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Merge branch 'main' of github.com:crewAIInc/crewAI into devin/1741108142-custom-llm-support
This commit is contained in:
Lorenze Jay
187
docs/changelog.mdx
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187
docs/changelog.mdx
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@@ -0,0 +1,187 @@
|
||||
---
|
||||
title: Changelog
|
||||
description: View the latest updates and changes to CrewAI
|
||||
icon: timeline
|
||||
---
|
||||
|
||||
<Update label="2025-03-17" 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="2025-03-10" 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="2025-02-12" 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="2025-01-28" 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="2025-01-20" 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="2025-01-04" 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="2024-12-05" 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="2024-12-04" 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="2024-11-25" 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="2024-11-13" 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>
|
||||
@@ -150,6 +150,8 @@ result = crew.kickoff(
|
||||
|
||||
Here are examples of how to use different types of knowledge sources:
|
||||
|
||||
Note: Please ensure that you create the ./knowldge folder. All source files (e.g., .txt, .pdf, .xlsx, .json) should be placed in this folder for centralized management.
|
||||
|
||||
### Text File Knowledge Source
|
||||
```python
|
||||
from crewai.knowledge.source.text_file_knowledge_source import TextFileKnowledgeSource
|
||||
@@ -460,12 +462,12 @@ class SpaceNewsKnowledgeSource(BaseKnowledgeSource):
|
||||
data = response.json()
|
||||
articles = data.get('results', [])
|
||||
|
||||
formatted_data = self._format_articles(articles)
|
||||
formatted_data = self.validate_content(articles)
|
||||
return {self.api_endpoint: formatted_data}
|
||||
except Exception as e:
|
||||
raise ValueError(f"Failed to fetch space news: {str(e)}")
|
||||
|
||||
def _format_articles(self, articles: list) -> str:
|
||||
def validate_content(self, articles: list) -> str:
|
||||
"""Format articles into readable text."""
|
||||
formatted = "Space News Articles:\n\n"
|
||||
for article in articles:
|
||||
|
||||
@@ -59,7 +59,7 @@ There are three ways to configure LLMs in CrewAI. Choose the method that best fi
|
||||
goal: Conduct comprehensive research and analysis
|
||||
backstory: A dedicated research professional with years of experience
|
||||
verbose: true
|
||||
llm: openai/gpt-4o-mini # your model here
|
||||
llm: openai/gpt-4o-mini # your model here
|
||||
# (see provider configuration examples below for more)
|
||||
```
|
||||
|
||||
@@ -111,7 +111,7 @@ There are three ways to configure LLMs in CrewAI. Choose the method that best fi
|
||||
## Provider Configuration Examples
|
||||
|
||||
|
||||
CrewAI supports a multitude of LLM providers, each offering unique features, authentication methods, and model capabilities.
|
||||
CrewAI supports a multitude of LLM providers, each offering unique features, authentication methods, and model capabilities.
|
||||
In this section, you'll find detailed examples that help you select, configure, and optimize the LLM that best fits your project's needs.
|
||||
|
||||
<AccordionGroup>
|
||||
@@ -121,7 +121,7 @@ In this section, you'll find detailed examples that help you select, configure,
|
||||
```toml Code
|
||||
# Required
|
||||
OPENAI_API_KEY=sk-...
|
||||
|
||||
|
||||
# Optional
|
||||
OPENAI_API_BASE=<custom-base-url>
|
||||
OPENAI_ORGANIZATION=<your-org-id>
|
||||
@@ -158,7 +158,11 @@ In this section, you'll find detailed examples that help you select, configure,
|
||||
|
||||
<Accordion title="Anthropic">
|
||||
```toml Code
|
||||
# Required
|
||||
ANTHROPIC_API_KEY=sk-ant-...
|
||||
|
||||
# Optional
|
||||
ANTHROPIC_API_BASE=<custom-base-url>
|
||||
```
|
||||
|
||||
Example usage in your CrewAI project:
|
||||
@@ -222,7 +226,7 @@ In this section, you'll find detailed examples that help you select, configure,
|
||||
AZURE_API_KEY=<your-api-key>
|
||||
AZURE_API_BASE=<your-resource-url>
|
||||
AZURE_API_VERSION=<api-version>
|
||||
|
||||
|
||||
# Optional
|
||||
AZURE_AD_TOKEN=<your-azure-ad-token>
|
||||
AZURE_API_TYPE=<your-azure-api-type>
|
||||
@@ -250,8 +254,42 @@ In this section, you'll find detailed examples that help you select, configure,
|
||||
model="bedrock/anthropic.claude-3-sonnet-20240229-v1:0"
|
||||
)
|
||||
```
|
||||
|
||||
Before using Amazon Bedrock, make sure you have boto3 installed in your environment
|
||||
|
||||
[Amazon Bedrock](https://docs.aws.amazon.com/bedrock/latest/userguide/models-regions.html) is a managed service that provides access to multiple foundation models from top AI companies through a unified API, enabling secure and responsible AI application development.
