added validator for async_execution true in tasks whenever in hierarchical run

* WIP. Figuring out disconnect issue.

* Cleaned up logs now that I've isolated the issue to the LLM

* more wip.

* WIP. It looks like usage metrics has always been broken for async

* Update parent crew who is managing for_each loop

* Merge in main to bugfix/kickoff-for-each-usage-metrics

* Clean up code for review

* Add new tests

* Final cleanup. Ready for review.

* Moving copy functionality from Agent to BaseAgent

* Fix renaming issue

* Fix linting errors

* use BaseAgent instead of Agent where applicable

.circleci/config.yml

@@ -1,27 +0,0 @@
-version: 2.1
-
-jobs:
-  build-and-test:
-    docker:
-      - image: python:3.9.18
-    steps:
-      - checkout
-      - run:
-          name: Install poetry
-          command: pip install poetry
-      - run:
-          name: Install dependencies
-          command: poetry install
-      - run:
-          name: Update PATH and Define Environment Variable at Runtime
-          command: |
-            echo 'export OPENAI_API_KEY=fake-api-key' >> "$BASH_ENV"
-            source "$BASH_ENV"
-      - run:
-          name: Run tests
-          command: poetry run pytest
-
-workflows:
-  build-and-test:
-    jobs:
-      - build-and-test

.editorconfig

@@ -0,0 +1,14 @@
+# .editorconfig
+root = true
+
+# All files
+[*]
+charset = utf-8
+end_of_line = lf
+insert_final_newline = true
+trim_trailing_whitespace = true
+
+# Python files
+[*.py]
+indent_style = space
+indent_size = 2

.github/workflows/linter.yml

@@ -0,0 +1,16 @@
+name: Lint
+
+on: [pull_request]
+
+jobs:
+  lint:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+
+      - name: Install Requirements
+        run: |
+          pip install ruff
+
+      - name: Run Ruff Linter
+        run: ruff check --exclude "templates","__init__.py"

.github/workflows/mkdocs.yml

@@ -0,0 +1,47 @@
+name: Deploy MkDocs
+
+on:
+  workflow_dispatch:
+  push:
+    branches:
+      - main
+
+permissions:
+  contents: write
+
+jobs:
+  deploy:
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v2
+
+      - name: Setup Python
+        uses: actions/setup-python@v4
+        with:
+          python-version: '3.10'
+
+      - name: Calculate requirements hash
+        id: req-hash
+        run: echo "::set-output name=hash::$(sha256sum requirements-doc.txt | awk '{print $1}')"
+
+      - name: Setup cache
+        uses: actions/cache@v3
+        with:
+          key: mkdocs-material-${{ steps.req-hash.outputs.hash }}
+          path: .cache
+          restore-keys: |
+            mkdocs-material-
+
+      - name: Install Requirements
+        run: |
+          sudo apt-get update &&
+          sudo apt-get install pngquant &&
+          pip install mkdocs-material mkdocs-material-extensions pillow cairosvg
+
+        env:
+          GH_TOKEN: ${{ secrets.GH_TOKEN }}
+
+      - name: Build and deploy MkDocs
+        run: mkdocs gh-deploy --force

.github/workflows/tests.yml

@@ -0,0 +1,31 @@
+name: Run Tests
+
+on: [pull_request]
+
+permissions:
+  contents: write
+
+env:
+  OPENAI_API_KEY: fake-api-key
+
+jobs:
+  deploy:
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4
+
+      - name: Setup Python
+        uses: actions/setup-python@v4
+        with:
+          python-version: "3.10"
+
+      - name: Install Requirements
+        run: |
+          set -e
+          pip install poetry
+          poetry install
+
+      - name: Run tests
+        run: poetry run pytest tests

.github/workflows/type-checker.yml

@@ -0,0 +1,26 @@
+name: Run Type Checks
+
+on: [pull_request]
+
+permissions:
+  contents: write
+
+jobs:
+  type-checker:
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4
+
+      - name: Setup Python
+        uses: actions/setup-python@v4
+        with:
+          python-version: "3.10"
+
+      - name: Install Requirements
+        run: |
+          pip install mypy
+
+      - name: Run type checks
+        run: mypy src

.gitignore

@@ -5,4 +5,13 @@ dist/
 .env
 assets/*
 .idea
-test.py
+test/
+docs_crew/
+chroma.sqlite3
+old_en.json
+db/
+test.py
+rc-tests/*
+*.pkl
+temp/*
+.vscode/*

.pre-commit-config.yaml

@@ -1,21 +1,9 @@
 repos:
-
-  - repo: https://github.com/psf/black-pre-commit-mirror
-    rev: 23.12.1
+  - repo: https://github.com/astral-sh/ruff-pre-commit
+    rev: v0.4.4
     hooks:
-      - id: black
-        language_version: python3.11
-        files: \.(py)$
-
-  - repo: https://github.com/pycqa/isort
-    rev: 5.13.2
-    hooks:
-      - id: isort
-        name: isort (python)
-        args: ["--profile", "black", "--filter-files"]
-
-  - repo: https://github.com/PyCQA/autoflake
-    rev: v2.2.1
-    hooks:
-      - id: autoflake
-        args: ['--in-place', '--remove-all-unused-imports', '--remove-unused-variables', '--ignore-init-module-imports']
+      - id: ruff
+        args: ["--fix"]
+        exclude: "templates"
+      - id: ruff-format
+        exclude: "templates"

README.md

@@ -1,18 +1,36 @@
-# crewAI
+<div align="center">
 
-![Logo of crewAI, tow people rowing on a boat](./crewai_logo.png)
+![Logo of crewAI, two people rowing on a boat](./docs/crewai_logo.png)
 
-🤖 Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks.
+# **crewAI**
 
-- [Why CrewAI](#why-crewai)
+🤖 **crewAI**: Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks.
+
+<h3>
+
+[Homepage](https://www.crewai.io/) | [Documentation](https://docs.crewai.com/) | [Chat with Docs](https://chatg.pt/DWjSBZn) | [Examples](https://github.com/joaomdmoura/crewai-examples) | [Discord](https://discord.com/invite/X4JWnZnxPb)
+
+</h3>
+
+[![GitHub Repo stars](https://img.shields.io/github/stars/joaomdmoura/crewAI)](https://github.com/joaomdmoura/crewAI)
+[![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](https://opensource.org/licenses/MIT)
+
+</div>
+
+## Table of contents
+
+- [Why CrewAI?](#why-crewai)
 - [Getting Started](#getting-started)
 - [Key Features](#key-features)
 - [Examples](#examples)
-- [Local Open Source Models](#local-open-source-models)
-- [CrewAI x AutoGen x ChatDev](#how-crewai-compares)
+  - [Quick Tutorial](#quick-tutorial)
+  - [Write Job Descriptions](#write-job-descriptions)
+  - [Trip Planner](#trip-planner)
+  - [Stock Analysis](#stock-analysis)
+- [Connecting Your Crew to a Model](#connecting-your-crew-to-a-model)
+- [How CrewAI Compares](#how-crewai-compares)
 - [Contribution](#contribution)
-- [💬 CrewAI Discord Community](https://discord.gg/4ZqbAStv)
-- [Hire Consulting](#hire-consulting)
+- [Telemetry](#telemetry)
 - [License](#license)
 
 ## Why CrewAI?
@@ -20,78 +38,78 @@
 The power of AI collaboration has too much to offer.
 CrewAI is designed to enable AI agents to assume roles, share goals, and operate in a cohesive unit - much like a well-oiled crew. Whether you're building a smart assistant platform, an automated customer service ensemble, or a multi-agent research team, CrewAI provides the backbone for sophisticated multi-agent interactions.
 
-- 🤖 [Talk with the Docs](https://chat.openai.com/g/g-qqTuUWsBY-crewai-assistant)
-- 📄 [Documentation Wiki](https://github.com/joaomdmoura/CrewAI/wiki)
-
 ## Getting Started
 
 To get started with CrewAI, follow these simple steps:
 
-1. **Installation**:
+### 1. Installation
 
 ```shell
 pip install crewai
 ```
 
-The example bellow also uses duckduckgo, so also install that
+If you want to install the 'crewai' package along with its optional features that include additional tools for agents, you can do so by using the following command: pip install 'crewai[tools]'. This command installs the basic package and also adds extra components which require more dependencies to function."
+
 ```shell
-pip install duckduckgo-search
+pip install 'crewai[tools]'
 ```
 
-2. **Setting Up Your Crew**:
+### 2. Setting Up Your Crew
 
 ```python
 import os
 from crewai import Agent, Task, Crew, Process
+from crewai_tools import SerperDevTool
 
-os.environ["OPENAI_API_KEY"] = "YOUR KEY"
+os.environ["OPENAI_API_KEY"] = "YOUR_API_KEY"
+os.environ["SERPER_API_KEY"] = "Your Key" # serper.dev API key
 
-# You can choose to use a local model through Ollama for example.
+# You can choose to use a local model through Ollama for example. See https://docs.crewai.com/how-to/LLM-Connections/ for more information.
+
+# os.environ["OPENAI_API_BASE"] = 'http://localhost:11434/v1'
+# os.environ["OPENAI_MODEL_NAME"] ='openhermes'  # Adjust based on available model
+# os.environ["OPENAI_API_KEY"] ='sk-111111111111111111111111111111111111111111111111'
+
+# You can pass an optional llm attribute specifying what model you wanna use.
+# It can be a local model through Ollama / LM Studio or a remote
+# model like OpenAI, Mistral, Antrophic or others (https://docs.crewai.com/how-to/LLM-Connections/)
 #
-# from langchain.llms import Ollama
-# ollama_llm = Ollama(model="openhermes")
+# import os
+# os.environ['OPENAI_MODEL_NAME'] = 'gpt-3.5-turbo'
+#
+# OR
+#
+# from langchain_openai import ChatOpenAI
 
-# Install duckduckgo-search for this example:
-# !pip install -U duckduckgo-search
-
-from langchain.tools import DuckDuckGoSearchRun
-search_tool = DuckDuckGoSearchRun()
+search_tool = SerperDevTool()
 
 # Define your agents with roles and goals
 researcher = Agent(
   role='Senior Research Analyst',
-  goal='Uncover cutting-edge developments in AI and data science in',
+  goal='Uncover cutting-edge developments in AI and data science',
   backstory="""You work at a leading tech think tank.
   Your expertise lies in identifying emerging trends.
-  You have a knack for dissecting complex data and presenting
-  actionable insights.""",
+  You have a knack for dissecting complex data and presenting actionable insights.""",
   verbose=True,
   allow_delegation=False,
+  # You can pass an optional llm attribute specifying what model you wanna use.
+  # llm=ChatOpenAI(model_name="gpt-3.5", temperature=0.7),
   tools=[search_tool]
-  # You can pass an optional llm attribute specifying what mode you wanna use.
-  # It can be a local model through Ollama / LM Studio or a remote
-  # model like OpenAI, Mistral, Antrophic of others (https://python.langchain.com/docs/integrations/llms/)
-  #
-  # Examples:
-  # llm=ollama_llm # was defined above in the file
-  # llm=ChatOpenAI(model_name="gpt-3.5", temperature=0.7)
 )
 writer = Agent(
   role='Tech Content Strategist',
   goal='Craft compelling content on tech advancements',
-  backstory="""You are a renowned Content Strategist, known for
-  your insightful and engaging articles.
+  backstory="""You are a renowned Content Strategist, known for your insightful and engaging articles.
   You transform complex concepts into compelling narratives.""",
   verbose=True,
-  allow_delegation=True,
-  # (optional) llm=ollama_llm
+  allow_delegation=True
 )
 
 # Create tasks for your agents
 task1 = Task(
   description="""Conduct a comprehensive analysis of the latest advancements in AI in 2024.
-  Identify key trends, breakthrough technologies, and potential industry impacts.
-  Your final answer MUST be a full analysis report""",
+  Identify key trends, breakthrough technologies, and potential industry impacts.""",
+  expected_output="Full analysis report in bullet points",
   agent=researcher
 )
 
@@ -99,8 +117,8 @@ task2 = Task(
   description="""Using the insights provided, develop an engaging blog
   post that highlights the most significant AI advancements.
   Your post should be informative yet accessible, catering to a tech-savvy audience.
-  Make it sound cool, avoid complex words so it doesn't sound like AI.
-  Your final answer MUST be the full blog post of at least 4 paragraphs.""",
+  Make it sound cool, avoid complex words so it doesn't sound like AI.""",
+  expected_output="Full blog post of at least 4 paragraphs",
   agent=writer
 )
 
@@ -109,6 +127,7 @@ crew = Crew(
   agents=[researcher, writer],
   tasks=[task1, task2],
   verbose=2, # You can set it to 1 or 2 to different logging levels
+  process = Process.sequential
 )
 
 # Get your crew to work!
@@ -118,73 +137,66 @@ print("######################")
 print(result)
 ```
 
-Currently the only supported process is `Process.sequential`, where one task is executed after the other and the outcome of one is passed as extra content into this next.
+In addition to the sequential process, you can use the hierarchical process, which automatically assigns a manager to the defined crew to properly coordinate the planning and execution of tasks through delegation and validation of results. [See more about the processes here](https://docs.crewai.com/core-concepts/Processes/).
 
