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Author SHA1 Message Date
lorenzejay
023a32758d docstrings added 2025-04-18 18:25:23 -07:00
lorenzejay
d7e97ed509 Enhance KnowledgeConfig with field descriptions
- Updated `results_limit` and `score_threshold` in `KnowledgeConfig` to use Pydantic's `Field` for improved documentation and clarity.
- Added descriptions to both parameters to provide better context for their usage in knowledge retrieval configuration.
2025-04-18 18:17:46 -07:00
lorenzejay
3981d8591a Update documentation to format parameter names in code style
- Changed the formatting of `results_limit` and `score_threshold` in the documentation to use code style for better clarity and emphasis.
- Ensured consistency in documentation presentation to enhance user understanding of configuration options.
2025-04-17 17:10:33 -07:00
lorenzejay
5d41ded7ed Add VCR support for agent tests with query limits and score thresholds
- Introduced `@pytest.mark.vcr` decorator in `agent_test.py` for tests involving knowledge sources, ensuring consistent recording of HTTP interactions.
- Added new YAML cassette files for `test_agent_with_knowledge_sources_with_query_limit_and_score_threshold` and `test_agent_with_knowledge_sources_with_query_limit_and_score_threshold_default`, capturing the expected API responses for these tests.
- Enhanced test reliability by utilizing VCR to manage external API calls during testing.
2025-04-17 16:48:16 -07:00
lorenzejay
3b72278631 Refactor agent tests to utilize mock knowledge storage
- Updated test cases in `agent_test.py` to use `KnowledgeStorage` for mocking knowledge sources, enhancing test reliability and clarity.
- Renamed `limit` to `results_limit` in `KnowledgeConfig` for consistency with recent changes.
- Ensured that knowledge queries are properly mocked to return expected results during tests.
2025-04-17 16:38:59 -07:00
lorenzejay
379d0faf52 Merge branch 'main' of github.com:crewAIInc/crewAI into improvement/knowledge-query-limits-and-context 2025-04-17 15:51:42 -07:00
lorenzejay
18db1cd294 Refactor knowledge configuration parameters in CrewAI
- Renamed `limit` to `results_limit` in `KnowledgeConfig`, `query_knowledge`, and `query` methods for consistency and clarity.
- Updated related documentation to reflect the new parameter name, ensuring users understand the configuration options for knowledge retrieval.
2025-04-17 15:49:03 -07:00
lorenzejay
d93e08a3a6 Enhance knowledge management in CrewAI
- Added `KnowledgeConfig` class to configure knowledge retrieval parameters such as `limit` and `score_threshold`.
- Updated `Agent` and `Crew` classes to utilize the new knowledge configuration for querying knowledge sources.
- Enhanced documentation to clarify the addition of knowledge sources at both agent and crew levels.
- Introduced new tips in documentation to guide users on knowledge source management and configuration.
2025-04-17 15:45:09 -07:00
12 changed files with 143 additions and 260 deletions

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@@ -4,36 +4,6 @@ description: View the latest updates and changes to CrewAI
icon: timeline
---
<Update label="2025-04-07" description="v0.114.0">
## Release Highlights
<Frame>
<img src="/images/v01140.png" />
</Frame>
**New Features & Enhancements**
- Agents as an atomic unit. (`Agent(...).kickoff()`)
- Support for [Custom LLM implementations](https://docs.crewai.com/guides/advanced/custom-llm).
- Integrated External Memory and [Opik observability](https://docs.crewai.com/how-to/opik-observability).
- Enhanced YAML extraction.
- Multimodal agent validation.
- Added Secure fingerprints for agents and crews.
**Core Improvements & Fixes**
- Improved serialization, agent copying, and Python compatibility.
- Added wildcard support to `emit()`
- Added support for additional router calls and context window adjustments.
- Fixed typing issues, validation, and import statements.
- Improved method performance.
- Enhanced agent task handling, event emissions, and memory management.
- Fixed CLI issues, conditional tasks, cloning behavior, and tool outputs.
**Documentation & Guides**
- Improved documentation structure, theme, and organization.
- Added guides for Local NVIDIA NIM with WSL2, W&B Weave, and Arize Phoenix.
- Updated tool configuration examples, prompts, and observability docs.
- Guide on using singular agents within Flows.
