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187
docs/changelog.mdx
Normal file
187
docs/changelog.mdx
Normal file
@@ -0,0 +1,187 @@
|
||||
---
|
||||
title: Changelog
|
||||
description: View the latest updates and changes to CrewAI
|
||||
icon: timeline
|
||||
---
|
||||
|
||||
<Update label="2024-03-17" description="v0.108.0">
|
||||
**Features**
|
||||
- Converted tabs to spaces in `crew.py` template
|
||||
- Enhanced LLM Streaming Response Handling and Event System
|
||||
- Included `model_name`
|
||||
- Enhanced Event Listener with rich visualization and improved logging
|
||||
- Added fingerprints
|
||||
|
||||
**Bug Fixes**
|
||||
- Fixed Mistral issues
|
||||
- Fixed a bug in documentation
|
||||
- Fixed type check error in fingerprint property
|
||||
|
||||
**Documentation Updates**
|
||||
- Improved tool documentation
|
||||
- Updated installation guide for the `uv` tool package
|
||||
- Added instructions for upgrading crewAI with the `uv` tool
|
||||
- Added documentation for `ApifyActorsTool`
|
||||
</Update>
|
||||
|
||||
<Update label="2024-03-10" description="v0.105.0">
|
||||
**Core Improvements & Fixes**
|
||||
- Fixed issues with missing template variables and user memory configuration
|
||||
- Improved async flow support and addressed agent response formatting
|
||||
- Enhanced memory reset functionality and fixed CLI memory commands
|
||||
- Fixed type issues, tool calling properties, and telemetry decoupling
|
||||
|
||||
**New Features & Enhancements**
|
||||
- Added Flow state export and improved state utilities
|
||||
- Enhanced agent knowledge setup with optional crew embedder
|
||||
- Introduced event emitter for better observability and LLM call tracking
|
||||
- Added support for Python 3.10 and ChatOllama from langchain_ollama
|
||||
- Integrated context window size support for the o3-mini model
|
||||
- Added support for multiple router calls
|
||||
|
||||
**Documentation & Guides**
|
||||
- Improved documentation layout and hierarchical structure
|
||||
- Added QdrantVectorSearchTool guide and clarified event listener usage
|
||||
- Fixed typos in prompts and updated Amazon Bedrock model listings
|
||||
</Update>
|
||||
|
||||
<Update label="2024-02-12" description="v0.102.0">
|
||||
**Core Improvements & Fixes**
|
||||
- Enhanced LLM Support: Improved structured LLM output, parameter handling, and formatting for Anthropic models
|
||||
- Crew & Agent Stability: Fixed issues with cloning agents/crews using knowledge sources, multiple task outputs in conditional tasks, and ignored Crew task callbacks
|
||||
- Memory & Storage Fixes: Fixed short-term memory handling with Bedrock, ensured correct embedder initialization, and added a reset memories function in the crew class
|
||||
- Training & Execution Reliability: Fixed broken training and interpolation issues with dict and list input types
|
||||
|
||||
**New Features & Enhancements**
|
||||
- Advanced Knowledge Management: Improved naming conventions and enhanced embedding configuration with custom embedder support
|
||||
- Expanded Logging & Observability: Added JSON format support for logging and integrated MLflow tracing documentation
|
||||
- Data Handling Improvements: Updated excel_knowledge_source.py to process multi-tab files
|
||||
- General Performance & Codebase Clean-Up: Streamlined enterprise code alignment and resolved linting issues
|
||||
- Adding new tool: `QdrantVectorSearchTool`
|
||||
|
||||
**Documentation & Guides**
|
||||
- Updated AI & Memory Docs: Improved Bedrock, Google AI, and long-term memory documentation
|
||||
- Task & Workflow Clarity: Added "Human Input" row to Task Attributes, Langfuse guide, and FileWriterTool documentation
|
||||
- Fixed Various Typos & Formatting Issues
|
||||
</Update>
|
||||
|
||||
<Update label="2024-01-28" description="v0.100.0">
|
||||
**Features**
|
||||
- Add Composio docs
|
||||
- Add SageMaker as a LLM provider
|
||||
|
||||
**Fixes**
|
||||
- Overall LLM connection issues
|
||||
- Using safe accessors on training
|
||||
- Add version check to crew_chat.py
|
||||
|
||||
**Documentation**
|
||||
- New docs for crewai chat
|
||||
- Improve formatting and clarity in CLI and Composio Tool docs
|
||||
</Update>
|
||||
|
||||
<Update label="2024-01-20" description="v0.98.0">
|
||||
**Features**
|
||||
- Conversation crew v1
|
||||
- Add unique ID to flow states
|
||||
- Add @persist decorator with FlowPersistence interface
|
||||
|
||||
**Integrations**
|
||||
- Add SambaNova integration
|
||||
- Add NVIDIA NIM provider in cli
|
||||
- Introducing VoyageAI
|
||||
|
||||
**Fixes**
|
||||
- Fix API Key Behavior and Entity Handling in Mem0 Integration
|
||||
- Fixed core invoke loop logic and relevant tests
|
||||
- Make tool inputs actual objects and not strings
|
||||
- Add important missing parts to creating tools
|
||||
- Drop litellm version to prevent windows issue
|
||||
- Before kickoff if inputs are none
|
||||
- Fixed typos, nested pydantic model issue, and docling issues
|
||||
</Update>
|
||||
|
||||
<Update label="2024-01-04" description="v0.95.0">
|
||||
**New Features**
|
||||
- Adding Multimodal Abilities to Crew