|
||||
|
||||
| Model | Context Window | Best For |
|
||||
|-------------------------|----------------------|-------------------------------------------------------------------|
|
||||
| Amazon Nova Pro | Up to 300k tokens | High-performance, model balancing accuracy, speed, and cost-effectiveness across diverse tasks. |
|
||||
| Amazon Nova Micro | Up to 128k tokens | High-performance, cost-effective text-only model optimized for lowest latency responses. |
|
||||
| Amazon Nova Lite | Up to 300k tokens | High-performance, affordable multimodal processing for images, video, and text with real-time capabilities. |
|
||||
| Claude 3.7 Sonnet | Up to 128k tokens | High-performance, best for complex reasoning, coding & AI agents |
|
||||
| Claude 3.5 Sonnet v2 | Up to 200k tokens | State-of-the-art model specialized in software engineering, agentic capabilities, and computer interaction at optimized cost. |
|
||||
| Claude 3.5 Sonnet | Up to 200k tokens | High-performance model delivering superior intelligence and reasoning across diverse tasks with optimal speed-cost balance. |
|
||||
| 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 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 |
|
||||
| Llama 3.1 405B Instruct | Up to 128k tokens | Advanced LLM for synthetic data generation, distillation, and inference for chatbots, coding, and domain-specific tasks. |
|
||||
| Llama 3.1 70B Instruct | Up to 128k tokens | Powers complex conversations with superior contextual understanding, reasoning and text generation. |
|
||||
| Llama 3.1 8B Instruct | Up to 128k tokens | Advanced state-of-the-art model with language understanding, superior reasoning, and text generation. |
|
||||
| Llama 3 70B Instruct | Up to 8k tokens | Powers complex conversations with superior contextual understanding, reasoning and text generation. |
|
||||
| Llama 3 8B Instruct | Up to 8k tokens | Advanced state-of-the-art LLM with language understanding, superior reasoning, and text generation. |
|
||||
| Titan Text G1 - Lite | Up to 4k tokens | Lightweight, cost-effective model optimized for English tasks and fine-tuning with focus on summarization and content generation. |
|
||||
| Titan Text G1 - Express | Up to 8k tokens | Versatile model for general language tasks, chat, and RAG applications with support for English and 100+ languages. |
|
||||
| Cohere Command | Up to 4k tokens | Model specialized in following user commands and delivering practical enterprise solutions. |
|
||||
| Jurassic-2 Mid | Up to 8,191 tokens | Cost-effective model balancing quality and affordability for diverse language tasks like Q&A, summarization, and content generation. |
|
||||
| Jurassic-2 Ultra | Up to 8,191 tokens | Model for advanced text generation and comprehension, excelling in complex tasks like analysis and content creation. |
|
||||
| Jamba-Instruct | Up to 256k tokens | Model with extended context window optimized for cost-effective text generation, summarization, and Q&A. |
|
||||
| Mistral 7B Instruct | Up to 32k tokens | This LLM follows instructions, completes requests, and generates creative text. |
|
||||
| Mistral 8x7B Instruct | Up to 32k tokens | An MOE LLM that follows instructions, completes requests, and generates creative text. |
|
||||
|
||||
</Accordion>
|
||||
|
||||
|
||||
<Accordion title="Amazon SageMaker">
|
||||
```toml Code
|
||||
AWS_ACCESS_KEY_ID=<your-access-key>
|
||||
@@ -368,6 +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:
|
||||
|
||||
@@ -396,7 +474,7 @@ In this section, you'll find detailed examples that help you select, configure,
|
||||
WATSONX_URL=<your-url>
|
||||
WATSONX_APIKEY=<your-apikey>
|
||||
WATSONX_PROJECT_ID=<your-project-id>
|
||||
|
||||
|
||||
# Optional
|
||||
WATSONX_TOKEN=<your-token>
|
||||
WATSONX_DEPLOYMENT_SPACE_ID=<your-space-id>
|
||||
@@ -413,7 +491,7 @@ In this section, you'll find detailed examples that help you select, configure,
|
||||
|
||||
<Accordion title="Ollama (Local LLMs)">
|
||||
1. Install Ollama: [ollama.ai](https://ollama.ai/)
|
||||
2. Run a model: `ollama run llama2`
|
||||
2. Run a model: `ollama run llama3`
|
||||
3. Configure:
|
||||
|
||||
```python Code
|
||||
@@ -522,7 +600,7 @@ In this section, you'll find detailed examples that help you select, configure,
|
||||
```toml Code
|
||||
OPENROUTER_API_KEY=<your-api-key>
|
||||
```
|
||||
|
||||
|
||||
Example usage in your CrewAI project:
|
||||
```python Code
|
||||
llm = LLM(
|
||||
@@ -645,7 +723,7 @@ Learn how to get the most out of your LLM configuration:
|
||||
- Small tasks (up to 4K tokens): Standard models
|
||||
- Medium tasks (between 4K-32K): Enhanced models
|
||||
- Large tasks (over 32K): Large context models
|
||||
|
||||
|
||||
```python
|
||||
# Configure model with appropriate settings
|
||||
llm = LLM(
|
||||
@@ -682,11 +760,11 @@ Learn how to get the most out of your LLM configuration:
|
||||
<Warning>
|
||||
Most authentication issues can be resolved by checking API key format and environment variable names.
