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Removing LangChain and Rebuilding Executor (#1322)

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* rebuilding executor

* removing langchain

* Making all tests good

* fixing types and adding ability for nor using system prompts

* improving types

* pleasing the types gods

* pleasing the types gods

* fixing parser, tools and executor

* making sure all tests pass

* final pass

* fixing type

* Updating Docs

* preparing to cut new version
This commit is contained in:
João Moura
2024-09-16 14:14:04 -03:00
committed by GitHub
parent 322780a5f3
commit e77442cf34
177 changed files with 27272 additions and 1618561 deletions
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30
docs/core-concepts/Agents.md
View File

@@ -11,31 +11,34 @@ description: What are crewAI Agents and how to use them.
<li class='leading-3'>Make decisions</li>
<li class='leading-3'>Communicate with other agents</li>
</ul>
<br/>
<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. |
| :------------------------- | :--------- | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| **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`. |
| **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 `False`.
| **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`. |
| **Allow Code Execution** *(optional)* | `allow_code_execution` | Enable code execution for the agent. Default is `False`. |
| **Max Retry Limit** *(optional)* | `max_retry_limit` | Maximum number of retries for an agent to execute a task when an error occurs. Default is `2`. |
| **Max Retry Limit** *(optional)* | `max_retry_limit` | Maximum number of retries for an agent to execute a task when an error occurs. Default is `2`.
| **Use Stop Words** *(optional)* | `use_stop_words` | Adds the ability to not use stop words (to support o1 models). Default is `True`. |
| **Use System Prompt** *(optional)* | `use_system_prompt` | Adds the ability to not use system prompt (to support o1 models). Default is `True`. |
| **Respect Context Window** *(optional)* | `respect_context_window` | Summary strategy to avoid overflowing the context window. Default is `True`. |
## Creating an Agent
@@ -63,7 +66,7 @@ agent = Agent(
max_rpm=None, # Optional
max_execution_time=None, # Optional
verbose=True, # Optional
allow_delegation=True, # Optional
allow_delegation=False, # Optional
step_callback=my_intermediate_step_callback, # Optional
cache=True, # Optional
system_template=my_system_template, # Optional
@@ -74,8 +77,11 @@ agent = Agent(
tools_handler=my_tools_handler, # Optional
cache_handler=my_cache_handler, # Optional
callbacks=[callback1, callback2], # Optional
allow_code_execution=True, # Optiona
allow_code_execution=True, # Optional
max_retry_limit=2, # Optional
use_stop_words=True, # Optional
use_system_prompt=True, # Optional
respect_context_window=True, # Optional
)
```
@@ -105,7 +111,7 @@ agent = Agent(
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.
CrewAI is a universal multi-agent framework that allows for all agents to work together to automate tasks and solve problems.
```py

2
docs/core-concepts/Collaboration.md
View File

@@ -27,7 +27,7 @@ The `Crew` class has been enriched with several attributes to support advanced f
- **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.
- **Output Logging (`output_log_file`)**: Specifies the file path for logging the output of the crew's execution.
- **Planning Mode (`planning`)**: Allows crews to plan their actions before executing tasks by setting `planning=True` when creating the `Crew` instance. This feature enhances coordination and efficiency.
- **Replay Feature**: Introduces a new CLI for listing tasks from the last run and replaying from a specific task, enhancing task management and troubleshooting.