|
||||
- Programatic Guardrails
|
||||
- HITL multiple rounds
|
||||
- Gemini 2.0 Support
|
||||
- CrewAI Flows Improvements
|
||||
- Add Workflow Permissions
|
||||
- Add support for langfuse with litellm
|
||||
- Portkey Integration with CrewAI
|
||||
- Add interpolate_only method and improve error handling
|
||||
- Docling Support
|
||||
- Weviate Support
|
||||
|
||||
**Fixes**
|
||||
- output_file not respecting system path
|
||||
- disk I/O error when resetting short-term memory
|
||||
- CrewJSONEncoder now accepts enums
|
||||
- Python max version
|
||||
- Interpolation for output_file in Task
|
||||
- Handle coworker role name case/whitespace properly
|
||||
- Add tiktoken as explicit dependency and document Rust requirement
|
||||
- Include agent knowledge in planning process
|
||||
- Change storage initialization to None for KnowledgeStorage
|
||||
- Fix optional storage checks
|
||||
- include event emitter in flows
|
||||
- Docstring, Error Handling, and Type Hints Improvements
|
||||
- Suppressed userWarnings from litellm pydantic issues
|
||||
</Update>
|
||||
|
||||
<Update label="2023-12-05" description="v0.86.0">
|
||||
**Changes**
|
||||
- Remove all references to pipeline and pipeline router
|
||||
- Add Nvidia NIM as provider in Custom LLM
|
||||
- Add knowledge demo + improve knowledge docs
|
||||
- Add HITL multiple rounds of followup
|
||||
- New docs about yaml crew with decorators
|
||||
- Simplify template crew
|
||||
</Update>
|
||||
|
||||
<Update label="2023-12-04" description="v0.85.0">
|
||||
**Features**
|
||||
- Added knowledge to agent level
|
||||
- Feat/remove langchain
|
||||
- Improve typed task outputs
|
||||
- Log in to Tool Repository on crewai login
|
||||
|
||||
**Fixes**
|
||||
- Fixes issues with result as answer not properly exiting LLM loop
|
||||
- Fix missing key name when running with ollama provider
|
||||
- Fix spelling issue found
|
||||
|
||||
**Documentation**
|
||||
- Update readme for running mypy
|
||||
- Add knowledge to mint.json
|
||||
- Update Github actions
|
||||
- Update Agents docs to include two approaches for creating an agent
|
||||
- Improvements to LLM Configuration and Usage
|
||||
</Update>
|
||||
|
||||
<Update label="2023-11-25" description="v0.83.0">
|
||||
**New Features**
|
||||
- New before_kickoff and after_kickoff crew callbacks
|
||||
- Support to pre-seed agents with Knowledge
|
||||
- Add support for retrieving user preferences and memories using Mem0
|
||||
|
||||
**Fixes**
|
||||
- Fix Async Execution
|
||||
- Upgrade chroma and adjust embedder function generator
|
||||
- Update CLI Watson supported models + docs
|
||||
- Reduce level for Bandit
|
||||
- Fixing all tests
|
||||
|
||||
**Documentation**
|
||||
- Update Docs
|
||||
</Update>
|
||||
|
||||
<Update label="2023-11-13" description="v0.80.0">
|
||||
**Fixes**
|
||||
- Fixing Tokens callback replacement bug
|
||||
- Fixing Step callback issue
|
||||
- Add cached prompt tokens info on usage metrics
|
||||
- Fix crew_train_success test
|
||||
</Update>
|
||||
@@ -270,7 +270,7 @@ In this section, you'll find detailed examples that help you select, configure,
|
||||
| Claude 3.5 Haiku | Up to 200k tokens | Fast, compact multimodal model optimized for quick responses and seamless human-like interactions |
|
||||
| Claude 3 Sonnet | Up to 200k tokens | Multimodal model balancing intelligence and speed for high-volume deployments. |
|
||||
| Claude 3 Haiku | Up to 200k tokens | Compact, high-speed multimodal model optimized for quick responses and natural conversational interactions |
|
||||
| Claude 3 Opus | Up to 200k tokens | Most advanced multimodal model excelling at complex tasks with human-like reasoning and superior contextual understanding. |
|
||||
| Claude 3 Opus | Up to 200k tokens | Most advanced multimodal model exceling at complex tasks with human-like reasoning and superior contextual understanding. |
|
||||
| Claude 2.1 | Up to 200k tokens | Enhanced version with expanded context window, improved reliability, and reduced hallucinations for long-form and RAG applications |
|
||||
| Claude | Up to 100k tokens | Versatile model excelling in sophisticated dialogue, creative content, and precise instruction following. |
|
||||
| Claude Instant | Up to 100k tokens | Fast, cost-effective model for everyday tasks like dialogue, analysis, summarization, and document Q&A |
|
||||
@@ -406,6 +406,46 @@ In this section, you'll find detailed examples that help you select, configure,
|
||||
| baichuan-inc/baichuan2-13b-chat | 4,096 tokens | Support Chinese and English chat, coding, math, instruction following, solving quizzes |
|
||||
</Accordion>
|
||||
|
||||
<Accordion title="Local NVIDIA NIM Deployed using WSL2">
|
||||
|
||||
NVIDIA NIM enables you to run powerful LLMs locally on your Windows machine using WSL2 (Windows Subsystem for Linux).
|
||||
This approach allows you to leverage your NVIDIA GPU for private, secure, and cost-effective AI inference without relying on cloud services.
|
||||
Perfect for development, testing, or production scenarios where data privacy or offline capabilities are required.