|
||||
</Warning>
|
||||
|
||||
|
||||
```bash
|
||||
# OpenAI
|
||||
OPENAI_API_KEY=sk-...
|
||||
|
||||
|
||||
# Anthropic
|
||||
ANTHROPIC_API_KEY=sk-ant-...
|
||||
```
|
||||
@@ -695,11 +773,11 @@ Learn how to get the most out of your LLM configuration:
|
||||
<Check>
|
||||
Always include the provider prefix in model names
|
||||
</Check>
|
||||
|
||||
|
||||
```python
|
||||
# Correct
|
||||
llm = LLM(model="openai/gpt-4")
|
||||
|
||||
|
||||
# Incorrect
|
||||
llm = LLM(model="gpt-4")
|
||||
```
|
||||
@@ -709,4 +787,9 @@ Learn how to get the most out of your LLM configuration:
|
||||
Use larger context models for extensive tasks
|
||||
</Tip>
|
||||
|
||||
```python
|
||||
# Large context model
|
||||
llm = LLM(model="openai/gpt-4o") # 128K tokens
|
||||
```
|
||||
</Tab>
|
||||
</Tabs>
|
||||
|
||||
@@ -60,7 +60,8 @@ my_crew = Crew(
|
||||
```python Code
|
||||
from crewai import Crew, Process
|
||||
from crewai.memory import LongTermMemory, ShortTermMemory, EntityMemory
|
||||
from crewai.memory.storage import LTMSQLiteStorage, RAGStorage
|
||||
from crewai.memory.storage.rag_storage import RAGStorage
|
||||
from crewai.memory.storage.ltm_sqlite_storage import LTMSQLiteStorage
|
||||
from typing import List, Optional
|
||||
|
||||
# Assemble your crew with memory capabilities
|
||||
@@ -119,7 +120,7 @@ Example using environment variables:
|
||||
import os
|
||||
from crewai import Crew
|
||||
from crewai.memory import LongTermMemory
|
||||
from crewai.memory.storage import LTMSQLiteStorage
|
||||
from crewai.memory.storage.ltm_sqlite_storage import LTMSQLiteStorage
|
||||
|
||||
# Configure storage path using environment variable
|
||||
storage_path = os.getenv("CREWAI_STORAGE_DIR", "./storage")
|
||||
@@ -148,7 +149,7 @@ crew = Crew(memory=True) # Uses default storage locations
|
||||
```python
|
||||
from crewai import Crew
|
||||
from crewai.memory import LongTermMemory
|
||||
from crewai.memory.storage import LTMSQLiteStorage
|
||||
from crewai.memory.storage.ltm_sqlite_storage import LTMSQLiteStorage
|
||||
|
||||
# Configure custom storage paths
|
||||
crew = Crew(
|
||||
|
||||
@@ -106,6 +106,7 @@ Here is a list of the available tools and their descriptions:
|
||||
|
||||
| Tool | Description |
|
||||
| :------------------------------- | :--------------------------------------------------------------------------------------------- |
|
||||
| **ApifyActorsTool** | A tool that integrates Apify Actors with your workflows for web scraping and automation tasks. |
|
||||
| **BrowserbaseLoadTool** | A tool for interacting with and extracting data from web browsers. |
|
||||
| **CodeDocsSearchTool** | A RAG tool optimized for searching through code documentation and related technical documents. |
|
||||
| **CodeInterpreterTool** | A tool for interpreting python code. |
|
||||
|
||||
225
docs/docs.json
Normal file
225
docs/docs.json
Normal file
@@ -0,0 +1,225 @@
|
||||
{
|
||||
"$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/bedrockinvokeagenttool",
|
||||
"tools/bedrockkbretriever",
|
||||
"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/"
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -1,4 +1,5 @@
|
||||
---title: Customizing Prompts
|
||||
---
|
||||
title: Customizing Prompts
|
||||
description: Dive deeper into low-level prompt customization for CrewAI, enabling super custom and complex use cases for different models and languages.
