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Merge pull request #1 from sriaradhyula/main
feat: collapse sriaradhyula/agentic-community-meta-agent
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‎.gitignore‎

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# Byte-compiled / optimized / DLL files
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__pycache__/
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*.py[cod]
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*$py.class
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MANIFEST
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# pyenv
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# pipenv
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# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
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# mkdocs documentation
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‎LICENSE‎

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Apache License
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Version 2.0, January 2004
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http://www.apache.org/licenses/

‎README.md‎

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## My Project
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# Meta-Everything Agents
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TODO: Fill this README out!
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Meta-Everything Agent implementations across multiple agentic AI frameworks, demonstrating practical patterns, performance comparisons, and architectural approaches to self-evolving AI systems.
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Be sure to:
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## Overview
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* Change the title in this README
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* Edit your repository description on GitHub
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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.
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### What is Meta-Everything Agent?
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Meta-Everything Agent is an AI system that embodies a "meta-everything" philosophy:
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- **Meta-Agent**: Dynamically spawns specialized agents for complex tasks
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- **Meta-Tooling**: Creates and modifies tools at runtime
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- **Meta-Learning**: Persistent cross-session memory for continuous improvement
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- **Meta-Cognition**: Self-reflection and confidence assessment for strategic decisions
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## Implementations
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### [Strands Agents SDK](/strands-agents)
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**Status**: Work in Progress
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**Focus**: Native implementation showcasing dynamic tool creation and swarm orchestration
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- Runtime tool generation with validation
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- Multi-agent coordination via swarm patterns
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- Memory persistence with FAISS/Mem0/OpenSearch
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- AWS Bedrock and Ollama model support
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### [LangGraph](/langgraph)
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**Status**: Not Started
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**Focus**: Graph-based agent orchestration and state management
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- Stateful agent workflows with checkpointing
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- Visual graph representation of agent interactions
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- Built-in persistence and time-travel debugging
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- Streaming and async execution patterns
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### [ADK (Agent Development Kit)](/adk)
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**Status**: Not Started
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**Focus**: Enterprise-grade agent deployment and monitoring
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- Production-ready agent scaffolding
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- Built-in observability and metrics
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- Distributed agent coordination
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- Model-agnostic architecture
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### [CrewAI](/crewai)
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**Status**: Not Started
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**Focus**: Role-based multi-agent collaboration
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- Hierarchical agent organization
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- Role and goal-driven agent behaviors
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- Process templates (sequential, hierarchical, custom)
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- Human-in-the-loop capabilities
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## Architecture
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```mermaid
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graph TB
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subgraph "Core Meta-Everything Agent Capabilities"
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M[Meta-Everything Philosophy]
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M --> MA[Meta-Agent]
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M --> MT[Meta-Tooling]
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M --> ML[Meta-Learning]
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M --> MC[Meta-Cognition]
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end
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subgraph "Framework Implementations"
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S[Strands Agents<br/>Dynamic Tools & Swarms]
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L[LangGraph<br/>Stateful Workflows]
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A[ADK<br/>Enterprise Scale]
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C[CrewAI<br/>Role-Based Teams]
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end
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MA --> S
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MA --> L
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MA --> A
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MA --> C
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style M fill:#1976d2,color:#fff
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style MA fill:#388e3c,color:#fff
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style MT fill:#f57c00,color:#fff
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style ML fill:#7b1fa2,color:#fff
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style MC fill:#d32f2f,color:#fff
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```
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## Key Differentiators
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| Feature | Strands Agents | LangGraph | ADK | CrewAI |
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|---------|---------------|-----------|-----|---------|
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| **Tool Creation** | Runtime generation | Graph nodes | Plugin system | Tool delegation |
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| **Multi-Agent** | Swarm patterns | Graph orchestration | Service mesh | Crew hierarchy |
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| **Memory** | FAISS/Mem0/OpenSearch | Checkpoints | State store | Shared context |
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| **Execution** | Step-based | Graph traversal | Event-driven | Process flows |
