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| [](https://discord.com/servers/agora-999382051935506503) | ||
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| # Python Package Template | ||
| # The Definitive Multi-Agent Marketing Course | ||
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| [](https://discord.gg/agora-999382051935506503) [](https://www.youtube.com/@kyegomez3242) [](https://www.linkedin.com/in/kye-g-38759a207/) [](https://x.com/kyegomezb) | ||
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| A easy, reliable, fluid template for python packages complete with docs, testing suites, readme's, github workflows, linting and much much more | ||
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| ## Installation | ||
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| You can install the package using pip | ||
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| This course teaches you how to leverage the Swarms framework to build sophisticated multi-agent systems for marketing automation, analysis, and optimization. Learn how to orchestrate multiple AI agents to handle complex marketing workflows across different channels and platforms. | ||
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| ### 📚 Course Overview | ||
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| This hands-on course will teach you how to: | ||
| - Build and deploy marketing-focused autonomous agents | ||
| - Orchestrate multi-agent systems for marketing automation | ||
| - Analyze marketing data using agent swarms | ||
| - Optimize marketing campaigns using collaborative AI | ||
| - Create scalable marketing workflows | ||
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| ### 🎯 Learning Objectives | ||
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| By the end of this course, you will be able to: | ||
| - Understand multi-agent architectures for marketing applications | ||
| - Build custom marketing agents using the Swarms framework | ||
| - Deploy agent swarms for social media management | ||
| - Create automated content generation pipelines | ||
| - Implement marketing analytics workflows | ||
| - Develop autonomous campaign optimization systems | ||
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| ### 📋 Course Outline | ||
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| #### Module 1: Foundations | ||
| 1. Introduction to Multi-Agent Systems | ||
| - Understanding agent collaboration | ||
| - Key concepts in swarm intelligence | ||
| - Marketing applications of multi-agent systems | ||
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| 2. Setting Up Your Environment | ||
| - Installing Swarms | ||
| - Configuring API keys | ||
| - Basic agent creation | ||
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| #### Module 2: Building Marketing Agents | ||
| 1. Social Media Agents | ||
| - Creating platform-specific agents | ||
| - Implementing posting strategies | ||
| - Engagement analysis | ||
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| 2. Content Generation Agents | ||
| - Copy generation workflows | ||
| - Multi-modal content creation | ||
| - Content optimization agents | ||
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| 3. Analytics Agents | ||
| - Data collection agents | ||
| - Metrics analysis | ||
| - Reporting automation | ||
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| #### Module 3: Multi-Agent Architectures | ||
| 1. Sequential Workflows | ||
| - Content creation pipelines | ||
| - Approval workflows | ||
| - Distribution chains | ||
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| 2. Concurrent Processing | ||
| - Parallel content generation | ||
| - Multi-channel posting | ||
| - Distributed analytics | ||
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| 3. Agent Rearrange Patterns | ||
| - Dynamic workflow optimization | ||
| - Adaptive campaign management | ||
| - A/B testing automation | ||
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| #### Module 4: Advanced Applications | ||
| 1. Campaign Optimization | ||
| - Performance monitoring agents | ||
| - Budget allocation systems | ||
| - Automated A/B testing | ||
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| 2. Customer Intelligence | ||
| - Sentiment analysis swarms | ||
| - Competitive monitoring | ||
| - Trend detection | ||
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| 3. Automated Reporting | ||
| - Data aggregation workflows | ||
| - Visualization agents | ||
| - Report generation systems | ||
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| ### 💻 Practical Projects | ||
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| 1. **Social Media Management System** | ||
| - Build a multi-agent system for managing multiple social media accounts | ||
| - Implement content scheduling and optimization | ||
| - Create engagement monitoring and response systems | ||
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| 2. **Content Generation Pipeline** | ||
| - Develop automated content creation workflows | ||
| - Implement multi-stage review processes | ||
| - Create distribution automation | ||
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| 3. **Marketing Analytics Dashboard** | ||
