Sync with SSF

CODE_OF_CONDUCT.md

@@ -1,76 +0,0 @@
-# Contributor Covenant Code of Conduct
-
-## Our Pledge
-
-In the interest of fostering an open and welcoming environment, we as
-contributors and maintainers pledge to making participation in our project and
-our community a harassment-free experience for everyone, regardless of age, body
-size, disability, ethnicity, sex characteristics, gender identity and expression,
-level of experience, education, socio-economic status, nationality, personal
-appearance, race, religion, or sexual identity and orientation.
-
-## Our Standards
-
-Examples of behavior that contributes to creating a positive environment
-include:
-
-* Using welcoming and inclusive language
-* Being respectful of differing viewpoints and experiences
-* Gracefully accepting constructive criticism
-* Focusing on what is best for the community
-* Showing empathy towards other community members
-
-Examples of unacceptable behavior by participants include:
-
-* The use of sexualized language or imagery and unwelcome sexual attention or
-  advances
-* Trolling, insulting/derogatory comments, and personal or political attacks
-* Public or private harassment
-* Publishing others' private information, such as a physical or electronic
-  address, without explicit permission
-* Other conduct which could reasonably be considered inappropriate in a
-  professional setting
-
-## Our Responsibilities
-
-Project maintainers are responsible for clarifying the standards of acceptable
-behavior and are expected to take appropriate and fair corrective action in
-response to any instances of unacceptable behavior.
-
-Project maintainers have the right and responsibility to remove, edit, or
-reject comments, commits, code, wiki edits, issues, and other contributions
-that are not aligned to this Code of Conduct, or to ban temporarily or
-permanently any contributor for other behaviors that they deem inappropriate,
-threatening, offensive, or harmful.
-
-## Scope
-
-This Code of Conduct applies both within project spaces and in public spaces
-when an individual is representing the project or its community. Examples of
-representing a project or community include using an official project e-mail
-address, posting via an official social media account, or acting as an appointed
-representative at an online or offline event. Representation of a project may be
-further defined and clarified by project maintainers.
-
-## Enforcement
-
-Instances of abusive, harassing, or otherwise unacceptable behavior may be
-reported by contacting the team at mozilla.ai. All
-complaints will be reviewed and investigated and will result in a response that
-is deemed necessary and appropriate to the circumstances. The project team is
-obligated to maintain confidentiality with regard to the reporter of an incident.
-Further details of specific enforcement policies may be posted separately.
-
-Project maintainers who do not follow or enforce the Code of Conduct in good
-faith may face temporary or permanent repercussions as determined by other
-members of the project's leadership.
-
-## Attribution
-
-This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4,
-available at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html
-
-[homepage]: https://www.contributor-covenant.org
-
-For answers to common questions about this code of conduct, see
-https://www.contributor-covenant.org/faq

CONTRIBUTING.md

@@ -1,45 +0,0 @@
-# Contributing to mozilla.ai Blueprints
-
-Thank you for your interest in contributing to this repository! This project is part of the Blueprints initiative, which empowers developers to integrate AI capabilities into their projects using open-source tools and models.
-
-We welcome all kinds of contributions, from improving customization, to extending capabilities, to fixing bugs. Whether you’re an experienced developer or just starting out, your support is highly appreciated.
-
----
-
-## **How to Contribute**
-
-### **Customize for your use-case or Extend It** 🔧
-- Fork this repo and customize it for your own use-case or even extend its capabilities.
-- We'd love to see what you've built! Provided your fork is public, we may reach out to you to feature your work on the [Blueprints Hub](https://developer-hub.mozilla.ai/) .
-
-### **Browse Existing Issues** 🔍
-- Check the Issues page to see if there are any tasks you'd like to tackle.
-- Look for issues labeled **`good first issue`** if you're new to the project—they're a great place to start.
-
-### **Report Issues** 🐛
-- Found a bug? Open a Bug Report by clicking on 'New Issue'
-- Provide as much detail as possible, including the steps to reproduce the issue and Expected vs. actual behavior
-
-### **Suggest Features** 🚀
-- Have an idea for improving the Blueprint? Open a Feature Request by clicking on 'New Issue'
-- Share why the feature is important and any alternative solutions you’ve considered.
-
-### **Submit Pull Requests** 💻
-- Fork the repository and create a new branch for your changes.
-- Install [pre-commit](https://pre-commit.com/) to ensure the code is formatted and standardized correctly, by running `pip install pre-commit` and then `pre-commit install`.
-- Ensure your branch is up-to-date with the main branch before submitting the PR
-- Please follow the PR template, adding as much detail as possible, including how to test the changes
-
----
-
-### **Guidelines for Contributions**
-
-**Coding Standards**
-- Follow PEP 8 for Python formatting.
-- Use clear variable and function names and add comments to improve readability.
-
-**Testing**
-- Test changes locally and in GitHub Codespaces to ensure functionality.
-
-**Documentation**
-- Update docs for changes to functionality and maintain consistency with existing docs.