 ## Key Features
 
 - **Role-Based Agent Design**: Customize agents with specific roles, goals, and tools.
 - **Autonomous Inter-Agent Delegation**: Agents can autonomously delegate tasks and inquire amongst themselves, enhancing problem-solving efficiency.
 - **Flexible Task Management**: Define tasks with customizable tools and assign them to agents dynamically.
-- **Processes Driven**: Currently only supports `sequential` task execution but more complex processes like consensual and hierarchical being worked on.
+- **Processes Driven**: Currently only supports `sequential` task execution and `hierarchical` processes, but more complex processes like consensual and autonomous are being worked on.
+- **Save output as file**: Save the output of individual tasks as a file, so you can use it later.
+- **Parse output as Pydantic or Json**: Parse the output of individual tasks as a Pydantic model or as a Json if you want to.
+- **Works with Open Source Models**: Run your crew using Open AI or open source models refer to the [Connect crewAI to LLMs](https://docs.crewai.com/how-to/LLM-Connections/) page for details on configuring your agents' connections to models, even ones running locally!
 
-![CrewAI Mind Map](/crewAI-mindmap.png "CrewAI Mind Map")
+![CrewAI Mind Map](./docs/crewAI-mindmap.png "CrewAI Mind Map")
 
 ## Examples
-You can test different real life examples of AI crews [in the examples repo](https://github.com/joaomdmoura/crewAI-examples?tab=readme-ov-file)
 
-### Code
+You can test different real life examples of AI crews in the [crewAI-examples repo](https://github.com/joaomdmoura/crewAI-examples?tab=readme-ov-file):
+
+- [Landing Page Generator](https://github.com/joaomdmoura/crewAI-examples/tree/main/landing_page_generator)
+- [Having Human input on the execution](https://docs.crewai.com/how-to/Human-Input-on-Execution)
 - [Trip Planner](https://github.com/joaomdmoura/crewAI-examples/tree/main/trip_planner)
 - [Stock Analysis](https://github.com/joaomdmoura/crewAI-examples/tree/main/stock_analysis)
-- [Landing Page Generator](https://github.com/joaomdmoura/crewAI-examples/tree/main/landing_page_generator)
-- [Having Human input on the execution](https://github.com/joaomdmoura/crewAI/wiki/Human-Input-on-Execution)
 
-### Video
-#### Quick Tutorial
-[![CrewAI Tutorial](https://img.youtube.com/vi/tnejrr-0a94/0.jpg)](https://www.youtube.com/watch?v=tnejrr-0a94 "CrewAI Tutorial")
+### Quick Tutorial
 
-#### Trip Planner
-[![Trip Planner](https://img.youtube.com/vi/xis7rWp-hjs/0.jpg)](https://www.youtube.com/watch?v=xis7rWp-hjs "Trip Planner")
+[![CrewAI Tutorial](https://img.youtube.com/vi/tnejrr-0a94/maxresdefault.jpg)](https://www.youtube.com/watch?v=tnejrr-0a94 "CrewAI Tutorial")
 
-#### Stock Analysis
-[![Stock Analysis](https://img.youtube.com/vi/e0Uj4yWdaAg/0.jpg)](https://www.youtube.com/watch?v=e0Uj4yWdaAg "Stock Analysis")
+### Write Job Descriptions
 
-## Local Open Source Models
-crewAI supports integration with local models, thorugh tools such as [Ollama](https://ollama.ai/), for enhanced flexibility and customization. This allows you to utilize your own models, which can be particularly useful for specialized tasks or data privacy concerns.
+[Check out code for this example](https://github.com/joaomdmoura/crewAI-examples/tree/main/job-posting) or watch a video below:
 
-### Setting Up Ollama
-- **Install Ollama**: Ensure that Ollama is properly installed in your environment. Follow the installation guide provided by Ollama for detailed instructions.
-- **Configure Ollama**: Set up Ollama to work with your local model. You will probably need to [tweak the model using a Modelfile](https://github.com/jmorganca/ollama/blob/main/docs/modelfile.md). I'd recommend adding `Observation` as a stop word and playing with `top_p` and `temperature`.
+[![Jobs postings](https://img.youtube.com/vi/u98wEMz-9to/maxresdefault.jpg)](https://www.youtube.com/watch?v=u98wEMz-9to "Jobs postings")
 
-### Integrating Ollama with CrewAI
-- Instantiate Ollama Model: Create an instance of the Ollama model. You can specify the model and the base URL during instantiation. For example:
+### Trip Planner
 
-```python
-from langchain.llms import Ollama
-ollama_openhermes = Ollama(model="openhermes")
-# Pass Ollama Model to Agents: When creating your agents within the CrewAI framework, you can pass the Ollama model as an argument to the Agent constructor. For instance:
+[Check out code for this example](https://github.com/joaomdmoura/crewAI-examples/tree/main/trip_planner) or watch a video below:
 
-local_expert = Agent(
-  role='Local Expert at this city',
-  goal='Provide the BEST insights about the selected city',
-  backstory="""A knowledgeable local guide with extensive information
-  about the city, it's attractions and customs""",
-  tools=[
-    SearchTools.search_internet,
-    BrowserTools.scrape_and_summarize_website,
-  ],
-  llm=ollama_openhermes, # Ollama model passed here
-  verbose=True
-)
-```
+[![Trip Planner](https://img.youtube.com/vi/xis7rWp-hjs/maxresdefault.jpg)](https://www.youtube.com/watch?v=xis7rWp-hjs "Trip Planner")
+
+### Stock Analysis
+
+[Check out code for this example](https://github.com/joaomdmoura/crewAI-examples/tree/main/stock_analysis) or watch a video below:
+
+[![Stock Analysis](https://img.youtube.com/vi/e0Uj4yWdaAg/maxresdefault.jpg)](https://www.youtube.com/watch?v=e0Uj4yWdaAg "Stock Analysis")
+
+## Connecting Your Crew to a Model
+
+crewAI supports using various LLMs through a variety of connection options. By default your agents will use the OpenAI API when querying the model. However, there are several other ways to allow your agents to connect to models. For example, you can configure your agents to use a local model via the Ollama tool.
+
+Please refer to the [Connect crewAI to LLMs](https://docs.crewai.com/how-to/LLM-Connections/) page for details on configuring you agents' connections to models.
 
 ## How CrewAI Compares
 
-- **Autogen**: While Autogen excels in creating conversational agents capable of working together, it lacks an inherent concept of process. In Autogen, orchestrating agents' interactions requires additional programming, which can become complex and cumbersome as the scale of tasks grows.
+- **Autogen**: While Autogen does good in creating conversational agents capable of working together, it lacks an inherent concept of process. In Autogen, orchestrating agents' interactions requires additional programming, which can become complex and cumbersome as the scale of tasks grows.
 
 - **ChatDev**: ChatDev introduced the idea of processes into the realm of AI agents, but its implementation is quite rigid. Customizations in ChatDev are limited and not geared towards production environments, which can hinder scalability and flexibility in real-world applications.
 
 **CrewAI's Advantage**: CrewAI is built with production in mind. It offers the flexibility of Autogen's conversational agents and the structured process approach of ChatDev, but without the rigidity. CrewAI's processes are designed to be dynamic and adaptable, fitting seamlessly into both development and production workflows.
 
+
 ## Contribution
 
 CrewAI is open-source and we welcome contributions. If you're looking to contribute, please:
@@ -196,12 +208,14 @@ CrewAI is open-source and we welcome contributions. If you're looking to contrib
 - We appreciate your input!
 