|
||||
|
||||
Here is a step-by-step guide to setting up a local NVIDIA NIM model:
|
||||
|
||||
1. Follow installation instructions from [NVIDIA Website](https://docs.nvidia.com/nim/wsl2/latest/getting-started.html)
|
||||
|
||||
2. Install the local model. For Llama 3.1-8b follow [instructions](https://build.nvidia.com/meta/llama-3_1-8b-instruct/deploy)
|
||||
|
||||
3. Configure your crewai local models:
|
||||
|
||||
```python Code
|
||||
from crewai.llm import LLM
|
||||
|
||||
local_nvidia_nim_llm = LLM(
|
||||
model="openai/meta/llama-3.1-8b-instruct", # it's an openai-api compatible model
|
||||
base_url="http://localhost:8000/v1",
|
||||
api_key="<your_api_key|any text if you have not configured it>", # api_key is required, but you can use any text
|
||||
)
|
||||
|
||||
# Then you can use it in your crew:
|
||||
|
||||
@CrewBase
|
||||
class MyCrew():
|
||||
# ...
|
||||
|
||||
@agent
|
||||
def researcher(self) -> Agent:
|
||||
return Agent(
|
||||
config=self.agents_config['researcher'],
|
||||
llm=local_nvidia_nim_llm
|
||||
)
|
||||
|
||||
# ...
|
||||
```
|
||||
</Accordion>
|
||||
|
||||
<Accordion title="Groq">
|
||||
Set the following environment variables in your `.env` file:
|
||||
|
||||
@@ -746,5 +786,5 @@ Learn how to get the most out of your LLM configuration:
|
||||
<Tip>
|
||||
Use larger context models for extensive tasks
|
||||
</Tip>
|
||||
|
||||
```
|
||||
</Tab>
|
||||
</Tabs>
|
||||
|
||||
223
docs/docs.json
Normal file
223
docs/docs.json
Normal file
@@ -0,0 +1,223 @@
|
||||
{
|
||||
"$schema": "https://mintlify.com/docs.json",
|
||||
"theme": "palm",
|
||||
"name": "CrewAI",
|
||||
"colors": {
|
||||
"primary": "#EB6658",
|
||||
"light": "#F3A78B",
|
||||
"dark": "#C94C3C"
|
||||
},
|
||||
"favicon": "favicon.svg",
|
||||
"navigation": {
|
||||
"tabs": [
|
||||
{
|
||||
"tab": "Get Started",
|
||||
"groups": [
|
||||
{
|
||||
"group": "Get Started",
|
||||
"pages": [
|
||||
"introduction",
|
||||
"installation",
|
||||
"quickstart",
|
||||
"changelog"
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "Guides",
|
||||
"pages": [
|
||||
{
|
||||
"group": "Concepts",
|
||||
"pages": [
|
||||
"guides/concepts/evaluating-use-cases"
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "Agents",
|
||||
"pages": [
|
||||
"guides/agents/crafting-effective-agents"
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "Crews",
|
||||
"pages": [
|
||||
"guides/crews/first-crew"
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "Flows",
|
||||
"pages": [
|
||||
"guides/flows/first-flow",
|
||||
"guides/flows/mastering-flow-state"
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "Advanced",
|
||||
"pages": [
|
||||
"guides/advanced/customizing-prompts",
|
||||
"guides/advanced/fingerprinting"
|
||||
]
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "Core Concepts",
|
||||
"pages": [
|
||||
"concepts/agents",
|
||||
"concepts/tasks",
|
||||
"concepts/crews",
|
||||
"concepts/flows",
|
||||
"concepts/knowledge",
|
||||
"concepts/llms",
|
||||
"concepts/processes",
|
||||
"concepts/collaboration",
|
||||
"concepts/training",
|
||||
"concepts/memory",
|
||||
"concepts/planning",
|
||||
"concepts/testing",
|
||||
"concepts/cli",
|
||||
"concepts/tools",
|
||||
"concepts/event-listener",
|
||||
"concepts/langchain-tools",
|
||||
"concepts/llamaindex-tools"
|
||||
]
|
||||
},
|
||||
{
|
||||
"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/agentops-observability",
|
||||
"how-to/langtrace-observability",
|
||||
"how-to/mlflow-observability",
|
||||
"how-to/openlit-observability",
|
||||
"how-to/portkey-observability",
|
||||
"how-to/langfuse-observability"
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "Tools",
|
||||
"pages": [
|
||||
"tools/aimindtool",
|
||||
"tools/apifyactorstool",
|
||||
"tools/bravesearchtool",
|
||||
"tools/browserbaseloadtool",
|
||||
"tools/codedocssearchtool",
|
||||
"tools/codeinterpretertool",
|
||||
"tools/composiotool",
|
||||
"tools/csvsearchtool",
|
||||
"tools/dalletool",
|
||||
"tools/directorysearchtool",
|
||||
"tools/directoryreadtool",
|
||||
"tools/docxsearchtool",
|
||||
"tools/exasearchtool",
|
||||
"tools/filereadtool",
|
||||
"tools/filewritetool",
|
||||
"tools/firecrawlcrawlwebsitetool",
|
||||
"tools/firecrawlscrapewebsitetool",
|
||||
"tools/firecrawlsearchtool",
|
||||
"tools/githubsearchtool",
|
||||
"tools/hyperbrowserloadtool",
|
||||
"tools/linkupsearchtool",
|
||||
"tools/llamaindextool",
|
||||
"tools/serperdevtool",
|
||||
"tools/s3readertool",
|
||||
"tools/s3writertool",
|
||||