73
docs/core-concepts/Crews.md
View File

@@ -13,18 +13,18 @@ A crew in crewAI represents a collaborative group of agents working together to
| :------------------------------------ | :--------------------- | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| **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. |
| **Process** _(optional)_ | `process` | The process flow (e.g., sequential, hierarchical) the crew follows. Default is `sequential`. |
| **Verbose** _(optional)_ | `verbose` | The verbosity level for logging during execution. Defaults to `False`. |
| **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. |
| **Max RPM** _(optional)_ | `max_rpm` | Maximum requests per minute the crew adheres to during execution. Defaults to `None`. |
| **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. |
| **Memory** _(optional)_ | `memory` | Utilized for storing execution memories (short-term, long-term, entity memory). Defaults to `False`. |
| **Cache** _(optional)_ | `cache` | Specifies whether to use a cache for storing the results of tools' execution. Defaults to `True`. |
| **Embedder** _(optional)_ | `embedder` | Configuration for the embedder to be used by the crew. Mostly used by memory for now. Default is `{"provider": "openai"}`. |
| **Full Output** _(optional)_ | `full_output` | Whether the crew should return the full output with all tasks outputs or just the final output. Defaults to `False`. |
| **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. |
@@ -38,65 +38,6 @@ A crew in crewAI represents a collaborative group of agents working together to
!!! 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
from crewai_tools import tool
@tool('DuckDuckGoSearch')
def search(search_query: str):
"""Search the web for information on a given topic"""
return DuckDuckGoSearchRun().run(search_query)
# Define agents with specific roles and tools
researcher = Agent(
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=[search]
)
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,
)
```
## Crew Output

155
docs/core-concepts/Memory.md
View File

@@ -4,16 +4,17 @@ description: Leveraging memory systems in the crewAI framework to enhance agent
---
## 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 remember 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. |
| Component | Description |
| :------------------- | :---------------------------------------------------------------------------------------------------------------------- |
| **Short-Term Memory**| Temporarily stores recent interactions and outcomes using `RAG`, 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. |
| **Entity Memory** | Captures and organizes information about entities (people, places, concepts) encountered during tasks, facilitating deeper understanding and relationship mapping. Uses `RAG` for storing entity information. |
| **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
@@ -27,12 +28,12 @@ description: Leveraging memory systems in the crewAI framework to enhance agent
## 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.
By default, the memory system is disabled, and you can ensure it is active by setting `memory=True` in the crew configuration. The memory will use OpenAI embeddings by default, but you can change it by setting `embedder` to a different model.
The 'embedder' only applies to **Short-Term Memory** which uses Chroma for RAG using EmbedChain package.
The **Long-Term Memory** uses SQLLite3 to store task results. Currently, there is no way to override these storage implementations.
The data storage files are saved into a platform specific location found using the appdirs package
and the name of the project which can be overridden using the **CREWAI_STORAGE_DIR** environment variable.
The 'embedder' only applies to **Short-Term Memory** which uses Chroma for RAG using the EmbedChain package.
The **Long-Term Memory** uses SQLite3 to store task results. Currently, there is no way to override these storage implementations.
The data storage files are saved into a platform-specific location found using the appdirs package,
and the name of the project can be overridden using the **CREWAI_STORAGE_DIR** environment variable.
### Example: Configuring Memory for a Crew
@@ -56,17 +57,17 @@ my_crew = Crew(
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'
}
}
agents=[...],
tasks=[...],
process=Process.sequential,
memory=True,
verbose=True,
embedder={
"provider": "openai",
"config": {
"model": 'text-embedding-3-small'
}
}
)
```
@@ -75,19 +76,19 @@ my_crew = Crew(
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"
}
}
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"
}
}
)
