AGENTOPS_API_KEY>
|
||||
```
|
||||
</Step>
|
||||
<Step title="Install AgentOps">
|
||||
Install AgentOps with:
|
||||
```bash
|
||||
pip install crewai[agentops]
|
||||
```
|
||||
or
|
||||
```bash
|
||||
pip install agentops
|
||||
```
|
||||
</Step>
|
||||
<Step title="Initialize AgentOps">
|
||||
Before using `Crew` in your script, include these lines:
|
||||
|
||||
```python
|
||||
import agentops
|
||||
agentops.init()
|
||||
```
|
||||
|
||||
This will initiate an AgentOps session as well as automatically track Crew agents. For further info on how to outfit more complex agentic systems,
|
||||
check out the [AgentOps documentation](https://docs.agentops.ai) or join the [Discord](https://discord.gg/j4f3KbeH).
|
||||
</Step>
|
||||
</Steps>
|
||||
|
||||
### Crew + AgentOps Examples
|
||||
|
||||
<CardGroup cols={3}>
|
||||
<Card
|
||||
title="Job Posting"
|
||||
color="#F3A78B"
|
||||
href="https://github.com/joaomdmoura/crewAI-examples/tree/main/job-posting"
|
||||
icon="briefcase"
|
||||
iconType="solid"
|
||||
>
|
||||
Example of a Crew agent that generates job posts.
|
||||
</Card>
|
||||
<Card
|
||||
title="Markdown Validator"
|
||||
color="#F3A78B"
|
||||
href="https://github.com/joaomdmoura/crewAI-examples/tree/main/markdown_validator"
|
||||
icon="markdown"
|
||||
iconType="solid"
|
||||
>
|
||||
Example of a Crew agent that validates Markdown files.
|
||||
</Card>
|
||||
<Card
|
||||
title="Instagram Post"
|
||||
color="#F3A78B"
|
||||
href="https://github.com/joaomdmoura/crewAI-examples/tree/main/instagram_post"
|
||||
icon="square-instagram"
|
||||
iconType="brands"
|
||||
>
|
||||
Example of a Crew agent that generates Instagram posts.
|
||||
</Card>
|
||||
</CardGroup>
|
||||
|
||||
### Further Information
|
||||
|
||||
To get started, create an [AgentOps account](https://agentops.ai/?=crew).
|
||||
|
||||
For feature requests or bug reports, please reach out to the AgentOps team on the [AgentOps Repo](https://github.com/AgentOps-AI/agentops).
|
||||
|
||||
#### Extra links
|
||||
|
||||
<a href="https://twitter.com/agentopsai/">🐦 Twitter</a>
|
||||
<span> • </span>
|
||||
<a href="https://discord.gg/JHPt4C7r">📢 Discord</a>
|
||||
<span> • </span>
|
||||
<a href="https://app.agentops.ai/?=crew">🖇️ AgentOps Dashboard</a>
|
||||
<span> • </span>
|
||||
<a href="https://docs.agentops.ai/introduction">📙 Documentation</a>
|
||||
@@ -1,17 +1,20 @@
|
||||
---
|
||||
title: Coding Agents
|
||||
description: Learn how to enable your crewAI Agents to write and execute code, and explore advanced features for enhanced functionality.
|
||||
description: Learn how to enable your CrewAI Agents to write and execute code, and explore advanced features for enhanced functionality.
|
||||
icon: rectangle-code
|
||||
---
|
||||
|
||||
## Introduction
|
||||
|
||||
crewAI Agents now have the powerful ability to write and execute code, significantly enhancing their problem-solving capabilities. This feature is particularly useful for tasks that require computational or programmatic solutions.
|
||||
CrewAI Agents now have the powerful ability to write and execute code, significantly enhancing their problem-solving capabilities. This feature is particularly useful for tasks that require computational or programmatic solutions.
|
||||
|
||||
## Enabling Code Execution
|
||||
|
||||
To enable code execution for an agent, set the `allow_code_execution` parameter to `True` when creating the agent. Here's an example:
|
||||
To enable code execution for an agent, set the `allow_code_execution` parameter to `True` when creating the agent.
|
||||
|
||||
```python
|
||||
Here's an example:
|
||||
|
||||
```python Code
|
||||
from crewai import Agent
|
||||
|
||||
coding_agent = Agent(
|
||||
@@ -22,31 +25,45 @@ coding_agent = Agent(
|
||||
)
|
||||
```
|
||||
|
||||
**Note**: The `allow_code_execution` parameter defaults to `False`.
|
||||
<Note>
|
||||
Note that `allow_code_execution` parameter defaults to `False`.
|
||||
</Note>
|
||||
|
||||
## Important Considerations
|
||||
|
||||
1. **Model Selection**: It is strongly recommended to use more capable models like Claude 3.5 Sonnet and GPT-4 when enabling code execution. These models have a better understanding of programming concepts and are more likely to generate correct and efficient code.
|
||||
1. **Model Selection**: It is strongly recommended to use more capable models like Claude 3.5 Sonnet and GPT-4 when enabling code execution.
|
||||
These models have a better understanding of programming concepts and are more likely to generate correct and efficient code.
|
||||
|
||||
2. **Error Handling**: The code execution feature includes error handling. If executed code raises an exception, the agent will receive the error message and can attempt to correct the code or provide alternative solutions. The `max_retry_limit` parameter, which defaults to 2, controls the maximum number of retries for a task.
|
||||
2. **Error Handling**: The code execution feature includes error handling. If executed code raises an exception, the agent will receive the error message and can attempt to correct the code or
|
||||
provide alternative solutions. The `max_retry_limit` parameter, which defaults to 2, controls the maximum number of retries for a task.
|
||||
|
||||
3. **Dependencies**: To use the code execution feature, you need to install the `crewai_tools` package. If not installed, the agent will log an info message: "Coding tools not available. Install crewai_tools."
|
||||
3. **Dependencies**: To use the code execution feature, you need to install the `crewai_tools` package. If not installed, the agent will log an info message:
|
||||
"Coding tools not available. Install crewai_tools."
|
||||
|
||||
## Code Execution Process
|
||||
|
||||
When an agent with code execution enabled encounters a task requiring programming:
|
||||
|
||||
1. The agent analyzes the task and determines that code execution is necessary.
|
||||
2. It formulates the Python code needed to solve the problem.
|
||||
3. The code is sent to the internal code execution tool (`CodeInterpreterTool`).
|
||||
4. The tool executes the code in a controlled environment and returns the result.
|
||||
5. The agent interprets the result and incorporates it into its response or uses it for further problem-solving.
|
||||
<Steps>
|
||||
<Step title="Task Analysis">
|
||||
The agent analyzes the task and determines that code execution is necessary.
|
||||
</Step>
|
||||
<Step title="Code Formulation">
|
||||
It formulates the Python code needed to solve the problem.
