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Merge branch 'main' into bugfix/kickoff-for-each-usage-metrics

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Brandon Hancock
2024-07-03 11:22:42 -04:00
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.gitignore vendored
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@@ -13,4 +13,5 @@ db/
test.py
rc-tests/*
*.pkl
temp/*
temp/*
.vscode/*

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README.md
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@@ -197,46 +197,6 @@ Please refer to the [Connect crewAI to LLMs](https://docs.crewai.com/how-to/LLM-
**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.
## Training
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.
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>
```
Replace `<n_iterations>` with the desired number of training iterations. This determines how many times the agents will go through the training process.
Remember to also replace the placeholder inputs with the actual values you want to use on the main.py file in the `train` function.
```python
def train():
"""
Train the crew for a given number of iterations.
"""
inputs = {"topic": "AI LLMs"}
try:
ProjectCreationCrew().crew().train(n_iterations=int(sys.argv[1]), inputs=inputs)
...
```
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!
## Contribution
CrewAI is open-source and we welcome contributions. If you're looking to contribute, please:

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docs/core-concepts/Agents.md
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@@ -16,24 +16,24 @@ description: What are crewAI Agents and how to use them.
## Agent Attributes
| Attribute | Description |
| :------------------------- | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| **Role** | Defines the agent's function within the crew. It determines the kind of tasks the agent is best suited for. |
| **Goal** | The individual objective that the agent aims to achieve. It guides the agent's decision-making process. |
| **Backstory** | Provides context to the agent's role and goal, enriching the interaction and collaboration dynamics. |
| **LLM** *(optional)* | 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)* | 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)* | 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` 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` is Tte 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` 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)* | Setting this to `True` configures the internal logger to provide detailed execution logs, aiding in debugging and monitoring. Default is `False`. |
| **Allow Delegation** *(optional)* | 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)* | 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)* | Indicates if the agent should use a cache for tool usage. Default is `True`. |
| **System Template** *(optional)* | Specifies the system format for the agent. Default is `None`. |
| **Prompt Template** *(optional)* | Specifies the prompt format for the agent. Default is `None`. |
| **Response Template** *(optional)* | Specifies the response format for the agent. Default is `None`. |
| 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

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docs/core-concepts/Crews.md
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@@ -8,29 +8,29 @@ A crew in crewAI represents a collaborative group of agents working together to
## Crew Attributes
| Attribute | Description |
| :-------------------------- | :----------------------------------------------------------- |
| **Tasks** | A list of tasks assigned to the crew. |
| **Agents** | A list of agents that are part of the crew. |
| **Process** *(optional)* | The process flow (e.g., sequential, hierarchical) the crew follows. |
| **Verbose** *(optional)* | The verbosity level for logging during execution. |
| **Manager LLM** *(optional)*| The language model used by the manager agent in a hierarchical process. **Required when using a hierarchical process.** |
| **Function Calling LLM** *(optional)* | 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)* | Optional configuration settings for the crew, in `Json` or `Dict[str, Any]` format. |
| **Max RPM** *(optional)* | Maximum requests per minute the crew adheres to during execution. |
| **Language** *(optional)* | Language used for the crew, defaults to English. |
| **Language File** *(optional)* | Path to the language file to be used for the crew. |
| **Memory** *(optional)* | Utilized for storing execution memories (short-term, long-term, entity memory). |
| **Cache** *(optional)* | Specifies whether to use a cache for storing the results of tools' execution. |
| **Embedder** *(optional)* | Configuration for the embedder to be used by the crew. Mostly used by memory for now. |
| **Full Output** *(optional)*| Whether the crew should return the full output with all tasks outputs or just the final output. |
| **Step Callback** *(optional)* | 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)* | A function that is called after the completion of each task. Useful for monitoring or additional operations post-task execution. |
| **Share Crew** *(optional)* | 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)* | 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` sets a custom agent that will be used as a manager. |
| **Manager Callbacks** *(optional)* | `manager_callbacks` takes a list of callback handlers to be executed by the manager agent when a hierarchical process is used. |
| **Prompt File** *(optional)* | Path to the prompt JSON file to be used for the crew. |
| 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.

