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Merge branch 'main' into feature/procedure_v2

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Brandon Hancock
2024-07-22 09:55:03 -04:00
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15
docs/core-concepts/Crews.md
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@@ -32,6 +32,7 @@ A crew in crewAI represents a collaborative group of agents working together to
| **Manager Agent** _(optional)_ | `manager_agent` | `manager` sets a custom agent that will be used as a manager. |
| **Manager Callbacks** _(optional)_ | `manager_callbacks` | `manager_callbacks` takes a list of callback handlers to be executed by the manager agent when a hierarchical process is used. |
| **Prompt File** _(optional)_ | `prompt_file` | Path to the prompt JSON file to be used for the crew. |
| **Planning** *(optional)* | `planning` | Adds planning ability to the Crew. When activated before each Crew iteration, all Crew data is sent to an AgentPlanner that will plan the tasks and this plan will be added to each task description.
!!! note "Crew Max RPM"
The `max_rpm` attribute sets the maximum number of requests per minute the crew can perform to avoid rate limits and will override individual agents' `max_rpm` settings if you set it.
@@ -45,6 +46,12 @@ When assembling a crew, you combine agents with complementary roles and tools, a
```python
from crewai import Crew, Agent, Task, Process
from langchain_community.tools import DuckDuckGoSearchRun
from crewai_tools import tool
@tool('DuckDuckGoSearch')
def search(search_query: str):
"""Search the web for information on a given topic"""
return DuckDuckGoSearchRun().run(search_query)
# Define agents with specific roles and tools
researcher = Agent(
@@ -55,7 +62,7 @@ researcher = Agent(
to the business.
You're currently working on a project to analyze the
trends and innovations in the space of artificial intelligence.""",
tools=[DuckDuckGoSearchRun()]
tools=[search]
)
writer = Agent(
@@ -213,9 +220,9 @@ These methods provide flexibility in how you manage and execute tasks within you
### Replaying from specific task:
You can now replay from a specific task using our cli command replay.
The replay_from_tasks feature in CrewAI allows you to replay from a specific task using the command-line interface (CLI). By running the command `crewai replay -t <task_id>`, you can specify the `task_id` for the replay process.
The replay feature in CrewAI allows you to replay from a specific task using the command-line interface (CLI). By running the command `crewai replay -t <task_id>`, you can specify the `task_id` for the replay process.
Kickoffs will now save the latest kickoffs returned task outputs locally for you to be able to replay from.
Kickoffs will now save the latest kickoffs returned task outputs locally for you to be able to replay from.
### Replaying from specific task Using the CLI
@@ -236,4 +243,4 @@ crewai log-tasks-outputs
crewai replay -t <task_id>
```
These commands let you replay from your latest kickoff tasks, still retaining context from previously executed tasks.
These commands let you replay from your latest kickoff tasks, still retaining context from previously executed tasks.

40
docs/core-concepts/Memory.md
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@@ -29,6 +29,11 @@ description: Leveraging memory systems in the crewAI framework to enhance agent
When configuring a crew, you can enable and customize each memory component to suit the crew's objectives and the nature of tasks it will perform.
By default, the memory system is disabled, and you can ensure it is active by setting `memory=True` in the crew configuration. The memory will use OpenAI Embeddings by default, but you can change it by setting `embedder` to a different model.
The 'embedder' only applies to **Short-Term Memory** which uses Chroma for RAG using EmbedChain package.
The **Long-Term Memory** uses SQLLite3 to store task results. Currently, there is no way to override these storage implementations.
The data storage files are saved into a platform specific location found using the appdirs package
and the name of the project which can be overridden using the **CREWAI_STORAGE_DIR** environment variable.
### Example: Configuring Memory for a Crew
```python
@@ -161,10 +166,43 @@ my_crew = Crew(
)
```
### Resetting Memory
```sh
crewai reset_memories [OPTIONS]
```
#### Resetting Memory Options
- **`-l, --long`**
- **Description:** Reset LONG TERM memory.
- **Type:** Flag (boolean)
- **Default:** False
- **`-s, --short`**
- **Description:** Reset SHORT TERM memory.
- **Type:** Flag (boolean)
- **Default:** False
- **`-e, --entities`**
- **Description:** Reset ENTITIES memory.
- **Type:** Flag (boolean)
- **Default:** False
- **`-k, --kickoff-outputs`**
- **Description:** Reset LATEST KICKOFF TASK OUTPUTS.
- **Type:** Flag (boolean)
- **Default:** False
- **`-a, --all`**
- **Description:** Reset ALL memories.
- **Type:** Flag (boolean)
- **Default:** False
## Benefits of Using crewAI's Memory System
- **Adaptive Learning:** Crews become more efficient over time, adapting to new information and refining their approach to tasks.
- **Enhanced Personalization:** Memory enables agents to remember user preferences and historical interactions, leading to personalized experiences.
- **Improved Problem Solving:** Access to a rich memory store aids agents in making more informed decisions, drawing on past learnings and contextual insights.
## Getting Started
Integrating crewAI's memory system into your projects is straightforward. By leveraging the provided memory components and configurations, you can quickly empower your agents with the ability to remember, reason, and learn from their interactions, unlocking new levels of intelligence and capability.
Integrating crewAI's memory system into your projects is straightforward. By leveraging the provided memory components and configurations, you can quickly empower your agents with the ability to remember, reason, and learn from their interactions, unlocking new levels of intelligence and capability.

