Meta-Everything Agents

Meta-Everything Agent implementations across multiple agentic AI frameworks, demonstrating practical patterns, performance comparisons, and architectural approaches to self-evolving AI systems.

Overview

This repository implements the same Meta-Everything Agent concept across four different agentic frameworks. Each implementation maintains the core functionality while showcasing the unique strengths, patterns, and practical limits of each SDK.

What is Meta-Everything Agent?

Meta-Everything Agent is an AI system that embodies a "meta-everything" philosophy:

• Meta-Agent: Dynamically spawns specialized agents for complex tasks
• Meta-Tooling: Creates and modifies tools at runtime
• Meta-Learning: Persistent cross-session memory for continuous improvement
• Meta-Cognition: Self-reflection and confidence assessment for strategic decisions

Implementations

Strands Agents SDK

Status: Work in Progress Focus: Native implementation showcasing dynamic tool creation and swarm orchestration

• Runtime tool generation with validation
• Multi-agent coordination via swarm patterns
• Memory persistence with FAISS/Mem0/OpenSearch
• AWS Bedrock and Ollama model support

LangGraph

Status: Not Started Focus: Graph-based agent orchestration and state management

• Stateful agent workflows with checkpointing
• Visual graph representation of agent interactions
• Built-in persistence and time-travel debugging
• Streaming and async execution patterns

ADK (Agent Development Kit)

Status: Not Started Focus: Enterprise-grade agent deployment and monitoring

• Production-ready agent scaffolding
• Built-in observability and metrics
• Distributed agent coordination
• Model-agnostic architecture

CrewAI

Status: Not Started Focus: Role-based multi-agent collaboration

• Hierarchical agent organization
• Role and goal-driven agent behaviors
• Process templates (sequential, hierarchical, custom)
• Human-in-the-loop capabilities

Architecture

graph TB
    subgraph "Core Meta-Everything Agent Capabilities"
        M[Meta-Everything Philosophy]
        M --> MA[Meta-Agent]
        M --> MT[Meta-Tooling]
        M --> ML[Meta-Learning]
        M --> MC[Meta-Cognition]
    end

    subgraph "Framework Implementations"
        S[Strands Agents<br/>Dynamic Tools & Swarms]
        L[LangGraph<br/>Stateful Workflows]
        A[ADK<br/>Enterprise Scale]
        C[CrewAI<br/>Role-Based Teams]
    end

    MA --> S
    MA --> L
    MA --> A
    MA --> C

    style M fill:#1976d2,color:#fff
    style MA fill:#388e3c,color:#fff
    style MT fill:#f57c00,color:#fff
    style ML fill:#7b1fa2,color:#fff
    style MC fill:#d32f2f,color:#fff

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Key Differentiators

Feature	Strands Agents	LangGraph	ADK	CrewAI
Tool Creation	Runtime generation	Graph nodes	Plugin system	Tool delegation
Multi-Agent	Swarm patterns	Graph orchestration	Service mesh	Crew hierarchy
Memory	FAISS/Mem0/OpenSearch	Checkpoints	State store	Shared context
Execution	Step-based	Graph traversal	Event-driven	Process flows

Implementation Patterns

Common Patterns Across All Frameworks

1. Confidence-Based Routing

   • High confidence (>80%): Direct execution
   • Medium (50-80%): Tool creation/enhancement
   • Low (<50%): Multi-agent delegation

2. Memory Persistence

   • Vector-based similarity search
   • Session continuity
   • Learning from past executions

3. Tool Lifecycle Management

   • Dynamic creation
   • Validation and testing
   • Runtime modification

Performance Benchmarks

Benchmarks coming soon - will compare:

• Task completion time
• Token efficiency
• Memory usage
• Success rates across task categories
• Framework overhead

Getting Started

Prerequisites

• Python 3.10+
• Git
• Framework-specific requirements (see individual READMEs)

Installation

# Clone the repository
git clone https://github.com/sriaradhyula/agentic-community-meta-agent.git
cd agentic-community-meta-agent

# Choose an implementation
cd strands-agents  # or langgraph, adk, crewai

# Follow framework-specific setup instructions

Quick Start

Each implementation maintains the same interface:

# Example for any framework
from metaagent import MetaEverythingAgent

agent = MetaEverythingAgent()
result = agent.run("Create a tool to analyze sentiment in text")

Use Cases

Dynamic Tool Creation

Meta-Everything Agent creates specialized tools on-demand for any task, testing and validating them before use.

Multi-Agent Orchestration

Complex problems trigger automatic spawning of specialized agents that collaborate to find solutions.

Continuous Learning

Every execution contributes to the knowledge base, improving future performance on similar tasks.

Self-Improvement

Meta-Everything Agent analyzes its own performance and adjusts strategies based on success metrics.

Project Structure

agentic-community-meta-agent/
├── README.md                 # This file
├── LICENSE                   # MIT License
├── strands-agents/          # Strands SDK implementation
│   └── MetaEverythingAgent/
│       ├── src/
│       ├── pyproject.toml
│       └── README.md
├── langgraph/               # LangGraph implementation (coming soon)
├── adk/                     # ADK implementation (coming soon)
└── crewai/                  # CrewAI implementation (coming soon)

Contributing

We welcome contributions! Areas of interest:

• Additional framework implementations
• Performance optimizations
• New meta-capabilities
• Benchmark suites
• Documentation improvements

Community

• Discussions: GitHub Discussions
• Issues: GitHub Issues
• Updates: Watch this repository for updates

Acknowledgments

• Strands Agents SDK for the foundational framework
• LangGraph for graph-based orchestration patterns
• ADK for enterprise agent patterns
• CrewAI for role-based agent collaboration

Security

See CONTRIBUTING for more information.

License

This project is licensed under the Apache-2.0 License.