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docs/core-concepts/Tasks.md
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@@ -11,20 +11,20 @@ Tasks within crewAI can be collaborative, requiring multiple agents to work toge
## Task Attributes
| Attribute | Description |
| :----------------------| :-------------------------------------------------------------------------------------------- |
| **Description** | A clear, concise statement of what the task entails. |
| **Agent** | The agent responsible for the task, assigned either directly or by the crew's process. |
| **Expected Output** | A detailed description of what the task's completion looks like. |
| **Tools** *(optional)* | The functions or capabilities the agent can utilize to perform the task. |
| **Async Execution** *(optional)* | If set, the task executes asynchronously, allowing progression without waiting for completion.|
| **Context** *(optional)* | Specifies tasks whose outputs are used as context for this task. |
| **Config** *(optional)* | Additional configuration details for the agent executing the task, allowing further customization. |
| **Output JSON** *(optional)* | Outputs a JSON object, requiring an OpenAI client. Only one output format can be set. |
| **Output Pydantic** *(optional)* | Outputs a Pydantic model object, requiring an OpenAI client. Only one output format can be set. |
| **Output File** *(optional)* | Saves the task output to a file. If used with `Output JSON` or `Output Pydantic`, specifies how the output is saved. |
| **Callback** *(optional)* | A Python callable that is executed with the task's output upon completion. |
| **Human Input** *(optional)* | Indicates if the task requires human feedback at the end, useful for tasks needing human oversight. |
| 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

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docs/how-to/Sequential.md
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@@ -37,10 +37,9 @@ writer = Agent(
backstory='A skilled writer with a talent for crafting compelling narratives'
)
# Define the tasks in sequence
research_task = Task(description='Gather relevant data...', agent=researcher)
analysis_task = Task(description='Analyze the data...', agent=analyst)
writing_task = Task(description='Compose the report...', agent=writer)
research_task = Task(description='Gather relevant data...', agent=researcher, expected_output='Raw Data')
analysis_task = Task(description='Analyze the data...', agent=analyst, expected_output='Data Insights')
writing_task = Task(description='Compose the report...', agent=writer, expected_output='Final Report')
# Form the crew with a sequential process
report_crew = Crew(
@@ -83,4 +82,4 @@ CrewAI tracks token usage across all tasks and agents. You can access these metr
1. **Order Matters**: Arrange tasks in a logical sequence where each task builds upon the previous one.
2. **Clear Task Descriptions**: Provide detailed descriptions for each task to guide the agents effectively.
3. **Appropriate Agent Selection**: Match agents' skills and roles to the requirements of each task.
4. **Use Context**: Leverage the context from previous tasks to inform subsequent ones
4. **Use Context**: Leverage the context from previous tasks to inform subsequent ones

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@@ -20,6 +20,7 @@ pip install 'crewai[tools]'
Remember that when using this tool, the code must be generated by the Agent itself. The code must be a Python3 code. And it will take some time for the first time to run because it needs to build the Docker image.
```python
from crewai import Agent
from crewai_tools import CodeInterpreterTool
Agent(
@@ -27,3 +28,14 @@ Agent(
tools=[CodeInterpreterTool()],
)
```
We also provide a simple way to use it directly from the Agent.
```python
from crewai import Agent
agent = Agent(
...
allow_code_execution=True,
)
```

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@@ -0,0 +1,72 @@
# ComposioTool Documentation
## Description
This tools is a wrapper around the composio toolset and gives your agent access to a wide variety of tools from the composio SDK.
## Installation
To incorporate this tool into your project, follow the installation instructions below:
```shell
pip install composio-core
pip install 'crewai[tools]'
```
after the installation is complete, either run `composio login` or export your composio API key as `COMPOSIO_API_KEY`.
## Example
The following example demonstrates how to initialize the tool and execute a github action:
1. Initialize toolset
```python
from composio import App
from crewai_tools import ComposioTool
from crewai import Agent, Task
tools = [ComposioTool.from_action(action=Action.GITHUB_ACTIVITY_STAR_REPO_FOR_AUTHENTICATED_USER)]
```
If you don't know what action you want to use, use `from_app` and `tags` filter to get relevant actions
```python
tools = ComposioTool.from_app(App.GITHUB, tags=["important"])
```
or use `use_case` to search relevant actions
```python
tools = ComposioTool.from_app(App.GITHUB, use_case="Star a github repository")
```
2. Define agent
```python
crewai_agent = Agent(
role="Github Agent",
goal="You take action on Github using Github APIs",
backstory=(
"You are AI agent that is responsible for taking actions on Github "
"on users behalf. You need to take action on Github using Github APIs"
),
verbose=True,
tools=tools,
)
```
3. Execute task
```python
task = Task(
description="Star a repo ComposioHQ/composio on GitHub",
agent=crewai_agent,
expected_output="if the star happened",
)
task.execute()
```
* More detailed list of tools can be found [here](https://app.composio.dev)

2
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@@ -148,6 +148,8 @@ nav:
- Tools Docs:
- Google Serper Search: 'tools/SerperDevTool.md'
- Browserbase Web Loader: 'tools/BrowserbaseLoadTool.md'
- Composio Tools: 'tools/ComposioTool.md'
- Code Interpreter: 'tools/CodeInterpreterTool.md'
- Scrape Website: 'tools/ScrapeWebsiteTool.md'
- Directory Read: 'tools/DirectoryReadTool.md'
- Exa Serch Web Loader: 'tools/EXASearchTool.md'

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poetry.lock generated
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@@ -323,17 +323,17 @@ lxml = ["lxml"]
[[package]]
name = "boto3"
version = "1.34.136"
version = "1.34.137"
description = "The AWS SDK for Python"
optional = false
python-versions = ">=3.8"
files = [
{file = "boto3-1.34.136-py3-none-any.whl", hash = "sha256:d41037e2c680ab8d6c61a0a4ee6bf1fdd9e857f43996672830a95d62d6f6fa79"},
{file = "boto3-1.34.136.tar.gz", hash = "sha256:0314e6598f59ee0f34eb4e6d1a0f69fa65c146d2b88a6e837a527a9956ec2731"},
{file = "boto3-1.34.137-py3-none-any.whl", hash = "sha256:7cb697d67fd138ceebc6f789919ae370c092a50c6b0ccc4ef483027935502eab"},
{file = "boto3-1.34.137.tar.gz", hash = "sha256:0b21b84db4619b3711a6f643d465a5a25e81231ee43615c55a20ff6b89c6cc3c"},
]
[package.dependencies]
botocore = ">=1.34.136,<1.35.0"
botocore = ">=1.34.137,<1.35.0"
jmespath = ">=0.7.1,<2.0.0"
s3transfer = ">=0.10.0,<0.11.0"
@@ -342,13 +342,13 @@ crt = ["botocore[crt] (>=1.21.0,<2.0a0)"]
[[package]]
name = "botocore"
version = "1.34.136"
version = "1.34.137"
description = "Low-level, data-driven core of boto 3."