|
||||
</Step>
|
||||
<Step title="Code Execution">
|
||||
The code is sent to the internal code execution tool (`CodeInterpreterTool`).
|
||||
</Step>
|
||||
<Step title="Result Interpretation">
|
||||
The agent interprets the result and incorporates it into its response or uses it for further problem-solving.
|
||||
</Step>
|
||||
</Steps>
|
||||
|
||||
## Example Usage
|
||||
|
||||
Here's a detailed example of creating an agent with code execution capabilities and using it in a task:
|
||||
|
||||
```python
|
||||
```python Code
|
||||
from crewai import Agent, Task, Crew
|
||||
|
||||
# Create an agent with code execution enabled
|
||||
@@ -75,4 +92,4 @@ result = analysis_crew.kickoff()
|
||||
print(result)
|
||||
```
|
||||
|
||||
In this example, the `coding_agent` can write and execute Python code to perform data analysis tasks.
|
||||
In this example, the `coding_agent` can write and execute Python code to perform data analysis tasks.
|
||||
@@ -1,15 +1,17 @@
|
||||
---
|
||||
title: Conditional Tasks
|
||||
description: Learn how to use conditional tasks in a crewAI kickoff
|
||||
icon: diagram-subtask
|
||||
---
|
||||
|
||||
## Introduction
|
||||
|
||||
Conditional Tasks in crewAI allow for dynamic workflow adaptation based on the outcomes of previous tasks. This powerful feature enables crews to make decisions and execute tasks selectively, enhancing the flexibility and efficiency of your AI-driven processes.
|
||||
Conditional Tasks in crewAI allow for dynamic workflow adaptation based on the outcomes of previous tasks.
|
||||
This powerful feature enables crews to make decisions and execute tasks selectively, enhancing the flexibility and efficiency of your AI-driven processes.
|
||||
|
||||
## Example Usage
|
||||
|
||||
```python
|
||||
```python Code
|
||||
from typing import List
|
||||
from pydantic import BaseModel
|
||||
from crewai import Agent, Crew
|
||||
@@ -1,10 +1,14 @@
|
||||
---
|
||||
title: Creating and Utilizing Tools in crewAI
|
||||
description: Comprehensive guide on crafting, using, and managing custom tools within the crewAI framework, including new functionalities and error handling.
|
||||
title: Create Custom Tools
|
||||
description: Comprehensive guide on crafting, using, and managing custom tools within the CrewAI framework, including new functionalities and error handling.
|
||||
icon: hammer
|
||||
---
|
||||
|
||||
## Creating and Utilizing Tools in crewAI
|
||||
This guide provides detailed instructions on creating custom tools for the crewAI framework and how to efficiently manage and utilize these tools, incorporating the latest functionalities such as tool delegation, error handling, and dynamic tool calling. It also highlights the importance of collaboration tools, enabling agents to perform a wide range of actions.
|
||||
## Creating and Utilizing Tools in CrewAI
|
||||
|
||||
This guide provides detailed instructions on creating custom tools for the CrewAI framework and how to efficiently manage and utilize these tools,
|
||||
incorporating the latest functionalities such as tool delegation, error handling, and dynamic tool calling. It also highlights the importance of collaboration tools,
|
||||
enabling agents to perform a wide range of actions.
|
||||
|
||||
### Prerequisites
|
||||
|
||||
@@ -18,7 +22,7 @@ pip install 'crewai[tools]'
|
||||
|
||||
To create a personalized tool, inherit from `BaseTool` and define the necessary attributes and the `_run` method.
|
||||
|
||||
```python
|
||||
```python Code
|
||||
from crewai_tools import BaseTool
|
||||
|
||||
class MyCustomTool(BaseTool):
|
||||
@@ -32,9 +36,10 @@ class MyCustomTool(BaseTool):
|
||||
|
||||
### Using the `tool` Decorator
|
||||
|
||||
Alternatively, you can use the tool decorator `@tool`. This approach allows you to define the tool's attributes and functionality directly within a function, offering a concise and efficient way to create specialized tools tailored to your needs.
|
||||
Alternatively, you can use the tool decorator `@tool`. This approach allows you to define the tool's attributes and functionality directly within a function,
|
||||
offering a concise and efficient way to create specialized tools tailored to your needs.
|
||||
|
||||
```python
|
||||
```python Code
|
||||
from crewai_tools import tool
|
||||
|
||||
@tool("Tool Name")
|
||||
@@ -48,7 +53,7 @@ def my_simple_tool(question: str) -> str:
|
||||
|
||||
To optimize tool performance with caching, define custom caching strategies using the `cache_function` attribute.
|
||||
|
||||
```python
|
||||
```python Code
|
||||
@tool("Tool with Caching")
|
||||
def cached_tool(argument: str) -> str:
|
||||
"""Tool functionality description."""
|
||||
@@ -61,4 +66,5 @@ def my_cache_strategy(arguments: dict, result: str) -> bool:
|
||||
cached_tool.cache_function = my_cache_strategy
|
||||
```
|
||||
|
||||
By adhering to these guidelines and incorporating new functionalities and collaboration tools into your tool creation and management processes, you can leverage the full capabilities of the crewAI framework, enhancing both the development experience and the efficiency of your AI agents.
|
||||
By adhering to these guidelines and incorporating new functionalities and collaboration tools into your tool creation and management processes,
|
||||
you can leverage the full capabilities of the CrewAI framework, enhancing both the development experience and the efficiency of your AI agents.
|
||||
@@ -1,12 +1,13 @@
|
||||
---
|
||||
title: Setting a Specific Agent as Manager in CrewAI
|
||||
title: Create Your Own Manager Agent
|
||||
description: Learn how to set a custom agent as the manager in CrewAI, providing more control over task management and coordination.
|
||||
|
||||
icon: user-shield
|
||||
---
|
||||
|
||||
# Setting a Specific Agent as Manager in CrewAI
|
||||
|
||||
CrewAI allows users to set a specific agent as the manager of the crew, providing more control over the management and coordination of tasks. This feature enables the customization of the managerial role to better fit your project's requirements.
|
||||
CrewAI allows users to set a specific agent as the manager of the crew, providing more control over the management and coordination of tasks.
|
||||
This feature enables the customization of the managerial role to better fit your project's requirements.