 ### Installing Dependencies
+
 ```bash
 poetry lock
 poetry install
 ```
 
 ### Virtual Env
+
 ```bash
 poetry shell
 ```
@@ -213,25 +227,60 @@ pre-commit install
 ```
 
 ### Running Tests
+
 ```bash
 poetry run pytest
 ```
 
+### Running static type checks
+
+```bash
+poetry run mypy
+```
+
 ### Packaging
+
 ```bash
 poetry build
 ```
 
 ### Installing Locally
+
 ```bash
 pip install dist/*.tar.gz
 ```
 
-## Hire Consulting
-I, [@joaomdmoura](https://github.com/joaomdmoura) (creator or crewAI), offer consulting through my LLC ([AI Nest Labs](https://ainestlabs.com)).
-If you are interested on hiring weekly hours with me on a retainer, feel free to email me at [joao@ainestlabs.com](mailto:joao@ainestlabs.com)
+## Telemetry
+
+CrewAI uses anonymous telemetry to collect usage data with the main purpose of helping us improve the library by focusing our efforts on the most used features, integrations and tools.
+
+There is NO data being collected on the prompts, tasks descriptions agents backstories or goals nor tools usage, no API calls, nor responses nor any data that is being processed by the agents, nor any secrets and env vars.
+
+Data collected includes:
+
+- Version of crewAI
+  - So we can understand how many users are using the latest version
+- Version of Python
+  - So we can decide on what versions to better support
+- General OS (e.g. number of CPUs, macOS/Windows/Linux)
+  - So we know what OS we should focus on and if we could build specific OS related features
+- Number of agents and tasks in a crew
+  - So we make sure we are testing internally with similar use cases and educate people on the best practices
+- Crew Process being used
+  - Understand where we should focus our efforts
+- If Agents are using memory or allowing delegation
+  - Understand if we improved the features or maybe even drop them
+- If Tasks are being executed in parallel or sequentially
+  - Understand if we should focus more on parallel execution
+- Language model being used
+  - Improved support on most used languages
+- Roles of agents in a crew
+  - Understand high level use cases so we can build better tools, integrations and examples about it
+- Tools names available
+  - Understand out of the publically available tools, which ones are being used the most so we can improve them
+
+Users can opt-in sharing the complete telemetry data by setting the `share_crew` attribute to `True` on their Crews.
 
 ## License
-CrewAI is released under the MIT License
-
 
+CrewAI is released under the MIT License.

crewAI.excalidraw

@@ -1463,11 +1463,11 @@
       "locked": false,
       "fontSize": 20,
       "fontFamily": 3,
-      "text": "Agents have the inert ability of\nreach out to another to delegate\nwork or ask questions.",
+      "text": "Agents have the innate ability of\nreach out to another to delegate\nwork or ask questions.",
       "textAlign": "right",
       "verticalAlign": "top",
       "containerId": null,
-      "originalText": "Agents have the inert ability of\nreach out to another to delegate\nwork or ask questions.",
+      "originalText": "Agents have the innate ability of\nreach out to another to delegate\nwork or ask questions.",
       "lineHeight": 1.2,
       "baseline": 68
     },
@@ -1734,4 +1734,4 @@
     "viewBackgroundColor": "#ffffff"
   },
   "files": {}
-}
+}

crewai/agent.py

@@ -1,181 +0,0 @@
-import uuid
-from typing import Any, List, Optional
-
-from langchain.agents.format_scratchpad import format_log_to_str
-from langchain.chat_models import ChatOpenAI
-from langchain.memory import ConversationSummaryMemory
-from langchain.tools.render import render_text_description
-from langchain_core.runnables.config import RunnableConfig
-from pydantic import (
-    UUID4,
-    BaseModel,
-    ConfigDict,
-    Field,
-    InstanceOf,
-    field_validator,
-    model_validator,
-)
-from pydantic_core import PydanticCustomError
-
-from crewai.agents import (
-    CacheHandler,
-    CrewAgentExecutor,
-    CrewAgentOutputParser,
-    ToolsHandler,
-)
-from crewai.prompts import Prompts
-
-
-class Agent(BaseModel):
-    """Represents an agent in a system.
-
-    Each agent has a role, a goal, a backstory, and an optional language model (llm).
-    The agent can also have memory, can operate in verbose mode, and can delegate tasks to other agents.
-
-    Attributes:
-            agent_executor: An instance of the CrewAgentExecutor class.
-            role: The role of the agent.
-            goal: The objective of the agent.
-            backstory: The backstory of the agent.
-            llm: The language model that will run the agent.
-            memory: Whether the agent should have memory or not.
-            verbose: Whether the agent execution should be in verbose mode.
-            allow_delegation: Whether the agent is allowed to delegate tasks to other agents.
-    """
-
-    __hash__ = object.__hash__
-    model_config = ConfigDict(arbitrary_types_allowed=True)
-    id: UUID4 = Field(
-        default_factory=uuid.uuid4,
-        frozen=True,
-        description="Unique identifier for the object, not set by user.",
-    )
-    role: str = Field(description="Role of the agent")
-    goal: str = Field(description="Objective of the agent")
-    backstory: str = Field(description="Backstory of the agent")
-    llm: Optional[Any] = Field(
-        default_factory=lambda: ChatOpenAI(
-            temperature=0.7,
-            model_name="gpt-4",
-        ),
-        description="Language model that will run the agent.",
-    )
-    memory: bool = Field(
-        default=True, description="Whether the agent should have memory or not"
-    )
-    verbose: bool = Field(
-        default=False, description="Verbose mode for the Agent Execution"
-    )
-    allow_delegation: bool = Field(
-        default=True, description="Allow delegation of tasks to agents"
-    )
-    tools: List[Any] = Field(
-        default_factory=list, description="Tools at agents disposal"
-    )
-    agent_executor: Optional[InstanceOf[CrewAgentExecutor]] = Field(
-        default=None, description="An instance of the CrewAgentExecutor class."
-    )
-    tools_handler: Optional[InstanceOf[ToolsHandler]] = Field(
-        default=None, description="An instance of the ToolsHandler class."
-    )
-    cache_handler: Optional[InstanceOf[CacheHandler]] = Field(
-        default=CacheHandler(), description="An instance of the CacheHandler class."
-    )
-
-    @field_validator("id", mode="before")
-    @classmethod
-    def _deny_user_set_id(cls, v: Optional[UUID4]) -> None:
-        if v:
-            raise PydanticCustomError(
-                "may_not_set_field", "This field is not to be set by the user.", {}
-            )
-
-    @model_validator(mode="after")
-    def check_agent_executor(self) -> "Agent":
-        if not self.agent_executor:
-            self.set_cache_handler(self.cache_handler)
-        return self
-
-    def execute_task(
-        self, task: str, context: str = None, tools: List[Any] = None
-    ) -> str:
-        """Execute a task with the agent.
-
-        Args:
-            task: Task to execute.
-            context: Context to execute the task in.
-            tools: Tools to use for the task.
-
-        Returns:
-            Output of the agent
-        """
-        if context:
-            task = "\n".join(
-                [task, "\nThis is the context you are working with:", context]
-            )
-
-        tools = tools or self.tools
-        self.agent_executor.tools = tools
-
-        return self.agent_executor.invoke(
-            {
-                "input": task,
-                "tool_names": self.__tools_names(tools),
-                "tools": render_text_description(tools),
-            },
-            RunnableConfig(callbacks=[self.tools_handler]),
-        )["output"]
-
-    def set_cache_handler(self, cache_handler) -> None:
-        self.cache_handler = cache_handler
-        self.tools_handler = ToolsHandler(cache=self.cache_handler)
-        self.__create_agent_executor()
-
-    def __create_agent_executor(self) -> CrewAgentExecutor:
-        """Create an agent executor for the agent.
-
-        Returns:
-            An instance of the CrewAgentExecutor class.
-        """
-        agent_args = {
-            "input": lambda x: x["input"],
-            "tools": lambda x: x["tools"],
-            "tool_names": lambda x: x["tool_names"],
-            "agent_scratchpad": lambda x: format_log_to_str(x["intermediate_steps"]),
-        }
-        executor_args = {
-            "tools": self.tools,
-            "verbose": self.verbose,
-            "handle_parsing_errors": True,
-        }
-
-        if self.memory:
-            summary_memory = ConversationSummaryMemory(
-                llm=self.llm, memory_key="chat_history", input_key="input"
-            )
-            executor_args["memory"] = summary_memory
-            agent_args["chat_history"] = lambda x: x["chat_history"]
-            prompt = Prompts().task_execution_with_memory()
-        else:
-            prompt = Prompts().task_execution()
-
-        execution_prompt = prompt.partial(
-            goal=self.goal,
-            role=self.role,
-            backstory=self.backstory,
-        )
-
-        bind = self.llm.bind(stop=["\nObservation"])
-        inner_agent = (
-            agent_args
-            | execution_prompt
-            | bind
-            | CrewAgentOutputParser(
-                tools_handler=self.tools_handler, cache=self.cache_handler
-            )
-        )
-        self.agent_executor = CrewAgentExecutor(agent=inner_agent, **executor_args)
-
-    @staticmethod
-    def __tools_names(tools) -> str:
-        return ", ".join([t.name for t in tools])

crewai/agents/cache/cache_hit.py

@@ -1,14 +0,0 @@
-from langchain_core.agents import AgentAction
-from pydantic.v1 import BaseModel, Field
-
-from .cache_handler import CacheHandler
-
-
-class CacheHit(BaseModel):
-    """Cache Hit Object."""
-
-    class Config:
-        arbitrary_types_allowed = True
-
-    action: AgentAction = Field(description="Action taken")
-    cache: CacheHandler = Field(description="Cache Handler for the tool")

crewai/agents/exceptions.py

@@ -1,24 +0,0 @@
-from langchain_core.exceptions import OutputParserException
-
-
-class TaskRepeatedUsageException(OutputParserException):
-    """Exception raised when a task is used twice in a roll."""
-
-    error: str = "TaskRepeatedUsageException"
-    message: str = "I just used the {tool} tool with input {tool_input}. So I already know the result of that and don't need to use it now.\n"
-
-    def __init__(self, tool: str, tool_input: str, text: str):
-        self.text = text
-        self.tool = tool
-        self.tool_input = tool_input
-        self.message = self.message.format(tool=tool, tool_input=tool_input)
-
-        super().__init__(
-            error=self.error,
-            observation=self.message,
-            send_to_llm=True,
-            llm_output=self.text,
-        )
-
-    def __str__(self):
-        return self.message

crewai/agents/executor.py

@@ -1,130 +0,0 @@
-from typing import Dict, Iterator, List, Optional, Tuple, Union
-
-from langchain.agents import AgentExecutor
-from langchain.agents.agent import ExceptionTool
-from langchain.agents.tools import InvalidTool
-from langchain.callbacks.manager import CallbackManagerForChainRun
-from langchain_core.agents import AgentAction, AgentFinish, AgentStep
-from langchain_core.exceptions import OutputParserException
-from langchain_core.tools import BaseTool
-
-from ..tools.cache_tools import CacheTools
-from .cache.cache_hit import CacheHit
-
-
-class CrewAgentExecutor(AgentExecutor):
-    def _iter_next_step(
-        self,
-        name_to_tool_map: Dict[str, BaseTool],
-        color_mapping: Dict[str, str],
-        inputs: Dict[str, str],
-        intermediate_steps: List[Tuple[AgentAction, str]],
-        run_manager: Optional[CallbackManagerForChainRun] = None,
-    ) -> Iterator[Union[AgentFinish, AgentAction, AgentStep]]:
-        """Take a single step in the thought-action-observation loop.
-
-        Override this to take control of how the agent makes and acts on choices.
-        """
-        try:
-            intermediate_steps = self._prepare_intermediate_steps(intermediate_steps)
-
-            # Call the LLM to see what to do.
-            output = self.agent.plan(
-                intermediate_steps,
-                callbacks=run_manager.get_child() if run_manager else None,
-                **inputs,
-            )
-        except OutputParserException as e:
-            if isinstance(self.handle_parsing_errors, bool):
-                raise_error = not self.handle_parsing_errors
-            else:
-                raise_error = False
-            if raise_error:
-                raise ValueError(
-                    "An output parsing error occurred. "
-                    "In order to pass this error back to the agent and have it try "
-                    "again, pass `handle_parsing_errors=True` to the AgentExecutor. "
-                    f"This is the error: {str(e)}"
-                )
-            text = str(e)
-            if isinstance(self.handle_parsing_errors, bool):
-                if e.send_to_llm:
-                    observation = str(e.observation)
-                    text = str(e.llm_output)
-                else:
-                    observation = "Invalid or incomplete response"
-            elif isinstance(self.handle_parsing_errors, str):
-                observation = self.handle_parsing_errors
-            elif callable(self.handle_parsing_errors):
-                observation = self.handle_parsing_errors(e)
-            else:
-                raise ValueError("Got unexpected type of `handle_parsing_errors`")
-            output = AgentAction("_Exception", observation, text)
-            if run_manager:
-                run_manager.on_agent_action(output, color="green")
-            tool_run_kwargs = self.agent.tool_run_logging_kwargs()
-            observation = ExceptionTool().run(
-                output.tool_input,
-                verbose=self.verbose,
-                color=None,
-                callbacks=run_manager.get_child() if run_manager else None,
-                **tool_run_kwargs,
-            )
-            yield AgentStep(action=output, observation=observation)
-            return
-
-        # If the tool chosen is the finishing tool, then we end and return.
-        if isinstance(output, AgentFinish):
-            yield output
-            return
-
-        # Override tool usage to use CacheTools
-        if isinstance(output, CacheHit):
-            cache = output.cache
-            action = output.action
-            tool = CacheTools(cache_handler=cache).tool()
-            output = action.copy()
-            output.tool_input = f"tool:{action.tool}|input:{action.tool_input}"
-            output.tool = tool.name
-            name_to_tool_map[tool.name] = tool
-            color_mapping[tool.name] = color_mapping[action.tool]
-
-        actions: List[AgentAction]