|
||||
icon: message-pen
|
||||
---
|
||||
|
||||
@@ -39,8 +39,7 @@ analysis_crew = Crew(
|
||||
agents=[coding_agent],
|
||||
tasks=[data_analysis_task],
|
||||
verbose=True,
|
||||
memory=False,
|
||||
respect_context_window=True # enable by default
|
||||
memory=False
|
||||
)
|
||||
|
||||
datasets = [
|
||||
|
||||
223
docs/mint.json
223
docs/mint.json
@@ -1,223 +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/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/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
|
||||
|
||||
99
docs/tools/apifyactorstool.mdx
Normal file
99
docs/tools/apifyactorstool.mdx
Normal file
@@ -0,0 +1,99 @@
|
||||
---
|
||||
title: Apify Actors
|
||||
description: "`ApifyActorsTool` lets you call Apify Actors to provide your CrewAI workflows with web scraping, crawling, data extraction, and web automation capabilities."
|
||||
# hack to use custom Apify icon
|
||||
icon: "); -webkit-mask-image: url('https://upload.wikimedia.org/wikipedia/commons/a/ae/Apify.svg');/*"
|
||||
---
|
||||
|
||||
# `ApifyActorsTool`
|
||||
|
||||
Integrate [Apify Actors](https://apify.com/actors) into your CrewAI workflows.
|
||||
|
||||
## Description
|
||||
|
||||
The `ApifyActorsTool` connects [Apify Actors](https://apify.com/actors), cloud-based programs for web scraping and automation, to your CrewAI workflows.
|
||||
Use any of the 4,000+ Actors on [Apify Store](https://apify.com/store) for use cases such as extracting data from social media, search engines, online maps, e-commerce sites, travel portals, or general websites.
|
||||
|
||||
For details, see the [Apify CrewAI integration](https://docs.apify.com/platform/integrations/crewai) in Apify documentation.
|
||||
|
||||
## Steps to get started
|
||||
|
||||
<Steps>
|
||||
<Step title="Install dependencies">
|
||||
Install `crewai[tools]` and `langchain-apify` using pip: `pip install 'crewai[tools]' langchain-apify`.
|
||||
</Step>
|
||||
<Step title="Obtain an Apify API token">
|
||||
Sign up to [Apify Console](https://console.apify.com/) and get your [Apify API token](https://console.apify.com/settings/integrations)..
|
||||
</Step>
|
||||
<Step title="Configure environment">
|
||||
Set your Apify API token as the `APIFY_API_TOKEN` environment variable to enable the tool's functionality.
|
||||
</Step>
|
||||
</Steps>
|
||||
|
||||
## Usage example
|
||||
|
||||
Use the `ApifyActorsTool` manually to run the [RAG Web Browser Actor](https://apify.com/apify/rag-web-browser) to perform a web search:
|
||||
|
||||
```python
|
||||
from crewai_tools import ApifyActorsTool
|
||||
|
||||
# Initialize the tool with an Apify Actor
|
||||
tool = ApifyActorsTool(actor_name="apify/rag-web-browser")
|
||||
|
||||
# Run the tool with input parameters
|
||||
results = tool.run(run_input={"query": "What is CrewAI?", "maxResults": 5})
|
||||
|
||||
# Process the results
|
||||
for result in results:
|
||||
print(f"URL: {result['metadata']['url']}")
|
||||
print(f"Content: {result.get('markdown', 'N/A')[:100]}...")
|
||||
```
|
||||
|
||||
### Expected output
|
||||
|
||||
Here is the output from running the code above:
|
||||
|
||||
```text
|
||||
URL: https://www.example.com/crewai-intro
|
||||
Content: CrewAI is a framework for building AI-powered workflows...
|
||||
URL: https://docs.crewai.com/
|
||||
Content: Official documentation for CrewAI...