```
@@ -96,18 +97,18 @@ my_crew = Crew(
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": "your_embedding_model_deployment_name"
}
}
agents=[...],
tasks=[...],
process=Process.sequential,
memory=True,
verbose=True,
embedder={
"provider": "azure_openai",
"config": {
"model": 'text-embedding-ada-002',
"deployment_name": "your_embedding_model_deployment_name"
}
}
)
```
@@ -116,14 +117,14 @@ my_crew = Crew(
from crewai import Crew, Agent, Task, Process
my_crew = Crew(
agents=[...],
tasks=[...],
process=Process.sequential,
memory=True,
verbose=True,
embedder={
"provider": "gpt4all"
}
agents=[...],
tasks=[...],
process=Process.sequential,
memory=True,
verbose=True,
embedder={
"provider": "gpt4all"
}
)
```
@@ -132,17 +133,17 @@ my_crew = Crew(
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'
}
}
agents=[...],
tasks=[...],
process=Process.sequential,
memory=True,
verbose=True,
embedder={
"provider": "vertexai",
"config": {
"model": 'textembedding-gecko'
}
}
)
```
@@ -151,18 +152,18 @@ my_crew = Crew(
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
}
}
agents=[...],
tasks=[...],
process=Process.sequential,
memory=True,
verbose=True,
embedder={
"provider": "cohere",
"config": {
"model": "embed-english-v3.0",
"vector_dimension": 1024
}
}
)
```

35
docs/core-concepts/Pipeline.md
View File

@@ -12,7 +12,7 @@ A pipeline in crewAI represents a structured workflow that allows for the sequen
Understanding the following terms is crucial for working effectively with pipelines:
- **Stage**: A distinct part of the pipeline, which can be either sequential (a single crew) or parallel (multiple crews executing concurrently).
- **Run**: A specific execution of the pipeline for a given set of inputs, representing a single instance of processing through the pipeline.
- **Kickoff**: A specific execution of the pipeline for a given set of inputs, representing a single instance of processing through the pipeline.
- **Branch**: Parallel executions within a stage (e.g., concurrent crew operations).
- **Trace**: The journey of an individual input through the entire pipeline, capturing the path and transformations it undergoes.
@@ -28,13 +28,13 @@ This represents a pipeline with three stages:
2. A parallel stage with two branches (crew2 and crew3 executing concurrently)
3. Another sequential stage (crew4)
Each input creates its own run, flowing through all stages of the pipeline. Multiple runs can be processed concurrently, each following the defined pipeline structure.
Each input creates its own kickoff, flowing through all stages of the pipeline. Multiple kickoffs can be processed concurrently, each following the defined pipeline structure.
## Pipeline Attributes
| Attribute | Parameters | Description |
| :--------- | :--------- | :---------------------------------------------------------------------------------------------- |
| **Stages** | `stages` | A list of crews, lists of crews, or routers representing the stages to be executed in sequence. |
| Attribute | Parameters | Description |
| :--------- | :---------- | :----------------------------------------------------------------------------------------------------------------- |
| **Stages** | `stages` | A list of `PipelineStage` (crews, lists of crews, or routers) representing the stages to be executed in sequence. |
## Creating a Pipeline
@@ -43,7 +43,7 @@ When creating a pipeline, you define a series of stages, each consisting of eith
### Example: Assembling a Pipeline
```python
from crewai import Crew, Agent, Task, Pipeline
from crewai import Crew, Process, Pipeline
# Define your crews
research_crew = Crew(
@@ -74,7 +74,8 @@ my_pipeline = Pipeline(
| Method | Description |
| :--------------- | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| **process_runs** | Executes the pipeline, processing all stages and returning the results. This method initiates one or more runs through the pipeline, handling the flow of data between stages. |
| **kickoff** | Executes the pipeline, processing all stages and returning the results. This method initiates one or more kickoffs through the pipeline, handling the flow of data between stages. |
| **process_runs** | Runs the pipeline for each input provided, handling the flow and transformation of data between stages. |
## Pipeline Output
@@ -99,12 +100,12 @@ The output of a pipeline in the crewAI framework is encapsulated within the `Pip
| Attribute | Parameters | Type | Description |
| :---------------- | :-------------- | :------------------------- | :-------------------------------------------------------------------------------------------- |
| **ID** | `id` | `UUID4` | A unique identifier for the run result. |