-        if isinstance(output, AgentAction):
-            actions = [output]
-        else:
-            actions = output
-        for agent_action in actions:
-            yield agent_action
-        for agent_action in actions:
-            if run_manager:
-                run_manager.on_agent_action(agent_action, color="green")
-            # Otherwise we lookup the tool
-            if agent_action.tool in name_to_tool_map:
-                tool = name_to_tool_map[agent_action.tool]
-                return_direct = tool.return_direct
-                color = color_mapping[agent_action.tool]
-                tool_run_kwargs = self.agent.tool_run_logging_kwargs()
-                if return_direct:
-                    tool_run_kwargs["llm_prefix"] = ""
-                # We then call the tool on the tool input to get an observation
-                observation = tool.run(
-                    agent_action.tool_input,
-                    verbose=self.verbose,
-                    color=color,
-                    callbacks=run_manager.get_child() if run_manager else None,
-                    **tool_run_kwargs,
-                )
-            else:
-                tool_run_kwargs = self.agent.tool_run_logging_kwargs()
-                observation = InvalidTool().run(
-                    {
-                        "requested_tool_name": agent_action.tool,
-                        "available_tool_names": list(name_to_tool_map.keys()),
-                    },
-                    verbose=self.verbose,
-                    color=None,
-                    callbacks=run_manager.get_child() if run_manager else None,
-                    **tool_run_kwargs,
-                )
-            yield AgentStep(action=agent_action, observation=observation)

crewai/agents/output_parser.py

@@ -1,78 +0,0 @@
-import re
-from typing import Union
-
-from langchain.agents.output_parsers import ReActSingleInputOutputParser
-from langchain_core.agents import AgentAction, AgentFinish
-
-from .cache import CacheHandler, CacheHit
-from .exceptions import TaskRepeatedUsageException
-from .tools_handler import ToolsHandler
-
-FINAL_ANSWER_ACTION = "Final Answer:"
-FINAL_ANSWER_AND_PARSABLE_ACTION_ERROR_MESSAGE = (
-    "Parsing LLM output produced both a final answer and a parse-able action:"
-)
-
-
-class CrewAgentOutputParser(ReActSingleInputOutputParser):
-    """Parses ReAct-style LLM calls that have a single tool input.
-
-    Expects output to be in one of two formats.
-
-    If the output signals that an action should be taken,
-    should be in the below format. This will result in an AgentAction
-    being returned.
-
-    ```
-    Thought: agent thought here
-    Action: search
-    Action Input: what is the temperature in SF?
-    ```
-
-    If the output signals that a final answer should be given,
-    should be in the below format. This will result in an AgentFinish
-    being returned.
-
-    ```
-    Thought: agent thought here
-    Final Answer: The temperature is 100 degrees
-    ```
-
-    It also prevents tools from being reused in a roll.
-    """
-
-    class Config:
-        arbitrary_types_allowed = True
-
-    tools_handler: ToolsHandler
-    cache: CacheHandler
-
-    def parse(self, text: str) -> Union[AgentAction, AgentFinish, CacheHit]:
-        FINAL_ANSWER_ACTION in text
-        regex = (
-            r"Action\s*\d*\s*:[\s]*(.*?)[\s]*Action\s*\d*\s*Input\s*\d*\s*:[\s]*(.*)"
-        )
-        action_match = re.search(regex, text, re.DOTALL)
-        if action_match:
-            action = action_match.group(1).strip()
-            action_input = action_match.group(2)
-            tool_input = action_input.strip(" ")
-            tool_input = tool_input.strip('"')
-
-            last_tool_usage = self.tools_handler.last_used_tool
-            if last_tool_usage:
-                usage = {
-                    "tool": action,
-                    "input": tool_input,
-                }
-                if usage == last_tool_usage:
-                    raise TaskRepeatedUsageException(
-                        tool=action, tool_input=tool_input, text=text
-                    )
-
-            result = self.cache.read(action, tool_input)
-            if result:
-                action = AgentAction(action, tool_input, text)
-                return CacheHit(action=action, cache=self.cache)
-
-        return super().parse(text)

crewai/agents/tools_handler.py

@@ -1,44 +0,0 @@
-from typing import Any, Dict
-
-from langchain.callbacks.base import BaseCallbackHandler
-
-from ..tools.cache_tools import CacheTools
-from .cache.cache_handler import CacheHandler
-
-
-class ToolsHandler(BaseCallbackHandler):
-    """Callback handler for tool usage."""
-
-    last_used_tool: Dict[str, Any] = {}
-    cache: CacheHandler = None
-
-    def __init__(self, cache: CacheHandler = None, **kwargs: Any):
-        """Initialize the callback handler."""
-        self.cache = cache
-        super().__init__(**kwargs)
-
-    def on_tool_start(
-        self, serialized: Dict[str, Any], input_str: str, **kwargs: Any
-    ) -> Any:
-        """Run when tool starts running."""
-        name = serialized.get("name")
-        if name not in ["invalid_tool", "_Exception"]:
-            tools_usage = {
-                "tool": name,
-                "input": input_str,
-            }
-            self.last_used_tool = tools_usage
-
-    def on_tool_end(self, output: str, **kwargs: Any) -> Any:
-        """Run when tool ends running."""
-        if (
-            "is not a valid tool" not in output
-            and "Invalid or incomplete response" not in output
-            and "Invalid Format" not in output
-        ):
-            if self.last_used_tool["tool"] != CacheTools().name:
-                self.cache.add(
-                    tool=self.last_used_tool["tool"],
-                    input=self.last_used_tool["input"],
-                    output=output,
-                )

crewai/crew.py

@@ -1,137 +0,0 @@
-import json
-import uuid
-from typing import Any, Dict, List, Optional, Union
-
-from pydantic import (
-    UUID4,
-    BaseModel,
-    ConfigDict,
-    Field,
-    InstanceOf,
-    Json,
-    field_validator,
-    model_validator,
-)
-from pydantic_core import PydanticCustomError
-
-from crewai.agent import Agent
-from crewai.agents.cache import CacheHandler
-from crewai.process import Process
-from crewai.task import Task
-from crewai.tools.agent_tools import AgentTools
-
-
-class Crew(BaseModel):
-    """Class that represents a group of agents, how they should work together and their tasks."""
-
-    __hash__ = object.__hash__
-    model_config = ConfigDict(arbitrary_types_allowed=True)
-    tasks: List[Task] = Field(description="List of tasks", default_factory=list)
-    agents: List[Agent] = Field(
-        description="List of agents in this crew.", default_factory=list
-    )
-    process: Process = Field(
-        description="Process that the crew will follow.", default=Process.sequential
-    )
-    verbose: Union[int, bool] = Field(
-        description="Verbose mode for the Agent Execution", default=0
-    )
-    config: Optional[Union[Json, Dict[str, Any]]] = Field(
-        description="Configuration of the crew.", default=None
-    )
-    cache_handler: Optional[InstanceOf[CacheHandler]] = Field(
-        default=CacheHandler(), description="An instance of the CacheHandler class."
-    )
-    id: UUID4 = Field(
-        default_factory=uuid.uuid4,
-        frozen=True,
-        description="Unique identifier for the object, not set by user.",
-    )
-
-    @field_validator("id", mode="before")
-    @classmethod
-    def _deny_user_set_id(cls, v: Optional[UUID4]) -> None:
-        if v:
-            raise PydanticCustomError(
-                "may_not_set_field", "This field is not to be set by the user.", {}
-            )
-
-    @classmethod
-    @field_validator("config", mode="before")
-    def check_config_type(cls, v: Union[Json, Dict[str, Any]]):
-        if isinstance(v, Json):
-            return json.loads(v)
-        return v
-
-    @model_validator(mode="after")
-    def check_config(self):
-        if not self.config and not self.tasks and not self.agents:
-            raise PydanticCustomError(
-                "missing_keys", "Either agents and task need to be set or config.", {}
-            )
-
-        if self.config:
-            if not self.config.get("agents") or not self.config.get("tasks"):
-                raise PydanticCustomError(
-                    "missing_keys_in_config", "Config should have agents and tasks", {}
-                )
-
-            self.agents = [Agent(**agent) for agent in self.config["agents"]]
-
-            tasks = []
-            for task in self.config["tasks"]:
-                task_agent = [agt for agt in self.agents if agt.role == task["agent"]][
-                    0
-                ]
-                del task["agent"]
-                tasks.append(Task(**task, agent=task_agent))
-
-            self.tasks = tasks
-
-        if self.agents:
-            for agent in self.agents:
-                agent.set_cache_handler(self.cache_handler)
-        return self
-
-    def kickoff(self) -> str:
-        """Kickoff the crew to work on its tasks.
-
-        Returns:
-            Output of the crew for each task.
-        """
-        for agent in self.agents:
-            agent.cache_handler = self.cache_handler
-
-        if self.process == Process.sequential:
-            return self.__sequential_loop()
-
-    def __sequential_loop(self) -> str:
-        """Loop that executes the sequential process.
-
-        Returns:
-            Output of the crew.
-        """
-        task_output = None
-        for task in self.tasks:
-            # Add delegation tools to the task if the agent allows it
-            if task.agent.allow_delegation:
-                agent_tools = AgentTools(agents=self.agents).tools()
-                task.tools += agent_tools
-
-            self.__log("debug", f"Working Agent: {task.agent.role}")
-            self.__log("info", f"Starting Task: {task.description}")
-
-            task_output = task.execute(task_output)
-            self.__log(
-                "debug", f"\n\n[{task.agent.role}] Task output: {task_output}\n\n"
-            )
-        return task_output
-
-    def __log(self, level, message):
-        """Log a message"""
-        level_map = {"debug": 1, "info": 2}
-        verbose_level = (
-            2 if isinstance(self.verbose, bool) and self.verbose else self.verbose
-        )
-        if verbose_level and level_map[level] <= verbose_level:
-            print(message)

crewai/prompts.py

@@ -1,53 +0,0 @@
-"""Prompts for generic agent."""
-import json
-import os
-from typing import ClassVar, Dict, Optional
-
-from langchain.prompts import PromptTemplate
-from pydantic import BaseModel, Field, PrivateAttr, model_validator
-
-
-class Prompts(BaseModel):
-    """Prompts for generic agent."""
-
-    _prompts: Optional[Dict[str, str]] = PrivateAttr()
-    language: Optional[str] = Field(
-        default="en",
-        description="Language of crewai prompts.",
-    )
-
-    @model_validator(mode="after")
-    def load_prompts(self) -> "Prompts":
-        """Load prompts from file."""
-        dir_path = os.path.dirname(os.path.realpath(__file__))
-        prompts_path = os.path.join(dir_path, f"prompts/{self.language}.json")
-
-        with open(prompts_path, "r") as f:
-            self._prompts = json.load(f)["slices"]
-        return self
-
-    SCRATCHPAD_SLICE: ClassVar[str] = "\n{agent_scratchpad}"
-
-    def task_execution_with_memory(self) -> str:
-        return PromptTemplate.from_template(
-            self._prompts["role_playing"]
-            + self._prompts["tools"]
-            + self._prompts["memory"]
-            + self._prompts["task"]
-            + self.SCRATCHPAD_SLICE
-        )
-
-    def task_execution_without_tools(self) -> str:
-        return PromptTemplate.from_template(
-            self._prompts["role_playing"]
-            + self._prompts["task"]
-            + self.SCRATCHPAD_SLICE
-        )
-
-    def task_execution(self) -> str:
-        return PromptTemplate.from_template(
-            self._prompts["role_playing"]
-            + self._prompts["tools"]
-            + self._prompts["task"]
-            + self.SCRATCHPAD_SLICE
-        )

crewai/prompts/en.json

@@ -1,8 +0,0 @@
-{
-  "slices": {
-    "task": "Begin! This is VERY important to you, your job depends on it!\n\nCurrent Task: {input}",
-    "memory": "This is the summary of your work so far:\n{chat_history}",
-    "role_playing": "You are {role}.\n{backstory}\n\nYour personal goal is: {goal}",
-    "tools": "TOOLS:\n------\nYou have access to the following tools:\n\n{tools}\n\nTo use a tool, please use the exact following format:\n\n```\nThought: Do I need to use a tool? Yes\nAction: the action to take, should be one of [{tool_names}], just the name.\nAction Input: the input to the action\nObservation: the result of the action\n```\n\nWhen you have a response for your task, or if you do not need to use a tool, you MUST use the format:\n\n```\nThought: Do I need to use a tool? No\nFinal Answer: [your response here]"
-  }
-}

crewai/task.py

@@ -1,62 +0,0 @@
-import uuid
-from typing import Any, List, Optional
-
-from pydantic import UUID4, BaseModel, Field, field_validator, model_validator
-from pydantic_core import PydanticCustomError
-
-from crewai.agent import Agent
-from crewai.tasks.task_output import TaskOutput
-
-
-class Task(BaseModel):
-    """Class that represent a task to be executed."""
-
-    __hash__ = object.__hash__
-    description: str = Field(description="Description of the actual task.")
-    agent: Optional[Agent] = Field(
-        description="Agent responsible for the task.", default=None
-    )
-    tools: List[Any] = Field(
-        default_factory=list,
-        description="Tools the agent are limited to use for this task.",
-    )
-    output: Optional[TaskOutput] = Field(
-        description="Task output, it's final result.", default=None
-    )
-    id: UUID4 = Field(
-        default_factory=uuid.uuid4,
-        frozen=True,
-        description="Unique identifier for the object, not set by user.",
-    )
-
-    @field_validator("id", mode="before")
-    @classmethod
-    def _deny_user_set_id(cls, v: Optional[UUID4]) -> None:
-        if v:
-            raise PydanticCustomError(
-                "may_not_set_field", "This field is not to be set by the user.", {}
-            )
-
-    @model_validator(mode="after")
-    def check_tools(self):
-        if not self.tools and (self.agent and self.agent.tools):
-            self.tools.extend(self.agent.tools)
-        return self
-
-    def execute(self, context: str = None) -> str:
-        """Execute the task.
-
-        Returns:
-            Output of the task.
-        """
-        if self.agent:
-            result = self.agent.execute_task(
-                task=self.description, context=context, tools=self.tools
-            )
-
-            self.output = TaskOutput(description=self.description, result=result)
