mirror of
https://github.com/crewAIInc/crewAI.git
synced 2026-01-07 15:18:29 +00:00
dfc4255f2f
* docs: Fix major memory system documentation issues - Remove misleading deprecation warnings, fix confusing comments, clearly separate three memory approaches, provide accurate examples that match implementation * fix: Correct broken image paths in README - Update crewai_logo.png and asset.png paths to point to docs/images/ directory instead of docs/ directly * docs: Add system prompt transparency and customization guide - Add 'Understanding Default System Instructions' section to address black-box concerns - Document what CrewAI automatically injects into prompts - Provide code examples to inspect complete system prompts - Show 3 methods to override default instructions - Include observability integration examples with Langfuse - Add best practices for production prompt management * docs: Fix implementation accuracy issues in memory documentation - Fix Ollama embedding URL parameter and remove unsupported Cohere input_type parameter * docs: Reference observability docs instead of showing specific tool examples * docs: Reorganize knowledge documentation for better developer experience - Move quickstart examples right after overview for immediate hands-on experience - Create logical learning progression: basics → configuration → advanced → troubleshooting - Add comprehensive agent vs crew knowledge guide with working examples - Consolidate debugging and troubleshooting in dedicated section - Organize best practices by topic in accordion format - Improve content flow from simple concepts to advanced features - Ensure all examples are grounded in actual codebase implementation * docs: enhance custom LLM documentation with comprehensive examples and accurate imports * docs: reorganize observability tools into dedicated section with comprehensive overview and improved navigation * docs: rename how-to section to learn and add comprehensive overview page * docs: finalize documentation reorganization and update navigation labels * docs: enhance README with comprehensive badges, navigation links, and getting started video
98 lines
3.6 KiB
Plaintext
98 lines
3.6 KiB
Plaintext
---
|
|
title: Human Input on Execution
|
|
description: Integrating CrewAI with human input during execution in complex decision-making processes and leveraging the full capabilities of the agent's attributes and tools.
|
|
icon: user-check
|
|
---
|
|
|
|
## Human input in agent execution
|
|
|
|
Human input is critical in several agent execution scenarios, allowing agents to request additional information or clarification when necessary.
|
|
This feature is especially useful in complex decision-making processes or when agents require more details to complete a task effectively.
|
|
|
|
## Using human input with CrewAI
|
|
|
|
To integrate human input into agent execution, set the `human_input` flag in the task definition. When enabled, the agent prompts the user for input before delivering its final answer.
|
|
This input can provide extra context, clarify ambiguities, or validate the agent's output.
|
|
|
|
### Example:
|
|
|
|
```shell
|
|
pip install crewai
|
|
```
|
|
|
|
```python Code
|
|
import os
|
|
from crewai import Agent, Task, Crew
|
|
from crewai_tools import SerperDevTool
|
|
|
|
os.environ["SERPER_API_KEY"] = "Your Key" # serper.dev API key
|
|
os.environ["OPENAI_API_KEY"] = "Your Key"
|
|
|
|
# Loading Tools
|
|
search_tool = SerperDevTool()
|
|
|
|
# Define your agents with roles, goals, tools, and additional attributes
|
|
researcher = Agent(
|
|
role='Senior Research Analyst',
|
|
goal='Uncover cutting-edge developments in AI and data science',
|
|
backstory=(
|
|
"You are a Senior Research Analyst at a leading tech think tank. "
|
|
"Your expertise lies in identifying emerging trends and technologies in AI and data science. "
|
|
"You have a knack for dissecting complex data and presenting actionable insights."
|
|
),
|
|
verbose=True,
|
|
allow_delegation=False,
|
|
tools=[search_tool]
|
|
)
|
|
writer = Agent(
|
|
role='Tech Content Strategist',
|
|
goal='Craft compelling content on tech advancements',
|
|
backstory=(
|
|
"You are a renowned Tech Content Strategist, known for your insightful and engaging articles on technology and innovation. "
|
|
"With a deep understanding of the tech industry, you transform complex concepts into compelling narratives."
|
|
),
|
|
verbose=True,
|
|
allow_delegation=True,
|
|
tools=[search_tool],
|
|
cache=False, # Disable cache for this agent
|
|
)
|
|
|
|
# Create tasks for your agents
|
|
task1 = Task(
|
|
description=(
|
|
"Conduct a comprehensive analysis of the latest advancements in AI in 2025. "
|
|
"Identify key trends, breakthrough technologies, and potential industry impacts. "
|
|
"Compile your findings in a detailed report. "
|
|
"Make sure to check with a human if the draft is good before finalizing your answer."
|
|
),
|
|
expected_output='A comprehensive full report on the latest AI advancements in 2025, leave nothing out',
|
|
agent=researcher,
|
|
human_input=True
|
|
)
|
|
|
|
task2 = Task(
|
|
description=(
|
|
"Using the insights from the researcher\'s report, develop an engaging blog post that highlights the most significant AI advancements. "
|
|
"Your post should be informative yet accessible, catering to a tech-savvy audience. "
|
|
"Aim for a narrative that captures the essence of these breakthroughs and their implications for the future."
|
|
),
|
|
expected_output='A compelling 3 paragraphs blog post formatted as markdown about the latest AI advancements in 2025',
|
|
agent=writer,
|
|
human_input=True
|
|
)
|
|
|
|
# Instantiate your crew with a sequential process
|
|
crew = Crew(
|
|
agents=[researcher, writer],
|
|
tasks=[task1, task2],
|
|
verbose=True,
|
|
memory=True,
|
|
planning=True # Enable planning feature for the crew
|
|
)
|
|
|
|
# Get your crew to work!
|
|
result = crew.kickoff()
|
|
|
|
print("######################")
|
|
print(result)
|
|
``` |