| **Raw** | `raw` | `str` | The raw output of the final stage in the pipeline run. |
| **Pydantic** | `pydantic` | `Optional[BaseModel]` | A Pydantic model object representing the structured output of the final stage, if applicable. |
| **JSON Dict** | `json_dict` | `Optional[Dict[str, Any]]` | A dictionary representing the JSON output of the final stage, if applicable. |
| **Token Usage** | `token_usage` | `Dict[str, Any]` | A summary of token usage across all stages of the pipeline run. |
| **Trace** | `trace` | `List[Any]` | A trace of the journey of inputs through the pipeline run. |
| **Crews Outputs** | `crews_outputs` | `List[CrewOutput]` | A list of `CrewOutput` objects, representing the outputs from each crew in the pipeline run. |
| **Raw** | `raw` | `str` | The raw output of the final stage in the pipeline kickoff. |
| **Pydantic** | `pydantic` | `Any` | A Pydantic model object representing the structured output of the final stage, if applicable. |
| **JSON Dict** | `json_dict` | `Union[Dict[str, Any], None]` | A dictionary representing the JSON output of the final stage, if applicable. |
| **Token Usage** | `token_usage` | `Dict[str, UsageMetrics]` | A summary of token usage across all stages of the pipeline kickoff. |
| **Trace** | `trace` | `List[Any]` | A trace of the journey of inputs through the pipeline kickoff. |
| **Crews Outputs** | `crews_outputs` | `List[CrewOutput]` | A list of `CrewOutput` objects, representing the outputs from each crew in the pipeline kickoff. |
### Pipeline Run Result Methods and Properties
@@ -112,7 +113,7 @@ The output of a pipeline in the crewAI framework is encapsulated within the `Pip
| :-------------- | :------------------------------------------------------------------------------------------------------- |
| **json** | Returns the JSON string representation of the run result if the output format of the final task is JSON. |
| **to_dict** | Converts the JSON and Pydantic outputs to a dictionary. |
| \***\*str\*\*** | Returns the string representation of the run result, prioritizing Pydantic, then JSON, then raw. |
| **str** | Returns the string representation of the run result, prioritizing Pydantic, then JSON, then raw. |
### Accessing Pipeline Outputs
@@ -247,7 +248,7 @@ main_pipeline = Pipeline(stages=[classification_crew, email_router])
inputs = [{"email": "..."}, {"email": "..."}] # List of email data
main_pipeline.kickoff(inputs=inputs)
main_pipeline.kickoff(inputs=inputs=inputs)
```
In this example, the router decides between an urgent pipeline and a normal pipeline based on the urgency score of the email. If the urgency score is greater than 7, it routes to the urgent pipeline; otherwise, it uses the normal pipeline. If the input doesn't include an urgency score, it defaults to just the classification crew.
@@ -261,7 +262,7 @@ In this example, the router decides between an urgent pipeline and a normal pipe
### Error Handling and Validation
The Pipeline class includes validation mechanisms to ensure the robustness of the pipeline structure:
The `Pipeline` class includes validation mechanisms to ensure the robustness of the pipeline structure:
- Validates that stages contain only Crew instances or lists of Crew instances.
- Prevents double nesting of stages to maintain a clear structure.
- Prevents double nesting of stages to maintain a clear structure.

7
docs/core-concepts/Planning.md
View File

@@ -43,7 +43,7 @@ my_crew = Crew(
### Example
When running the base case example, you will see something like the following output, which represents the output of the AgentPlanner responsible for creating the step-by-step logic to add to the Agents tasks.
When running the base case example, you will see something like the following output, which represents the output of the AgentPlanner responsible for creating the step-by-step logic to add to the Agents' tasks.
```
[2024-07-15 16:49:11][INFO]: Planning the crew execution
@@ -96,7 +96,7 @@ A list with 10 bullet points of the most relevant information about AI LLMs.
**Agent Goal:** Create detailed reports based on AI LLMs data analysis and research findings
**Task Expected Output:** A fully fledge report with the main topics, each with a full section of information. Formatted as markdown without '```'
**Task Expected Output:** A fully fledged report with the main topics, each with a full section of information. Formatted as markdown without '```'
**Task Tools:** None specified
@@ -130,5 +130,4 @@ A list with 10 bullet points of the most relevant information about AI LLMs.
- Double-check formatting and make any necessary adjustments.