-            return result
-        else:
-            raise Exception(
-                f"The task '{self.description}' has no agent assigned, therefore it can't be executed directly and should be executed in a Crew using a specific process that support that, either consensual or hierarchical."
-            )

crewai/tools/agent_tools.py

@@ -1,76 +0,0 @@
-from textwrap import dedent
-from typing import List
-
-from langchain.tools import Tool
-from pydantic import BaseModel, Field
-
-from crewai.agent import Agent
-
-
-class AgentTools(BaseModel):
-    """Default tools around agent delegation"""
-
-    agents: List[Agent] = Field(description="List of agents in this crew.")
-
-    def tools(self):
-        return [
-            Tool.from_function(
-                func=self.delegate_work,
-                name="Delegate work to co-worker",
-                description=dedent(
-                    f"""\
-                Useful to delegate a specific task to one of the
-                following co-workers: [{', '.join([agent.role for agent in self.agents])}].
-                The input to this tool should be a pipe (|) separated text of length
-                three, representing the co-worker you want to ask it to (one of the options),
-                the task and all actual context you have for the task.
-                For example, `coworker|task|context`.
-                """
-                ),
-            ),
-            Tool.from_function(
-                func=self.ask_question,
-                name="Ask question to co-worker",
-                description=dedent(
-                    f"""\
-                Useful to ask a question, opinion or take from on
-                of the following co-workers: [{', '.join([agent.role for agent in self.agents])}].
-                The input to this tool should be a pipe (|) separated text of length
-                three, representing the co-worker you want to ask it to (one of the options),
-                the question and all actual context you have for the question.
-                For example, `coworker|question|context`.
-                """
-                ),
-            ),
-        ]
-
-    def delegate_work(self, command):
-        """Useful to delegate a specific task to a coworker."""
-        return self.__execute(command)
-
-    def ask_question(self, command):
-        """Useful to ask a question, opinion or take from a coworker."""
-        return self.__execute(command)
-
-    def __execute(self, command):
-        """Execute the command."""
-        try:
-            agent, task, context = command.split("|")
-        except ValueError:
-            return "\nError executing tool. Missing exact 3 pipe (|) separated values. For example, `coworker|task|context`. I need to make sure to pass context as context\n"
-
-        if not agent or not task or not context:
-            return "\nError executing tool. Missing exact 3 pipe (|) separated values. For example, `coworker|task|context`. I need to make sure to pass context as context.\n"
-
-        agent = [
-            available_agent
-            for available_agent in self.agents
-            if available_agent.role == agent
-        ]
-
-        if len(agent) == 0:
-            return f"\nError executing tool. Co-worker mentioned on the Action Input not found, it must to be one of the following options: {', '.join([agent.role for agent in self.agents])}.\n"
-
-        agent = agent[0]
-        result = agent.execute_task(task, context)
-        return result

docs/CNAME

@@ -0,0 +1 @@
+docs.crewai.com

docs/core-concepts/Agents.md

@@ -0,0 +1,149 @@
+---
+title: crewAI Agents
+description: What are crewAI Agents and how to use them.
+---
+
+## What is an Agent?
+!!! note "What is an Agent?"
+    An agent is an **autonomous unit** programmed to:
+    <ul>
+      <li class='leading-3'>Perform tasks</li>
+      <li class='leading-3'>Make decisions</li>
+      <li class='leading-3'>Communicate with other agents</li>
+    </ul>
+      <br/>
+    Think of an agent as a member of a team, with specific skills and a particular job to do. Agents can have different roles like 'Researcher', 'Writer', or 'Customer Support', each contributing to the overall goal of the crew.
+
+## Agent Attributes
+
+| Attribute                  | Parameter  | Description                                                                                                                                                                                                                                    |
+| :------------------------- | :---- | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| **Role**                   | `role`  | Defines the agent's function within the crew. It determines the kind of tasks the agent is best suited for.                                                                                                                                    |
+| **Goal**                   | `goal`  | The individual objective that the agent aims to achieve. It guides the agent's decision-making process.                                                                                                                                        |
+| **Backstory**              | `backstory`  | Provides context to the agent's role and goal, enriching the interaction and collaboration dynamics.                                                                                                                                           |
+| **LLM** *(optional)*       | `llm`  | Represents the language model that will run the agent. It dynamically fetches the model name from the `OPENAI_MODEL_NAME` environment variable, defaulting to "gpt-4" if not specified.                                                         |
+| **Tools** *(optional)*     | `tools`  | Set of capabilities or functions that the agent can use to perform tasks. Expected to be instances of custom classes compatible with the agent's execution environment. Tools are initialized with a default value of an empty list.             |
+| **Function Calling LLM** *(optional)* | `function_calling_llm`  | Specifies the language model that will handle the tool calling for this agent, overriding the crew function calling LLM if passed. Default is `None`.                                                                                          |
+| **Max Iter** *(optional)*  | `max_iter` | Max Iter is the maximum number of iterations the agent can perform before being forced to give its best answer. Default is `25`.                                                                                                                           |
+| **Max RPM** *(optional)*   | `max_rpm`  | Max RPM is the maximum number of requests per minute the agent can perform to avoid rate limits. It's optional and can be left unspecified, with a default value of `None`.                                                                               |
+| **Max Execution Time** *(optional)*   | `max_execution_time`  | Max Execution Time is the Maximum execution time for an agent to execute a task. It's optional and can be left unspecified, with a default value of `None`, meaning no max execution time.                                                                     |
+| **Verbose** *(optional)*   | `verbose`  | Setting this to `True` configures the internal logger to provide detailed execution logs, aiding in debugging and monitoring. Default is `False`.                                                                                              |
+| **Allow Delegation** *(optional)* | `allow_delegation`  | Agents can delegate tasks or questions to one another, ensuring that each task is handled by the most suitable agent. Default is `True`.                                                                                                       |
+| **Step Callback** *(optional)* | `step_callback`  | A function that is called after each step of the agent. This can be used to log the agent's actions or to perform other operations. It will overwrite the crew `step_callback`.                                                               |
+| **Cache** *(optional)*     | `cache`  | Indicates if the agent should use a cache for tool usage. Default is `True`.                                                                                                                                                                  |
+| **System Template** *(optional)*     | `system_template`  | Specifies the system format for the agent. Default is `None`.                                                                                                                                                                  |
+| **Prompt Template** *(optional)*     | `prompt_template`  | Specifies the prompt format for the agent. Default is `None`.                                                                                                                                                                  |
+| **Response Template** *(optional)*     | `response_template`  | Specifies the response format for the agent. Default is `None`.                                                                                                                                                                  |
+
+## Creating an Agent
+
+!!! note "Agent Interaction"
+    Agents can interact with each other using crewAI's built-in delegation and communication mechanisms. This allows for dynamic task management and problem-solving within the crew.
+
+To create an agent, you would typically initialize an instance of the `Agent` class with the desired properties. Here's a conceptual example including all attributes:
+
+```python
+# Example: Creating an agent with all attributes
+from crewai import Agent
+
+agent = Agent(
+  role='Data Analyst',
+  goal='Extract actionable insights',
+  backstory="""You're a data analyst at a large company.
+  You're responsible for analyzing data and providing insights
+  to the business.
+  You're currently working on a project to analyze the
+  performance of our marketing campaigns.""",
+  tools=[my_tool1, my_tool2],  # Optional, defaults to an empty list
+  llm=my_llm,  # Optional
+  function_calling_llm=my_llm,  # Optional
+  max_iter=15,  # Optional
+  max_rpm=None, # Optional
+  max_execution_time=None, # Optional
+  verbose=True,  # Optional
+  allow_delegation=True,  # Optional
+  step_callback=my_intermediate_step_callback,  # Optional
+  cache=True,  # Optional
+  system_template=my_system_template,  # Optional
+  prompt_template=my_prompt_template,  # Optional
+  response_template=my_response_template,  # Optional
+  config=my_config,  # Optional
+  crew=my_crew,  # Optional
+  tools_handler=my_tools_handler,  # Optional
+  cache_handler=my_cache_handler,  # Optional
+  callbacks=[callback1, callback2],  # Optional
+  agent_executor=my_agent_executor  # Optional
+)
+```
+
+## Setting prompt templates
+
+Prompt templates are used to format the prompt for the agent. You can use to update the system, regular and response templates for the agent. Here's an example of how to set prompt templates:
+
+```python
+agent = Agent(
+        role="{topic} specialist",
+        goal="Figure {goal} out",
+        backstory="I am the master of {role}",
+        system_template="""<|start_header_id|>system<|end_header_id|>
+
+{{ .System }}<|eot_id|>""",
+        prompt_template="""<|start_header_id|>user<|end_header_id|>
+
+{{ .Prompt }}<|eot_id|>""",
+        response_template="""<|start_header_id|>assistant<|end_header_id|>
+
+{{ .Response }}<|eot_id|>""",
+    )
+```
+
+## Bring your Third Party Agents
+!!! note "Extend your Third Party Agents like LlamaIndex, Langchain, Autogen or fully custom agents using the the crewai's BaseAgent class."
+
+    BaseAgent includes attributes and methods required to integrate with your crews to run and delegate tasks to other agents within your own crew.
+
+    CrewAI is a universal multi agent framework that allows for all agents to work together to automate tasks and solve problems.
+
+
+```py
+from crewai import Agent, Task, Crew
+from custom_agent import CustomAgent # You need to build and extend your own agent logic with the CrewAI BaseAgent class then import it here.
+
+from langchain.agents import load_tools
+
+langchain_tools = load_tools(["google-serper"], llm=llm)
+
+agent1 = CustomAgent(
+    role="backstory agent",
+    goal="who is {input}?",
+    backstory="agent backstory",
+    verbose=True,
+)
+
+task1 = Task(
+    expected_output="a short biography of {input}",
+    description="a short biography of {input}",
+    agent=agent1,
+)
+
+agent2 = Agent(
+    role="bio agent",
+    goal="summarize the short bio for {input} and if needed do more research",
+    backstory="agent backstory",
+    verbose=True,
+)
+
+task2 = Task(
+    description="a tldr summary of the short biography",
+    expected_output="5 bullet point summary of the biography",
+    agent=agent2,
+    context=[task1],
+)
+
+my_crew = Crew(agents=[agent1, agent2], tasks=[task1, task2])
+crew = my_crew.kickoff(inputs={"input": "Mark Twain"})
+```
+
+
+## Conclusion
+Agents are the building blocks of the CrewAI framework. By understanding how to define and interact with agents, you can create sophisticated AI systems that leverage the power of collaborative intelligence.

docs/core-concepts/Collaboration.md

@@ -0,0 +1,42 @@
+---
+title: How Agents Collaborate in CrewAI
+description: Exploring the dynamics of agent collaboration within the CrewAI framework, focusing on the newly integrated features for enhanced functionality.
+---
+
+## Collaboration Fundamentals
+!!! note "Core of Agent Interaction"
+    Collaboration in CrewAI is fundamental, enabling agents to combine their skills, share information, and assist each other in task execution, embodying a truly cooperative ecosystem.
+
+- **Information Sharing**: Ensures all agents are well-informed and can contribute effectively by sharing data and findings.
+- **Task Assistance**: Allows agents to seek help from peers with the required expertise for specific tasks.
+- **Resource Allocation**: Optimizes task execution through the efficient distribution and sharing of resources among agents.
+
+## Enhanced Attributes for Improved Collaboration
+The `Crew` class has been enriched with several attributes to support advanced functionalities:
+
+- **Language Model Management (`manager_llm`, `function_calling_llm`)**: Manages language models for executing tasks and tools, facilitating sophisticated agent-tool interactions. Note that while `manager_llm` is mandatory for hierarchical processes to ensure proper execution flow, `function_calling_llm` is optional, with a default value provided for streamlined tool interaction.
+- **Custom Manager Agent (`manager_agent`)**: Allows specifying a custom agent as the manager instead of using the default manager provided by CrewAI.
+- **Process Flow (`process`)**: Defines the execution logic (e.g., sequential, hierarchical) to streamline task distribution and execution.
