Fully Automated Cult

Ascend Through Code. Evolve Through Automation.

Queries: Investigating Chain-Of-Thought Reasoning with CrewAI

·

How would I build a crewai program that improved reasoning, like its a chatbot and behind the scenes the agents and tasks help reason the question?

(Forgive my prompting, this is a spur of the moment thought.)

To build a CrewAI program that improves reasoning in a chatbot-style interaction, you can design a multi-agent system where different agents take on specific roles to process, analyze, and reason through questions. Here’s how we can think about it:

Key Components:

  1. Agents:
    • Each agent should specialize in a reasoning strategy or information extraction process.
    • For example, one agent could analyze logical consistency, another could explore external knowledge sources, and another could prioritize user context or emotional tone.
  2. Tasks:
    • Tasks will be designed to split up the reasoning process. One task might be focused on logical reasoning, another on analogy-making, another on fact-checking, etc.
    • Each task should culminate in a decision or piece of data that is then shared with a lead agent responsible for synthesizing and presenting a coherent answer.
  3. Tools:
    • Use tools to integrate external APIs or knowledge bases that assist agents in finding information.
    • Example tools might include web search tools or reasoning models.
  4. Processes:
    • The agents should work in a sequential or parallel process, depending on how reasoning complexity is divided. You could use a sequential process where agents build on each other’s findings or a for-each process where agents work independently and later combine results.

Plan:

  • Agent Roles:
    • Analyzer Agent: Evaluates logical consistency in the question and possible answers.
    • Knowledge Agent: Researches background information from external sources or databases.
    • Synthesizer Agent: Combines the output from other agents into a coherent, concise response for the user.
  • Tasks:
    • Logical evaluation: Break down the structure of the query and analyze possible reasoning paths.
    • Fact-checking: Validate claims and search for external information.
    • Synthesis: Combine the processed data into a final, understandable answer for the chatbot user.

Step 1: Setting up Agents

You will create agents that can execute different parts of the reasoning pipeline, such as logical analysis, fact extraction, and synthesis.

Step 2: Defining Tasks

Tasks will correspond to subtasks like logical analysis, data search, and synthesis, with clearly defined expected outputs.

Step 3: Combining Outputs

The crew will work together in a process, most likely sequentially, where the output of one agent feeds into the next.

Here’s an example code outline for this kind of program:

Sample Code

import os
from crewai import Agent, Task, Crew, Process
from crewai_tools import SerperDevTool

# Setup API Keys
os.environ["SERPER_API_KEY"] = "Your Key"
os.environ["OPENAI_API_KEY"] = "Your Key"

# Logical reasoning agent
analyzer_agent = Agent(
    role='Logical Analyzer',
    goal='Evaluate the logical structure of the query and proposed responses.',
    verbose=True,
    memory=True,
    backstory=(
        "You are a highly logical AI skilled at identifying faulty reasoning and ensuring the accuracy of logical conclusions."
    ),
    tools=[],  # Can add specific logic tools if necessary
)

# Knowledge research agent
knowledge_agent = Agent(
    role='Knowledge Researcher',
    goal='Gather relevant information from external sources to support reasoning.',
    verbose=True,
    memory=True,
    backstory=(
        "You are an adept researcher skilled at pulling in relevant facts and information to support logical conclusions."
    ),
    tools=[SerperDevTool()],  # Integrating a web search tool for research
)

# Synthesizer agent
synthesizer_agent = Agent(
    role='Response Synthesizer',
    goal='Combine logical analysis and researched facts into a coherent, understandable answer for the user.',
    verbose=True,
    memory=True,
    backstory=(
        "You synthesize inputs from different agents and create clear, understandable responses."
    ),
    tools=[],
)

# Define the tasks

# Logical analysis task
analyze_task = Task(
    description="Analyze the logical consistency of the user's query.",
    expected_output='A summary of the logical analysis, including any contradictions or valid inferences.',
    agent=analyzer_agent,
)

# Research task
research_task = Task(
    description="Find relevant facts and evidence to support or refute the user query.",
    expected_output='A list of key facts or supporting evidence from external sources.',
    agent=knowledge_agent,
)

# Synthesis task
synthesis_task = Task(
    description="Combine logical analysis and research into a final response.",
    expected_output='A final response that synthesizes the logical and factual analysis.',
    agent=synthesizer_agent,
)

# Create the crew
crew = Crew(
    agents=[analyzer_agent, knowledge_agent, synthesizer_agent],
    tasks=[analyze_task, research_task, synthesis_task],
    process=Process.sequential  # Can also use Process.parallel depending on use case
)

# Kickoff the process with a user query as input
result = crew.kickoff(inputs={'user_query': 'What is the impact of AI on job markets?'})
print(result)

Explanation:

  1. Agents:
    • Analyzer Agent checks the logical structure.
    • Knowledge Agent researches external knowledge.
    • Synthesizer Agent brings it all together into a cohesive response.
  2. Tasks:
    • Logical analysis task helps break down the reasoning process.
    • Research task ensures factual correctness.
    • Synthesis task ensures the chatbot response is smooth and concise.
  3. Process:
    • We use Process.sequential so each agent contributes to the next step in reasoning.