"tools/scrapegraphscrapetool",
|
||||
"tools/scrapeelementfromwebsitetool",
|
||||
"tools/jsonsearchtool",
|
||||
"tools/mdxsearchtool",
|
||||
"tools/mysqltool",
|
||||
"tools/multiontool",
|
||||
"tools/nl2sqltool",
|
||||
"tools/patronustools",
|
||||
"tools/pdfsearchtool",
|
||||
"tools/pgsearchtool",
|
||||
"tools/qdrantvectorsearchtool",
|
||||
"tools/ragtool",
|
||||
"tools/scrapewebsitetool",
|
||||
"tools/scrapflyscrapetool",
|
||||
"tools/seleniumscrapingtool",
|
||||
"tools/snowflakesearchtool",
|
||||
"tools/spidertool",
|
||||
"tools/txtsearchtool",
|
||||
"tools/visiontool",
|
||||
"tools/weaviatevectorsearchtool",
|
||||
"tools/websitesearchtool",
|
||||
"tools/xmlsearchtool",
|
||||
"tools/youtubechannelsearchtool",
|
||||
"tools/youtubevideosearchtool"
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "Telemetry",
|
||||
"pages": [
|
||||
"telemetry"
|
||||
]
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
"tab": "Examples",
|
||||
"groups": [
|
||||
{
|
||||
"group": "Examples",
|
||||
"pages": [
|
||||
"examples/example"
|
||||
]
|
||||
}
|
||||
]
|
||||
}
|
||||
],
|
||||
"global": {
|
||||
"anchors": [
|
||||
{
|
||||
"anchor": "Community",
|
||||
"href": "https://community.crewai.com",
|
||||
"icon": "discourse"
|
||||
}
|
||||
]
|
||||
}
|
||||
},
|
||||
"logo": {
|
||||
"light": "crew_only_logo.png",
|
||||
"dark": "crew_only_logo.png"
|
||||
},
|
||||
"appearance": {
|
||||
"default": "dark",
|
||||
"strict": false
|
||||
},
|
||||
"navbar": {
|
||||
"primary": {
|
||||
"type": "github",
|
||||
"href": "https://github.com/crewAIInc/crewAI"
|
||||
}
|
||||
},
|
||||
"search": {
|
||||
"prompt": "Search CrewAI docs"
|
||||
},
|
||||
"seo": {
|
||||
"indexing": "navigable"
|
||||
},
|
||||
"footer": {
|
||||
"socials": {
|
||||
"website": "https://crewai.com",
|
||||
"x": "https://x.com/crewAIInc",
|
||||
"github": "https://github.com/crewAIInc/crewAI",
|
||||
"linkedin": "https://www.linkedin.com/company/crewai-inc",
|
||||
"youtube": "https://youtube.com/@crewAIInc",
|
||||
"reddit": "https://www.reddit.com/r/crewAIInc/"
|
||||
}
|
||||
}
|
||||
}
|
||||
225
docs/mint.json
225
docs/mint.json
@@ -1,225 +0,0 @@
|
||||
{
|
||||
"name": "CrewAI",
|
||||
"theme": "venus",
|
||||
"logo": {
|
||||
"dark": "crew_only_logo.png",
|
||||
"light": "crew_only_logo.png"
|
||||
},
|
||||
"favicon": "favicon.svg",
|
||||
"colors": {
|
||||
"primary": "#EB6658",
|
||||
"light": "#F3A78B",
|
||||
"dark": "#C94C3C",
|
||||
"anchors": {
|
||||
"from": "#737373",
|
||||
"to": "#EB6658"
|
||||
}
|
||||
},
|
||||
"seo": {
|
||||
"indexHiddenPages": false
|
||||
},
|
||||
"modeToggle": {
|
||||
"default": "dark",
|
||||
"isHidden": false
|
||||
},
|
||||
"feedback": {
|
||||
"suggestEdit": true,
|
||||
"raiseIssue": true,
|
||||
"thumbsRating": true
|
||||
},
|
||||
"topbarCtaButton": {
|
||||
"type": "github",
|
||||
"url": "https://github.com/crewAIInc/crewAI"
|
||||
},
|
||||
"primaryTab": {
|
||||
"name": "Get Started"
|
||||
},
|
||||
"tabs": [
|
||||
{
|
||||
"name": "Examples",
|
||||
"url": "examples"
|
||||
}
|
||||
],
|
||||
"anchors": [
|
||||
{
|
||||
"name": "Community",
|
||||
"icon": "discourse",
|
||||
"url": "https://community.crewai.com"
|
||||
},
|
||||
{
|
||||
"name": "Changelog",
|
||||
"icon": "timeline",
|
||||
"url": "https://github.com/crewAIInc/crewAI/releases"
|
||||
}
|
||||
],
|
||||
"navigation": [
|
||||
{
|
||||
"group": "Get Started",
|
||||
"pages": [
|
||||
"introduction",
|
||||
"installation",
|
||||
"quickstart"
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "Guides",
|
||||
"pages": [
|
||||
{
|
||||
"group": "Concepts",
|
||||
"pages": [
|
||||
"guides/concepts/evaluating-use-cases"
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "Agents",
|
||||
"pages": [
|
||||
"guides/agents/crafting-effective-agents"
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "Crews",
|
||||
"pages": [
|
||||
"guides/crews/first-crew"
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "Flows",
|
||||
"pages": [
|
||||
"guides/flows/first-flow",
|
||||
"guides/flows/mastering-flow-state"
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "Advanced",
|
||||
"pages": [
|
||||
"guides/advanced/customizing-prompts",
|
||||
"guides/advanced/fingerprinting"
|
||||
]
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "Core Concepts",
|
||||
"pages": [
|
||||
"concepts/agents",
|
||||
"concepts/tasks",
|
||||
"concepts/crews",
|
||||
"concepts/flows",
|
||||
"concepts/knowledge",
|
||||
"concepts/llms",
|
||||
"concepts/processes",
|
||||
"concepts/collaboration",