optional = false
python-versions = ">=3.8"
files = [
{file = "botocore-1.34.136-py3-none-any.whl", hash = "sha256:c63fe9032091fb9e9477706a3ebfa4d0c109b807907051d892ed574f9b573e61"},
{file = "botocore-1.34.136.tar.gz", hash = "sha256:7f7135178692b39143c8f152a618d2a3b71065a317569a7102d2306d4946f42f"},
{file = "botocore-1.34.137-py3-none-any.whl", hash = "sha256:a980fa4adec4bfa23fff70a3512622e9412c69c791898a52cafc2458b0be6040"},
{file = "botocore-1.34.137.tar.gz", hash = "sha256:e29c8e9bfda0b20a1997792968e85868bfce42fefad9730f633a81adcff3f2ef"},
]
[package.dependencies]
@@ -359,137 +359,6 @@ urllib3 = {version = ">=1.25.4,<2.2.0 || >2.2.0,<3", markers = "python_version >
[package.extras]
crt = ["awscrt (==0.20.11)"]
[[package]]
name = "brotli"
version = "1.1.0"
description = "Python bindings for the Brotli compression library"
optional = false
python-versions = "*"
files = [
{file = "Brotli-1.1.0-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:e1140c64812cb9b06c922e77f1c26a75ec5e3f0fb2bf92cc8c58720dec276752"},
{file = "Brotli-1.1.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:c8fd5270e906eef71d4a8d19b7c6a43760c6abcfcc10c9101d14eb2357418de9"},
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{file = "Brotli-1.1.0-cp310-cp310-win32.whl", hash = "sha256:be36e3d172dc816333f33520154d708a2657ea63762ec16b62ece02ab5e4daf2"},
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]
[package.dependencies]
typing-extensions = ">=4.6.0,<4.7.0 || >4.7.0"
[[package]]
name = "pydantic-core"
version = "2.20.0"
@@ -4356,23 +4052,6 @@ files = [
{file = "pyflakes-3.2.0.tar.gz", hash = "sha256:1c61603ff154621fb2a9172037d84dca3500def8c8b630657d1701f026f8af3f"},
]
[[package]]
name = "pygithub"
version = "1.59.1"
description = "Use the full Github API v3"
optional = false
python-versions = ">=3.7"
files = [
{file = "PyGithub-1.59.1-py3-none-any.whl", hash = "sha256:3d87a822e6c868142f0c2c4bf16cce4696b5a7a4d142a7bd160e1bdf75bc54a9"},
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]
[package.dependencies]
deprecated = "*"
pyjwt = {version = ">=2.4.0", extras = ["crypto"]}
pynacl = ">=1.4.0"
requests = ">=2.14.0"
[[package]]
name = "pygments"
version = "2.18.0"
@@ -4387,26 +4066,6 @@ files = [
[package.extras]
windows-terminal = ["colorama (>=0.4.6)"]
[[package]]
name = "pyjwt"
version = "2.8.0"
description = "JSON Web Token implementation in Python"
optional = false
python-versions = ">=3.7"
files = [
{file = "PyJWT-2.8.0-py3-none-any.whl", hash = "sha256:59127c392cc44c2da5bb3192169a91f429924e17aff6534d70fdc02ab3e04320"},
{file = "PyJWT-2.8.0.tar.gz", hash = "sha256:57e28d156e3d5c10088e0c68abb90bfac3df82b40a71bd0daa20c65ccd5c23de"},
]
[package.dependencies]
cryptography = {version = ">=3.4.0", optional = true, markers = "extra == \"crypto\""}
[package.extras]
crypto = ["cryptography (>=3.4.0)"]
dev = ["coverage[toml] (==5.0.4)", "cryptography (>=3.4.0)", "pre-commit", "pytest (>=6.0.0,<7.0.0)", "sphinx (>=4.5.0,<5.0.0)", "sphinx-rtd-theme", "zope.interface"]
docs = ["sphinx (>=4.5.0,<5.0.0)", "sphinx-rtd-theme", "zope.interface"]
tests = ["coverage[toml] (==5.0.4)", "pytest (>=6.0.0,<7.0.0)"]
[[package]]
name = "pylance"
version = "0.9.18"
@@ -4449,32 +4108,6 @@ pyyaml = "*"
[package.extras]
extra = ["pygments (>=2.12)"]
[[package]]
name = "pynacl"
version = "1.5.0"
description = "Python binding to the Networking and Cryptography (NaCl) library"
optional = false
python-versions = ">=3.6"
files = [
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{file = "PyNaCl-1.5.0-cp36-abi3-musllinux_1_1_x86_64.whl", hash = "sha256:61f642bf2378713e2c2e1de73444a3778e5f0a38be6fee0fe532fe30060282ff"},
{file = "PyNaCl-1.5.0-cp36-abi3-win32.whl", hash = "sha256:e46dae94e34b085175f8abb3b0aaa7da40767865ac82c928eeb9e57e1ea8a543"},