</Update>
<Update label="2025-03-17" description="v0.108.0">
**Features**
- Converted tabs to spaces in `crew.py` template

View File

@@ -179,78 +179,7 @@ def crew(self) -> Crew:
```
</Note>
### 10. Deploy
Deploy the crew or flow to [CrewAI Enterprise](https://app.crewai.com).
- **Authentication**: You need to be authenticated to deploy to CrewAI Enterprise.
```shell Terminal
crewai signup
```
If you already have an account, you can login with:
```shell Terminal
crewai login
```
- **Create a deployment**: Once you are authenticated, you can create a deployment for your crew or flow from the root of your localproject.
```shell Terminal
crewai deploy create
```
- Reads your local project configuration.
- Prompts you to confirm the environment variables (like `OPENAI_API_KEY`, `SERPER_API_KEY`) found locally. These will be securely stored with the deployment on the Enterprise platform. Ensure your sensitive keys are correctly configured locally (e.g., in a `.env` file) before running this.
- Links the deployment to the corresponding remote GitHub repository (it usually detects this automatically).
- **Deploy the Crew**: Once you are authenticated, you can deploy your crew or flow to CrewAI Enterprise.
```shell Terminal
crewai deploy push
```
- Initiates the deployment process on the CrewAI Enterprise platform.
- Upon successful initiation, it will output the Deployment created successfully! message along with the Deployment Name and a unique Deployment ID (UUID).
- **Deployment Status**: You can check the status of your deployment with:
```shell Terminal
crewai deploy status
```
This fetches the latest deployment status of your most recent deployment attempt (e.g., `Building Images for Crew`, `Deploy Enqueued`, `Online`).
- **Deployment Logs**: You can check the logs of your deployment with:
```shell Terminal
crewai deploy logs
```
This streams the deployment logs to your terminal.
- **List deployments**: You can list all your deployments with:
```shell Terminal
crewai deploy list
```
This lists all your deployments.
- **Delete a deployment**: You can delete a deployment with:
```shell Terminal
crewai deploy remove
```
This deletes the deployment from the CrewAI Enterprise platform.
- **Help Command**: You can get help with the CLI with:
```shell Terminal
crewai deploy --help
```
This shows the help message for the CrewAI Deploy CLI.
Watch this video tutorial for a step-by-step demonstration of deploying your crew to [CrewAI Enterprise](http://app.crewai.com) using the CLI.
<iframe
width="100%"
height="400"
src="https://www.youtube.com/embed/3EqSV-CYDZA"
title="CrewAI Deployment Guide"
frameborder="0"
style={{ borderRadius: '10px' }}
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
allowfullscreen
></iframe>
### 11. API Keys
### 10. API Keys
When running ```crewai create crew``` command, the CLI will first show you the top 5 most common LLM providers and ask you to select one.

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@@ -1,10 +1,10 @@
---
title: LangChain Tool
description: The `LangChainTool` is a wrapper for LangChain tools and query engines.
title: Using LangChain Tools
description: Learn how to integrate LangChain tools with CrewAI agents to enhance search-based queries and more.
icon: link
---
## `LangChainTool`
## Using LangChain Tools
<Info>
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.

View File

@@ -0,0 +1,71 @@
---
title: Using LlamaIndex Tools
description: Learn how to integrate LlamaIndex tools with CrewAI agents to enhance search-based queries and more.
icon: toolbox
---
## Using LlamaIndex Tools
<Info>
CrewAI seamlessly integrates with LlamaIndexs comprehensive toolkit for RAG (Retrieval-Augmented Generation) and agentic pipelines, enabling advanced search-based queries and more.
</Info>
Here are the available built-in tools offered by LlamaIndex.
```python Code
from crewai import Agent
from crewai_tools import LlamaIndexTool
# Example 1: Initialize from FunctionTool
from llama_index.core.tools import FunctionTool
your_python_function = lambda ...: ...