|
||||
"concepts/training",
|
||||
"concepts/memory",
|
||||
"concepts/planning",
|
||||
"concepts/testing",
|
||||
"concepts/cli",
|
||||
"concepts/tools",
|
||||
"concepts/event-listener",
|
||||
"concepts/langchain-tools",
|
||||
"concepts/llamaindex-tools"
|
||||
]
|
||||
},
|
||||
{
|
||||
"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/agentops-observability",
|
||||
"how-to/langtrace-observability",
|
||||
"how-to/mlflow-observability",
|
||||
"how-to/openlit-observability",
|
||||
"how-to/portkey-observability",
|
||||
"how-to/langfuse-observability"
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "Examples",
|
||||
"pages": [
|
||||
"examples/example"
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "Tools",
|
||||
"pages": [
|
||||
"tools/aimindtool",
|
||||
"tools/apifyactorstool",
|
||||
"tools/bravesearchtool",
|
||||
"tools/browserbaseloadtool",
|
||||
"tools/codedocssearchtool",
|
||||
"tools/codeinterpretertool",
|
||||
"tools/composiotool",
|
||||
"tools/csvsearchtool",
|
||||
"tools/dalletool",
|
||||
"tools/directorysearchtool",
|
||||
"tools/directoryreadtool",
|
||||
"tools/docxsearchtool",
|
||||
"tools/exasearchtool",
|
||||
"tools/filereadtool",
|
||||
"tools/filewritetool",
|
||||
"tools/firecrawlcrawlwebsitetool",
|
||||
"tools/firecrawlscrapewebsitetool",
|
||||
"tools/firecrawlsearchtool",
|
||||
"tools/githubsearchtool",
|
||||
"tools/hyperbrowserloadtool",
|
||||
"tools/linkupsearchtool",
|
||||
"tools/llamaindextool",
|
||||
"tools/serperdevtool",
|
||||
"tools/s3readertool",
|
||||
"tools/s3writertool",
|
||||
"tools/scrapegraphscrapetool",
|
||||
"tools/scrapeelementfromwebsitetool",
|
||||
"tools/jsonsearchtool",
|
||||
"tools/mdxsearchtool",
|
||||
"tools/mysqltool",
|
||||
"tools/multiontool",
|
||||
"tools/nl2sqltool",
|
||||
"tools/patronustools",
|
||||
"tools/pdfsearchtool",
|
||||
"tools/pgsearchtool",
|
||||
"tools/qdrantvectorsearchtool",
|
||||
"tools/ragtool",
|
||||
"tools/scrapewebsitetool",
|
||||
"tools/scrapflyscrapetool",
|
||||
"tools/seleniumscrapingtool",
|
||||
"tools/snowflakesearchtool",
|
||||
"tools/spidertool",
|
||||
"tools/txtsearchtool",
|
||||
"tools/visiontool",
|
||||
"tools/weaviatevectorsearchtool",
|
||||
"tools/websitesearchtool",
|
||||
"tools/xmlsearchtool",
|
||||
"tools/youtubechannelsearchtool",
|
||||
"tools/youtubevideosearchtool"
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "Telemetry",
|
||||
"pages": [
|
||||
"telemetry"
|
||||
]
|
||||
}
|
||||
],
|
||||
"search": {
|
||||
"prompt": "Search CrewAI docs"
|
||||
},
|
||||
"footerSocials": {
|
||||
"website": "https://crewai.com",
|
||||
"x": "https://x.com/crewAIInc",
|
||||
"github": "https://github.com/crewAIInc/crewAI",
|
||||
"linkedin": "https://www.linkedin.com/company/crewai-inc",
|
||||
"youtube": "https://youtube.com/@crewAIInc"
|
||||
}
|
||||
}
|
||||
@@ -300,7 +300,7 @@ email_summarizer:
|
||||
```
|
||||
|
||||
<Tip>
|
||||
Note how we use the same name for the agent in the `tasks.yaml` (`email_summarizer_task`) file as the method name in the `crew.py` (`email_summarizer_task`) file.
|
||||
Note how we use the same name for the task in the `tasks.yaml` (`email_summarizer_task`) file as the method name in the `crew.py` (`email_summarizer_task`) file.
|
||||
</Tip>
|
||||
|
||||
```yaml tasks.yaml
|
||||
|
||||
@@ -7,8 +7,10 @@ icon: file-code
|
||||
# `JSONSearchTool`
|
||||
|
||||
<Note>
|
||||
The JSONSearchTool is currently in an experimental phase. This means the tool is under active development, and users might encounter unexpected behavior or changes.
|
||||
We highly encourage feedback on any issues or suggestions for improvements.
|
||||
The JSONSearchTool is currently in an experimental phase. This means the tool
|
||||
is under active development, and users might encounter unexpected behavior or
|
||||
changes. We highly encourage feedback on any issues or suggestions for
|
||||
improvements.
|
||||
</Note>
|
||||
|
||||
## Description
|
||||
@@ -60,7 +62,7 @@ tool = JSONSearchTool(
|
||||
# stream=true,
|
||||
},
|
||||
},
|
||||
"embedder": {
|
||||
"embedding_model": {
|
||||
"provider": "google", # or openai, ollama, ...
|
||||
"config": {
|
||||
"model": "models/embedding-001",
|
||||
@@ -70,4 +72,4 @@ tool = JSONSearchTool(
|
||||
},
|
||||
}
|
||||
)
|
||||
```
|
||||
```
|
||||
|
||||
@@ -8,8 +8,8 @@ icon: vector-square
|
||||
|
||||
## Description
|
||||
|
||||
The `RagTool` is designed to answer questions by leveraging the power of Retrieval-Augmented Generation (RAG) through EmbedChain.