|
||||
|
||||
## Using the `manager_agent` Attribute
|
||||
|
||||
@@ -16,7 +17,7 @@ The `manager_agent` attribute allows you to define a custom agent to manage the
|
||||
|
||||
### Example
|
||||
|
||||
```python
|
||||
```python Code
|
||||
import os
|
||||
from crewai import Agent, Task, Crew, Process
|
||||
|
||||
@@ -71,7 +72,7 @@ result = crew.kickoff()
|
||||
|
||||
If you're using the hierarchical process and don't want to set a custom manager agent, you can specify the language model for the manager:
|
||||
|
||||
```python
|
||||
```python Code
|
||||
from langchain_openai import ChatOpenAI
|
||||
|
||||
manager_llm = ChatOpenAI(model_name="gpt-4")
|
||||
@@ -84,4 +85,6 @@ crew = Crew(
|
||||
)
|
||||
```
|
||||
|
||||
Note: Either `manager_agent` or `manager_llm` must be set when using the hierarchical process.
|
||||
<Note>
|
||||
Either `manager_agent` or `manager_llm` must be set when using the hierarchical process.
|
||||
</Note>
|
||||
111
docs/how-to/customizing-agents.mdx
Normal file
111
docs/how-to/customizing-agents.mdx
Normal file
@@ -0,0 +1,111 @@
|
||||
---
|
||||
title: Customize Agents
|
||||
description: A comprehensive guide to tailoring agents for specific roles, tasks, and advanced customizations within the CrewAI framework.
|
||||
icon: user-pen
|
||||
---
|
||||
|
||||
## Customizable Attributes
|
||||
|
||||
Crafting an efficient CrewAI team hinges on the ability to dynamically tailor your AI agents to meet the unique requirements of any project. This section covers the foundational attributes you can customize.
|
||||
|
||||
### Key Attributes for Customization
|
||||
|
||||
| Attribute | Description |
|
||||
|:-----------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------|
|
||||
| **Role** | Specifies the agent's job within the crew, such as 'Analyst' or 'Customer Service Rep'. |
|
||||
| **Goal** | Defines the agent’s objectives, aligned with its role and the crew’s overarching mission. |
|
||||
| **Backstory** | Provides depth to the agent's persona, enhancing motivations and engagements within the crew. |
|
||||
| **Tools** *(Optional)* | Represents the capabilities or methods the agent uses for tasks, from simple functions to complex integrations. |
|
||||
| **Cache** *(Optional)* | Determines if the agent should use a cache for tool usage. |
|
||||
| **Max RPM** | Sets the maximum requests per minute (`max_rpm`). Can be set to `None` for unlimited requests to external services. |
|
||||
| **Verbose** *(Optional)* | Enables detailed logging for debugging and optimization, providing insights into execution processes. |
|
||||
| **Allow Delegation** *(Optional)* | Controls task delegation to other agents, default is `False`. |
|
||||
| **Max Iter** *(Optional)* | Limits the maximum number of iterations (`max_iter`) for a task to prevent infinite loops, with a default of 25. |
|
||||
| **Max Execution Time** *(Optional)* | Sets the maximum time allowed for an agent to complete a task. |
|
||||
| **System Template** *(Optional)* | Defines the system format for the agent. |
|
||||
| **Prompt Template** *(Optional)* | Defines the prompt format for the agent. |
|
||||
| **Response Template** *(Optional)* | Defines the response format for the agent. |
|
||||
| **Use System Prompt** *(Optional)* | Controls whether the agent will use a system prompt during task execution. |
|
||||
| **Respect Context Window** | Enables a sliding context window by default, maintaining context size. |
|
||||
| **Max Retry Limit** | Sets the maximum number of retries (`max_retry_limit`) for an agent in case of errors. |
|
||||
|
||||
## Advanced Customization Options
|
||||
|
||||
Beyond the basic attributes, CrewAI allows for deeper customization to enhance an agent's behavior and capabilities significantly.
|
||||
|
||||
### Language Model Customization
|
||||
|
||||
Agents can be customized with specific language models (`llm`) and function-calling language models (`function_calling_llm`), offering advanced control over their processing and decision-making abilities.
|
||||
It's important to note that setting the `function_calling_llm` allows for overriding the default crew function-calling language model, providing a greater degree of customization.
|
||||
|
||||
## Performance and Debugging Settings
|
||||
|
||||
Adjusting an agent's performance and monitoring its operations are crucial for efficient task execution.
|
||||
|
||||
### Verbose Mode and RPM Limit
|
||||
|
||||
- **Verbose Mode**: Enables detailed logging of an agent's actions, useful for debugging and optimization. Specifically, it provides insights into agent execution processes, aiding in the optimization of performance.
|
||||
- **RPM Limit**: Sets the maximum number of requests per minute (`max_rpm`). This attribute is optional and can be set to `None` for no limit, allowing for unlimited queries to external services if needed.
|
||||
|
||||
### Maximum Iterations for Task Execution
|
||||
|
||||
The `max_iter` attribute allows users to define the maximum number of iterations an agent can perform for a single task, preventing infinite loops or excessively long executions.
|
||||
The default value is set to 25, providing a balance between thoroughness and efficiency. Once the agent approaches this number, it will try its best to give a good answer.
|
||||
|
||||
## Customizing Agents and Tools
|
||||
|
||||
Agents are customized by defining their attributes and tools during initialization. Tools are critical for an agent's functionality, enabling them to perform specialized tasks.
|
||||
The `tools` attribute should be an array of tools the agent can utilize, and it's initialized as an empty list by default. Tools can be added or modified post-agent initialization to adapt to new requirements.
|
||||
|
||||
```shell
|
||||
pip install 'crewai[tools]'
|
||||
```
|
||||
|
||||
### Example: Assigning Tools to an Agent
|
||||
|
||||
```python Code
|
||||
import os
|
||||
from crewai import Agent
|
||||
from crewai_tools import SerperDevTool
|
||||
|
||||
# Set API keys for tool initialization
|
||||
os.environ["OPENAI_API_KEY"] = "Your Key"
|
||||
os.environ["SERPER_API_KEY"] = "Your Key"
|
||||
|
||||
# Initialize a search tool
|
||||
search_tool = SerperDevTool()
|
||||
|
||||
# Initialize the agent with advanced options
|
||||
agent = Agent(
|
||||
role='Research Analyst',
|
||||
goal='Provide up-to-date market analysis',
|
||||
backstory='An expert analyst with a keen eye for market trends.',
|
||||
tools=[search_tool],
|
||||
memory=True, # Enable memory
|
||||
verbose=True,
|
||||
max_rpm=None, # No limit on requests per minute
|
||||
max_iter=25, # Default value for maximum iterations
|
||||
)
|
||||
```
|
||||
|
||||
## Delegation and Autonomy
|
||||
|
||||
Controlling an agent's ability to delegate tasks or ask questions is vital for tailoring its autonomy and collaborative dynamics within the CrewAI framework. By default,
|
||||
the `allow_delegation` attribute is now set to `False`, disabling agents to seek assistance or delegate tasks as needed. This default behavior can be changed to promote collaborative problem-solving and
|
||||
efficiency within the CrewAI ecosystem. If needed, delegation can be enabled to suit specific operational requirements.