{file = "PyNaCl-1.5.0-cp36-abi3-win_amd64.whl", hash = "sha256:20f42270d27e1b6a29f54032090b972d97f0a1b0948cc52392041ef7831fee93"},
{file = "PyNaCl-1.5.0.tar.gz", hash = "sha256:8ac7448f09ab85811607bdd21ec2464495ac8b7c66d146bf545b0f08fb9220ba"},
]
[package.dependencies]
cffi = ">=1.4.1"
[package.extras]
docs = ["sphinx (>=1.6.5)", "sphinx-rtd-theme"]
tests = ["hypothesis (>=3.27.0)", "pytest (>=3.2.1,!=3.3.0)"]
[[package]]
name = "pypdf"
version = "4.2.0"
@@ -5102,18 +4735,18 @@ files = [
[[package]]
name = "setuptools"
version = "70.1.1"
version = "70.2.0"
description = "Easily download, build, install, upgrade, and uninstall Python packages"
optional = false
python-versions = ">=3.8"
files = [
{file = "setuptools-70.1.1-py3-none-any.whl", hash = "sha256:a58a8fde0541dab0419750bcc521fbdf8585f6e5cb41909df3a472ef7b81ca95"},
{file = "setuptools-70.1.1.tar.gz", hash = "sha256:937a48c7cdb7a21eb53cd7f9b59e525503aa8abaf3584c730dc5f7a5bec3a650"},
{file = "setuptools-70.2.0-py3-none-any.whl", hash = "sha256:b8b8060bb426838fbe942479c90296ce976249451118ef566a5a0b7d8b78fb05"},
{file = "setuptools-70.2.0.tar.gz", hash = "sha256:bd63e505105011b25c3c11f753f7e3b8465ea739efddaccef8f0efac2137bac1"},
]
[package.extras]
docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "pyproject-hooks (!=1.1)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-inline-tabs", "sphinx-lint", "sphinx-notfound-page (>=1,<2)", "sphinx-reredirects", "sphinxcontrib-towncrier"]
testing = ["build[virtualenv] (>=1.0.3)", "filelock (>=3.4.0)", "importlib-metadata", "ini2toml[lite] (>=0.14)", "jaraco.develop (>=7.21)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "jaraco.test", "mypy (==1.10.0)", "packaging (>=23.2)", "pip (>=19.1)", "pyproject-hooks (!=1.1)", "pytest (>=6,!=8.1.1)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-home (>=0.5)", "pytest-mypy", "pytest-perf", "pytest-ruff (>=0.3.2)", "pytest-subprocess", "pytest-timeout", "pytest-xdist (>=3)", "tomli", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"]
doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "pyproject-hooks (!=1.1)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-inline-tabs", "sphinx-lint", "sphinx-notfound-page (>=1,<2)", "sphinx-reredirects", "sphinxcontrib-towncrier"]
test = ["build[virtualenv] (>=1.0.3)", "filelock (>=3.4.0)", "importlib-metadata", "ini2toml[lite] (>=0.14)", "jaraco.develop (>=7.21)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "jaraco.test", "mypy (==1.10.0)", "packaging (>=23.2)", "pip (>=19.1)", "pyproject-hooks (!=1.1)", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-home (>=0.5)", "pytest-mypy", "pytest-perf", "pytest-ruff (>=0.3.2)", "pytest-subprocess", "pytest-timeout", "pytest-xdist (>=3)", "tomli", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"]
[[package]]
name = "shapely"
@@ -5194,17 +4827,6 @@ files = [
{file = "six-1.16.0.tar.gz", hash = "sha256:1e61c37477a1626458e36f7b1d82aa5c9b094fa4802892072e49de9c60c4c926"},
]
[[package]]
name = "smmap"
version = "5.0.1"
description = "A pure Python implementation of a sliding window memory map manager"
optional = false
python-versions = ">=3.7"
files = [
{file = "smmap-5.0.1-py3-none-any.whl", hash = "sha256:e6d8668fa5f93e706934a62d7b4db19c8d9eb8cf2adbb75ef1b675aa332b69da"},
{file = "smmap-5.0.1.tar.gz", hash = "sha256:dceeb6c0028fdb6734471eb07c0cd2aae706ccaecab45965ee83f11c8d3b1f62"},
]
[[package]]
name = "sniffio"
version = "1.3.1"
@@ -5714,6 +5336,21 @@ files = [
{file = "typing_extensions-4.12.2.tar.gz", hash = "sha256:1a7ead55c7e559dd4dee8856e3a88b41225abfe1ce8df57b7c13915fe121ffb8"},
]
[[package]]
name = "typing-inspect"
version = "0.9.0"
description = "Runtime inspection utilities for typing module."