|
||||
It provides a dynamic knowledge base that can be queried to retrieve relevant information from various data sources.
|
||||
The `RagTool` is designed to answer questions by leveraging the power of Retrieval-Augmented Generation (RAG) through EmbedChain.
|
||||
It provides a dynamic knowledge base that can be queried to retrieve relevant information from various data sources.
|
||||
This tool is particularly useful for applications that require access to a vast array of information and need to provide contextually relevant answers.
|
||||
|
||||
## Example
|
||||
@@ -138,7 +138,7 @@ config = {
|
||||
"model": "gpt-4",
|
||||
}
|
||||
},
|
||||
"embedder": {
|
||||
"embedding_model": {
|
||||
"provider": "openai",
|
||||
"config": {
|
||||
"model": "text-embedding-ada-002"
|
||||
@@ -151,4 +151,4 @@ rag_tool = RagTool(config=config, summarize=True)
|
||||
|
||||
## Conclusion
|
||||
|
||||
The `RagTool` provides a powerful way to create and query knowledge bases from various data sources. By leveraging Retrieval-Augmented Generation, it enables agents to access and retrieve relevant information efficiently, enhancing their ability to provide accurate and contextually appropriate responses.
|
||||
The `RagTool` provides a powerful way to create and query knowledge bases from various data sources. By leveraging Retrieval-Augmented Generation, it enables agents to access and retrieve relevant information efficiently, enhancing their ability to provide accurate and contextually appropriate responses.
|
||||
|
||||
@@ -17,9 +17,9 @@ dependencies = [
|
||||
"pdfplumber>=0.11.4",
|
||||
"regex>=2024.9.11",
|
||||
# Telemetry and Monitoring
|
||||
"opentelemetry-api>=1.22.0",
|
||||
"opentelemetry-sdk>=1.22.0",
|
||||
"opentelemetry-exporter-otlp-proto-http>=1.22.0",
|
||||
"opentelemetry-api>=1.30.0",
|
||||
"opentelemetry-sdk>=1.30.0",
|
||||
"opentelemetry-exporter-otlp-proto-http>=1.30.0",
|
||||
# Data Handling
|
||||
"chromadb>=0.5.23",
|
||||
"openpyxl>=3.1.5",
|
||||
|
||||
@@ -124,9 +124,9 @@ class CrewAgentParser:
|
||||
)
|
||||
|
||||
def _extract_thought(self, text: str) -> str:
|
||||
thought_index = text.find("\n\nAction")
|
||||
thought_index = text.find("\nAction")
|
||||
if thought_index == -1:
|
||||
thought_index = text.find("\n\nFinal Answer")
|
||||
thought_index = text.find("\nFinal Answer")
|
||||
if thought_index == -1:
|
||||
return ""
|
||||
thought = text[:thought_index].strip()
|
||||
@@ -136,7 +136,7 @@ class CrewAgentParser:
|
||||
|
||||
def _clean_action(self, text: str) -> str:
|
||||
"""Clean action string by removing non-essential formatting characters."""
|
||||
return re.sub(r"^\s*\*+\s*|\s*\*+\s*$", "", text).strip()
|
||||
return text.strip().strip("*").strip()
|
||||
|
||||
def _safe_repair_json(self, tool_input: str) -> str:
|
||||
UNABLE_TO_REPAIR_JSON_RESULTS = ['""', "{}"]
|
||||
|
||||
@@ -1,4 +1,5 @@
|
||||
import subprocess
|
||||
from functools import lru_cache
|
||||
|
||||
|
||||
class Repository:
|
||||
@@ -35,6 +36,7 @@ class Repository:
|
||||
encoding="utf-8",
|
||||
).strip()
|
||||
|
||||
@lru_cache(maxsize=None)
|
||||
def is_git_repo(self) -> bool:
|
||||
"""Check if the current directory is a git repository."""
|
||||
try:
|
||||
|
||||
@@ -1,7 +1,7 @@
|
||||
import os
|
||||
from typing import Any, Dict, List
|
||||
|
||||
from mem0 import MemoryClient
|
||||
from mem0 import Memory, MemoryClient
|
||||
|
||||
from crewai.memory.storage.interface import Storage
|
||||
|
||||
@@ -32,13 +32,16 @@ class Mem0Storage(Storage):
|
||||
mem0_org_id = config.get("org_id")
|
||||
mem0_project_id = config.get("project_id")
|
||||
|
||||
# Initialize MemoryClient with available parameters
|
||||
if mem0_org_id and mem0_project_id:
|
||||
self.memory = MemoryClient(
|
||||
api_key=mem0_api_key, org_id=mem0_org_id, project_id=mem0_project_id
|
||||
)
|
||||
# Initialize MemoryClient or Memory based on the presence of the mem0_api_key
|
||||
if mem0_api_key:
|
||||
if mem0_org_id and mem0_project_id:
|
||||
self.memory = MemoryClient(
|
||||
api_key=mem0_api_key, org_id=mem0_org_id, project_id=mem0_project_id
|
||||
)
|
||||
else:
|
||||
self.memory = MemoryClient(api_key=mem0_api_key)
|
||||
else:
|
||||
self.memory = MemoryClient(api_key=mem0_api_key)
|
||||
self.memory = Memory() # Fallback to Memory if no Mem0 API key is provided
|
||||
|
||||
def _sanitize_role(self, role: str) -> str:
|
||||
"""
|
||||
|
||||
@@ -281,8 +281,16 @@ class Telemetry:
|
||||
return self._safe_telemetry_operation(operation)
|
||||
|
||||
def task_ended(self, span: Span, task: Task, crew: Crew):
|
||||
"""Records task execution in a crew."""