|
||||
|
||||
### Example: Disabling Delegation for an Agent
|
||||
|
||||
```python Code
|
||||
agent = Agent(
|
||||
role='Content Writer',
|
||||
goal='Write engaging content on market trends',
|
||||
backstory='A seasoned writer with expertise in market analysis.',
|
||||
allow_delegation=True # Enabling delegation
|
||||
)
|
||||
```
|
||||
|
||||
## Conclusion
|
||||
|
||||
Customizing agents in CrewAI by setting their roles, goals, backstories, and tools, alongside advanced options like language model customization, memory, performance settings, and delegation preferences,
|
||||
equips a nuanced and capable AI team ready for complex challenges.
|
||||
50
docs/how-to/force-tool-output-as-result.mdx
Normal file
50
docs/how-to/force-tool-output-as-result.mdx
Normal file
@@ -0,0 +1,50 @@
|
||||
---
|
||||
title: Force Tool Output as Result
|
||||
description: Learn how to force tool output as the result in an Agent's task in CrewAI.
|
||||
icon: wrench-simple
|
||||
---
|
||||
|
||||
## Introduction
|
||||
|
||||
In CrewAI, you can force the output of a tool as the result of an agent's task.
|
||||
This feature is useful when you want to ensure that the tool output is captured and returned as the task result, avoiding any agent modification during the task execution.
|
||||
|
||||
## Forcing Tool Output as Result
|
||||
|
||||
To force the tool output as the result of an agent's task, you need to set the `result_as_answer` parameter to `True` when adding a tool to the agent.
|
||||
This parameter ensures that the tool output is captured and returned as the task result, without any modifications by the agent.
|
||||
|
||||
Here's an example of how to force the tool output as the result of an agent's task:
|
||||
|
||||
```python Code
|
||||
from crewai.agent import Agent
|
||||
from my_tool import MyCustomTool
|
||||
|
||||
# Create a coding agent with the custom tool
|
||||
coding_agent = Agent(
|
||||
role="Data Scientist",
|
||||
goal="Produce amazing reports on AI",
|
||||
backstory="You work with data and AI",
|
||||
tools=[MyCustomTool(result_as_answer=True)],
|
||||
)
|
||||
|
||||
# Assuming the tool's execution and result population occurs within the system
|
||||
task_result = coding_agent.execute_task(task)
|
||||
```
|
||||
|
||||
## Workflow in Action
|
||||
|
||||
<Steps>
|
||||
<Step title="Task Execution">
|
||||
The agent executes the task using the tool provided.
|
||||
</Step>
|
||||
<Step title="Tool Output">
|
||||
The tool generates the output, which is captured as the task result.
|
||||
</Step>
|
||||
<Step title="Agent Interaction">
|
||||
The agent may reflect and take learnings from the tool but the output is not modified.
|
||||
</Step>
|
||||
<Step title="Result Return">
|
||||
The tool output is returned as the task result without any modifications.
|
||||
</Step>
|
||||
</Steps>
|
||||
@@ -1,18 +1,26 @@
|
||||
---
|
||||
title: Implementing the Hierarchical Process in CrewAI
|
||||
title: Hierarchical Process
|
||||
description: A comprehensive guide to understanding and applying the hierarchical process within your CrewAI projects, updated to reflect the latest coding practices and functionalities.
|
||||
icon: sitemap
|
||||
---
|
||||
|
||||
## Introduction
|
||||
The hierarchical process in CrewAI introduces a structured approach to task management, simulating traditional organizational hierarchies for efficient task delegation and execution. This systematic workflow enhances project outcomes by ensuring tasks are handled with optimal efficiency and accuracy.
|
||||
|
||||
!!! note "Complexity and Efficiency"
|
||||
The hierarchical process in CrewAI introduces a structured approach to task management, simulating traditional organizational hierarchies for efficient task delegation and execution.
|
||||
This systematic workflow enhances project outcomes by ensuring tasks are handled with optimal efficiency and accuracy.
|
||||
|
||||
<Tip>
|
||||
The hierarchical process is designed to leverage advanced models like GPT-4, optimizing token usage while handling complex tasks with greater efficiency.
|
||||
</Tip>
|
||||
|
||||
## Hierarchical Process Overview
|
||||
By default, tasks in CrewAI are managed through a sequential process. However, adopting a hierarchical approach allows for a clear hierarchy in task management, where a 'manager' agent coordinates the workflow, delegates tasks, and validates outcomes for streamlined and effective execution. This manager agent can now be either automatically created by CrewAI or explicitly set by the user.
|
||||
|
||||
By default, tasks in CrewAI are managed through a sequential process. However, adopting a hierarchical approach allows for a clear hierarchy in task management,
|
||||
where a 'manager' agent coordinates the workflow, delegates tasks, and validates outcomes for streamlined and effective execution. This manager agent can now be either
|
||||
automatically created by CrewAI or explicitly set by the user.
|
||||
|
||||
### Key Features
|
||||
|
||||
- **Task Delegation**: A manager agent allocates tasks among crew members based on their roles and capabilities.
|
||||
- **Result Validation**: The manager evaluates outcomes to ensure they meet the required standards.
|
||||
- **Efficient Workflow**: Emulates corporate structures, providing an organized approach to task management.
|
||||
@@ -25,15 +33,21 @@ By default, tasks in CrewAI are managed through a sequential process. However, a
|
||||
|
||||
|
||||
## Implementing the Hierarchical Process
|
||||
To utilize the hierarchical process, it's essential to explicitly set the process attribute to `Process.hierarchical`, as the default behavior is `Process.sequential`. Define a crew with a designated manager and establish a clear chain of command.
|
||||
|
||||
!!! note "Tools and Agent Assignment"
|
||||
Assign tools at the agent level to facilitate task delegation and execution by the designated agents under the manager's guidance. Tools can also be specified at the task level for precise control over tool availability during task execution.
|
||||
To utilize the hierarchical process, it's essential to explicitly set the process attribute to `Process.hierarchical`, as the default behavior is `Process.sequential`.
|
||||
Define a crew with a designated manager and establish a clear chain of command.
|
||||
|
||||
!!! note "Manager LLM Requirement"
|
||||
Configuring the `manager_llm` parameter is crucial for the hierarchical process. The system requires a manager LLM to be set up for proper function, ensuring tailored decision-making.
|
||||
<Tip>
|
||||
Assign tools at the agent level to facilitate task delegation and execution by the designated agents under the manager's guidance.
|
||||
Tools can also be specified at the task level for precise control over tool availability during task execution.
|
||||
</Tip>
|
||||
|
||||
```python
|
||||
<Tip>
|
||||
Configuring the `manager_llm` parameter is crucial for the hierarchical process.
|
||||
The system requires a manager LLM to be set up for proper function, ensuring tailored decision-making.