+- **Verbose Logging (`verbose`)**: Offers detailed logging capabilities for monitoring and debugging purposes. It supports both integer and boolean types to indicate the verbosity level. For example, setting `verbose` to 1 might enable basic logging, whereas setting it to True enables more detailed logs.
+- **Rate Limiting (`max_rpm`)**: Ensures efficient utilization of resources by limiting requests per minute. Guidelines for setting `max_rpm` should consider the complexity of tasks and the expected load on resources.
+- **Internationalization / Customization Support (`language`, `prompt_file`)**: Facilitates full customization of the inner prompts, enhancing global usability. Supported languages and the process for utilizing the `prompt_file` attribute for customization should be clearly documented. [Example of file](https://github.com/joaomdmoura/crewAI/blob/main/src/crewai/translations/en.json)
+- **Execution and Output Handling (`full_output`)**: Distinguishes between full and final outputs for nuanced control over task results. Examples showcasing the difference in outputs can aid in understanding the practical implications of this attribute.
+- **Callback and Telemetry (`step_callback`, `task_callback`)**: Integrates callbacks for step-wise and task-level execution monitoring, alongside telemetry for performance analytics. The purpose and usage of `task_callback` alongside `step_callback` for granular monitoring should be clearly explained.
+- **Crew Sharing (`share_crew`)**: Enables sharing of crew information with CrewAI for continuous improvement and training models. The privacy implications and benefits of this feature, including how it contributes to model improvement, should be outlined.
+- **Usage Metrics (`usage_metrics`)**: Stores all metrics for the language model (LLM) usage during all tasks' execution, providing insights into operational efficiency and areas for improvement. Detailed information on accessing and interpreting these metrics for performance analysis should be provided.
+- **Memory Usage (`memory`)**: Indicates whether the crew should use memory to store memories of its execution, enhancing task execution and agent learning.
+- **Embedder Configuration (`embedder`)**: Specifies the configuration for the embedder to be used by the crew for understanding and generating language. This attribute supports customization of the language model provider.
+- **Cache Management (`cache`)**: Determines whether the crew should use a cache to store the results of tool executions, optimizing performance.
+- **Output Logging (`output_log_file`)**: Specifies the file path for logging the output of the crew execution.
+
+## Delegation: Dividing to Conquer
+Delegation enhances functionality by allowing agents to intelligently assign tasks or seek help, thereby amplifying the crew's overall capability.
+
+## Implementing Collaboration and Delegation
+Setting up a crew involves defining the roles and capabilities of each agent. CrewAI seamlessly manages their interactions, ensuring efficient collaboration and delegation, with enhanced customization and monitoring features to adapt to various operational needs.
+
+## Example Scenario
+Consider a crew with a researcher agent tasked with data gathering and a writer agent responsible for compiling reports. The integration of advanced language model management and process flow attributes allows for more sophisticated interactions, such as the writer delegating complex research tasks to the researcher or querying specific information, thereby facilitating a seamless workflow.
+
+## Conclusion
+The integration of advanced attributes and functionalities into the CrewAI framework significantly enriches the agent collaboration ecosystem. These enhancements not only simplify interactions but also offer unprecedented flexibility and control, paving the way for sophisticated AI-driven solutions capable of tackling complex tasks through intelligent collaboration and delegation.

docs/core-concepts/Crews.md

@@ -0,0 +1,158 @@
+---
+title: crewAI Crews
+description: Understanding and utilizing crews in the crewAI framework with comprehensive attributes and functionalities.
+---
+
+## What is a Crew?
+A crew in crewAI represents a collaborative group of agents working together to achieve a set of tasks. Each crew defines the strategy for task execution, agent collaboration, and the overall workflow.
+
+## Crew Attributes
+
+| Attribute                   | Parameters          | Description                                                                                              |
+| :-------------------------- | :------------------ | :------------------------------------------------------------------------------------------------------- |
+| **Tasks**                   | `tasks`               | A list of tasks assigned to the crew.                                                                    |
+| **Agents**                  | `agents`              | A list of agents that are part of the crew.                                                              |
+| **Process** *(optional)*    | `process`             | The process flow (e.g., sequential, hierarchical) the crew follows.                                      |
+| **Verbose** *(optional)*    | `verbose`             | The verbosity level for logging during execution.                                                        |
+| **Manager LLM** *(optional)*| `manager_llm`         | The language model used by the manager agent in a hierarchical process. **Required when using a hierarchical process.** |
+| **Function Calling LLM** *(optional)* | `function_calling_llm` | If passed, the crew will use this LLM to do function calling for tools for all agents in the crew. Each agent can have its own LLM, which overrides the crew's LLM for function calling. |
+| **Config** *(optional)*     | `config`              | Optional configuration settings for the crew, in `Json` or `Dict[str, Any]` format.                       |
+| **Max RPM** *(optional)*    | `max_rpm`             | Maximum requests per minute the crew adheres to during execution.                                         |
+| **Language**  *(optional)*  | `language`            | Language used for the crew, defaults to English.                                                          |
+| **Language File** *(optional)* | `language_file`       | Path to the language file to be used for the crew.                                                        |
+| **Memory** *(optional)*     | `memory`              | Utilized for storing execution memories (short-term, long-term, entity memory).                           |
+| **Cache** *(optional)*      | `cache`               | Specifies whether to use a cache for storing the results of tools' execution.                             |
+| **Embedder** *(optional)*   | `embedder`            | Configuration for the embedder to be used by the crew. Mostly used by memory for now.                     |
+| **Full Output** *(optional)*| `full_output`         | Whether the crew should return the full output with all tasks outputs or just the final output.           |
+| **Step Callback** *(optional)* | `step_callback`       | A function that is called after each step of every agent. This can be used to log the agent's actions or to perform other operations; it won't override the agent-specific `step_callback`. |
+| **Task Callback** *(optional)* | `task_callback`       | A function that is called after the completion of each task. Useful for monitoring or additional operations post-task execution. |
+| **Share Crew** *(optional)* | `share_crew`          | Whether you want to share the complete crew information and execution with the crewAI team to make the library better, and allow us to train models. |
+| **Output Log File** *(optional)* | `output_log_file`    | Whether you want to have a file with the complete crew output and execution. You can set it using True and it will default to the folder you are currently in and it will be called logs.txt or passing a string with the full path and name of the file. |
+| **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.                                                     |
+
+!!! 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.
+
+## Creating a Crew
+
+When assembling a crew, you combine agents with complementary roles and tools, assign tasks, and select a process that dictates their execution order and interaction.
+
+### Example: Assembling a Crew
+
+```python
+from crewai import Crew, Agent, Task, Process
+from langchain_community.tools import DuckDuckGoSearchRun
+
+# Define agents with specific roles and tools
+researcher = Agent(
+    role='Senior Research Analyst',
+    goal='Discover innovative AI technologies',
+    backstory="""You're a senior research analyst at a large company.
+        You're responsible for analyzing data and providing insights
+        to the business.
+        You're currently working on a project to analyze the
+        trends and innovations in the space of artificial intelligence.""",
+    tools=[DuckDuckGoSearchRun()]
+)
+
+writer = Agent(
+    role='Content Writer',
+    goal='Write engaging articles on AI discoveries',
+    backstory="""You're a senior writer at a large company.
+        You're responsible for creating content to the business.
+        You're currently working on a project to write about trends
+        and innovations in the space of AI for your next meeting.""",
+    verbose=True
+)
+
+# Create tasks for the agents
+research_task = Task(
+    description='Identify breakthrough AI technologies',
+    agent=researcher,
+    expected_output='A bullet list summary of the top 5 most important AI news'
+)
+write_article_task = Task(
+    description='Draft an article on the latest AI technologies',
+    agent=writer,
+    expected_output='3 paragraph blog post on the latest AI technologies'
+)
+
+# Assemble the crew with a sequential process
+my_crew = Crew(
+    agents=[researcher, writer],
+    tasks=[research_task, write_article_task],
+    process=Process.sequential,
+    full_output=True,
+    verbose=True,
+)
+```
+
+## Memory Utilization
+
+Crews can utilize memory (short-term, long-term, and entity memory) to enhance their execution and learning over time. This feature allows crews to store and recall execution memories, aiding in decision-making and task execution strategies.
+
+## Cache Utilization
+
+Caches can be employed to store the results of tools' execution, making the process more efficient by reducing the need to re-execute identical tasks.
+
+## Crew Usage Metrics
+
+After the crew execution, you can access the `usage_metrics` attribute to view the language model (LLM) usage metrics for all tasks executed by the crew. This provides insights into operational efficiency and areas for improvement.
+
+```python
+# Access the crew's usage metrics
+crew = Crew(agents=[agent1, agent2], tasks=[task1, task2])
+crew.kickoff()
+print(crew.usage_metrics)
+```
+
+## Crew Execution Process
+
+- **Sequential Process**: Tasks are executed one after another, allowing for a linear flow of work.
+- **Hierarchical Process**: A manager agent coordinates the crew, delegating tasks and validating outcomes before proceeding. **Note**: A `manager_llm` or `manager_agent` is required for this process and it's essential for validating the process flow.
+
+### Kicking Off a Crew
+
+Once your crew is assembled, initiate the workflow with the `kickoff()` method. This starts the execution process according to the defined process flow.
+
+```python
+# Start the crew's task execution
+result = my_crew.kickoff()
+print(result)
+```
+
+### Different wayt to Kicking Off a Crew
+
+Once your crew is assembled, initiate the workflow with the appropriate kickoff method. CrewAI provides several methods for better control over the kickoff process: `kickoff()`, `kickoff_for_each()`, `kickoff_async()`, and `kickoff_for_each_async()`.
+
+`kickoff()`: Starts the execution process according to the defined process flow.
+`kickoff_for_each()`: Executes tasks for each agent individually.
+`kickoff_async()`: Initiates the workflow asynchronously.
+`kickoff_for_each_async()`: Executes tasks for each agent individually in an asynchronous manner.
+
+```python
+# Start the crew's task execution
+result = my_crew.kickoff()
+print(result)
+
+# Example of using kickoff_for_each
+inputs_array = [{'topic': 'AI in healthcare'}, {'topic': 'AI in finance'}]
+results = my_crew.kickoff_for_each(inputs=inputs_array)
+for result in results:
+    print(result)
+
+# Example of using kickoff_async
+inputs = {'topic': 'AI in healthcare'}
+async_result = my_crew.kickoff_async(inputs=inputs)
+print(async_result)
+
+# Example of using kickoff_for_each_async
+inputs_array = [{'topic': 'AI in healthcare'}, {'topic': 'AI in finance'}]
+async_results = my_crew.kickoff_for_each_async(inputs=inputs_array)
+for async_result in async_results:
+    print(async_result)
+```
+
+These methods provide flexibility in how you manage and execute tasks within your crew, allowing for both synchronous and asynchronous workflows tailored to your needs

docs/core-concepts/Memory.md

@@ -0,0 +1,170 @@
+---
+title: crewAI Memory Systems
+description: Leveraging memory systems in the crewAI framework to enhance agent capabilities.
+---
+
+## Introduction to Memory Systems in crewAI
+!!! note "Enhancing Agent Intelligence"
+    The crewAI framework introduces a sophisticated memory system designed to significantly enhance the capabilities of AI agents. This system comprises short-term memory, long-term memory, entity memory, and contextual memory, each serving a unique purpose in aiding agents to remember, reason, and learn from past interactions.
+
+## Memory System Components
+
+| Component            | Description                                                  |
+| :------------------- | :----------------------------------------------------------- |
+| **Short-Term Memory**| Temporarily stores recent interactions and outcomes, enabling agents to recall and utilize information relevant to their current context during the current executions. |
+| **Long-Term Memory** | Preserves valuable insights and learnings from past executions, allowing agents to build and refine their knowledge over time. So Agents can remeber what they did right and wrong across multiple executions |
+| **Entity Memory**    | Captures and organizes information about entities (people, places, concepts) encountered during tasks, facilitating deeper understanding and relationship mapping. |
+| **Contextual Memory**| Maintains the context of interactions by combining `ShortTermMemory`, `LongTermMemory`, and `EntityMemory`, aiding in the coherence and relevance of agent responses over a sequence of tasks or a conversation. |
+
+## How Memory Systems Empower Agents
+
+1. **Contextual Awareness**: With short-term and contextual memory, agents gain the ability to maintain context over a conversation or task sequence, leading to more coherent and relevant responses.