|
||||
"""Records the completion of a task execution in a crew.
|
||||
|
||||
Args:
|
||||
span (Span): The OpenTelemetry span tracking the task execution
|
||||
task (Task): The task that was completed
|
||||
crew (Crew): The crew context in which the task was executed
|
||||
|
||||
Note:
|
||||
If share_crew is enabled, this will also record the task output
|
||||
"""
|
||||
def operation():
|
||||
if crew.share_crew:
|
||||
self._add_attribute(
|
||||
@@ -297,8 +305,13 @@ class Telemetry:
|
||||
self._safe_telemetry_operation(operation)
|
||||
|
||||
def tool_repeated_usage(self, llm: Any, tool_name: str, attempts: int):
|
||||
"""Records the repeated usage 'error' of a tool by an agent."""
|
||||
"""Records when a tool is used repeatedly, which might indicate an issue.
|
||||
|
||||
Args:
|
||||
llm (Any): The language model being used
|
||||
tool_name (str): Name of the tool being repeatedly used
|
||||
attempts (int): Number of attempts made with this tool
|
||||
"""
|
||||
def operation():
|
||||
tracer = trace.get_tracer("crewai.telemetry")
|
||||
span = tracer.start_span("Tool Repeated Usage")
|
||||
@@ -317,8 +330,13 @@ class Telemetry:
|
||||
self._safe_telemetry_operation(operation)
|
||||
|
||||
def tool_usage(self, llm: Any, tool_name: str, attempts: int):
|
||||
"""Records the usage of a tool by an agent."""
|
||||
"""Records the usage of a tool by an agent.
|
||||
|
||||
Args:
|
||||
llm (Any): The language model being used
|
||||
tool_name (str): Name of the tool being used
|
||||
attempts (int): Number of attempts made with this tool
|
||||
"""
|
||||
def operation():
|
||||
tracer = trace.get_tracer("crewai.telemetry")
|
||||
span = tracer.start_span("Tool Usage")
|
||||
@@ -337,8 +355,11 @@ class Telemetry:
|
||||
self._safe_telemetry_operation(operation)
|
||||
|
||||
def tool_usage_error(self, llm: Any):
|
||||
"""Records the usage of a tool by an agent."""
|
||||
"""Records when a tool usage results in an error.
|
||||
|
||||
Args:
|
||||
llm (Any): The language model being used when the error occurred
|
||||
"""
|
||||
def operation():
|
||||
tracer = trace.get_tracer("crewai.telemetry")
|
||||
span = tracer.start_span("Tool Usage Error")
|
||||
@@ -357,6 +378,14 @@ class Telemetry:
|
||||
def individual_test_result_span(
|
||||
self, crew: Crew, quality: float, exec_time: int, model_name: str
|
||||
):
|
||||
"""Records individual test results for a crew execution.
|
||||
|
||||
Args:
|
||||
crew (Crew): The crew being tested
|
||||
quality (float): Quality score of the execution
|
||||
exec_time (int): Execution time in seconds
|
||||
model_name (str): Name of the model used
|
||||
"""
|
||||
def operation():
|
||||
tracer = trace.get_tracer("crewai.telemetry")
|
||||
span = tracer.start_span("Crew Individual Test Result")
|
||||
@@ -383,6 +412,14 @@ class Telemetry:
|
||||
inputs: dict[str, Any] | None,
|
||||
model_name: str,
|
||||
):
|
||||
"""Records the execution of a test suite for a crew.
|
||||
|
||||
Args:
|
||||
crew (Crew): The crew being tested
|
||||
iterations (int): Number of test iterations
|
||||
inputs (dict[str, Any] | None): Input parameters for the test
|
||||
model_name (str): Name of the model used in testing
|
||||
"""
|
||||
def operation():
|
||||
tracer = trace.get_tracer("crewai.telemetry")
|
||||
span = tracer.start_span("Crew Test Execution")
|
||||
@@ -408,6 +445,7 @@ class Telemetry:
|
||||
self._safe_telemetry_operation(operation)
|
||||
|
||||
def deploy_signup_error_span(self):
|
||||
"""Records when an error occurs during the deployment signup process."""
|
||||
def operation():
|
||||
tracer = trace.get_tracer("crewai.telemetry")
|
||||
span = tracer.start_span("Deploy Signup Error")
|
||||
@@ -417,6 +455,11 @@ class Telemetry:
|
||||
self._safe_telemetry_operation(operation)
|
||||
|
||||
def start_deployment_span(self, uuid: Optional[str] = None):
|
||||
"""Records the start of a deployment process.
|
||||
|
||||
Args:
|
||||
uuid (Optional[str]): Unique identifier for the deployment
|
||||
"""
|
||||
def operation():
|
||||
tracer = trace.get_tracer("crewai.telemetry")
|
||||
span = tracer.start_span("Start Deployment")
|
||||
@@ -428,6 +471,7 @@ class Telemetry:
|
||||
self._safe_telemetry_operation(operation)
|
||||
|
||||
def create_crew_deployment_span(self):
|
||||
"""Records the creation of a new crew deployment."""
|
||||
def operation():
|
||||
tracer = trace.get_tracer("crewai.telemetry")
|
||||
span = tracer.start_span("Create Crew Deployment")
|
||||
@@ -437,6 +481,12 @@ class Telemetry:
|
||||
self._safe_telemetry_operation(operation)
|
||||
|
||||
def get_crew_logs_span(self, uuid: Optional[str], log_type: str = "deployment"):
|
||||
"""Records the retrieval of crew logs.