|
||||
</Tip>
|
||||
|
||||
```python Code
|
||||
from langchain_openai import ChatOpenAI
|
||||
from crewai import Crew, Process, Agent
|
||||
|
||||
@@ -75,9 +89,12 @@ project_crew = Crew(
|
||||
```
|
||||
|
||||
### Workflow in Action
|
||||
|
||||
1. **Task Assignment**: The manager assigns tasks strategically, considering each agent's capabilities and available tools.
|
||||
2. **Execution and Review**: Agents complete their tasks with the option for asynchronous execution and callback functions for streamlined workflows.
|
||||
3. **Sequential Task Progression**: Despite being a hierarchical process, tasks follow a logical order for smooth progression, facilitated by the manager's oversight.
|
||||
|
||||
## Conclusion
|
||||
Adopting the hierarchical process in CrewAI, with the correct configurations and understanding of the system's capabilities, facilitates an organized and efficient approach to project management. Utilize the advanced features and customizations to tailor the workflow to your specific needs, ensuring optimal task execution and project success.
|
||||
|
||||
Adopting the hierarchical process in CrewAI, with the correct configurations and understanding of the system's capabilities, facilitates an organized and efficient approach to project management.
|
||||
Utilize the advanced features and customizations to tailor the workflow to your specific needs, ensuring optimal task execution and project success.
|
||||
@@ -1,15 +1,18 @@
|
||||
---
|
||||
title: Human Input on Execution
|
||||
description: Integrating CrewAI with human input during execution in complex decision-making processes and leveraging the full capabilities of the agent's attributes and tools.
|
||||
icon: user-check
|
||||
---
|
||||
|
||||
# Human Input in Agent Execution
|
||||
## Human input in agent execution
|
||||
|
||||
Human input is critical in several agent execution scenarios, allowing agents to request additional information or clarification when necessary. This feature is especially useful in complex decision-making processes or when agents require more details to complete a task effectively.
|
||||
Human input is critical in several agent execution scenarios, allowing agents to request additional information or clarification when necessary.
|
||||
This feature is especially useful in complex decision-making processes or when agents require more details to complete a task effectively.
|
||||
|
||||
## Using Human Input with CrewAI
|
||||
## Using human input with CrewAI
|
||||
|
||||
To integrate human input into agent execution, set the `human_input` flag in the task definition. When enabled, the agent prompts the user for input before delivering its final answer. This input can provide extra context, clarify ambiguities, or validate the agent's output.
|
||||
To integrate human input into agent execution, set the `human_input` flag in the task definition. When enabled, the agent prompts the user for input before delivering its final answer.
|
||||
This input can provide extra context, clarify ambiguities, or validate the agent's output.
|
||||
|
||||
### Example:
|
||||
|
||||
@@ -17,7 +20,7 @@ To integrate human input into agent execution, set the `human_input` flag in the
|
||||
pip install crewai
|
||||
```
|
||||
|
||||
```python
|
||||
```python Code
|
||||
import os
|
||||
from crewai import Agent, Task, Crew
|
||||
from crewai_tools import SerperDevTool
|
||||
@@ -1,11 +1,13 @@
|
||||
---
|
||||
title: Kickoff Async
|
||||
title: Kickoff Crew Asynchronously
|
||||
description: Kickoff a Crew Asynchronously
|
||||
icon: rocket-launch
|
||||
---
|
||||
|
||||
## Introduction
|
||||
|
||||
CrewAI provides the ability to kickoff a crew asynchronously, allowing you to start the crew execution in a non-blocking manner. This feature is particularly useful when you want to run multiple crews concurrently or when you need to perform other tasks while the crew is executing.
|
||||
CrewAI provides the ability to kickoff a crew asynchronously, allowing you to start the crew execution in a non-blocking manner.
|
||||
This feature is particularly useful when you want to run multiple crews concurrently or when you need to perform other tasks while the crew is executing.
|
||||
|
||||
## Asynchronous Crew Execution
|
||||
|
||||
@@ -13,7 +15,7 @@ To kickoff a crew asynchronously, use the `kickoff_async()` method. This method
|
||||
|
||||
### Method Signature
|
||||
|
||||
```python
|
||||
```python Code
|
||||
def kickoff_async(self, inputs: dict) -> CrewOutput:
|
||||
```
|
||||
|
||||
@@ -37,7 +39,7 @@ def kickoff_async(self, inputs: dict) -> CrewOutput:
|
||||
|
||||
Here's an example of how to kickoff a crew asynchronously using asyncio and awaiting the result:
|
||||
|
||||
```python
|
||||
```python Code
|
||||
import asyncio
|
||||
from crewai import Crew, Agent, Task
|
||||
|
||||
@@ -74,7 +76,7 @@ asyncio.run(async_crew_execution())
|
||||
|
||||
In this example, we'll show how to kickoff multiple crews asynchronously and wait for all of them to complete using `asyncio.gather()`:
|
||||
|
||||
```python
|
||||
```python Code
|
||||
import asyncio
|
||||
from crewai import Crew, Agent, Task
|
||||
|
||||
@@ -1,17 +1,22 @@
|
||||
---
|
||||
title: Kickoff For Each
|
||||
description: Kickoff a Crew for a List
|
||||
title: Kickoff Crew for Each
|
||||
description: Kickoff Crew for Each Item in a List
|
||||
icon: at
|
||||
---
|
||||
|
||||
## Introduction
|
||||
CrewAI provides the ability to kickoff a crew for each item in a list, allowing you to execute the crew for each item in the list. This feature is particularly useful when you need to perform the same set of tasks for multiple items.
|
||||
|
||||
CrewAI provides the ability to kickoff a crew for each item in a list, allowing you to execute the crew for each item in the list.
|
||||
This feature is particularly useful when you need to perform the same set of tasks for multiple items.
|
||||
|
||||
## Kicking Off a Crew for Each Item
|
||||
To kickoff a crew for each item in a list, use the `kickoff_for_each()` method. This method executes the crew for each item in the list, allowing you to process multiple items efficiently.
|
||||
|
||||
To kickoff a crew for each item in a list, use the `kickoff_for_each()` method.
|
||||
This method executes the crew for each item in the list, allowing you to process multiple items efficiently.
|
||||
|
||||
Here's an example of how to kickoff a crew for each item in a list:
|
||||
|
||||
```python
|
||||
```python Code
|
||||
from crewai import Crew, Agent, Task
|
||||
|
||||
# Create an agent with code execution enabled
|
||||
72
docs/how-to/langtrace-observability.mdx
Normal file
72
docs/how-to/langtrace-observability.mdx
Normal file
@@ -0,0 +1,72 @@
|
||||
---
|
||||
title: Agent Monitoring with Langtrace
|
||||
description: How to monitor cost, latency, and performance of CrewAI Agents using Langtrace, an external observability tool.
|
||||
icon: chart-line
|
||||
---
|
||||
|
||||
# Langtrace Overview
|
||||
|
||||
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.
|
||||
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.
|
||||
This integration allows you to log hyperparameters, monitor performance regressions, and establish a process for continuous improvement of your Agents.