|
||||
```
|
||||
|
||||
The `ApifyActorsTool` automatically fetches the Actor definition and input schema from Apify using the provided `actor_name` and then constructs the tool description and argument schema. This means you need to specify only a valid `actor_name`, and the tool handles the rest when used with agents—no need to specify the `run_input`. Here's how it works:
|
||||
|
||||
```python
|
||||
from crewai import Agent
|
||||
from crewai_tools import ApifyActorsTool
|
||||
|
||||
rag_browser = ApifyActorsTool(actor_name="apify/rag-web-browser")
|
||||
|
||||
agent = Agent(
|
||||
role="Research Analyst",
|
||||
goal="Find and summarize information about specific topics",
|
||||
backstory="You are an experienced researcher with attention to detail",
|
||||
tools=[rag_browser],
|
||||
)
|
||||
```
|
||||
|
||||
You can run other Actors from [Apify Store](https://apify.com/store) simply by changing the `actor_name` and, when using it manually, adjusting the `run_input` based on the Actor input schema.
|
||||
|
||||
For an example of usage with agents, see the [CrewAI Actor template](https://apify.com/templates/python-crewai).
|
||||
|
||||
## Configuration
|
||||
|
||||
The `ApifyActorsTool` requires these inputs to work:
|
||||
|
||||
- **`actor_name`**
|
||||
The ID of the Apify Actor to run, e.g., `"apify/rag-web-browser"`. Browse all Actors on [Apify Store](https://apify.com/store).
|
||||
- **`run_input`**
|
||||
A dictionary of input parameters for the Actor when running the tool manually.
|
||||
- For example, for the `apify/rag-web-browser` Actor: `{"query": "search term", "maxResults": 5}`
|
||||
- See the Actor's [input schema](https://apify.com/apify/rag-web-browser/input-schema) for the list of input parameters.
|
||||
|
||||
## Resources
|
||||
|
||||
- **[Apify](https://apify.com/)**: Explore the Apify platform.
|
||||
- **[How to build an AI agent on Apify](https://blog.apify.com/how-to-build-an-ai-agent/)** - A complete step-by-step guide to creating, publishing, and monetizing AI agents on the Apify platform.
|
||||
- **[RAG Web Browser Actor](https://apify.com/apify/rag-web-browser)**: A popular Actor for web search for LLMs.
|
||||
- **[CrewAI Integration Guide](https://docs.apify.com/platform/integrations/crewai)**: Follow the official guide for integrating Apify and CrewAI.
|
||||
187
docs/tools/bedrockinvokeagenttool.mdx
Normal file
187
docs/tools/bedrockinvokeagenttool.mdx
Normal file
@@ -0,0 +1,187 @@
|
||||
---
|
||||
title: Bedrock Invoke Agent Tool
|
||||
description: Enables CrewAI agents to invoke Amazon Bedrock Agents and leverage their capabilities within your workflows
|
||||
icon: aws
|
||||
---
|
||||
|
||||
# `BedrockInvokeAgentTool`
|
||||
|
||||
The `BedrockInvokeAgentTool` enables CrewAI agents to invoke Amazon Bedrock Agents and leverage their capabilities within your workflows.
|
||||
|
||||
## Installation
|
||||
|
||||
```bash
|
||||
uv pip install 'crewai[tools]'
|
||||
```
|
||||
|
||||
## Requirements
|
||||
|
||||
- AWS credentials configured (either through environment variables or AWS CLI)
|
||||
- `boto3` and `python-dotenv` packages
|
||||
- Access to Amazon Bedrock Agents
|
||||
|
||||
## Usage
|
||||
|
||||
Here's how to use the tool with a CrewAI agent:
|
||||
|
||||
```python {2, 4-8}
|
||||
from crewai import Agent, Task, Crew
|
||||
from crewai_tools.aws.bedrock.agents.invoke_agent_tool import BedrockInvokeAgentTool
|
||||
|
||||
# Initialize the tool
|
||||
agent_tool = BedrockInvokeAgentTool(
|
||||
agent_id="your-agent-id",
|
||||
agent_alias_id="your-agent-alias-id"
|
||||
)
|
||||
|
||||
# Create a CrewAI agent that uses the tool
|
||||
aws_expert = Agent(
|
||||
role='AWS Service Expert',
|
||||
goal='Help users understand AWS services and quotas',
|
||||
backstory='I am an expert in AWS services and can provide detailed information about them.',
|
||||
tools=[agent_tool],
|
||||
verbose=True
|
||||
)
|
||||
|
||||
# Create a task for the agent
|
||||
quota_task = Task(
|
||||