optional = false
python-versions = "*"
files = [
{file = "typing_inspect-0.9.0-py3-none-any.whl", hash = "sha256:9ee6fc59062311ef8547596ab6b955e1b8aa46242d854bfc78f4f6b0eff35f9f"},
{file = "typing_inspect-0.9.0.tar.gz", hash = "sha256:b23fc42ff6f6ef6954e4852c1fb512cdd18dbea03134f91f856a95ccc9461f78"},
]
[package.dependencies]
mypy-extensions = ">=0.3.0"
typing-extensions = ">=3.7.4"
[[package]]
name = "ujson"
version = "5.10.0"
@@ -6375,41 +6012,6 @@ files = [
idna = ">=2.0"
multidict = ">=4.0"
[[package]]
name = "youtube-transcript-api"
version = "0.6.2"
description = "This is an python API which allows you to get the transcripts/subtitles for a given YouTube video. It also works for automatically generated subtitles, supports translating subtitles and it does not require a headless browser, like other selenium based solutions do!"
optional = false
python-versions = "*"
files = [
{file = "youtube_transcript_api-0.6.2-py3-none-any.whl", hash = "sha256:019dbf265c6a68a0591c513fff25ed5a116ce6525832aefdfb34d4df5567121c"},
{file = "youtube_transcript_api-0.6.2.tar.gz", hash = "sha256:cad223d7620633cec44f657646bffc8bbc5598bd8e70b1ad2fa8277dec305eb7"},
]
[package.dependencies]
requests = "*"
[[package]]
name = "yt-dlp"
version = "2023.12.30"
description = "A youtube-dl fork with additional features and patches"
optional = false
python-versions = ">=3.8"
files = [
{file = "yt-dlp-2023.12.30.tar.gz", hash = "sha256:a11862e57721b0a0f0883dfeb5a4d79ba213a2d4c45e1880e9fd70f8e6570c38"},
{file = "yt_dlp-2023.12.30-py2.py3-none-any.whl", hash = "sha256:c00d9a71d64472ad441bcaa1ec0c3797d6e60c9f934f270096a96fe51657e7b3"},
]
[package.dependencies]
brotli = {version = "*", markers = "implementation_name == \"cpython\""}
brotlicffi = {version = "*", markers = "implementation_name != \"cpython\""}
certifi = "*"
mutagen = "*"
pycryptodomex = "*"
requests = ">=2.31.0,<3"
urllib3 = ">=1.26.17,<3"
websockets = ">=12.0"
[[package]]
name = "zipp"
version = "3.19.2"
@@ -6431,4 +6033,4 @@ tools = ["crewai-tools"]
[metadata]
lock-version = "2.0"
python-versions = ">=3.10,<=3.13"
content-hash = "93cf2828a0b5157c46d35cd95db4b8c69461b0473e6d9f10643064b2bb2b6e15"
content-hash = "d4ea0d71723ecc2bad629c387dd786b6a96c553b1e3e298516fdfcd2059d1019"

14
pyproject.toml
View File

@@ -1,6 +1,6 @@
[tool.poetry]
name = "crewai"
version = "0.35.4"
version = "0.35.8"
description = "Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks."
authors = ["Joao Moura <joao@crewai.com>"]
readme = "README.md"
@@ -14,22 +14,24 @@ Repository = "https://github.com/joaomdmoura/crewai"
[tool.poetry.dependencies]
python = ">=3.10,<=3.13"
pydantic = "^2.4.2"
langchain = ">=0.2,<=0.3"
langchain = ">=0.1.4,<0.2.0"
openai = "^1.13.3"
opentelemetry-api = "^1.22.0"
opentelemetry-sdk = "^1.22.0"
opentelemetry-exporter-otlp-proto-http = "^1.22.0"
instructor = "1.3.3"
regex = "^2023.12.25"
crewai-tools = { version = "^0.4.1", optional = true }
crewai-tools = { version = "^0.4.6", optional = true }
click = "^8.1.7"
python-dotenv = "^1.0.0"
embedchain-crewai = {extras = ["github", "youtube"], version = "^0.1.114"}
appdirs = "^1.4.4"
jsonref = "^1.1.0"
agentops = { version = "^0.1.9", optional = true }
embedchain = "^0.1.113"
[tool.poetry.extras]
tools = ["crewai-tools"]
agentops = ["agentops"]
[tool.poetry.group.dev.dependencies]
isort = "^5.13.2"
@@ -43,7 +45,7 @@ mkdocs-material = { extras = ["imaging"], version = "^9.5.7" }
mkdocs-material-extensions = "^1.3.1"
pillow = "^10.2.0"
cairosvg = "^2.7.1"
crewai-tools = "^0.4.1"
crewai-tools = "^0.4.6"
[tool.poetry.group.test.dependencies]
pytest = "^8.0.0"
@@ -60,4 +62,4 @@ exclude = ["cli/templates/main.py", "cli/templates/crew.py"]
[build-system]
requires = ["poetry-core"]
build-backend = "poetry.core.masonry.api"
build-backend = "poetry.core.masonry.api"

22
src/crewai/agent.py
View File

@@ -18,7 +18,20 @@ from crewai.utilities.constants import TRAINED_AGENTS_DATA_FILE, TRAINING_DATA_F
from crewai.utilities.token_counter_callback import TokenCalcHandler
from crewai.utilities.training_handler import CrewTrainingHandler
agentops = None
try:
import agentops
from agentops import track_agent
except ImportError:
def track_agent():
def noop(f):
return f
return noop
@track_agent()
class Agent(BaseAgent):
"""Represents an agent in a system.
