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Update the test PRs and update README to run demo using CLI
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README.md

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@@ -249,14 +249,13 @@ agenspy agent run "Review PR https://github.com/stanfordnlp/dspy/pull/8277" --re
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agenspy protocol test mcp
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```
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- Manage Servers:
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- Run example:
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```bash
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agenspy server --help
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agenspy demo github-pr
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```
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## 📚 Documentation
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For detailed documentation, including API reference, examples, and advanced usage, please visit our [documentation site](TBC). (coming soon)
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## 📚 Examples
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See the examples/ directory for complete examples:
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Get your OpenAI API key OPENAI_API_KEY from [here](https://platform.openai.com/api-keys) and optionally GITHUB_TOKEN from [here](https://github.com/settings/tokens) and set as ENV variables. You might also need to install nodejs and npm to run the nodejs server.
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- `basic_mcp_demo.py` - Simple MCP agent
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- `comprehensive_mcp_demo.py` - Comprehensive MCP agent
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- `github_pr_review.py` - GitHub PR review agent
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- `multi_protocol_demo.py` - Multi-protocol agent (Experimental Mock)
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- `python_server_demo.py` - Python MCP server
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Run the examples with:
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```bash
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agenspy demo github-pr
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```
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Or Run manually using Python:
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## 🤝 Contributing
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```bash
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python examples/github_pr_review.py
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```
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We welcome contributions! Please see our [Contributing Guide](CONTRIBUTING.md) for details on how to contribute to the project.
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## 🔗 Resources
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- [DSPy Documentation](https://dspy.ai/)
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- [Model Context Protocol](https://modelcontextprotocol.io/)
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- [Agent2Agent Protocol](https://google.github.io/A2A/)
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- [GitHub Repository](https://github.com/superagenticai/Agenspy)
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## 🚀 Future Roadmap
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The end goal is to merge this tool in the dspy main repo and make it a first-class citizen of the DSPy ecosystem. However, if it doesn't fit there then it can be used independently as a protocol-first AI agent framework. Here are some food for thought for future work:
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### Merge into DSPy
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The end goal is to merge this tool in the dspy main repo and make it a first-class citizen of the DSPy ecosystem. However, if it doesn't fit there then it can be used independently as a protocol-first AI agent framework.
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### Get DSPy Listed in Google A2A Agent Directory
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Implementations of A2A and Get DSPy Listed in A2A Agent Directory (here)[https://github.com/google/A2A/blob/main/samples/python/agents/README.md] by building DSPy agents that utilize the A2A protocol.
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### Future Work
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Alternately, Agenspy can be developed independently as a protocol-first AI agent framework. Here are some food for thought for future work:
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- **Protocol Layer**: WebSocket and gRPC support for real-time, high-performance agent communication
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- **Agent Framework**: Enhanced orchestration, state management, and network discovery
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- **Production Readiness**: Monitoring, load balancing, and fault tolerance features
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- **Developer Tools**: Improved CLI, web dashboard, and debugging utilities
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- **Ecosystem**: Cloud integrations and database adapters for popular services
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Interested in contributing? Check out our [open issues](https://github.com/superagenticai/Agenspy/issues) or start a discussion about new features!
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## 🤝 Contributing
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We welcome contributions! Please see our [Contributing Guide](CONTRIBUTING.md) for details on how to contribute to the project.
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## 📄 License
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This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
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## 🔗 Resources
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- [DSPy Documentation](https://dspy.ai/)
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- [Model Context Protocol](https://modelcontextprotocol.io/)
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- [Agent2Agent Protocol](https://google.github.io/A2A/)
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- [GitHub Repository](https://github.com/superagenticai/Agenspy)
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## 📬 Contact
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agenspy/cli/main.py

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from .commands.config import config_group
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from .commands.protocols import protocol_group
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from .commands.server import server_group
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from .commands.demo import demo_group
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from .commands.workflow import workflow_group
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@click.group()
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cli.add_command(protocol_group)
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cli.add_command(server_group)
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cli.add_command(config_group)
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cli.add_command(demo_group, name="demo")
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cli.add_command(workflow_group, name="workflow")
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def main():

examples/github_pr_review.py

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# Test with a real repository
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test_repos = [
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"https://github.com/microsoft/vscode/pull/200000", # Likely to exist
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"https://github.com/facebook/react/pull/25000", # Fallback
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"https://github.com/stanfordnlp/dspy/pull/8242", # Likely to exist
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"https://github.com/stanfordnlp/dspy/pull/8277", # Fallback
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"https://github.com/example/test-repo/pull/1" # Mock fallback
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]
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# Review a PR
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result = agent(
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pr_url="https://github.com/example/awesome-project/pull/42",
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pr_url="https://github.com/stanfordnlp/dspy/pull/8277",
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review_focus="security"
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)
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