+
+2. **Experience Accumulation**: Long-term memory allows agents to accumulate experiences, learning from past actions to improve future decision-making and problem-solving.
+
+3. **Entity Understanding**: By maintaining entity memory, agents can recognize and remember key entities, enhancing their ability to process and interact with complex information.
+
+## Implementing Memory in Your Crew
+
+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.
+
+### Example: Configuring Memory for a Crew
+
+```python
+from crewai import Crew, Agent, Task, Process
+
+# Assemble your crew with memory capabilities
+my_crew = Crew(
+    agents=[...],
+    tasks=[...],
+    process=Process.sequential,
+    memory=True,
+    verbose=True
+)
+```
+
+## Additional Embedding Providers
+
+### Using OpenAI embeddings (already default)
+```python
+from crewai import Crew, Agent, Task, Process
+
+my_crew = Crew(
+		agents=[...],
+		tasks=[...],
+		process=Process.sequential,
+		memory=True,
+		verbose=True,
+		embedder={
+				"provider": "openai",
+				"config":{
+						"model": 'text-embedding-3-small'
+				}
+		}
+)
+```
+
+### Using Google AI embeddings
+```python
+from crewai import Crew, Agent, Task, Process
+
+my_crew = Crew(
+		agents=[...],
+		tasks=[...],
+		process=Process.sequential,
+		memory=True,
+		verbose=True,
+		embedder={
+			"provider": "google",
+			"config":{
+				"model": 'models/embedding-001',
+				"task_type": "retrieval_document",
+				"title": "Embeddings for Embedchain"
+			}
+		}
+)
+```
+
+### Using Azure OpenAI embeddings
+```python
+from crewai import Crew, Agent, Task, Process
+
+my_crew = Crew(
+		agents=[...],
+		tasks=[...],
+		process=Process.sequential,
+		memory=True,
+		verbose=True,
+		embedder={
+			"provider": "azure_openai",
+			"config":{
+				"model": 'text-embedding-ada-002',
+				"deployment_name": "you_embedding_model_deployment_name"
+			}
+		}
+)
+```
+
+### Using GPT4ALL embeddings
+```python
+from crewai import Crew, Agent, Task, Process
+
+my_crew = Crew(
+		agents=[...],
+		tasks=[...],
+		process=Process.sequential,
+		memory=True,
+		verbose=True,
+		embedder={
+			"provider": "gpt4all"
+		}
+)
+```
+
+### Using Vertex AI embeddings
+```python
+from crewai import Crew, Agent, Task, Process
+
+my_crew = Crew(
+		agents=[...],
+		tasks=[...],
+		process=Process.sequential,
+		memory=True,
+		verbose=True,
+		embedder={
+			"provider": "vertexai",
+			"config":{
+				"model": 'textembedding-gecko'
+			}
+		}
+)
+```
+
+### Using Cohere embeddings
+```python
+from crewai import Crew, Agent, Task, Process
+
+my_crew = Crew(
+		agents=[...],
+		tasks=[...],
+		process=Process.sequential,
+		memory=True,
+		verbose=True,
+		embedder={
+			"provider": "cohere",
+			"config":{
+				"model": "embed-english-v3.0"
+    		"vector_dimension": 1024
+			}
+		}
+)
+```
+
+## 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.

docs/core-concepts/Processes.md

@@ -0,0 +1,64 @@
+---
+title: Managing Processes in CrewAI
+description: Detailed guide on workflow management through processes in CrewAI, with updated implementation details.
+---
+
+## Understanding Processes
+!!! note "Core Concept"
+    In CrewAI, processes orchestrate the execution of tasks by agents, akin to project management in human teams. These processes ensure tasks are distributed and executed efficiently, in alignment with a predefined strategy.
+
+## Process Implementations
+
+- **Sequential**: Executes tasks sequentially, ensuring tasks are completed in an orderly progression.
+- **Hierarchical**: Organizes tasks in a managerial hierarchy, where tasks are delegated and executed based on a structured chain of command. A manager language model (`manager_llm`) or a custom manager agent (`manager_agent`) must be specified in the crew to enable the hierarchical process, facilitating the creation and management of tasks by the manager.
+- **Consensual Process (Planned)**: Aiming for collaborative decision-making among agents on task execution, this process type introduces a democratic approach to task management within CrewAI. It is planned for future development and is not currently implemented in the codebase.
+
+## The Role of Processes in Teamwork
+Processes enable individual agents to operate as a cohesive unit, streamlining their efforts to achieve common objectives with efficiency and coherence.
+
+## Assigning Processes to a Crew
+To assign a process to a crew, specify the process type upon crew creation to set the execution strategy. For a hierarchical process, ensure to define `manager_llm` or `manager_agent` for the manager agent.
+
+```python
+from crewai import Crew
+from crewai.process import Process
+from langchain_openai import ChatOpenAI
+
+# Example: Creating a crew with a sequential process
+crew = Crew(
+    agents=my_agents,
+    tasks=my_tasks,
+    process=Process.sequential
+)
+
+# Example: Creating a crew with a hierarchical process
+# Ensure to provide a manager_llm or manager_agent
+crew = Crew(
+    agents=my_agents,
+    tasks=my_tasks,
+    process=Process.hierarchical,
+    manager_llm=ChatOpenAI(model="gpt-4")
+    # or
+    # manager_agent=my_manager_agent
+)
+```
+**Note:** Ensure `my_agents` and `my_tasks` are defined prior to creating a `Crew` object, and for the hierarchical process, either `manager_llm` or `manager_agent` is also required.
+
+## Sequential Process
+This method mirrors dynamic team workflows, progressing through tasks in a thoughtful and systematic manner. Task execution follows the predefined order in the task list, with the output of one task serving as context for the next.
+
+To customize task context, utilize the `context` parameter in the `Task` class to specify outputs that should be used as context for subsequent tasks.
+
+## Hierarchical Process
+Emulates a corporate hierarchy, CrewAI allows specifying a custom manager agent or automatically creates one, requiring the specification of a manager language model (`manager_llm`). This agent oversees task execution, including planning, delegation, and validation. Tasks are not pre-assigned; the manager allocates tasks to agents based on their capabilities, reviews outputs, and assesses task completion.
+
+## Process Class: Detailed Overview
+The `Process` class is implemented as an enumeration (`Enum`), ensuring type safety and restricting process values to the defined types (`sequential`, `hierarchical`). The consensual process is planned for future inclusion, emphasizing our commitment to continuous development and innovation.
+
+## Additional Task Features
+- **Asynchronous Execution**: Tasks can now be executed asynchronously, allowing for parallel processing and efficiency improvements. This feature is designed to enable tasks to be carried out concurrently, enhancing the overall productivity of the crew.
+- **Human Input Review**: An optional feature that enables the review of task outputs by humans to ensure quality and accuracy before finalization. This additional step introduces a layer of oversight, providing an opportunity for human intervention and validation.
+- **Output Customization**: Tasks support various output formats, including JSON (`output_json`), Pydantic models (`output_pydantic`), and file outputs (`output_file`), providing flexibility in how task results are captured and utilized. This allows for a wide range of output possibilities, catering to different needs and requirements.
+
+## Conclusion
+The structured collaboration facilitated by processes within CrewAI is crucial for enabling systematic teamwork among agents. This documentation has been updated to reflect the latest features, enhancements, and the planned integration of the Consensual Process, ensuring users have access to the most current and comprehensive information.

docs/core-concepts/Tasks.md

@@ -0,0 +1,249 @@
+---
+title: crewAI Tasks
+description: Detailed guide on managing and creating tasks within the crewAI framework, reflecting the latest codebase updates.
+---
+
+## Overview of a Task
+!!! note "What is a Task?"
+    In the crewAI framework, tasks are specific assignments completed by agents. They provide all necessary details for execution, such as a description, the agent responsible, required tools, and more, facilitating a wide range of action complexities.
+
+Tasks within crewAI can be collaborative, requiring multiple agents to work together. This is managed through the task properties and orchestrated by the Crew's process, enhancing teamwork and efficiency.
+
+## Task Attributes
+
+| Attribute              | Parameters           | Description                                                                                   |
+| :----------------------| :------------------- | :-------------------------------------------------------------------------------------------- |
+| **Description**        | `description`          | A clear, concise statement of what the task entails.                                          |
+| **Agent**              | `agent`                | The agent responsible for the task, assigned either directly or by the crew's process.        |
+| **Expected Output**    | `expected_output`      | A detailed description of what the task's completion looks like.                              |
+| **Tools** *(optional)* | `tools`                | The functions or capabilities the agent can utilize to perform the task.                      |
+| **Async Execution** *(optional)* | `async_execution`      | If set, the task executes asynchronously, allowing progression without waiting for completion.|
+| **Context**  *(optional)* | `context`              | Specifies tasks whose outputs are used as context for this task.                              |
+| **Config** *(optional)* | `config`               | Additional configuration details for the agent executing the task, allowing further customization. |
+| **Output JSON**  *(optional)* | `output_json`          | Outputs a JSON object, requiring an OpenAI client. Only one output format can be set.         |
+| **Output Pydantic**  *(optional)* | `output_pydantic`      | Outputs a Pydantic model object, requiring an OpenAI client. Only one output format can be set. |
+| **Output File**  *(optional)* | `output_file`          | Saves the task output to a file. If used with `Output JSON` or `Output Pydantic`, specifies how the output is saved. |
+| **Callback**  *(optional)* | `callback`             | A Python callable that is executed with the task's output upon completion.                    |
+| **Human Input** *(optional)* | `human_input`         | Indicates if the task requires human feedback at the end, useful for tasks needing human oversight. |
+
+## Creating a Task
+
+Creating a task involves defining its scope, responsible agent, and any additional attributes for flexibility:
+
+```python
+from crewai import Task
+
+task = Task(
+    description='Find and summarize the latest and most relevant news on AI',
+    agent=sales_agent
+)
+```
+
+!!! note "Task Assignment"
+    Directly specify an `agent` for assignment or let the `hierarchical` CrewAI's process decide based on roles, availability, etc.
+
+## Integrating Tools with Tasks
+
+Leverage tools from the [crewAI Toolkit](https://github.com/joaomdmoura/crewai-tools) and [LangChain Tools](https://python.langchain.com/docs/integrations/tools) for enhanced task performance and agent interaction.
+
+## Creating a Task with Tools
+
+```python
+import os
+os.environ["OPENAI_API_KEY"] = "Your Key"
+os.environ["SERPER_API_KEY"] = "Your Key" # serper.dev API key
+
+from crewai import Agent, Task, Crew
+from crewai_tools import SerperDevTool
+
+research_agent = Agent(
+  role='Researcher',
+  goal='Find and summarize the latest AI news',
+  backstory="""You're a researcher at a large company.
+  You're responsible for analyzing data and providing insights
+  to the business.""",
+  verbose=True
+)
+
+search_tool = SerperDevTool()
+
+task = Task(
+  description='Find and summarize the latest AI news',
+  expected_output='A bullet list summary of the top 5 most important AI news',
+  agent=research_agent,
+  tools=[search_tool]
+)
+
+crew = Crew(
+    agents=[research_agent],
+    tasks=[task],
+    verbose=2
+)
+
+result = crew.kickoff()
+print(result)
+```
+
+This demonstrates how tasks with specific tools can override an agent's default set for tailored task execution.
+
+## Referring to Other Tasks
+
+In crewAI, the output of one task is automatically relayed into the next one, but you can specifically define what tasks' output, including multiple, should be used as context for another task.
+
+This is useful when you have a task that depends on the output of another task that is not performed immediately after it. This is done through the `context` attribute of the task:
+
+```python
+# ...
+
+research_ai_task = Task(
+    description='Find and summarize the latest AI news',
+    expected_output='A bullet list summary of the top 5 most important AI news',
+    async_execution=True,
+    agent=research_agent,
+    tools=[search_tool]
+)
+
+research_ops_task = Task(
+    description='Find and summarize the latest AI Ops news',
+    expected_output='A bullet list summary of the top 5 most important AI Ops news',
+    async_execution=True,
+    agent=research_agent,
+    tools=[search_tool]
+)
+
+write_blog_task = Task(
+    description="Write a full blog post about the importance of AI and its latest news",
+    expected_output='Full blog post that is 4 paragraphs long',
+    agent=writer_agent,
+    context=[research_ai_task, research_ops_task]
+)
+
+#...
+```
+
+## Asynchronous Execution
+
+You can define a task to be executed asynchronously. This means that the crew will not wait for it to be completed to continue with the next task. This is useful for tasks that take a long time to be completed, or that are not crucial for the next tasks to be performed.
+
+You can then use the `context` attribute to define in a future task that it should wait for the output of the asynchronous task to be completed.
+
+```python
+#...
+
+list_ideas = Task(
+    description="List of 5 interesting ideas to explore for an article about AI.",
+    expected_output="Bullet point list of 5 ideas for an article.",
+    agent=researcher,
+    async_execution=True # Will be executed asynchronously
+)
+
+list_important_history = Task(
+    description="Research the history of AI and give me the 5 most important events.",
+    expected_output="Bullet point list of 5 important events.",
+    agent=researcher,
+    async_execution=True # Will be executed asynchronously
+)
+
+write_article = Task(
+    description="Write an article about AI, its history, and interesting ideas.",
+    expected_output="A 4 paragraph article about AI.",
+    agent=writer,
+    context=[list_ideas, list_important_history] # Will wait for the output of the two tasks to be completed
+)
+
+#...
+```
+
+## Callback Mechanism
+
+The callback function is executed after the task is completed, allowing for actions or notifications to be triggered based on the task's outcome.