@@ -47,6 +60,8 @@ class Agent(BaseAgent):
default=None,
description="Maximum execution time for an agent to execute a task",
)
agent_ops_agent_name: str = None
agent_ops_agent_id: str = None
cache_handler: InstanceOf[CacheHandler] = Field(
default=None, description="An instance of the CacheHandler class."
)
@@ -82,6 +97,7 @@ class Agent(BaseAgent):
def __init__(__pydantic_self__, **data):
config = data.pop("config", {})
super().__init__(**config, **data)
__pydantic_self__.agent_ops_agent_name = __pydantic_self__.role
@model_validator(mode="after")
def set_agent_executor(self) -> "Agent":
@@ -99,6 +115,12 @@ class Agent(BaseAgent):
):
self.llm.callbacks.append(token_handler)
if agentops and not any(
isinstance(handler, agentops.LangchainCallbackHandler) for handler in self.llm.callbacks
):
agentops.stop_instrumenting()
self.llm.callbacks.append(agentops.LangchainCallbackHandler())
if not self.agent_executor:
if not self.cache_handler:
self.cache_handler = CacheHandler()

3
src/crewai/agents/agent_builder/base_agent.py
View File

@@ -15,8 +15,9 @@ from pydantic import (
)
from pydantic_core import PydanticCustomError
from crewai.agents import CacheHandler, ToolsHandler
from crewai.agents.agent_builder.utilities.base_token_process import TokenProcess
from crewai.agents.cache.cache_handler import CacheHandler
from crewai.agents.tools_handler import ToolsHandler
from crewai.utilities import I18N, Logger, RPMController
T = TypeVar("T", bound="BaseAgent")

118
src/crewai/agents/agent_builder/base_agent_executor_mixin.py
View File

@@ -1,65 +1,109 @@
import time
from typing import TYPE_CHECKING, Optional
from crewai.memory.entity.entity_memory_item import EntityMemoryItem
from crewai.memory.long_term.long_term_memory_item import LongTermMemoryItem
from crewai.memory.short_term.short_term_memory_item import ShortTermMemoryItem
from crewai.utilities.converter import ConverterError
from crewai.utilities.evaluators.task_evaluator import TaskEvaluator
from crewai.utilities import I18N
if TYPE_CHECKING:
from crewai.crew import Crew
from crewai.task import Task
from crewai.agents.agent_builder.base_agent import BaseAgent
class CrewAgentExecutorMixin:
crew: Optional["Crew"]
crew_agent: Optional["BaseAgent"]
task: Optional["Task"]
iterations: int
force_answer_max_iterations: int
have_forced_answer: bool
_i18n: I18N
def _should_force_answer(self) -> bool:
"""Determine if a forced answer is required based on iteration count."""
return (
self.iterations == self.force_answer_max_iterations
) and not self.have_forced_answer
def _create_short_term_memory(self, output) -> None:
"""Create and save a short-term memory item if conditions are met."""
if (
self.crew
and self.crew_agent
and self.task
and "Action: Delegate work to coworker" not in output.log
):
try:
memory = ShortTermMemoryItem(
data=output.log,
agent=self.crew_agent.role,
metadata={
"observation": self.task.description,
},
)
if (
hasattr(self.crew, "_short_term_memory")
and self.crew._short_term_memory
):
self.crew._short_term_memory.save(memory)
except Exception as e:
print(f"Failed to add to short term memory: {e}")
pass
def _create_long_term_memory(self, output) -> None:
"""Create and save long-term and entity memory items based on evaluation."""