description="Find out the current service quotas for EC2 in us-west-2 and explain any recent changes.",
|
||||
agent=aws_expert
|
||||
)
|
||||
|
||||
# Create a crew with the agent
|
||||
crew = Crew(
|
||||
agents=[aws_expert],
|
||||
tasks=[quota_task],
|
||||
verbose=2
|
||||
)
|
||||
|
||||
# Run the crew
|
||||
result = crew.kickoff()
|
||||
print(result)
|
||||
```
|
||||
|
||||
## Tool Arguments
|
||||
|
||||
| Argument | Type | Required | Default | Description |
|
||||
|:---------|:-----|:---------|:--------|:------------|
|
||||
| **agent_id** | `str` | Yes | None | The unique identifier of the Bedrock agent |
|
||||
| **agent_alias_id** | `str` | Yes | None | The unique identifier of the agent alias |
|
||||
| **session_id** | `str` | No | timestamp | The unique identifier of the session |
|
||||
| **enable_trace** | `bool` | No | False | Whether to enable trace for debugging |
|
||||
| **end_session** | `bool` | No | False | Whether to end the session after invocation |
|
||||
| **description** | `str` | No | None | Custom description for the tool |
|
||||
|
||||
## Environment Variables
|
||||
|
||||
```bash
|
||||
BEDROCK_AGENT_ID=your-agent-id # Alternative to passing agent_id
|
||||
BEDROCK_AGENT_ALIAS_ID=your-agent-alias-id # Alternative to passing agent_alias_id
|
||||
AWS_REGION=your-aws-region # Defaults to us-west-2
|
||||
AWS_ACCESS_KEY_ID=your-access-key # Required for AWS authentication
|
||||
AWS_SECRET_ACCESS_KEY=your-secret-key # Required for AWS authentication
|
||||
```
|
||||
|
||||
## Advanced Usage
|
||||
|
||||
### Multi-Agent Workflow with Session Management
|
||||
|
||||
```python {2, 4-22}
|
||||
from crewai import Agent, Task, Crew, Process
|
||||
from crewai_tools.aws.bedrock.agents.invoke_agent_tool import BedrockInvokeAgentTool
|
||||
|
||||
# Initialize tools with session management
|
||||
initial_tool = BedrockInvokeAgentTool(
|
||||
agent_id="your-agent-id",
|
||||
agent_alias_id="your-agent-alias-id",
|
||||
session_id="custom-session-id"
|
||||
)
|
||||
|
||||
followup_tool = BedrockInvokeAgentTool(
|
||||
agent_id="your-agent-id",
|
||||
agent_alias_id="your-agent-alias-id",
|
||||
session_id="custom-session-id"
|
||||
)
|
||||
|
||||
final_tool = BedrockInvokeAgentTool(
|
||||
agent_id="your-agent-id",
|
||||
agent_alias_id="your-agent-alias-id",
|
||||
session_id="custom-session-id",
|
||||
end_session=True
|
||||
)
|
||||
|
||||
# Create agents for different stages
|
||||
researcher = Agent(
|
||||
role='AWS Service Researcher',
|
||||
goal='Gather information about AWS services',
|
||||
backstory='I am specialized in finding detailed AWS service information.',
|
||||
tools=[initial_tool]
|
||||
)
|
||||
|
||||
analyst = Agent(
|
||||
role='Service Compatibility Analyst',
|
||||
goal='Analyze service compatibility and requirements',
|
||||
backstory='I analyze AWS services for compatibility and integration possibilities.',
|
||||
tools=[followup_tool]
|
||||
)
|
||||
|
||||
summarizer = Agent(
|
||||
role='Technical Documentation Writer',
|
||||
goal='Create clear technical summaries',
|
||||
backstory='I specialize in creating clear, concise technical documentation.',
|
||||
tools=[final_tool]
|
||||
)
|
||||
|
||||
# Create tasks
|
||||
research_task = Task(
|
||||
description="Find all available AWS services in us-west-2 region.",
|
||||
agent=researcher
|
||||
)
|
||||
|
||||
analysis_task = Task(
|
||||
description="Analyze which services support IPv6 and their implementation requirements.",
|
||||
agent=analyst
|
||||
)
|
||||
|
||||
summary_task = Task(
|
||||
description="Create a summary of IPv6-compatible services and their key features.",
|
||||
agent=summarizer
|
||||
)
|
||||
|
||||
# Create a crew with the agents and tasks
|
||||
crew = Crew(
|
||||
agents=[researcher, analyst, summarizer],
|
||||
tasks=[research_task, analysis_task, summary_task],
|
||||
process=Process.sequential,
|
||||
verbose=2
|
||||
)
|
||||
|
||||
# Run the crew
|
||||
result = crew.kickoff()
|
||||
```
|
||||
|
||||
## Use Cases
|
||||
|
||||
### Hybrid Multi-Agent Collaborations
|