119
docs/core-concepts/Planning.md Normal file
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@@ -0,0 +1,119 @@
---
title: crewAI Planning
description: Learn how to add planning to your crewAI Crew and improve their performance.
---
## Introduction
The planning feature in CrewAI allows you to add planning capability to your crew. When enabled, before each Crew iteration, all Crew information is sent to an AgentPlanner that will plan the tasks step by step, and this plan will be added to each task description.
### Using the Planning Feature
Getting started with the planning feature is very easy, the only step required is to add `planning=True` to your Crew:
```python
from crewai import Crew, Agent, Task, Process
# Assemble your crew with planning capabilities
my_crew = Crew(
agents=self.agents,
tasks=self.tasks,
process=Process.sequential,
planning=True,
)
```
From this point on, your crew will have planning enabled, and the tasks will be planned before each iteration.
### Example
When running the base case example, you will see something like the following output, which represents the output of the AgentPlanner responsible for creating the step-by-step logic to add to the Agents tasks.
```bash
[2024-07-15 16:49:11][INFO]: Planning the crew execution
**Step-by-Step Plan for Task Execution**
**Task Number 1: Conduct a thorough research about AI LLMs**
**Agent:** AI LLMs Senior Data Researcher
**Agent Goal:** Uncover cutting-edge developments in AI LLMs
**Task Expected Output:** A list with 10 bullet points of the most relevant information about AI LLMs
**Task Tools:** None specified
**Agent Tools:** None specified
**Step-by-Step Plan:**
1. **Define Research Scope:**
- Determine the specific areas of AI LLMs to focus on, such as advancements in architecture, use cases, ethical considerations, and performance metrics.
2. **Identify Reliable Sources:**
- List reputable sources for AI research, including academic journals, industry reports, conferences (e.g., NeurIPS, ACL), AI research labs (e.g., OpenAI, Google AI), and online databases (e.g., IEEE Xplore, arXiv).
3. **Collect Data:**
- Search for the latest papers, articles, and reports published in 2023 and early 2024.
- Use keywords like "Large Language Models 2024", "AI LLM advancements", "AI ethics 2024", etc.
4. **Analyze Findings:**
- Read and summarize the key points from each source.
- Highlight new techniques, models, and applications introduced in the past year.
5. **Organize Information:**
- Categorize the information into relevant topics (e.g., new architectures, ethical implications, real-world applications).
- Ensure each bullet point is concise but informative.
6. **Create the List:**
- Compile the 10 most relevant pieces of information into a bullet point list.
- Review the list to ensure clarity and relevance.
**Expected Output:**
A list with 10 bullet points of the most relevant information about AI LLMs.
---
**Task Number 2: Review the context you got and expand each topic into a full section for a report**
**Agent:** AI LLMs Reporting Analyst
**Agent Goal:** Create detailed reports based on AI LLMs data analysis and research findings
**Task Expected Output:** A fully fledge report with the main topics, each with a full section of information. Formatted as markdown without '```'
**Task Tools:** None specified
**Agent Tools:** None specified
**Step-by-Step Plan:**
1. **Review the Bullet Points:**
- Carefully read through the list of 10 bullet points provided by the AI LLMs Senior Data Researcher.
2. **Outline the Report:**
- Create an outline with each bullet point as a main section heading.
- Plan sub-sections under each main heading to cover different aspects of the topic.
3. **Research Further Details:**
- For each bullet point, conduct additional research if necessary to gather more detailed information.
- Look for case studies, examples, and statistical data to support each section.
4. **Write Detailed Sections:**
- Expand each bullet point into a comprehensive section.
- Ensure each section includes an introduction, detailed explanation, examples, and a conclusion.
- Use markdown formatting for headings, subheadings, lists, and emphasis.
5. **Review and Edit:**
- Proofread the report for clarity, coherence, and correctness.
- Make sure the report flows logically from one section to the next.
- Format the report according to markdown standards.
6. **Finalize the Report:**
- Ensure the report is complete with all sections expanded and detailed.
- Double-check formatting and make any necessary adjustments.
**Expected Output:**
A fully-fledged report with the main topics, each with a full section of information. Formatted as markdown without '```'.
---
```