og_tool = FunctionTool.from_defaults(
your_python_function,
name="<name>",
description='<description>'
)
tool = LlamaIndexTool.from_tool(og_tool)
# Example 2: Initialize from LlamaHub Tools
from llama_index.tools.wolfram_alpha import WolframAlphaToolSpec
wolfram_spec = WolframAlphaToolSpec(app_id="<app_id>")
wolfram_tools = wolfram_spec.to_tool_list()
tools = [LlamaIndexTool.from_tool(t) for t in wolfram_tools]
# Example 3: Initialize Tool from a LlamaIndex Query Engine
query_engine = index.as_query_engine()
query_tool = LlamaIndexTool.from_query_engine(
query_engine,
name="Uber 2019 10K Query Tool",
description="Use this tool to lookup the 2019 Uber 10K Annual Report"
)
# 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]
)
# rest of the code ...
```
## Steps to Get Started
To effectively use the LlamaIndexTool, follow these steps:
<Steps>
<Step title="Package Installation">
Make sure that `crewai[tools]` package is installed in your Python environment:
<CodeGroup>
```shell Terminal
pip install 'crewai[tools]'
```
</CodeGroup>
</Step>
<Step title="Install and Use LlamaIndex">
Follow the LlamaIndex documentation [LlamaIndex Documentation](https://docs.llamaindex.ai/) to set up a RAG/agent pipeline.
</Step>
</Steps>

View File

@@ -1,13 +1,9 @@
---
title: Custom LLM Implementation
description: Learn how to create custom LLM implementations in CrewAI.
icon: code
---
## Custom LLM Implementations
# Custom LLM Implementations
CrewAI now supports custom LLM implementations through the `BaseLLM` abstract base class. This allows you to create your own LLM implementations that don't rely on litellm's authentication mechanism.
## Using Custom LLM Implementations
To create a custom LLM implementation, you need to:
1. Inherit from the `BaseLLM` abstract base class

View File

@@ -8,27 +8,25 @@
"dark": "#C94C3C"
},
"favicon": "favicon.svg",
"contextual": {
"options": ["copy", "view", "chatgpt", "claude"]
},
"navigation": {
"tabs": [
{
"tab": "Documentation",
"tab": "Get Started",
"groups": [
{
"group": "Get Started",
"pages": [
"introduction",
"installation",
"quickstart"
"quickstart",
"changelog"
]
},
{
"group": "Guides",
"pages": [
{
"group": "Strategy",
"group": "Concepts",
"pages": [
"guides/concepts/evaluating-use-cases"
]
@@ -81,6 +79,41 @@
"concepts/event-listener"
]
},
{
"group": "How to Guides",
"pages": [
"how-to/create-custom-tools",
"how-to/sequential-process",
"how-to/hierarchical-process",
"how-to/custom-manager-agent",
"how-to/llm-connections",
"how-to/customizing-agents",
"how-to/multimodal-agents",
"how-to/coding-agents",
"how-to/force-tool-output-as-result",
"how-to/human-input-on-execution",
"how-to/kickoff-async",
"how-to/kickoff-for-each",
"how-to/replay-tasks-from-latest-crew-kickoff",
"how-to/conditional-tasks",
"how-to/langchain-tools",
"how-to/llamaindex-tools"
]
},
{
"group": "Agent Monitoring & Observability",
"pages": [
"how-to/agentops-observability",
"how-to/arize-phoenix-observability",
"how-to/langfuse-observability",
"how-to/langtrace-observability",
"how-to/mlflow-observability",
"how-to/openlit-observability",
"how-to/opik-observability",
"how-to/portkey-observability",
"how-to/weave-integration"
]
},
{
"group": "Tools",
"pages": [
@@ -108,7 +141,6 @@
"tools/hyperbrowserloadtool",
"tools/linkupsearchtool",
"tools/llamaindextool",
"tools/langchaintool",
"tools/serperdevtool",
"tools/s3readertool",
"tools/s3writertool",
@@ -138,40 +170,6 @@
"tools/youtubevideosearchtool"
]
},
{
"group": "Agent Monitoring & Observability",
"pages": [
"how-to/agentops-observability",
"how-to/arize-phoenix-observability",
"how-to/langfuse-observability",
"how-to/langtrace-observability",
"how-to/mlflow-observability",
"how-to/openlit-observability",
"how-to/opik-observability",
"how-to/portkey-observability",
"how-to/weave-integration"
]
},
{
"group": "Learn",
"pages": [
"how-to/conditional-tasks",
"how-to/coding-agents",
"how-to/create-custom-tools",
"how-to/custom-llm",
"how-to/custom-manager-agent",
"how-to/customizing-agents",
"how-to/force-tool-output-as-result",
"how-to/hierarchical-process",
"how-to/human-input-on-execution",
"how-to/kickoff-async",
"how-to/kickoff-for-each",
"how-to/llm-connections",
"how-to/multimodal-agents",
"how-to/replay-tasks-from-latest-crew-kickoff",
"how-to/sequential-process"
]
},
{
"group": "Telemetry",
"pages": [
@@ -190,35 +188,19 @@