|
||||
|
||||
Args:
|
||||
uuid (Optional[str]): Unique identifier for the crew
|
||||
log_type (str, optional): Type of logs being retrieved. Defaults to "deployment".
|
||||
"""
|
||||
def operation():
|
||||
tracer = trace.get_tracer("crewai.telemetry")
|
||||
span = tracer.start_span("Get Crew Logs")
|
||||
@@ -449,6 +499,11 @@ class Telemetry:
|
||||
self._safe_telemetry_operation(operation)
|
||||
|
||||
def remove_crew_span(self, uuid: Optional[str] = None):
|
||||
"""Records the removal of a crew.
|
||||
|
||||
Args:
|
||||
uuid (Optional[str]): Unique identifier for the crew being removed
|
||||
"""
|
||||
def operation():
|
||||
tracer = trace.get_tracer("crewai.telemetry")
|
||||
span = tracer.start_span("Remove Crew")
|
||||
@@ -574,6 +629,11 @@ class Telemetry:
|
||||
self._safe_telemetry_operation(operation)
|
||||
|
||||
def flow_creation_span(self, flow_name: str):
|
||||
"""Records the creation of a new flow.
|
||||
|
||||
Args:
|
||||
flow_name (str): Name of the flow being created
|
||||
"""
|
||||
def operation():
|
||||
tracer = trace.get_tracer("crewai.telemetry")
|
||||
span = tracer.start_span("Flow Creation")
|
||||
@@ -584,6 +644,12 @@ class Telemetry:
|
||||
self._safe_telemetry_operation(operation)
|
||||
|
||||
def flow_plotting_span(self, flow_name: str, node_names: list[str]):
|
||||
"""Records flow visualization/plotting activity.
|
||||
|
||||
Args:
|
||||
flow_name (str): Name of the flow being plotted
|
||||
node_names (list[str]): List of node names in the flow
|
||||
"""
|
||||
def operation():
|
||||
tracer = trace.get_tracer("crewai.telemetry")
|
||||
span = tracer.start_span("Flow Plotting")
|
||||
@@ -595,6 +661,12 @@ class Telemetry:
|
||||
self._safe_telemetry_operation(operation)
|
||||
|
||||
def flow_execution_span(self, flow_name: str, node_names: list[str]):
|
||||
"""Records the execution of a flow.
|
||||
|
||||
Args:
|
||||
flow_name (str): Name of the flow being executed
|
||||
node_names (list[str]): List of nodes being executed in the flow
|
||||
"""
|
||||
def operation():
|
||||
tracer = trace.get_tracer("crewai.telemetry")
|
||||
span = tracer.start_span("Flow Execution")
|
||||
|
||||
@@ -337,11 +337,23 @@ class ToolUsage:
|
||||
return "\n--\n".join(descriptions)
|
||||
|
||||
def _function_calling(self, tool_string: str):
|
||||
model = (
|
||||
InstructorToolCalling
|
||||
if self.function_calling_llm.supports_function_calling()
|
||||
else ToolCalling
|
||||
supports_function_calling = (
|
||||
self.function_calling_llm.supports_function_calling()
|
||||
)
|
||||
|
||||
if not supports_function_calling:
|
||||
import warnings
|
||||
|
||||
warnings.warn(
|
||||
"The model you're using doesn't natively support function calling. "
|
||||
"CrewAI will attempt to use a workaround, but this may be less reliable. "
|
||||
"Consider using a model with native function calling support for better results.",
|
||||
UserWarning,
|
||||
stacklevel=2,
|
||||
)
|
||||
|
||||
model = InstructorToolCalling if supports_function_calling else ToolCalling
|
||||
|
||||
converter = Converter(
|
||||
text=f"Only tools available:\n###\n{self._render()}\n\nReturn a valid schema for the tool, the tool name must be exactly equal one of the options, use this text to inform the valid output schema:\n\n### TEXT \n{tool_string}",
|
||||
llm=self.function_calling_llm,
|
||||
|
||||
@@ -96,6 +96,10 @@ class CrewPlanner:
|
||||
tasks_summary = []
|
||||
for idx, task in enumerate(self.tasks):
|
||||
knowledge_list = self._get_agent_knowledge(task)
|
||||
agent_tools = (
|
||||
f"[{', '.join(str(tool) for tool in task.agent.tools)}]" if task.agent and task.agent.tools else '"agent has no tools"',
|
||||
f',\n "agent_knowledge": "[\\"{knowledge_list[0]}\\"]"' if knowledge_list and str(knowledge_list) != "None" else ""
|
||||
)
|
||||
task_summary = f"""
|
||||
Task Number {idx + 1} - {task.description}
|
||||
"task_description": {task.description}
|
||||
@@ -103,10 +107,7 @@ class CrewPlanner:
|
||||
"agent": {task.agent.role if task.agent else "None"}
|
||||
"agent_goal": {task.agent.goal if task.agent else "None"}
|
||||
"task_tools": {task.tools}
|
||||
"agent_tools": %s%s""" % (
|
||||
f"[{', '.join(str(tool) for tool in task.agent.tools)}]" if task.agent and task.agent.tools else '"agent has no tools"',
|
||||
f',\n "agent_knowledge": "[\\"{knowledge_list[0]}\\"]"' if knowledge_list and str(knowledge_list) != "None" else ""
|
||||
)
|
||||
"agent_tools": {"".join(agent_tools)}"""
|
||||
|
||||
tasks_summary.append(task_summary)
|
||||
return " ".join(tasks_summary)
|
||||
|
||||
Reference in New Issue
Block a user