10
docs/core-concepts/Tools.md
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@@ -100,16 +100,24 @@ Here is a list of the available tools and their descriptions:
| Tool | Description |
| :-------------------------- | :-------------------------------------------------------------------------------------------- |
| **BrowserbaseLoadTool** | A tool for interacting with and extracting data from web browsers. |
| **CodeDocsSearchTool** | A RAG tool optimized for searching through code documentation and related technical documents. |
| **CodeInterpreterTool** | A tool for interpreting python code. |
| **ComposioTool** | Enables use of Composio tools. |
| **CSVSearchTool** | A RAG tool designed for searching within CSV files, tailored to handle structured data. |
| **DirectorySearchTool** | A RAG tool for searching within directories, useful for navigating through file systems. |
| **DOCXSearchTool** | A RAG tool aimed at searching within DOCX documents, ideal for processing Word files. |
| **DirectoryReadTool** | Facilitates reading and processing of directory structures and their contents. |
| **EXASearchTool** | A tool designed for performing exhaustive searches across various data sources. |
| **FileReadTool** | Enables reading and extracting data from files, supporting various file formats. |
| **FirecrawlSearchTool** | A tool to search webpages using Firecrawl and return the results. |
| **FirecrawlCrawlWebsiteTool** | A tool for crawling webpages using Firecrawl. |
| **FirecrawlScrapeWebsiteTool** | A tool for scraping webpages url using Firecrawl and returning its contents. |
| **GithubSearchTool** | A RAG tool for searching within GitHub repositories, useful for code and documentation search.|
| **SerperDevTool** | A specialized tool for development purposes, with specific functionalities under development. |
| **TXTSearchTool** | A RAG tool focused on searching within text (.txt) files, suitable for unstructured data. |
| **JSONSearchTool** | A RAG tool designed for searching within JSON files, catering to structured data handling. |
| **LlamaIndexTool** | Enables the use of LlamaIndex tools. |
| **MDXSearchTool** | A RAG tool tailored for searching within Markdown (MDX) files, useful for documentation. |
| **PDFSearchTool** | A RAG tool aimed at searching within PDF documents, ideal for processing scanned documents. |
| **PGSearchTool** | A RAG tool optimized for searching within PostgreSQL databases, suitable for database queries. |
@@ -120,8 +128,6 @@ Here is a list of the available tools and their descriptions:
| **XMLSearchTool** | A RAG tool designed for searching within XML files, suitable for structured data formats. |
| **YoutubeChannelSearchTool**| A RAG tool for searching within YouTube channels, useful for video content analysis. |
| **YoutubeVideoSearchTool** | A RAG tool aimed at searching within YouTube videos, ideal for video data extraction. |
| **BrowserbaseTool** | A tool for interacting with and extracting data from web browsers. |
| **ExaSearchTool** | A tool designed for performing exhaustive searches across various data sources. |
## Creating your own Tools

8
docs/how-to/Creating-a-Crew-and-kick-it-off.md
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@@ -21,14 +21,16 @@ Define your agents with distinct roles, backstories, and enhanced capabilities.
import os
from langchain.llms import OpenAI
from crewai import Agent
from crewai_tools import SerperDevTool, BrowserbaseTool, ExaSearchTool
from crewai_tools import SerperDevTool, BrowserbaseLoadTool, EXASearchTool
os.environ["OPENAI_API_KEY"] = "Your OpenAI Key"
os.environ["SERPER_API_KEY"] = "Your Serper Key"
os.environ["BROWSERBASE_API_KEY"] = "Your BrowserBase Key"
os.environ["BROWSERBASE_PROJECT_ID"] = "Your BrowserBase Project Id"
search_tool = SerperDevTool()
browser_tool = BrowserbaseTool()
exa_search_tool = ExaSearchTool()
browser_tool = BrowserbaseLoadTool()
exa_search_tool = EXASearchTool()
# Creating a senior researcher agent with advanced configurations
researcher = Agent(

4
docs/how-to/Replay-tasks-from-latest-Crew-Kickoff.md
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@@ -36,14 +36,14 @@ To replay from a task programmatically, use the following steps:
2. Execute the replay command within a try-except block to handle potential errors.
```python
def replay_from_task():
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 uses the previous kickoffs inputs
try:
YourCrewName_Crew().crew().replay_from_task(task_id=task_id, inputs=inputs)
YourCrewName_Crew().crew().replay(task_id=task_id, inputs=inputs)
except Exception as e:
raise Exception(f"An error occurred while replaying the crew: {e}")

4
docs/how-to/Start-a-New-CrewAI-Project.md
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@@ -9,7 +9,7 @@ Welcome to the ultimate guide for starting a new CrewAI project. This document w
## Prerequisites
We assume you have already installed CrewAI. If not, please refer to the [installation guide](how-to/Installing-CrewAI.md) to install CrewAI and its dependencies.
We assume you have already installed CrewAI. If not, please refer to the [installation guide](https://docs.crewai.com/how-to/Installing-CrewAI/) to install CrewAI and its dependencies.
## Creating a New Project
@@ -134,4 +134,4 @@ This will initialize your crew of AI agents and begin task execution as defined
## Deploying Your Project
The easiest way to deploy your crew is through [CrewAI+](https://www.crewai.com/crewaiplus), where you can deploy your crew in a few clicks.
The easiest way to deploy your crew is through [CrewAI+](https://www.crewai.com/crewaiplus), where you can deploy your crew in a few clicks.

5
docs/index.md
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@@ -43,6 +43,11 @@ Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By
Memory
</a>
</li>
<li>
<a href="./core-concepts/Planning">
Planning
</a>
</li>
</ul>
</div>
<div style="width:30%">