[Feature Request]: Swarm Related Features showcase

[alecsolder]

Is your feature request related to a problem? Please describe.

Swarms as extremely useful as-is, but there are some features that would be useful to have and would continue to make them better

Describe the solution you'd like

Feedback and Direction for Concept Development

Overview

This iteration is intended to:

• Prove that the concepts work together cohesively.

There is a lot of future work to be done, many more are included in the code as TODOs, but some examples are:

• Separate these concepts into more defined and manageable components in the future.
• Refine naming conventions, which currently lack consistency due to changes throughout development.

Note: a lot of the conceptual naming/wording in this is work in progress so they may be confusing.

Core Concept

The general idea is to create a system that:

• Observes the problem-solving process (e.g., function/tool usage) and augments it with strategies like the scientific method (as an idea) in order to get data about tool use.
• Collects, stores and makes that knowledge available to other agents and functions without relying on message passing.

Key Features

1. Information Gathering:

   • Collect insights from all interactions and results in the conversation.
     • Example: If the agent runs SHOW TABLES; early in the process, that information should be recorded.
     • Example: If certain parameters consistently result in errors, that behavior should be logged.
   • Record these insights in a structured and reusable format.

2. Feedback Loop:

   • Dynamically update system messages and function docstrings with gathered information.
   • This creates a loop where each iteration becomes faster and more efficient due to improved contextual knowledge.

3. Knowledge Format:

   • Store information in a concrete data format:
     • Enables analysis by another LLM for accuracy and optimization.
     • Allows permanent storage for future use as a prompt.
   • Effectively acts as a training framework for functions.

Applications

• Enable a system to explore using a tool, gather insights, and provide tips for subsequent runs.
• Build a domain-specific prompt-tuning framework for functions/agents:
  • Train a general-purpose agent to specialize in a domain through iterative experimentation.
  • For instance:
    • Use training data to simulate questions.
    • Adjust knowledge dynamically to achieve correct answers or reduce iterations.
      • Can use TOT/MCST to do this, one route decides to gather information A first other route decides info B, one may be better than the other

Benefits

• Automates the discovery of knowledge required by the agent.
• Reduces reliance on manual intervention for prompt engineering.
• Creates a self-experimenting system that improves over time through trial and error.

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Components

1. Knowledge Source/Knowledge Generator

• Goal: Share knowledge between agents in a swarm without relying on message history alone.

• Data Models:

  • Maintain knowledge as Python objects instead of message history.
  • Register agents with each other to share relevant information:
    • Example: SQL query agent may pull knowledge from a RAG agent.
    • Example: RAG agent might not care about SQL query knowledge.
    • Examplne: Reasoning agent cares about knowledge from the SQL agent but not RAG agent, so you now have a teired memory system where information can stop at certain levels or bubble up.

• Implementation:

  • Wrap function use in a "scientific method"-like loop (very loose):
    • Treat functions as "experiments."
    • Generate hypotheses for each tool run.
    • Evaluate results to gain insights.

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2. Sub-Swarm

• Purpose: Handle complex retry loops when an agent lacks sufficient information or effective prompts.

• Design:

  • Similar to how a ReasoningAgent creates a sub-conversation.
  • Sub-swarms allow:
    • Functions to execute within the sub-swarm.
    • Results to bubble back up to the main swarm.
  • Enables state transitions via SwarmResult and context variable updates.
  • Enables functions to modify context_variables

• Advantages:

  • Reduces overall context size.
  • Allows for validation and corrects facts in the knowledge directly, ensuring reliable information flow.
  • Allows flexible and traceable solutions.

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Key Challenges

1. Structuring Knowledge:

   • Ensuring that gathered knowledge is accurate, relevant, and trustworthy.
   • Building systems to validate and compact this knowledge effectively.

2. Reducing Hallucinations:

   • By trusting only pre-populated and validated knowledge, reasoning agents could potentially reduce hallucination risks.

3. Flexibility:

   • Ensuring the system works across different domains and tools while maintaining modularity.

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Next Steps

1. Refine the Structure:

   • Focus on making the knowledge-sharing process seamless and efficient.
   • Separate concepts (e.g., KnowledgeSource, Sub-Swarm) into well-defined modules.

2. Experimentation and Testing:

   • Test the hypothesis-driven loop for tool usage.
   • Evaluate how effectively the system trains itself on domain-specific tasks.

3. Naming Conventions:

   • Establish consistent terminology to avoid confusion as concepts evolve.

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Closing Thoughts

The key to success lies in making the knowledge structure robust and trustworthy. If the reasoning agent can rely entirely on validated knowledge, it could significantly improve accuracy and reduce hallucinations. Additionally, modularizing components like the Sub-Swarm and Knowledge Generator will make the system more flexible and scalable in various applications.

Additional context

PR: #417