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
## Setup Instructions
|
||||
|
||||
<Steps>
|
||||
<Step title="Sign up for Langtrace">
|
||||
Sign up by visiting [https://langtrace.ai/signup](https://langtrace.ai/signup).
|
||||
</Step>
|
||||
<Step title="Create a project">
|
||||
Set the project type to `CrewAI` and generate an API key.
|
||||
</Step>
|
||||
<Step title="Install Langtrace in your CrewAI project">
|
||||
Use the following command:
|
||||
|
||||
```bash
|
||||
pip install langtrace-python-sdk
|
||||
```
|
||||
</Step>
|
||||
<Step title="Import Langtrace">
|
||||
Import and initialize Langtrace at the beginning of your script, before any CrewAI imports:
|
||||
|
||||
```python
|
||||
from langtrace_python_sdk import langtrace
|
||||
langtrace.init(api_key='<LANGTRACE_API_KEY>')
|
||||
|
||||
# Now import CrewAI modules
|
||||
from crewai import Agent, Task, Crew
|
||||
```
|
||||
</Step>
|
||||
</Steps>
|
||||
|
||||
### Features and Their Application to CrewAI
|
||||
|
||||
1. **LLM Token and Cost Tracking**
|
||||
|
||||
- Monitor the token usage and associated costs for each CrewAI agent interaction.
|
||||
|
||||
2. **Trace Graph for Execution Steps**
|
||||
|
||||
- Visualize the execution flow of your CrewAI tasks, including latency and logs.
|
||||
- Useful for identifying bottlenecks in your agent workflows.
|
||||
|
||||
3. **Dataset Curation with Manual Annotation**
|
||||
|
||||
- Create datasets from your CrewAI task outputs for future training or evaluation.
|
||||
|
||||
4. **Prompt Versioning and Management**
|
||||
|
||||
- Keep track of different versions of prompts used in your CrewAI agents.
|
||||
- Useful for A/B testing and optimizing agent performance.
|
||||
|
||||
5. **Prompt Playground with Model Comparisons**
|
||||
|
||||
- Test and compare different prompts and models for your CrewAI agents before deployment.
|
||||
|
||||
6. **Testing and Evaluations**
|
||||
|
||||
- Set up automated tests for your CrewAI agents and tasks.
|
||||
182
docs/how-to/llm-connections.mdx
Normal file
182
docs/how-to/llm-connections.mdx
Normal file
@@ -0,0 +1,182 @@
|
||||
---
|
||||
title: Connect to any LLM
|
||||
description: Comprehensive guide on integrating CrewAI with various Large Language Models (LLMs) using LiteLLM, including supported providers and configuration options.
|
||||
icon: brain-circuit
|
||||
---
|
||||
|
||||
## Connect CrewAI to LLMs
|
||||
|
||||
CrewAI uses LiteLLM to connect to a wide variety of Language Models (LLMs). This integration provides extensive versatility, allowing you to use models from numerous providers with a simple, unified interface.
|
||||
|
||||
<Note>
|
||||
By default, CrewAI uses the `gpt-4o-mini` model. This is determined by the `OPENAI_MODEL_NAME` environment variable, which defaults to "gpt-4o-mini" if not set.
|
||||
You can easily configure your agents to use a different model or provider as described in this guide.
|
||||
</Note>
|
||||
|
||||
## Supported Providers
|
||||
|
||||
LiteLLM supports a wide range of providers, including but not limited to:
|
||||
|
||||
- OpenAI
|
||||
- Anthropic
|
||||
- Google (Vertex AI, Gemini)
|
||||
- Azure OpenAI
|
||||
- AWS (Bedrock, SageMaker)
|
||||
- Cohere
|
||||
- Hugging Face
|
||||
- Ollama
|
||||
- Mistral AI
|
||||
- Replicate
|
||||
- Together AI
|
||||
- AI21
|
||||
- Cloudflare Workers AI
|
||||
- DeepInfra
|
||||
- Groq
|
||||
- And many more!
|
||||
|
||||
For a complete and up-to-date list of supported providers, please refer to the [LiteLLM Providers documentation](https://docs.litellm.ai/docs/providers).
|
||||
|
||||
## Changing the LLM
|
||||
|
||||
To use a different LLM with your CrewAI agents, you have several options:
|
||||
|
||||
<Tabs>
|
||||
<Tab title="Using a String Identifier">
|
||||
Pass the model name as a string when initializing the agent:
|
||||
<CodeGroup>
|
||||
```python Code
|
||||
from crewai import Agent
|
||||
|
||||
# Using OpenAI's GPT-4
|
||||
openai_agent = Agent(
|
||||
role='OpenAI Expert',
|
||||
goal='Provide insights using GPT-4',
|
||||
backstory="An AI assistant powered by OpenAI's latest model.",
|
||||
llm='gpt-4'
|
||||
)
|
||||
|
||||
# Using Anthropic's Claude
|
||||
claude_agent = Agent(
|
||||
role='Anthropic Expert',
|
||||
goal='Analyze data using Claude',
|
||||
backstory="An AI assistant leveraging Anthropic's language model.",
|
||||
llm='claude-2'
|
||||
)
|
||||
```
|
||||
</CodeGroup>
|
||||
</Tab>
|
||||
<Tab title="Using the LLM Class">
|
||||
For more detailed configuration, use the LLM class:
|
||||
<CodeGroup>
|
||||
```python Code
|
||||
from crewai import Agent, LLM
|
||||
|
||||
llm = LLM(
|
||||
model="gpt-4",
|
||||
temperature=0.7,
|
||||
base_url="https://api.openai.com/v1",
|
||||
api_key="your-api-key-here"
|
||||
)
|
||||
|
||||
agent = Agent(
|
||||
role='Customized LLM Expert',
|
||||
goal='Provide tailored responses',
|
||||
backstory="An AI assistant with custom LLM settings.",
|
||||
llm=llm
|
||||
)
|
||||
```
|
||||
</CodeGroup>
|
||||
</Tab>
|
||||
</Tabs>
|
||||
|
||||
## Configuration Options
|
||||
|
||||
When configuring an LLM for your agent, you have access to a wide range of parameters:
|
||||
|
||||
| Parameter | Type | Description |
|
||||
|:----------|:-----:|:-------------|
|
||||
| **model** | `str` | The name of the model to use (e.g., "gpt-4", "claude-2") |
|
||||
| **temperature** | `float` | Controls randomness in output (0.0 to 1.0) |
|
||||
| **max_tokens** | `int` | Maximum number of tokens to generate |
|
||||
| **top_p** | `float` | Controls diversity of output (0.0 to 1.0) |
|
||||
| **frequency_penalty** | `float` | Penalizes new tokens based on their frequency in the text so far |
|
||||
| **presence_penalty** | `float` | Penalizes new tokens based on their presence in the text so far |
|
||||
| **stop** | `str`, `List[str]` | Sequence(s) to stop generation |
|
||||
| **base_url** | `str` | The base URL for the API endpoint |
|
||||
| **api_key** | `str` | Your API key for authentication |
|
||||
|
||||
For a complete list of parameters and their descriptions, refer to the LLM class documentation.