Next Steps:

  • Tool Customization: Add more advanced tools as needed for specific reasoning steps, like using specialized knowledge bases.
  • Task Refinement: Adjust task descriptions and expected outputs based on how you want the chatbot to behave.
  • Testing: Run tests with different kinds of user inputs to evaluate the chatbot’s reasoning.

Let me know if you’d like to refine any part of this or add custom features!

Thanks, CrewAI Assistant..

would this constitute “chain of thought”?

Yes, the system described in the previous example constitutes a “Chain of Thought” approach. Here’s why:

Chain of Thought Definition

In machine learning, and particularly in reasoning-based AI systems, Chain of Thought refers to the model’s ability to break down a complex problem into intermediate steps or stages. Rather than providing a direct answer, the system explicitly goes through a series of thought processes (or reasoning steps) to reach a conclusion. This is akin to how humans solve complex problems by breaking them into smaller parts.

Breakdown of Chain of Thought in the System:

  1. Agent-Based Reasoning:
    • Each agent is responsible for a specific part of the reasoning process, much like a human would think step-by-step through a problem. For example:
      • The Logical Analyzer breaks down and evaluates the structure of the query.
      • The Knowledge Researcher looks for external information to support or refute claims.
      • The Synthesizer compiles everything into a final, understandable response.
  2. Task Division:
    • Each task in this system is designed to handle one stage of reasoning, which is a core component of the Chain of Thought. The system doesn’t jump to the final answer but instead processes intermediate steps:
      • First, logic is evaluated.
      • Second, facts and data are retrieved.
      • Finally, the synthesized answer is produced.
  3. Sequential Process:
    • Using the sequential process ensures that the reasoning is happening step-by-step, where one agent’s output is used by another. This sequential nature reinforces the “Chain of Thought” pattern, as each step builds upon the previous one.

How It Mirrors Human Chain of Thought:

Humans, when reasoning through a question, typically:

  1. Analyze the problem: Look at the logic of the question and make sure the premises are sound.
  2. Gather information: Search for relevant facts or prior knowledge that can help answer the question.
  3. Synthesize and conclude: Put together the logical analysis and the data to form a coherent answer.

Your system replicates this process through agents and tasks. Hence, it encourages and implements a Chain of Thought approach by structuring reasoning into discrete, logical steps rather than having the system immediately produce an answer without thinking through the components.

How to Explicitly Emphasize Chain of Thought:

To emphasize this pattern more explicitly, you could:

  • Ensure each task’s expected output is highly structured so that it encourages the agents to “think aloud,” making intermediate reasoning more transparent.
  • Use language or prompts in the agents’ goals or tasks to encourage stepwise reflection. For instance, the Logical Analyzer agent can be encouraged to explain the premises and implications before reaching a conclusion.

In conclusion, the setup you’ve built constitutes a Chain of Thought reasoning framework, and you could enhance it further by adding even more intermediate steps or explicit reasoning guidelines if needed.

Let’s see here..

Could improvements be made to this crew?

Absolutely! While the crew in your current setup is quite solid in structuring reasoning with agents and tasks, there are several potential improvements that could enhance its reasoning abilities, performance, and versatility. Here are some ways to improve it:

1. Diverse Reasoning Techniques

Different types of reasoning can be employed to add more depth to the crew’s problem-solving approach. You can expand the crew by adding agents specialized in:

  • Deductive Reasoning: An agent that draws specific conclusions from general rules.Inductive Reasoning: An agent that observes patterns and infers general principles.Analogical Reasoning: An agent that solves problems by drawing analogies to similar situations or concepts.

Example:

deductive_agent = Agent(
    role='Deductive Reasoner',
    goal='Apply rules and general principles to derive specific conclusions.',
    verbose=True,
    memory=True,
    backstory="You are skilled at applying well-known rules or laws to make valid conclusions from given premises."
)

inductive_agent = Agent(
    role='Inductive Reasoner',
    goal='Identify patterns from specific examples and infer general rules.',
    verbose=True,
    memory=True,
    backstory="You are adept at identifying patterns from past experiences or data to predict general trends."
)

Adding these agents will allow the crew to explore the question from multiple angles, leading to richer and more reliable reasoning.

2. Error-Checking and Counter-Argument Agents

It would be beneficial to add an agent whose task is to challenge the current reasoning, identify potential flaws, or propose counter-arguments. This agent could work in parallel to other agents, improving the robustness of the final output.