| - Build data collection and analysis agents | ||
| - Create automated reporting systems | ||
| - Implement performance optimization agents | ||
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| ### 🛠️ Technical Requirements | ||
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| - Python 3.10 or higher | ||
| - Swarms library (`pip install -U swarms`) | ||
| - OpenAI API key | ||
| - Anthropic API key (optional) | ||
| - Social media API access tokens | ||
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| ### 📦 Getting Started | ||
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| 1. Clone the course repository: | ||
| ```bash | ||
| pip install -e . | ||
| git clone https://github.com/yourusername/marketing-swarms-course | ||
| cd marketing-swarms-course | ||
| ``` | ||
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| # Usage | ||
| ```python | ||
| print("hello world") | ||
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| 2. Install requirements: | ||
| ```bash | ||
| pip install -r requirements.txt | ||
| ``` | ||
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| 3. Set up your environment variables: | ||
| ```bash | ||
| export OPENAI_API_KEY="your-api-key" | ||
| export ANTHROPIC_API_KEY="your-api-key" | ||
| ``` | ||
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| ### 🎓 Prerequisites | ||
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| ### Code Quality 🧹 | ||
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| - `make style` to format the code | ||
| - `make check_code_quality` to check code quality (PEP8 basically) | ||
| - `black .` | ||
| - `ruff . --fix` | ||
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| ### Tests 🧪 | ||
| - Basic Python programming knowledge | ||
| - Understanding of marketing concepts | ||
| - Familiarity with APIs and web services | ||
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| [`pytests`](https://docs.pytest.org/en/7.1.x/) is used to run our tests. | ||
| ### 📚 Resources | ||
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| ### Publish on PyPi 🚀 | ||
| - [Swarms Documentation](https://docs.swarms.world) | ||
| - [Marketing Agents Examples](https://github.com/The-Swarm-Corporation/swarms-examples) | ||
| - [Community Discord](https://discord.gg/kS3rwKs3ZC) | ||
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| **Important**: Before publishing, edit `__version__` in [src/__init__](/src/__init__.py) to match the wanted new version. | ||
| ### 👥 Community Support | ||
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| ``` | ||
| poetry build | ||
| poetry publish | ||
| ``` | ||
| - Join our [Discord community](https://discord.gg/kS3rwKs3ZC) for real-time support | ||
| - Participate in weekly office hours | ||
| - Share your projects and get feedback from peers | ||
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| ### CI/CD 🤖 | ||
| ### 📝 Assessment | ||
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| We use [GitHub actions](https://github.com/features/actions) to automatically run tests and check code quality when a new PR is done on `main`. | ||
| - Weekly coding assignments | ||
| - Two mini-projects | ||
| - Final capstone project | ||
| - Peer reviews and feedback | ||
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| On any pull request, we will check the code quality and tests. | ||
| ### 🏆 Certification | ||
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| When a new release is created, we will try to push the new code to PyPi. We use [`twine`](https://twine.readthedocs.io/en/stable/) to make our life easier. | ||
| Upon completion of all course requirements, you will receive: | ||
| - A certificate of completion | ||
| - Portfolio-ready projects | ||
| - Access to the alumni network | ||
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| The **correct steps** to create a new realease are the following: | ||
| - edit `__version__` in [src/__init__](/src/__init__.py) to match the wanted new version. | ||
| - create a new [`tag`](https://git-scm.com/docs/git-tag) with the release name, e.g. `git tag v0.0.1 && git push origin v0.0.1` or from the GitHub UI. | ||
| - create a new release from GitHub UI | ||
| ### 📅 Course Schedule | ||
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| The CI will run when you create the new release. | ||
| - Duration: 8 weeks | ||
| - Weekly live sessions: 2 hours | ||
| - Office hours: 1 hour per week | ||
| - Estimated study time: 10-15 hours per week | ||
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| # Docs | ||
| We use MK docs. This repo comes with the zeta docs. All the docs configurations are already here along with the readthedocs configs. | ||
| ### 🤝 Contributing | ||
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| We welcome contributions to improve the course materials. Please see our [contributing guidelines](CONTRIBUTING.md) for more information. | ||
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| ### 📄 License | ||
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| # License | ||
| MIT | ||
| This course is licensed under the GNU Affero General Public License v3.0. |