]
}
]
},
{
"tab": "Releases",
"groups": [
{
"group": "Releases",
"pages": [
"changelog"
]
}
]
}
],
"global": {
"anchors": [
{
"anchor": "Website",
"href": "https://crewai.com",
"icon": "globe"
},
{
"anchor": "Forum",
"anchor": "Community",
"href": "https://community.crewai.com",
"icon": "discourse"
},
{
"anchor": "Get Help",
"href": "mailto:support@crewai.com",
"icon": "headset"
"anchor": "Tutorials",
"href": "https://www.youtube.com/@crewAIInc",
"icon": "youtube"
}
]
}
@@ -232,12 +214,6 @@
"strict": false
},
"navbar": {
"links": [
{
"label": "Start Free Trial",
"href": "https://app.crewai.com"
}
],
"primary": {
"type": "github",
"href": "https://github.com/crewAIInc/crewAI"
@@ -247,12 +223,7 @@
"prompt": "Search CrewAI docs"
},
"seo": {
"indexing": "all"
},
"errors": {
"404": {
"redirect": true
}
"indexing": "navigable"
},
"footer": {
"socials": {

View File

@@ -1,5 +1,5 @@
---
title: Custom Manager Agent
title: Create Your Own Manager Agent
description: Learn how to set a custom agent as the manager in CrewAI, providing more control over task management and coordination.
icon: user-shield
---

View File

@@ -20,8 +20,10 @@ Here's an example of how to replay from a task:
To use the replay feature, follow these steps:
<Steps>
<Step title="Open your terminal or command prompt."></Step>
<Step title="Navigate to the directory where your CrewAI project is located."></Step>
<Step title="Open your terminal or command prompt.">
</Step>
<Step title="Navigate to the directory where your CrewAI project is located.">
</Step>
<Step title="Run the following commands:">
To view the latest kickoff task_ids use:

Binary file not shown.

Before

Width:  |  Height:  |  Size: 2.4 MiB

View File

@@ -336,22 +336,9 @@ email_summarizer_task:
- research_task
```
## Deploying Your Crew
## Deploying Your Project
The easiest way to deploy your crew to production is through [CrewAI Enterprise](http://app.crewai.com).
Watch this video tutorial for a step-by-step demonstration of deploying your crew to [CrewAI Enterprise](http://app.crewai.com) using the CLI.
<iframe
width="100%"
height="400"
src="https://www.youtube.com/embed/3EqSV-CYDZA"
title="CrewAI Deployment Guide"
frameborder="0"
style={{ borderRadius: '10px' }}
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
allowfullscreen
></iframe>
The easiest way to deploy your crew is through [CrewAI Enterprise](http://app.crewai.com), where you can deploy your crew in a few clicks.
<CardGroup cols={2}>
<Card

View File

@@ -8,29 +8,11 @@ icon: code-simple
## Description
The `CodeInterpreterTool` enables CrewAI agents to execute Python 3 code that they generate autonomously. This functionality is particularly valuable as it allows agents to create code, execute it, obtain the results, and utilize that information to inform subsequent decisions and actions.
The `CodeInterpreterTool` enables CrewAI agents to execute Python 3 code that they generate autonomously. The code is run in a secure, isolated Docker container, ensuring safety regardless of the content. This functionality is particularly valuable as it allows agents to create code, execute it, obtain the results, and utilize that information to inform subsequent decisions and actions.
There are several ways to use this tool:
### Docker Container (Recommended)
This is the primary option. The code runs in a secure, isolated Docker container, ensuring safety regardless of its content.
Make sure Docker is installed and running on your system. If you dont have it, you can install it from [here](https://docs.docker.com/get-docker/).
### Sandbox environment
If Docker is unavailable — either not installed or not accessible for any reason — the code will be executed in a restricted Python environment - called sandbox.
This environment is very limited, with strict restrictions on many modules and built-in functions.
### Unsafe Execution
**NOT RECOMMENDED FOR PRODUCTION**
This mode allows execution of any Python code, including dangerous calls to `sys, os..` and similar modules. [Check out](/tools/codeinterpretertool#enabling-unsafe-mode) how to enable this mode
## Logging
The `CodeInterpreterTool` logs the selected execution strategy to STDOUT
## Requirements
- Docker must be installed and running on your system. If you don't have it, you can install it from [here](https://docs.docker.com/get-docker/).