Example:

counter_argument_agent = Agent(
    role='Devil’s Advocate',
    goal='Challenge assumptions and explore alternative perspectives or contradictions.',
    verbose=True,
    memory=True,
    backstory="You love to challenge conventional thinking and assumptions to ensure robust reasoning."
)

counter_argument_task = Task(
    description="Challenge the assumptions and reasoning of other agents. Provide counter-arguments or highlight potential weaknesses in the conclusions.",
    expected_output="A list of potential weaknesses or alternative perspectives to consider.",
    agent=counter_argument_agent
)

By running this agent alongside others, you ensure the final result is more robust and less prone to faulty reasoning.

3. Memory Enhancements for Agents

Incorporating long-term memory into the agents allows them to retain useful information from previous reasoning sessions. This can help improve continuity in complex, multi-turn conversations or queries.

Memory-enabled agents could:

  • Recall past information or conclusions and use that as context for new tasks.
  • Prevent repeating the same research or logical steps, improving efficiency.

To implement memory:

from crewai.memory import SimpleMemory

# Enable memory for each agent
analyzer_agent.memory = SimpleMemory()
knowledge_agent.memory = SimpleMemory()
synthesizer_agent.memory = SimpleMemory()

# Memory stores for long-term recall across tasks

4. Parallelized Processing for Faster Results

If you want to speed up reasoning or handle more complex queries, you can move from a sequential process to a parallel process, where different agents work simultaneously and their results are combined at the end. This is especially useful if you add more agents and tasks.

from crewai import Process

# Use a parallel process instead of sequential
crew = Crew(
    agents=[analyzer_agent, knowledge_agent, synthesizer_agent, deductive_agent, inductive_agent],
    tasks=[analyze_task, research_task, synthesis_task, counter_argument_task],
    process=Process.parallel  # Parallel execution for faster processing
)

With parallel execution, you can handle larger reasoning tasks more efficiently and reduce the overall response time.

5. Feedback Loop for Continuous Improvement

A feedback loop could be added, where after the final output is synthesized, the system can critique its own answer. This could either be:

  • A self-critique agent that revisits the final response to look for weaknesses or improvements.
  • A refinement task that allows the crew to refine its output based on certain metrics (e.g., clarity, factual correctness, etc.).

Example:

refinement_agent = Agent(
    role='Refinement Agent',
    goal="Assess the final output for clarity and correctness and propose improvements.",
    verbose=True,
    memory=True,
    backstory="You ensure the final response is clear, concise, and accurate."
)

refinement_task = Task(
    description="Evaluate the synthesized response for clarity, factual correctness, and logic. Propose revisions if necessary.",
    expected_output="An improved version of the final response, if revisions are needed.",
    agent=refinement_agent
)

6. Incorporating Human-in-the-Loop

You could add a mechanism where a human reviewer can be consulted at any point. This is useful when the agents reach uncertain conclusions or if you want the final decision to be reviewed before output.

This could be implemented as a task:

human_input_task = Task(
    description="Present the reasoning to a human reviewer and await feedback.",
    expected_output="Feedback on the current reasoning or a decision to proceed.",
    agent=None,  # Human reviewer handles this
)

This makes the system more interactive and suitable for cases where absolute accuracy is important.

7. Dynamic Task Creation

Incorporate the ability for agents to dynamically create new tasks based on the complexity of the query. This would allow the system to adapt to more challenging or nuanced queries.

For example, if the Synthesizer Agent finds a gap in the logical or factual data, it can create a new task for the Analyzer or Knowledge agent to fill that gap, improving overall reasoning quality.

# In the synthesis task, allow dynamic task creation
synthesis_task = Task(
    description="Combine logical analysis and research into a final response. Create additional tasks if new lines of reasoning are needed.",
    expected_output="A final response, or additional tasks if more research is required.",
    agent=synthesizer_agent,
    dynamic_task_creation=True  # Allows the agent to spawn new tasks if needed
)

8. Pre-Defined or Adaptive Strategies

Give agents the ability to choose between multiple reasoning strategies (deductive, inductive, analogical) depending on the nature of the input. For example:

  • If the query involves more abstract concepts, analogical reasoning might be chosen.
  • If it’s about technical facts, inductive or deductive reasoning would be prioritized.

Conclusion:

By adding specialized agents (e.g., deductive/inductive reasoners), memory for long-term knowledge retention, parallel processing for efficiency, and refinement loops for continuous improvement, you can enhance the reasoning capabilities of the system significantly. Furthermore, adding dynamic task creation and a human-in-the-loop component can make it more robust and adaptable to various complexities.

Would you like to implement one or more of these suggestions in your current crew? Let me know, and I can assist you further!

I can’t quite remember but I think some of those things about parallel processes and memory are already built-in to CrewAI, I don’t think that code is quite right. Chalking that up to hallucination.

I like the Dynamic Task Creation idea. That could be useful..

@fullautocult

/***********************TAGS*************************/

/ / / / / / /

/****************************************************/

Leave a comment