**Expected Output:**
A fully-fledged report with the main topics, each with a full section of information. Formatted as markdown without '```'.
```
A fully fledged report with the main topics, each with a full section of information. Formatted as markdown without '```'.

49
docs/core-concepts/Tasks.md
View File

@@ -1,3 +1,4 @@
```markdown
---
title: crewAI Tasks
description: Detailed guide on managing and creating tasks within the crewAI framework, reflecting the latest codebase updates.
@@ -12,22 +13,22 @@ Tasks within crewAI can be collaborative, requiring multiple agents to work toge
## 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. Defaults to an empty list. |
| **Async Execution** _(optional)_ | `async_execution` | If set, the task executes asynchronously, allowing progression without waiting for completion. Defaults to False. |
| **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. Defaults to None. |
| **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. |
| **Output** _(optional)_ | `output` | An instance of `TaskOutput`, containing the raw, JSON, and Pydantic output plus additional details. |
| **Callback** _(optional)_ | `callback` | A 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. Defaults to False.|
| **Converter Class** _(optional)_ | `converter_cls` | A converter class used to export structured output. Defaults to None. |
| Attribute | Parameters | Type | Description |
| :------------------------------- | :---------------- | :---------------------------- | :------------------------------------------------------------------------------------------------------------------- |
| **Description** | `description` | `str` | A clear, concise statement of what the task entails. |
| **Agent** | `agent` | `Optional[BaseAgent]` | The agent responsible for the task, assigned either directly or by the crew's process. |
| **Expected Output** | `expected_output` | `str` | A detailed description of what the task's completion looks like. |
| **Tools** _(optional)_ | `tools` | `Optional[List[Any]]` | The functions or capabilities the agent can utilize to perform the task. Defaults to an empty list. |
| **Async Execution** _(optional)_ | `async_execution` | `Optional[bool]` | If set, the task executes asynchronously, allowing progression without waiting for completion. Defaults to False. |
| **Context** _(optional)_ | `context` | `Optional[List["Task"]]` | Specifies tasks whose outputs are used as context for this task. |
| **Config** _(optional)_ | `config` | `Optional[Dict[str, Any]]` | Additional configuration details for the agent executing the task, allowing further customization. Defaults to None. |
| **Output JSON** _(optional)_ | `output_json` | `Optional[Type[BaseModel]]` | Outputs a JSON object, requiring an OpenAI client. Only one output format can be set. |
| **Output Pydantic** _(optional)_ | `output_pydantic` | `Optional[Type[BaseModel]]` | Outputs a Pydantic model object, requiring an OpenAI client. Only one output format can be set. |
| **Output File** _(optional)_ | `output_file` | `Optional[str]` | Saves the task output to a file. If used with `Output JSON` or `Output Pydantic`, specifies how the output is saved. |
| **Output** _(optional)_ | `output` | `Optional[TaskOutput]` | An instance of `TaskOutput`, containing the raw, JSON, and Pydantic output plus additional details. |
| **Callback** _(optional)_ | `callback` | `Optional[Any]` | A callable that is executed with the task's output upon completion. |
| **Human Input** _(optional)_ | `human_input` | `Optional[bool]` | Indicates if the task should involve human review at the end, useful for tasks needing human oversight. Defaults to False.|
| **Converter Class** _(optional)_ | `converter_cls` | `Optional[Type[Converter]]` | A converter class used to export structured output. Defaults to None. |
## Creating a Task
@@ -49,28 +50,28 @@ Directly specify an `agent` for assignment or let the `hierarchical` CrewAI's pr
## Task Output
!!! note "Understanding Task Outputs"
The output of a task in the crewAI framework is encapsulated within the `TaskOutput` class. This class provides a structured way to access results of a task, including various formats such as raw strings, JSON, and Pydantic models.
The output of a task in the crewAI framework is encapsulated within the `TaskOutput` class. This class provides a structured way to access results of a task, including various formats such as raw output, JSON, and Pydantic models.
By default, the `TaskOutput` will only include the `raw` output. A `TaskOutput` will only include the `pydantic` or `json_dict` output if the original `Task` object was configured with `output_pydantic` or `output_json`, respectively.