## Installation
@@ -92,32 +74,18 @@ programmer_agent = Agent(
)
```
### Enabling `unsafe_mode`
```python Code
from crewai_tools import CodeInterpreterTool
code = """
import os
os.system("ls -la")
"""
CodeInterpreterTool(unsafe_mode=True).run(code=code)
```
## Parameters
The `CodeInterpreterTool` accepts the following parameters during initialization:
- **user_dockerfile_path**: Optional. Path to a custom Dockerfile to use for the code interpreter container.
- **user_docker_base_url**: Optional. URL to the Docker daemon to use for running the container.
- **unsafe_mode**: Optional. Whether to run code directly on the host machine instead of in a Docker container or sandbox. Default is `False`. Use with caution!
- **default_image_tag**: Optional. Default Docker image tag. Default is `code-interpreter:latest`
- **unsafe_mode**: Optional. Whether to run code directly on the host machine instead of in a Docker container. Default is `False`. Use with caution!
When using the tool with an agent, the agent will need to provide:
- **code**: Required. The Python 3 code to execute.
- **libraries_used**: Optional. A list of libraries used in the code that need to be installed. Default is `[]`
- **libraries_used**: Required. A list of libraries used in the code that need to be installed.
## Agent Integration Example
@@ -184,7 +152,7 @@ class CodeInterpreterTool(BaseTool):
if self.unsafe_mode:
return self.run_code_unsafe(code, libraries_used)
else:
return self.run_code_safety(code, libraries_used)
return self.run_code_in_docker(code, libraries_used)
```
The tool performs the following steps:
@@ -200,9 +168,8 @@ The tool performs the following steps:
By default, the `CodeInterpreterTool` runs code in an isolated Docker container, which provides a layer of security. However, there are still some security considerations to keep in mind:
1. The Docker container has access to the current working directory, so sensitive files could potentially be accessed.
2. If the Docker container is unavailable and the code needs to run safely, it will be executed in a sandbox environment. For security reasons, installing arbitrary libraries is not allowed
3. The `unsafe_mode` parameter allows code to be executed directly on the host machine, which should only be used in trusted environments.
4. Be cautious when allowing agents to install arbitrary libraries, as they could potentially include malicious code.
2. The `unsafe_mode` parameter allows code to be executed directly on the host machine, which should only be used in trusted environments.
3. Be cautious when allowing agents to install arbitrary libraries, as they could potentially include malicious code.
## Conclusion

View File

@@ -117,9 +117,7 @@ class ToolCommand(BaseCommand, PlusAPIMixin):
published_handle = publish_response.json()["handle"]
console.print(
f"Successfully published `{published_handle}` ({project_version}).\n\n"
+ "⚠️ Security checks are running in the background. Your tool will be available once these are complete.\n"
+ f"You can monitor the status or access your tool here:\nhttps://app.crewai.com/crewai_plus/tools/{published_handle}",
f"Successfully published {published_handle} ({project_version}).\nInstall it in other projects with crewai tool install {published_handle}",
style="bold green",
)
@@ -155,12 +153,8 @@ class ToolCommand(BaseCommand, PlusAPIMixin):
login_response_json = login_response.json()
settings = Settings()
settings.tool_repository_username = login_response_json["credential"][
"username"
]
settings.tool_repository_password = login_response_json["credential"][
"password"
]
settings.tool_repository_username = login_response_json["credential"]["username"]
settings.tool_repository_password = login_response_json["credential"]["password"]
settings.dump()
console.print(
@@ -185,7 +179,7 @@ class ToolCommand(BaseCommand, PlusAPIMixin):
capture_output=False,
env=self._build_env_with_credentials(repository_handle),
text=True,
check=True,
check=True
)
if add_package_result.stderr:
@@ -210,11 +204,7 @@ class ToolCommand(BaseCommand, PlusAPIMixin):
settings = Settings()
env = os.environ.copy()
env[f"UV_INDEX_{repository_handle}_USERNAME"] = str(
settings.tool_repository_username or ""
)
env[f"UV_INDEX_{repository_handle}_PASSWORD"] = str(
settings.tool_repository_password or ""
)
env[f"UV_INDEX_{repository_handle}_USERNAME"] = str(settings.tool_repository_username or "")
env[f"UV_INDEX_{repository_handle}_PASSWORD"] = str(settings.tool_repository_password or "")
return env