if (
self.crew
and self.crew.memory
and "Action: Delegate work to coworker" not in output.log
and self.crew._long_term_memory
and self.crew._entity_memory
and self.task
and self.crew_agent
):
memory = ShortTermMemoryItem(
data=output.log,
agent=self.crew_agent.role,
metadata={
"observation": self.task.description,
},
)
self.crew._short_term_memory.save(memory)
try:
ltm_agent = TaskEvaluator(self.crew_agent)
evaluation = ltm_agent.evaluate(self.task, output.log)
def _create_long_term_memory(self, output) -> None:
if self.crew and self.crew.memory:
ltm_agent = TaskEvaluator(self.crew_agent)
evaluation = ltm_agent.evaluate(self.task, output.log)
if isinstance(evaluation, ConverterError):
return
if isinstance(evaluation, ConverterError):
return
long_term_memory = LongTermMemoryItem(
task=self.task.description,
agent=self.crew_agent.role,
quality=evaluation.quality,
datetime=str(time.time()),
expected_output=self.task.expected_output,
metadata={
"suggestions": evaluation.suggestions,
"quality": evaluation.quality,
},
)
self.crew._long_term_memory.save(long_term_memory)
for entity in evaluation.entities:
entity_memory = EntityMemoryItem(
name=entity.name,
type=entity.type,
description=entity.description,
relationships="\n".join([f"- {r}" for r in entity.relationships]),
long_term_memory = LongTermMemoryItem(
task=self.task.description,
agent=self.crew_agent.role,
quality=evaluation.quality,
datetime=str(time.time()),
expected_output=self.task.expected_output,
metadata={
"suggestions": evaluation.suggestions,
"quality": evaluation.quality,
},
)
self.crew._entity_memory.save(entity_memory)
self.crew._long_term_memory.save(long_term_memory)
for entity in evaluation.entities:
entity_memory = EntityMemoryItem(
name=entity.name,
type=entity.type,
description=entity.description,
relationships="\n".join(
[f"- {r}" for r in entity.relationships]
),
)
self.crew._entity_memory.save(entity_memory)
except AttributeError as e:
print(f"Missing attributes for long term memory: {e}")
pass
except Exception as e:
print(f"Failed to add to long term memory: {e}")
pass
def _ask_human_input(self, final_answer: dict) -> str:
"""Get human input."""
"""Prompt human input for final decision making."""
return input(
self._i18n.slice("getting_input").format(final_answer=final_answer)
)

16
src/crewai/agents/executor.py
View File

@@ -1,6 +1,14 @@
import threading
import time
from typing import Any, Dict, Iterator, List, Optional, Tuple, Union
from typing import (
Any,
Dict,
Iterator,
List,
Optional,
Tuple,
Union,
)
from langchain.agents import AgentExecutor
from langchain.agents.agent import ExceptionTool
@@ -11,13 +19,15 @@ from langchain_core.exceptions import OutputParserException
from langchain_core.tools import BaseTool
from langchain_core.utils.input import get_color_mapping
from pydantic import InstanceOf
from crewai.agents.agent_builder.base_agent_executor_mixin import CrewAgentExecutorMixin
from crewai.agents.agent_builder.base_agent_executor_mixin import (
CrewAgentExecutorMixin,
)
from crewai.agents.tools_handler import ToolsHandler
from crewai.tools.tool_usage import ToolUsage, ToolUsageErrorException
from crewai.utilities import I18N
from crewai.utilities.constants import TRAINING_DATA_FILE
from crewai.utilities.training_handler import CrewTrainingHandler
from crewai.utilities import I18N
class CrewAgentExecutor(AgentExecutor, CrewAgentExecutorMixin):

2
src/crewai/cli/templates/pyproject.toml
View File

@@ -6,7 +6,7 @@ authors = ["Your Name <you@example.com>"]
[tool.poetry.dependencies]
python = ">=3.10,<=3.13"
crewai = { extras = ["tools"], version = "^0.35.4" }
crewai = { extras = ["tools"], version = "^0.35.8" }
[tool.poetry.scripts]
{{folder_name}} = "{{folder_name}}.main:run"

9
src/crewai/crew.py
View File

@@ -31,6 +31,11 @@ from crewai.utilities import I18N, FileHandler, Logger, RPMController
from crewai.utilities.evaluators.task_evaluator import TaskEvaluator
from crewai.utilities.training_handler import CrewTrainingHandler
try:
import agentops
except ImportError:
agentops = None
class Crew(BaseModel):
"""
@@ -547,6 +552,10 @@ class Crew(BaseModel):
def _finish_execution(self, output) -> None:
if self.max_rpm:
self._rpm_controller.stop_rpm_counter()
if agentops:
agentops.end_session(
end_state="Success", end_state_reason="Finished Execution", is_auto_end=True
)
self._telemetry.end_crew(self, output)
def calculate_usage_metrics(self) -> Dict[str, int]:

33
src/crewai/tools/tool_usage.py
View File

@@ -11,6 +11,12 @@ from crewai.telemetry import Telemetry
from crewai.tools.tool_calling import InstructorToolCalling, ToolCalling
from crewai.utilities import I18N, Converter, ConverterError, Printer
agentops = None
try:
import agentops
except ImportError:
pass
OPENAI_BIGGER_MODELS = ["gpt-4"]
@@ -91,15 +97,16 @@ class ToolUsage:
self.task.increment_tools_errors()
self._printer.print(content=f"\n\n{error}\n", color="red")
return error
return f"{self._use(tool_string=tool_string, tool=tool, calling=calling)}" # type: ignore # BUG?: "_use" of "ToolUsage" does not return a value (it only ever returns None)