### Task Output Attributes
| Attribute | Parameters | Type | Description |
| :---------------- | :-------------- | :------------------------- | :------------------------------------------------------------------------------------------------- |
| **Description** | `description` | `str` | A brief description of the task. |
| **Summary** | `summary` | `Optional[str]` | A short summary of the task, auto-generated from the first 10 words of the description. |
| **Description** | `description` | `str` | Description of the task. |
| **Summary** | `summary` | `Optional[str]` | Summary of the task, auto-generated from the first 10 words of the description. |
| **Raw** | `raw` | `str` | The raw output of the task. This is the default format for the output. |
| **Pydantic** | `pydantic` | `Optional[BaseModel]` | A Pydantic model object representing the structured output of the task. |
| **JSON Dict** | `json_dict` | `Optional[Dict[str, Any]]` | A dictionary representing the JSON output of the task. |
| **Agent** | `agent` | `str` | The agent that executed the task. |
| **Output Format** | `output_format` | `OutputFormat` | The format of the task output, with options including RAW, JSON, and Pydantic. The default is RAW. |
### Task Output Methods and Properties
### Task Methods and Properties
| Method/Property | Description |
| :-------------- | :------------------------------------------------------------------------------------------------ |
| **json** | Returns the JSON string representation of the task output if the output format is JSON. |
| **to_dict** | Converts the JSON and Pydantic outputs to a dictionary. |
| \***\*str\*\*** | Returns the string representation of the task output, prioritizing Pydantic, then JSON, then raw. |
| **str** | Returns the string representation of the task output, prioritizing Pydantic, then JSON, then raw. |
### Accessing Task Outputs
@@ -234,7 +235,7 @@ def callback_function(output: TaskOutput):
print(f"""
Task completed!
Task: {output.description}
Output: {output.raw_output}
Output: {output.raw}
""")
research_task = Task(
@@ -275,7 +276,7 @@ result = crew.kickoff()
print(f"""
Task completed!
Task: {task1.output.description}
Output: {task1.output.raw_output}
Output: {task1.output.raw}
""")
```
@@ -313,4 +314,4 @@ save_output_task = Task(
## 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.
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.

36
docs/core-concepts/Testing.md
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@@ -9,7 +9,7 @@ Testing is a crucial part of the development process, and it is essential to ens
### Using the Testing Feature
We added the CLI command `crewai test` to make it easy to test your crew. This command will run your crew for a specified number of iterations and provide detailed performance metrics. The parameters are `n_iterations` and `model` which are optional and default to 2 and `gpt-4o-mini` respectively. For now, the only provider available is OpenAI.
We added the CLI command `crewai test` to make it easy to test your crew. This command will run your crew for a specified number of iterations and provide detailed performance metrics. The parameters are `n_iterations` and `model`, which are optional and default to 2 and `gpt-4o-mini` respectively. For now, the only provider available is OpenAI.
```bash
crewai test
@@ -21,20 +21,36 @@ If you want to run more iterations or use a different model, you can specify the
crewai test --n_iterations 5 --model gpt-4o
```
or using the short forms:
```bash
crewai test -n 5 -m gpt-4o
```
When you run the `crewai test` command, the crew will be executed for the specified number of iterations, and the performance metrics will be displayed at the end of the run.
A table of scores at the end will show the performance of the crew in terms of the following metrics:
```
Task Scores
(1-10 Higher is better)
┏━━━━━━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━━━━━━┓
┃ Tasks/Crew ┃ Run 1 ┃ Run 2 ┃ Avg. Total ┃
┡━━━━━━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━━━━━━┩
│ Task 1 │ 10.0 │ 9.0 │ 9.5 │
│ Task 2 │ 9.0 │ 9.0 │ 9.0 │
│ Crew │ 9.5 │ 9.0 │ 9.2 │
└────────────┴───────┴───────┴────────────┘
Tasks Scores
(1-10 Higher is better)
┏━━━━━━━━━━━━━━━━━━━━┯━━━━━━━┯━━━━━━━┯━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ Tasks/Crew/Agents │ Run 1 │ Run 2 │ Avg. Total │ Agents │ ┃
┠────────────────────┼───────┼───────┼────────────┼────────────────────────────────┼─────────────────────────────────┨
┃ Task 1 │ 9.0 │ 9.5 │ 9.2 │ - Professional Insights │ ┃
┃ │ │ │ │ Researcher │ ┃
┃ │ │ │ │ │ ┃
┃ Task 2 │ 9.0 │ 10.0 │ 9.5 │ - Company Profile Investigator │ ┃
┃ │ │ │ │ │ ┃