||||
- Create workflows where CrewAI agents collaborate with managed Bedrock agents running as services in AWS
|
||||
- Enable scenarios where sensitive data processing happens within your AWS environment while other agents operate externally
|
||||
- Bridge on-premises CrewAI agents with cloud-based Bedrock agents for distributed intelligence workflows
|
||||
|
||||
### Data Sovereignty and Compliance
|
||||
- Keep data-sensitive agentic workflows within your AWS environment while allowing external CrewAI agents to orchestrate tasks
|
||||
- Maintain compliance with data residency requirements by processing sensitive information only within your AWS account
|
||||
- Enable secure multi-agent collaborations where some agents cannot access your organization's private data
|
||||
|
||||
### Seamless AWS Service Integration
|
||||
- Access any AWS service through Amazon Bedrock Actions without writing complex integration code
|
||||
- Enable CrewAI agents to interact with AWS services through natural language requests
|
||||
- Leverage pre-built Bedrock agent capabilities to interact with AWS services like Bedrock Knowledge Bases, Lambda, and more
|
||||
|
||||
### Scalable Hybrid Agent Architectures
|
||||
- Offload computationally intensive tasks to managed Bedrock agents while lightweight tasks run in CrewAI
|
||||
- Scale agent processing by distributing workloads between local CrewAI agents and cloud-based Bedrock agents
|
||||
|
||||
### Cross-Organizational Agent Collaboration
|
||||
- Enable secure collaboration between your organization's CrewAI agents and partner organizations' Bedrock agents
|
||||
- Create workflows where external expertise from Bedrock agents can be incorporated without exposing sensitive data
|
||||
- Build agent ecosystems that span organizational boundaries while maintaining security and data control
|
||||
165
docs/tools/bedrockkbretriever.mdx
Normal file
165
docs/tools/bedrockkbretriever.mdx
Normal file
@@ -0,0 +1,165 @@
|
||||
---
|
||||
title: 'Bedrock Knowledge Base Retriever'
|
||||
description: 'Retrieve information from Amazon Bedrock Knowledge Bases using natural language queries'
|
||||
icon: aws
|
||||
---
|
||||
|
||||
# `BedrockKBRetrieverTool`
|
||||
|
||||
The `BedrockKBRetrieverTool` enables CrewAI agents to retrieve information from Amazon Bedrock Knowledge Bases using natural language queries.
|
||||
|
||||
## Installation
|
||||
|
||||
```bash
|
||||
uv pip install 'crewai[tools]'
|
||||
```
|
||||
|
||||
## Requirements
|
||||
|
||||
- AWS credentials configured (either through environment variables or AWS CLI)
|
||||
- `boto3` and `python-dotenv` packages
|
||||
- Access to Amazon Bedrock Knowledge Base
|
||||
|
||||
## Usage
|
||||
|
||||
Here's how to use the tool with a CrewAI agent:
|
||||
|
||||
```python {2, 4-17}
|
||||
from crewai import Agent, Task, Crew
|
||||
from crewai_tools.aws.bedrock.knowledge_base.retriever_tool import BedrockKBRetrieverTool
|
||||
|
||||
# Initialize the tool
|
||||
kb_tool = BedrockKBRetrieverTool(
|
||||
knowledge_base_id="your-kb-id",
|
||||
number_of_results=5
|
||||
)
|
||||
|
||||
# Create a CrewAI agent that uses the tool
|
||||
researcher = Agent(
|
||||
role='Knowledge Base Researcher',
|
||||
goal='Find information about company policies',
|
||||
backstory='I am a researcher specialized in retrieving and analyzing company documentation.',
|
||||
tools=[kb_tool],
|
||||
verbose=True
|
||||
)
|
||||
|
||||
# Create a task for the agent
|
||||
research_task = Task(
|
||||
description="Find our company's remote work policy and summarize the key points.",
|
||||
agent=researcher
|
||||
)
|
||||
|
||||
# Create a crew with the agent
|
||||
crew = Crew(
|
||||
agents=[researcher],
|
||||
tasks=[research_task],
|
||||
verbose=2
|
||||
)
|
||||
|
||||
# Run the crew
|
||||
result = crew.kickoff()
|
||||
print(result)
|
||||
```
|
||||
|
||||
## Tool Arguments
|
||||
|
||||
| Argument | Type | Required | Default | Description |
|
||||
|:---------|:-----|:---------|:---------|:-------------|
|
||||