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## Implementation Patterns
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### Common Patterns Across All Frameworks
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1. **Confidence-Based Routing**
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- High confidence (>80%): Direct execution
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- Medium (50-80%): Tool creation/enhancement
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- Low (<50%): Multi-agent delegation
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2. **Memory Persistence**
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- Vector-based similarity search
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- Session continuity
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- Learning from past executions
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3. **Tool Lifecycle Management**
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- Dynamic creation
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- Validation and testing
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- Runtime modification
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## Performance Benchmarks
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*Benchmarks coming soon - will compare:*
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- Task completion time
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- Token efficiency
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- Memory usage
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- Success rates across task categories
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- Framework overhead
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## Getting Started
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### Prerequisites
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- Python 3.10+
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- Git
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- Framework-specific requirements (see individual READMEs)
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### Installation
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```bash
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# Clone the repository
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git clone https://github.com/sriaradhyula/agentic-community-meta-agent.git
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cd agentic-community-meta-agent
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# Choose an implementation
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cd strands-agents # or langgraph, adk, crewai
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# Follow framework-specific setup instructions
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```
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### Quick Start
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Each implementation maintains the same interface:
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```python
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# Example for any framework
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from metaagent import MetaEverythingAgent
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agent = MetaEverythingAgent()
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result = agent.run("Create a tool to analyze sentiment in text")
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```
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## Use Cases
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### Dynamic Tool Creation
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Meta-Everything Agent creates specialized tools on-demand for any task, testing and validating them before use.
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### Multi-Agent Orchestration
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Complex problems trigger automatic spawning of specialized agents that collaborate to find solutions.
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### Continuous Learning
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Every execution contributes to the knowledge base, improving future performance on similar tasks.
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### Self-Improvement
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Meta-Everything Agent analyzes its own performance and adjusts strategies based on success metrics.
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## Project Structure
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```
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agentic-community-meta-agent/
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├── README.md # This file
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├── LICENSE # MIT License
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├── strands-agents/ # Strands SDK implementation
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│ └── MetaEverythingAgent/
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│ ├── src/
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│ ├── pyproject.toml
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│ └── README.md
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├── langgraph/ # LangGraph implementation (coming soon)
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├── adk/ # ADK implementation (coming soon)
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└── crewai/ # CrewAI implementation (coming soon)
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```
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## Contributing
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We welcome contributions! Areas of interest:
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- Additional framework implementations
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- Performance optimizations
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- New meta-capabilities
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- Benchmark suites
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- Documentation improvements
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## Community
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- **Discussions**: [GitHub Discussions](https://github.com/sriaradhyula/agentic-community-meta-agent/discussions)
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- **Issues**: [GitHub Issues](https://github.com/sriaradhyula/agentic-community-meta-agent/issues)
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- **Updates**: Watch this repository for updates
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## Acknowledgments
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- [Strands Agents SDK](https://github.com/strands-agents/sdk-python) for the foundational framework
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- [LangGraph](https://github.com/langchain-ai/langgraph) for graph-based orchestration patterns
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- [ADK](https://github.com/example/adk) for enterprise agent patterns
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- [CrewAI](https://github.com/joaomdmoura/crewAI) for role-based agent collaboration
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## Security
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See [CONTRIBUTING](CONTRIBUTING.md#security-issue-notifications) for more information.
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## License
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This project is licensed under the Apache-2.0 License.
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This project is licensed under the [Apache-2.0 License](LICENSE).

‎meta-everything-agent/adk/README.md‎

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# Meta-Everything Agent (ADK Implementation)

‎meta-everything-agent/crew-ai/README.md‎

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# Meta-Everything Agent (Crew AI Implementation)

‎meta-everything-agent/langgraph/README.md‎

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# Meta-Everything Agent (LangGraph Implementation)

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