|
||||
|
||||
## Connecting to OpenAI-Compatible LLMs
|
||||
|
||||
You can connect to OpenAI-compatible LLMs using either environment variables or by setting specific attributes on the LLM class:
|
||||
|
||||
<Tabs>
|
||||
<Tab title="Using Environment Variables">
|
||||
<CodeGroup>
|
||||
```python Code
|
||||
import os
|
||||
|
||||
os.environ["OPENAI_API_KEY"] = "your-api-key"
|
||||
os.environ["OPENAI_API_BASE"] = "https://api.your-provider.com/v1"
|
||||
os.environ["OPENAI_MODEL_NAME"] = "your-model-name"
|
||||
```
|
||||
</CodeGroup>
|
||||
</Tab>
|
||||
<Tab title="Using LLM Class Attributes">
|
||||
<CodeGroup>
|
||||
```python Code
|
||||
llm = LLM(
|
||||
model="custom-model-name",
|
||||
api_key="your-api-key",
|
||||
base_url="https://api.your-provider.com/v1"
|
||||
)
|
||||
agent = Agent(llm=llm, ...)
|
||||
```
|
||||
</CodeGroup>
|
||||
</Tab>
|
||||
</Tabs>
|
||||
|
||||
## Using Local Models with Ollama
|
||||
|
||||
For local models like those provided by Ollama:
|
||||
|
||||
<Steps>
|
||||
<Step title="Download and install Ollama">
|
||||
[Click here to download and install Ollama](https://ollama.com/download)
|
||||
</Step>
|
||||
<Step title="Pull the desired model">
|
||||
For example, run `ollama pull llama3.2` to download the model.
|
||||
</Step>
|
||||
<Step title="Configure your agent">
|
||||
<CodeGroup>
|
||||
```python Code
|
||||
agent = Agent(
|
||||
role='Local AI Expert',
|
||||
goal='Process information using a local model',
|
||||
backstory="An AI assistant running on local hardware.",
|
||||
llm=LLM(model="ollama/llama3.2", base_url="http://localhost:11434")
|
||||
)
|
||||
```
|
||||
</CodeGroup>
|
||||
</Step>
|
||||
</Steps>
|
||||
|
||||
## Changing the Base API URL
|
||||
|
||||
You can change the base API URL for any LLM provider by setting the `base_url` parameter:
|
||||
|
||||
```python Code
|
||||
llm = LLM(
|
||||
model="custom-model-name",
|
||||
base_url="https://api.your-provider.com/v1",
|
||||
api_key="your-api-key"
|
||||
)
|
||||
agent = Agent(llm=llm, ...)
|
||||
```
|
||||
|
||||
This is particularly useful when working with OpenAI-compatible APIs or when you need to specify a different endpoint for your chosen provider.
|
||||
|
||||
## Conclusion
|
||||
|
||||
By leveraging LiteLLM, CrewAI offers seamless integration with a vast array of LLMs. This flexibility allows you to choose the most suitable model for your specific needs, whether you prioritize performance, cost-efficiency, or local deployment. Remember to consult the [LiteLLM documentation](https://docs.litellm.ai/docs/) for the most up-to-date information on supported models and configuration options.
|
||||
80
docs/how-to/replay-tasks-from-latest-crew-kickoff.mdx
Normal file
80
docs/how-to/replay-tasks-from-latest-crew-kickoff.mdx
Normal file
@@ -0,0 +1,80 @@
|
||||
---
|
||||
title: Replay Tasks from Latest Crew Kickoff
|
||||
description: Replay tasks from the latest crew.kickoff(...)
|
||||
icon: arrow-right
|
||||
---
|
||||
|
||||
## Introduction
|
||||
|
||||
CrewAI provides the ability to replay from a task specified from the latest crew kickoff. This feature is particularly useful when you've finished a kickoff and may want to retry certain tasks or don't need to refetch data over and your agents already have the context saved from the kickoff execution so you just need to replay the tasks you want to.
|
||||
|
||||
<Note>
|
||||
You must run `crew.kickoff()` before you can replay a task.
|
||||
Currently, only the latest kickoff is supported, so if you use `kickoff_for_each`, it will only allow you to replay from the most recent crew run.
|
||||
</Note>
|
||||
|
||||
Here's an example of how to replay from a task:
|
||||
|
||||
### Replaying from Specific Task Using the CLI
|
||||
|
||||
To use the replay feature, follow these steps:
|
||||
|
||||
<Steps>
|
||||
<Step title="Open your terminal or command prompt.">
|
||||
</Step>
|
||||
<Step title="Navigate to the directory where your CrewAI project is located.">
|
||||
</Step>
|
||||
<Step title="Run the following commands:">
|
||||
To view the latest kickoff task_ids use:
|
||||
|
||||
```shell
|
||||
crewai log-tasks-outputs
|
||||
```
|
||||
|
||||
Once you have your `task_id` to replay, use:
|
||||
|
||||
```shell
|
||||
crewai replay -t <task_id>
|
||||
```
|
||||
</Step>
|
||||
</Steps>
|
||||
|
||||
<Note>
|
||||
Ensure `crewai` is installed and configured correctly in your development environment.
|
||||
</Note>
|
||||
|
||||
### Replaying from a Task Programmatically
|
||||
|
||||
To replay from a task programmatically, use the following steps:
|
||||
|
||||
<Steps>
|
||||
<Step title="Specify the `task_id` and input parameters for the replay process.">
|
||||
Specify the `task_id` and input parameters for the replay process.
|
||||
</Step>
|
||||
<Step title="Execute the replay command within a try-except block to handle potential errors.">
|
||||
Execute the replay command within a try-except block to handle potential errors.
|
||||
<CodeGroup>
|
||||
```python Code
|
||||
def replay():
|
||||
"""
|
||||
Replay the crew execution from a specific task.
|
||||
"""
|
||||
task_id = '<task_id>'
|
||||
inputs = {"topic": "CrewAI Training"} # This is optional; you can pass in the inputs you want to replay; otherwise, it uses the previous kickoff's inputs.