return f"{self._use(tool_string=tool_string, tool=tool, calling=calling)}" # type: ignore # BUG?: "_use" of "ToolUsage" does not return a value (it only ever returns None)
def _use(
self,
tool_string: str,
tool: BaseTool,
calling: Union[ToolCalling, InstructorToolCalling],
) -> str: # TODO: Fix this return type --> finecwg : I updated return type to str
if self._check_tool_repeated_usage(calling=calling): # type: ignore # _check_tool_repeated_usage of "ToolUsage" does not return a value (it only ever returns None)
) -> str: # TODO: Fix this return type
tool_event = agentops.ToolEvent(name=calling.tool_name) if agentops else None
if self._check_tool_repeated_usage(calling=calling): # type: ignore # _check_tool_repeated_usage of "ToolUsage" does not return a value (it only ever returns None)
try:
result = self._i18n.errors("task_repeated_usage").format(
tool_names=self.tools_names
@@ -110,13 +117,13 @@ class ToolUsage:
tool_name=tool.name,
attempts=self._run_attempts,
)
result = self._format_result(result=result) # type: ignore # "_format_result" of "ToolUsage" does not return a value (it only ever returns None)
result = self._format_result(result=result) # type: ignore # "_format_result" of "ToolUsage" does not return a value (it only ever returns None)
return result # type: ignore # Fix the reutrn type of this function
except Exception:
self.task.increment_tools_errors()
result = None # type: ignore # Incompatible types in assignment (expression has type "None", variable has type "str")
result = None # type: ignore # Incompatible types in assignment (expression has type "None", variable has type "str")
if self.tools_handler.cache:
result = self.tools_handler.cache.read( # type: ignore # Incompatible types in assignment (expression has type "str | None", variable has type "str")
@@ -133,7 +140,7 @@ class ToolUsage:
if calling.arguments:
try:
acceptable_args = tool.args_schema.schema()["properties"].keys() # type: ignore # Item "None" of "type[BaseModel] | None" has no attribute "schema"
acceptable_args = tool.args_schema.schema()["properties"].keys() # type: ignore # Item "None" of "type[BaseModel] | None" has no attribute "schema"
arguments = {
k: v
for k, v in calling.arguments.items()
@@ -145,7 +152,7 @@ class ToolUsage:
arguments = calling.arguments
result = tool._run(**arguments)
else:
arguments = calling.arguments.values() # type: ignore # Incompatible types in assignment (expression has type "dict_values[str, Any]", variable has type "dict[str, Any]")
arguments = calling.arguments.values() # type: ignore # Incompatible types in assignment (expression has type "dict_values[str, Any]", variable has type "dict[str, Any]")
result = tool._run(*arguments)
else:
result = tool._run()
@@ -164,6 +171,10 @@ class ToolUsage:
return error # type: ignore # No return value expected
self.task.increment_tools_errors()
if agentops:
agentops.record(
agentops.ErrorEvent(exception=e, trigger_event=tool_event)
)
return self.use(calling=calling, tool_string=tool_string) # type: ignore # No return value expected
if self.tools_handler:
@@ -184,18 +195,20 @@ class ToolUsage:
)
self._printer.print(content=f"\n\n{result}\n", color="purple")
if agentops:
agentops.record(tool_event)
self._telemetry.tool_usage(
llm=self.function_calling_llm,
tool_name=tool.name,
attempts=self._run_attempts,
)
result = self._format_result(result=result) # type: ignore # "_format_result" of "ToolUsage" does not return a value (it only ever returns None)
)
result = self._format_result(result=result) # type: ignore # "_format_result" of "ToolUsage" does not return a value (it only ever returns None)
return result # type: ignore # No return value expected
def _format_result(self, result: Any) -> None:
self.task.used_tools += 1
if self._should_remember_format(): # type: ignore # "_should_remember_format" of "ToolUsage" does not return a value (it only ever returns None)
result = self._remember_format(result=result) # type: ignore # "_remember_format" of "ToolUsage" does not return a value (it only ever returns None)
result = self._remember_format(result=result) # type: ignore # "_remember_format" of "ToolUsage" does not return a value (it only ever returns None)
return result
def _should_remember_format(self) -> None:

12
src/crewai/utilities/evaluators/task_evaluator.py
View File

@@ -5,6 +5,17 @@ from pydantic import BaseModel, Field
from crewai.utilities import Converter
from crewai.utilities.pydantic_schema_parser import PydanticSchemaParser
agentops = None
try:
import agentops
from agentops import track_agent
except ImportError:
def track_agent(name):
def noop(f):
return f
return noop
class Entity(BaseModel):
@@ -38,6 +49,7 @@ class TrainingTaskEvaluation(BaseModel):
)
@track_agent(name="Task Evaluator")
class TaskEvaluator:
def __init__(self, original_agent):
self.llm = original_agent.llm

2
src/crewai/utilities/logger.py
View File

@@ -1,7 +1,7 @@
from datetime import datetime
from crewai.utilities.printer import Printer
from datetime import datetime
class Logger:
_printer = Printer()