| **knowledge_base_id** | `str` | Yes | None | The unique identifier of the knowledge base (0-10 alphanumeric characters) |
|
||||
| **number_of_results** | `int` | No | 5 | Maximum number of results to return |
|
||||
| **retrieval_configuration** | `dict` | No | None | Custom configurations for the knowledge base query |
|
||||
| **guardrail_configuration** | `dict` | No | None | Content filtering settings |
|
||||
| **next_token** | `str` | No | None | Token for pagination |
|
||||
|
||||
## Environment Variables
|
||||
|
||||
```bash
|
||||
BEDROCK_KB_ID=your-knowledge-base-id # Alternative to passing knowledge_base_id
|
||||
AWS_REGION=your-aws-region # Defaults to us-east-1
|
||||
AWS_ACCESS_KEY_ID=your-access-key # Required for AWS authentication
|
||||
AWS_SECRET_ACCESS_KEY=your-secret-key # Required for AWS authentication
|
||||
```
|
||||
|
||||
## Response Format
|
||||
|
||||
The tool returns results in JSON format:
|
||||
|
||||
```json
|
||||
{
|
||||
"results": [
|
||||
{
|
||||
"content": "Retrieved text content",
|
||||
"content_type": "text",
|
||||
"source_type": "S3",
|
||||
"source_uri": "s3://bucket/document.pdf",
|
||||
"score": 0.95,
|
||||
"metadata": {
|
||||
"additional": "metadata"
|
||||
}
|
||||
}
|
||||
],
|
||||
"nextToken": "pagination-token",
|
||||
"guardrailAction": "NONE"
|
||||
}
|
||||
```
|
||||
|
||||
## Advanced Usage
|
||||
|
||||
### Custom Retrieval Configuration
|
||||
|
||||
```python
|
||||
kb_tool = BedrockKBRetrieverTool(
|
||||
knowledge_base_id="your-kb-id",
|
||||
retrieval_configuration={
|
||||
"vectorSearchConfiguration": {
|
||||
"numberOfResults": 10,
|
||||
"overrideSearchType": "HYBRID"
|
||||
}
|
||||
}
|
||||
)
|
||||
|
||||
policy_expert = Agent(
|
||||
role='Policy Expert',
|
||||
goal='Analyze company policies in detail',
|
||||
backstory='I am an expert in corporate policy analysis with deep knowledge of regulatory requirements.',
|
||||
tools=[kb_tool]
|
||||
)
|
||||
```
|
||||
|
||||
## Supported Data Sources
|
||||
|
||||
- Amazon S3
|
||||
- Confluence
|
||||
- Salesforce
|
||||
- SharePoint
|
||||
- Web pages
|
||||
- Custom document locations
|
||||
- Amazon Kendra
|
||||
- SQL databases
|
||||
|
||||
## Use Cases
|
||||
|
||||
### Enterprise Knowledge Integration
|
||||
- Enable CrewAI agents to access your organization's proprietary knowledge without exposing sensitive data
|
||||
- Allow agents to make decisions based on your company's specific policies, procedures, and documentation
|
||||
- Create agents that can answer questions based on your internal documentation while maintaining data security
|
||||
|
||||
### Specialized Domain Knowledge
|
||||
- Connect CrewAI agents to domain-specific knowledge bases (legal, medical, technical) without retraining models
|
||||
- Leverage existing knowledge repositories that are already maintained in your AWS environment
|
||||
- Combine CrewAI's reasoning with domain-specific information from your knowledge bases
|
||||
|
||||
### Data-Driven Decision Making
|
||||
- Ground CrewAI agent responses in your actual company data rather than general knowledge
|
||||
- Ensure agents provide recommendations based on your specific business context and documentation
|
||||
- Reduce hallucinations by retrieving factual information from your knowledge bases
|
||||
|
||||
### Scalable Information Access
|
||||
- Access terabytes of organizational knowledge without embedding it all into your models
|
||||
- Dynamically query only the relevant information needed for specific tasks
|
||||
- Leverage AWS's scalable infrastructure to handle large knowledge bases efficiently
|
||||
|
||||
### Compliance and Governance
|
||||
- Ensure CrewAI agents provide responses that align with your company's approved documentation
|
||||
- Create auditable trails of information sources used by your agents
|
||||
- Maintain control over what information sources your agents can access
|
||||
@@ -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.
|
||||
|
||||
Reference in New Issue
Block a user