|
||||
try:
|
||||
YourCrewName_Crew().crew().replay(task_id=task_id, inputs=inputs)
|
||||
|
||||
except subprocess.CalledProcessError as e:
|
||||
raise Exception(f"An error occurred while replaying the crew: {e}")
|
||||
|
||||
except Exception as e:
|
||||
raise Exception(f"An unexpected error occurred: {e}")
|
||||
```
|
||||
</CodeGroup>
|
||||
</Step>
|
||||
</Steps>
|
||||
|
||||
## Conclusion
|
||||
|
||||
With the above enhancements and detailed functionality, replaying specific tasks in CrewAI has been made more efficient and robust.
|
||||
Ensure you follow the commands and steps precisely to make the most of these features.
|
||||
@@ -1,23 +1,30 @@
|
||||
---
|
||||
title: Using the Sequential Processes in crewAI
|
||||
description: A comprehensive guide to utilizing the sequential processes for task execution in crewAI projects.
|
||||
title: Sequential Processes
|
||||
description: A comprehensive guide to utilizing the sequential processes for task execution in CrewAI projects.
|
||||
icon: forward
|
||||
---
|
||||
|
||||
## Introduction
|
||||
CrewAI offers a flexible framework for executing tasks in a structured manner, supporting both sequential and hierarchical processes. This guide outlines how to effectively implement these processes to ensure efficient task execution and project completion.
|
||||
|
||||
CrewAI offers a flexible framework for executing tasks in a structured manner, supporting both sequential and hierarchical processes.
|
||||
This guide outlines how to effectively implement these processes to ensure efficient task execution and project completion.
|
||||
|
||||
## Sequential Process Overview
|
||||
The sequential process ensures tasks are executed one after the other, following a linear progression. This approach is ideal for projects requiring tasks to be completed in a specific order.
|
||||
|
||||
The sequential process ensures tasks are executed one after the other, following a linear progression.
|
||||
This approach is ideal for projects requiring tasks to be completed in a specific order.
|
||||
|
||||
### Key Features
|
||||
|
||||
- **Linear Task Flow**: Ensures orderly progression by handling tasks in a predetermined sequence.
|
||||
- **Simplicity**: Best suited for projects with clear, step-by-step tasks.
|
||||
- **Easy Monitoring**: Facilitates easy tracking of task completion and project progress.
|
||||
|
||||
## Implementing the Sequential Process
|
||||
|
||||
To use the sequential process, assemble your crew and define tasks in the order they need to be executed.
|
||||
|
||||
```python
|
||||
```python Code
|
||||
from crewai import Crew, Process, Agent, Task, TaskOutput, CrewOutput
|
||||
|
||||
# Define your agents
|
||||
@@ -38,9 +45,21 @@ writer = Agent(
|
||||
)
|
||||
|
||||
# Define your tasks
|
||||
research_task = Task(description='Gather relevant data...', agent=researcher, expected_output='Raw Data')
|
||||
analysis_task = Task(description='Analyze the data...', agent=analyst, expected_output='Data Insights')
|
||||
writing_task = Task(description='Compose the report...', agent=writer, expected_output='Final Report')
|
||||
research_task = Task(
|
||||
description='Gather relevant data...',
|
||||
agent=researcher,
|
||||
expected_output='Raw Data'
|
||||
)
|
||||
analysis_task = Task(
|
||||
description='Analyze the data...',
|
||||
agent=analyst,
|
||||
expected_output='Data Insights'
|
||||
)
|
||||
writing_task = Task(
|
||||
description='Compose the report...',
|
||||
agent=writer,
|
||||
expected_output='Final Report'
|
||||
)
|
||||
|
||||
# Form the crew with a sequential process
|
||||
report_crew = Crew(
|
||||
@@ -58,9 +77,11 @@ crew_output: CrewOutput = result.output
|
||||
```
|
||||
|
||||
### Note:
|
||||
|
||||
Each task in a sequential process **must** have an agent assigned. Ensure that every `Task` includes an `agent` parameter.
|
||||
|
||||
### Workflow in Action
|
||||
|
||||
1. **Initial Task**: In a sequential process, the first agent completes their task and signals completion.
|
||||
2. **Subsequent Tasks**: Agents pick up their tasks based on the process type, with outcomes of preceding tasks or directives guiding their execution.
|
||||
3. **Completion**: The process concludes once the final task is executed, leading to project completion.
|
||||
@@ -68,28 +89,39 @@ Each task in a sequential process **must** have an agent assigned. Ensure that e
|
||||
## Advanced Features
|
||||
|
||||
### Task Delegation
|
||||
In sequential processes, if an agent has `allow_delegation` set to `True`, they can delegate tasks to other agents in the crew. This feature is automatically set up when there are multiple agents in the crew.
|
||||
|
||||
In sequential processes, if an agent has `allow_delegation` set to `True`, they can delegate tasks to other agents in the crew.
|
||||
This feature is automatically set up when there are multiple agents in the crew.
|
||||
|
||||
### Asynchronous Execution
|
||||
Tasks can be executed asynchronously, allowing for parallel processing when appropriate. To create an asynchronous task, set `async_execution=True` when defining the task.
|
||||
|
||||
Tasks can be executed asynchronously, allowing for parallel processing when appropriate.
|
||||
To create an asynchronous task, set `async_execution=True` when defining the task.
|
||||
|
||||
### Memory and Caching
|
||||
|
||||
CrewAI supports both memory and caching features:
|
||||
|
||||
- **Memory**: Enable by setting `memory=True` when creating the Crew. This allows agents to retain information across tasks.
|
||||
- **Caching**: By default, caching is enabled. Set `cache=False` to disable it.
|
||||
|
||||
### Callbacks
|
||||
|
||||
You can set callbacks at both the task and step level:
|
||||
|
||||
- `task_callback`: Executed after each task completion.
|
||||
- `step_callback`: Executed after each step in an agent's execution.
|
||||
|
||||
### Usage Metrics
|
||||
|
||||
CrewAI tracks token usage across all tasks and agents. You can access these metrics after execution.
|
||||
|
||||
## Best Practices for Sequential Processes
|
||||
|
||||
1. **Order Matters**: Arrange tasks in a logical sequence where each task builds upon the previous one.
|
||||
2. **Clear Task Descriptions**: Provide detailed descriptions for each task to guide the agents effectively.
|
||||
3. **Appropriate Agent Selection**: Match agents' skills and roles to the requirements of each task.
|
||||
4. **Use Context**: Leverage the context from previous tasks to inform subsequent ones.
|
||||
|
||||
This updated documentation ensures that details accurately reflect the latest changes in the codebase and clearly describes how to leverage new features and configurations. The content is kept simple and direct to ensure easy understanding.
|
||||
This updated documentation ensures that details accurately reflect the latest changes in the codebase and clearly describes how to leverage new features and configurations.
|
||||
The content is kept simple and direct to ensure easy understanding.
|
||||
Reference in New Issue
Block a user