┃ Task 3 │ 9.0 │ 9.0 │ 9.0 │ - Automation Insights │ ┃
┃ │ │ │ │ Specialist │ ┃
┃ │ │ │ │ │ ┃
┃ Task 4 │ 9.0 │ 9.0 │ 9.0 │ - Final Report Compiler │ ┃
┃ │ │ │ │ │ - Automation Insights ┃
┃ │ │ │ │ │ Specialist ┃
┃ Crew │ 9.00 │ 9.38 │ 9.2 │ │ ┃
┃ Execution Time (s) │ 126 │ 145 │ 135 │ │ ┃
┗━━━━━━━━━━━━━━━━━━━━┷━━━━━━━┷━━━━━━━┷━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛
```
The example above shows the test results for two runs of the crew with two tasks, with the average total score for each task and the crew as a whole.

21
docs/core-concepts/Tools.md
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@@ -106,7 +106,7 @@ Here is a list of the available tools and their descriptions:
| **CodeInterpreterTool** | A tool for interpreting python code. |
| **ComposioTool** | Enables use of Composio tools. |
| **CSVSearchTool** | A RAG tool designed for searching within CSV files, tailored to handle structured data. |
| **DALL-E Tool** | A tool for generating images using the DALL-E API. |
| **DALL-E Tool** | A tool for generating images using the DALL-E API. |
| **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. |
@@ -114,7 +114,7 @@ Here is a list of the available tools and their descriptions:
| **FileReadTool** | Enables reading and extracting data from files, supporting various file formats. |
| **FirecrawlSearchTool** | A tool to search webpages using Firecrawl and return the results. |
| **FirecrawlCrawlWebsiteTool** | A tool for crawling webpages using Firecrawl. |
| **FirecrawlScrapeWebsiteTool** | A tool for scraping webpages url using Firecrawl and returning its contents. |
| **FirecrawlScrapeWebsiteTool** | A tool for scraping webpages URL using Firecrawl and returning its contents. |
| **GithubSearchTool** | A RAG tool for searching within GitHub repositories, useful for code and documentation search.|
| **SerperDevTool** | A specialized tool for development purposes, with specific functionalities under development. |
| **TXTSearchTool** | A RAG tool focused on searching within text (.txt) files, suitable for unstructured data. |
@@ -123,14 +123,14 @@ Here is a list of the available tools and their descriptions:
| **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. |
| **Vision Tool** | A tool for generating images using the DALL-E API. |
| **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. |
| **Vision Tool** | A tool for generating images using the DALL-E API. |
| **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. |
## Creating your own Tools
@@ -144,6 +144,7 @@ pip install 'crewai[tools]'
```
Once you do that there are two main ways for one to create a crewAI tool:
### Subclassing `BaseTool`
```python

2
docs/core-concepts/Training-Crew.md
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@@ -16,7 +16,7 @@ To use the training feature, follow these steps:
3. Run the following command:
```shell
crewai train -n <n_iterations> <filename>
crewai train -n <n_iterations> <filename> (optional)
```
!!! note "Replace `<n_iterations>` with the desired number of training iterations and `<filename>` with the appropriate filename ending with `.pkl`."

3
docs/core-concepts/Using-LangChain-Tools.md
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@@ -5,9 +5,10 @@ description: Learn how to integrate LangChain tools with CrewAI agents to enhanc
## 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/)
CrewAI seamlessly integrates with LangChain’s comprehensive [list of tools](https://python.langchain.com/docs/integrations/tools/), all of which can be used with crewAI.
```python
import os
from crewai import Agent
from langchain.agents import Tool
from langchain.utilities import GoogleSerperAPIWrapper

10
docs/core-concepts/Using-LlamaIndex-Tools.md
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@@ -35,10 +35,10 @@ query_tool = LlamaIndexTool.from_query_engine(
# Create and assign the tools to an agent
agent = Agent(
role='Research Analyst',
goal='Provide up-to-date market analysis',
backstory='An expert analyst with a keen eye for market trends.',
tools=[tool, *tools, query_tool]
role='Research Analyst',
goal='Provide up-to-date market analysis',
backstory='An expert analyst with a keen eye for market trends.',
tools=[tool, *tools, query_tool]
)
# rest of the code ...
@@ -54,4 +54,4 @@ To effectively use the LlamaIndexTool, follow these steps:
pip install 'crewai[tools]'
```
2. **Install and Use LlamaIndex**: Follow LlamaIndex documentation [LlamaIndex Documentation](https://docs.llamaindex.ai/) to set up a RAG/agent pipeline.
2. **Install and Use LlamaIndex**: Follow the LlamaIndex documentation [LlamaIndex Documentation](https://docs.llamaindex.ai/) to set up a RAG/agent pipeline.