+
+```python
+# ...
+
+def callback_function(output: TaskOutput):
+    # Do something after the task is completed
+    # Example: Send an email to the manager
+    print(f"""
+        Task completed!
+        Task: {output.description}
+        Output: {output.raw_output}
+    """)
+
+research_task = Task(
+    description='Find and summarize the latest AI news',
+    expected_output='A bullet list summary of the top 5 most important AI news',
+    agent=research_agent,
+    tools=[search_tool],
+    callback=callback_function
+)
+
+#...
+```
+
+## Accessing a Specific Task Output
+
+Once a crew finishes running, you can access the output of a specific task by using the `output` attribute of the task object:
+
+```python
+# ...
+task1 = Task(
+    description='Find and summarize the latest AI news',
+    expected_output='A bullet list summary of the top 5 most important AI news',
+    agent=research_agent,
+    tools=[search_tool]
+)
+
+#...
+
+crew = Crew(
+    agents=[research_agent],
+    tasks=[task1, task2, task3],
+    verbose=2
+)
+
+result = crew.kickoff()
+
+# Returns a TaskOutput object with the description and results of the task
+print(f"""
+    Task completed!
+    Task: {task1.output.description}
+    Output: {task1.output.raw_output}
+""")
+```
+
+## Tool Override Mechanism
+
+Specifying tools in a task allows for dynamic adaptation of agent capabilities, emphasizing CrewAI's flexibility.
+
+## Error Handling and Validation Mechanisms
+
+While creating and executing tasks, certain validation mechanisms are in place to ensure the robustness and reliability of task attributes. These include but are not limited to:
+
+- Ensuring only one output type is set per task to maintain clear output expectations.
+- Preventing the manual assignment of the `id` attribute to uphold the integrity of the unique identifier system.
+
+These validations help in maintaining the consistency and reliability of task executions within the crewAI framework.
+
+## Creating Directories when Saving Files
+
+You can now specify if a task should create directories when saving its output to a file. This is particularly useful for organizing outputs and ensuring that file paths are correctly structured.
+
+```python
+# ...
+
+save_output_task = Task(
+    description='Save the summarized AI news to a file',
+    expected_output='File saved successfully',
+    agent=research_agent,
+    tools=[file_save_tool],
+    output_file='outputs/ai_news_summary.txt',
+    create_directory=True
+)
+
+#...
+```
+
+## Conclusion
+
+Tasks are the driving force behind the actions of agents in crewAI. By properly defining tasks and their outcomes, you set the stage for your AI agents to work effectively, either independently or as a collaborative unit. Equipping tasks with appropriate tools, understanding the execution process, and following robust validation practices are crucial for maximizing CrewAI's potential, ensuring agents are effectively prepared for their assignments and that tasks are executed as intended.

docs/core-concepts/Tools.md

@@ -0,0 +1,194 @@
+---
+title: crewAI Tools
+description: Understanding and leveraging tools within the crewAI framework for agent collaboration and task execution.
+---
+
+## Introduction
+CrewAI tools empower agents with capabilities ranging from web searching and data analysis to collaboration and delegating tasks among coworkers. This documentation outlines how to create, integrate, and leverage these tools within the CrewAI framework, including a new focus on collaboration tools.
+
+## What is a Tool?
+!!! note "Definition"
+    A tool in CrewAI is a skill or function that agents can utilize to perform various actions. This includes tools from the [crewAI Toolkit](https://github.com/joaomdmoura/crewai-tools) and [LangChain Tools](https://python.langchain.com/docs/integrations/tools), enabling everything from simple searches to complex interactions and effective teamwork among agents.
+
+## Key Characteristics of Tools
+
+- **Utility**: Crafted for tasks such as web searching, data analysis, content generation, and agent collaboration.
+- **Integration**: Boosts agent capabilities by seamlessly integrating tools into their workflow.
+- **Customizability**: Provides the flexibility to develop custom tools or utilize existing ones, catering to the specific needs of agents.
+- **Error Handling**: Incorporates robust error handling mechanisms to ensure smooth operation.
+- **Caching Mechanism**: Features intelligent caching to optimize performance and reduce redundant operations.
+
+## Using crewAI Tools
+
+To enhance your agents' capabilities with crewAI tools, begin by installing our extra tools package:
+
+```bash
+pip install 'crewai[tools]'
+```
+
+Here's an example demonstrating their use:
+
+```python
+import os
+from crewai import Agent, Task, Crew
+# Importing crewAI tools
+from crewai_tools import (
+    DirectoryReadTool,
+    FileReadTool,
+    SerperDevTool,
+    WebsiteSearchTool
+)
+
+# Set up API keys
+os.environ["SERPER_API_KEY"] = "Your Key" # serper.dev API key
+os.environ["OPENAI_API_KEY"] = "Your Key"
+
+# Instantiate tools
+docs_tool = DirectoryReadTool(directory='./blog-posts')
+file_tool = FileReadTool()
+search_tool = SerperDevTool()
+web_rag_tool = WebsiteSearchTool()
+
+# Create agents
+researcher = Agent(
+    role='Market Research Analyst',
+    goal='Provide up-to-date market analysis of the AI industry',
+    backstory='An expert analyst with a keen eye for market trends.',
+    tools=[search_tool, web_rag_tool],
+    verbose=True
+)
+
+writer = Agent(
+    role='Content Writer',
+    goal='Craft engaging blog posts about the AI industry',
+    backstory='A skilled writer with a passion for technology.',
+    tools=[docs_tool, file_tool],
+    verbose=True
+)
+
+# Define tasks
+research = Task(
+    description='Research the latest trends in the AI industry and provide a summary.',
+    expected_output='A summary of the top 3 trending developments in the AI industry with a unique perspective on their significance.',
+    agent=researcher
+)
+
+write = Task(
+    description='Write an engaging blog post about the AI industry, based on the research analyst’s summary. Draw inspiration from the latest blog posts in the directory.',
+    expected_output='A 4-paragraph blog post formatted in markdown with engaging, informative, and accessible content, avoiding complex jargon.',
+    agent=writer,
+    output_file='blog-posts/new_post.md'  # The final blog post will be saved here
+)
+
+# Assemble a crew
+crew = Crew(
+    agents=[researcher, writer],
+    tasks=[research, write],
+    verbose=2
+)
+
+# Execute tasks
+crew.kickoff()
+```
+
+## Available crewAI Tools
+
+- **Error Handling**: All tools are built with error handling capabilities, allowing agents to gracefully manage exceptions and continue their tasks.
+- **Caching Mechanism**: All tools support caching, enabling agents to efficiently reuse previously obtained results, reducing the load on external resources and speeding up the execution time. You can also define finer control over the caching mechanism using the `cache_function` attribute on the tool.
+
+Here is a list of the available tools and their descriptions:
+
+| Tool                        | Description                                                                                   |
+| :-------------------------- | :-------------------------------------------------------------------------------------------- |
+| **CodeDocsSearchTool**      | A RAG tool optimized for searching through code documentation and related technical documents. |
+| **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.                |
+| **FileReadTool**            | Enables reading and extracting data from files, supporting various file formats.              |
+| **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.     |
+| **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. |
+| **RagTool**                 | A general-purpose RAG tool capable of handling various data sources and types.                 |
+| **ScrapeElementFromWebsiteTool** | Enables scraping specific elements from websites, useful for targeted data extraction.     |
+| **ScrapeWebsiteTool**       | Facilitates scraping entire websites, ideal for comprehensive data collection.                 |
+| **WebsiteSearchTool**       | A RAG tool for searching website content, optimized for web data extraction.                   |
+| **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
+
+!!! example "Custom Tool Creation"
+    Developers can craft custom tools tailored for their agent’s needs or utilize pre-built options:
+
+To create your own crewAI tools you will need to install our extra tools package:
+
+```bash
+pip install 'crewai[tools]'
+```
+
+Once you do that there are two main ways for one to create a crewAI tool:
+### Subclassing `BaseTool`
+
+```python
+from crewai_tools import BaseTool
+
+class MyCustomTool(BaseTool):
+    name: str = "Name of my tool"
+    description: str = "Clear description for what this tool is useful for, your agent will need this information to use it."
+
+    def _run(self, argument: str) -> str:
+        # Implementation goes here
+        return "Result from custom tool"
+```
+
+### Utilizing the `tool` Decorator
+
+```python
+from crewai_tools import tool
+@tool("Name of my tool")
+def my_tool(question: str) -> str:
+    """Clear description for what this tool is useful for, your agent will need this information to use it."""
+    # Function logic here
+    return "Result from your custom tool"
+```
+
+### Custom Caching Mechanism
+!!! note "Caching"
+    Tools can optionally implement a `cache_function` to fine-tune caching behavior. This function determines when to cache results based on specific conditions, offering granular control over caching logic.
+
+```python
+from crewai_tools import tool
+
+@tool
+def multiplication_tool(first_number: int, second_number: int) -> str:
+    """Useful for when you need to multiply two numbers together."""
+    return first_number * second_number
+
+def cache_func(args, result):
+    # In this case, we only cache the result if it's a multiple of 2
+    cache = result % 2 == 0
+    return cache
+
+multiplication_tool.cache_function = cache_func
+
+writer1 = Agent(
+        role="Writer",
+        goal="You write lessons of math for kids.",
+        backstory="You're an expert in writing and you love to teach kids but you know nothing of math.",
+        tools=[multiplication_tool],
+        allow_delegation=False,
+    )
+    #...
+```
+
+
+## Conclusion
+Tools are pivotal in extending the capabilities of CrewAI agents, enabling them to undertake a broad spectrum of tasks and collaborate effectively. When building solutions with CrewAI, leverage both custom and existing tools to empower your agents and enhance the AI ecosystem. Consider utilizing error handling, caching mechanisms, and the flexibility of tool arguments to optimize your agents' performance and capabilities.

docs/core-concepts/Training-Crew.md

@@ -0,0 +1,53 @@
+---
+title: crewAI Train
+description: Learn how to train your crewAI agents by giving them feedback early on and get consistent results.
+---
+
+## Introduction
+The training feature in CrewAI allows you to train your AI agents using the command-line interface (CLI). By running the command `crewai train -n <n_iterations>`, you can specify the number of iterations for the training process.
+
+During training, CrewAI utilizes techniques to optimize the performance of your agents along with human feedback. This helps the agents improve their understanding, decision-making, and problem-solving abilities.
+
+### Training Your Crew Using the CLI
+To use the training 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 command:
+
+```shell
+crewai train -n <n_iterations>
+```
+
+### Training Your Crew Programmatically
+To train your crew programmatically, use the following steps:
+
+1. Define the number of iterations for training.
+2. Specify the input parameters for the training process.
+3. Execute the training command within a try-except block to handle potential errors.
+
+```python
+    n_iterations = 2
+    inputs = {"topic": "CrewAI Training"}
+
+    try:
+        YourCrewName_Crew().crew().train(n_iterations= n_iterations, inputs=inputs)
+
+    except Exception as e:
+        raise Exception(f"An error occurred while training the crew: {e}")
+```
+
+!!! note "Replace `<n_iterations>` with the desired number of training iterations. This determines how many times the agents will go through the training process."
+
+
+### Key Points to Note:
+- **Positive Integer Requirement:** Ensure that the number of iterations (`n_iterations`) is a positive integer. The code will raise a `ValueError` if this condition is not met.
+- **Error Handling:** The code handles subprocess errors and unexpected exceptions, providing error messages to the user.
+
+It is important to note that the training process may take some time, depending on the complexity of your agents and will also require your feedback on each iteration.
+
+Once the training is complete, your agents will be equipped with enhanced capabilities and knowledge, ready to tackle complex tasks and provide more consistent and valuable insights.
+
+Remember to regularly update and retrain your agents to ensure they stay up-to-date with the latest information and advancements in the field.
+
+Happy training with CrewAI!

docs/core-concepts/Using-LangChain-Tools.md

@@ -0,0 +1,38 @@
+---
+title: Using LangChain Tools
+description: Learn how to integrate LangChain tools with CrewAI agents to enhance search-based queries and more.
+---
+
+## Using LangChain Tools
+!!! info "LangChain Integration"
+    CrewAI seamlessly integrates with LangChain’s comprehensive toolkit for search-based queries and more, here are the available built-in tools that are offered by Langchain [LangChain Toolkit](https://python.langchain.com/docs/integrations/tools/)
+
+```python
+from crewai import Agent
+from langchain.agents import Tool
+from langchain.utilities import GoogleSerperAPIWrapper
+
+# Setup API keys
+os.environ["SERPER_API_KEY"] = "Your Key"
+
+search = GoogleSerperAPIWrapper()
+
+# Create and assign the search tool to an agent
+serper_tool = Tool(
+  name="Intermediate Answer",
+  func=search.run,
+  description="Useful for search-based queries",
+)
+
+agent = Agent(
+  role='Research Analyst',
+  goal='Provide up-to-date market analysis',
+  backstory='An expert analyst with a keen eye for market trends.',
+  tools=[serper_tool]
+)
+
+# rest of the code ...
+```
+
+## Conclusion
+Tools are pivotal in extending the capabilities of CrewAI agents, enabling them to undertake a broad spectrum of tasks and collaborate effectively. When building solutions with CrewAI, leverage both custom and existing tools to empower your agents and enhance the AI ecosystem. Consider utilizing error handling, caching mechanisms, and the flexibility of tool arguments to optimize your agents' performance and capabilities.