Dockerfile

@@ -9,8 +9,8 @@ RUN apt-get update && apt-get install -y \
     git \
     && rm -rf /var/lib/apt/lists/*
 
-COPY demo/requirements.txt ./
-COPY demo/ ./demo/
+COPY requirements.txt ./
+COPY . ./demo/
 
 RUN pip3 install -r requirements.txt
 

LICENSE

@@ -1,201 +0,0 @@
-                                 Apache License
-                           Version 2.0, January 2004
-                        http://www.apache.org/licenses/
-
-   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
-
-   1. Definitions.
-
-      "License" shall mean the terms and conditions for use, reproduction,
-      and distribution as defined by Sections 1 through 9 of this document.
-
-      "Licensor" shall mean the copyright owner or entity authorized by
-      the copyright owner that is granting the License.
-
-      "Legal Entity" shall mean the union of the acting entity and all
-      other entities that control, are controlled by, or are under common
-      control with that entity. For the purposes of this definition,
-      "control" means (i) the power, direct or indirect, to cause the
-      direction or management of such entity, whether by contract or
-      otherwise, or (ii) ownership of fifty percent (50%) or more of the
-      outstanding shares, or (iii) beneficial ownership of such entity.
-
-      "You" (or "Your") shall mean an individual or Legal Entity
-      exercising permissions granted by this License.
-
-      "Source" form shall mean the preferred form for making modifications,
-      including but not limited to software source code, documentation
-      source, and configuration files.
-
-      "Object" form shall mean any form resulting from mechanical
-      transformation or translation of a Source form, including but
-      not limited to compiled object code, generated documentation,
-      and conversions to other media types.
-
-      "Work" shall mean the work of authorship, whether in Source or
-      Object form, made available under the License, as indicated by a
-      copyright notice that is included in or attached to the work
-      (an example is provided in the Appendix below).
-
-      "Derivative Works" shall mean any work, whether in Source or Object
-      form, that is based on (or derived from) the Work and for which the
-      editorial revisions, annotations, elaborations, or other modifications
-      represent, as a whole, an original work of authorship. For the purposes
-      of this License, Derivative Works shall not include works that remain
-      separable from, or merely link (or bind by name) to the interfaces of,
-      the Work and Derivative Works thereof.
-
-      "Contribution" shall mean any work of authorship, including
-      the original version of the Work and any modifications or additions
-      to that Work or Derivative Works thereof, that is intentionally
-      submitted to Licensor for inclusion in the Work by the copyright owner
-      or by an individual or Legal Entity authorized to submit on behalf of
-      the copyright owner. For the purposes of this definition, "submitted"
-      means any form of electronic, verbal, or written communication sent
-      to the Licensor or its representatives, including but not limited to
-      communication on electronic mailing lists, source code control systems,
-      and issue tracking systems that are managed by, or on behalf of, the
-      Licensor for the purpose of discussing and improving the Work, but
-      excluding communication that is conspicuously marked or otherwise
-      designated in writing by the copyright owner as "Not a Contribution."
-
-      "Contributor" shall mean Licensor and any individual or Legal Entity
-      on behalf of whom a Contribution has been received by Licensor and
-      subsequently incorporated within the Work.
-
-   2. Grant of Copyright License. Subject to the terms and conditions of
-      this License, each Contributor hereby grants to You a perpetual,
-      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
-      copyright license to reproduce, prepare Derivative Works of,
-      publicly display, publicly perform, sublicense, and distribute the
-      Work and such Derivative Works in Source or Object form.
-
-   3. Grant of Patent License. Subject to the terms and conditions of
-      this License, each Contributor hereby grants to You a perpetual,
-      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
-      (except as stated in this section) patent license to make, have made,
-      use, offer to sell, sell, import, and otherwise transfer the Work,
-      where such license applies only to those patent claims licensable
-      by such Contributor that are necessarily infringed by their
-      Contribution(s) alone or by combination of their Contribution(s)
-      with the Work to which such Contribution(s) was submitted. If You
-      institute patent litigation against any entity (including a
-      cross-claim or counterclaim in a lawsuit) alleging that the Work
-      or a Contribution incorporated within the Work constitutes direct
-      or contributory patent infringement, then any patent licenses
-      granted to You under this License for that Work shall terminate
-      as of the date such litigation is filed.
-
-   4. Redistribution. You may reproduce and distribute copies of the
-      Work or Derivative Works thereof in any medium, with or without
-      modifications, and in Source or Object form, provided that You
-      meet the following conditions:
-
-      (a) You must give any other recipients of the Work or
-          Derivative Works a copy of this License; and
-
-      (b) You must cause any modified files to carry prominent notices
-          stating that You changed the files; and
-
-      (c) You must retain, in the Source form of any Derivative Works
-          that You distribute, all copyright, patent, trademark, and
-          attribution notices from the Source form of the Work,
-          excluding those notices that do not pertain to any part of
-          the Derivative Works; and
-
-      (d) If the Work includes a "NOTICE" text file as part of its
-          distribution, then any Derivative Works that You distribute must
-          include a readable copy of the attribution notices contained
-          within such NOTICE file, excluding those notices that do not
-          pertain to any part of the Derivative Works, in at least one
-          of the following places: within a NOTICE text file distributed
-          as part of the Derivative Works; within the Source form or
-          documentation, if provided along with the Derivative Works; or,
-          within a display generated by the Derivative Works, if and
-          wherever such third-party notices normally appear. The contents
-          of the NOTICE file are for informational purposes only and
-          do not modify the License. You may add Your own attribution
-          notices within Derivative Works that You distribute, alongside
-          or as an addendum to the NOTICE text from the Work, provided
-          that such additional attribution notices cannot be construed
-          as modifying the License.
-
-      You may add Your own copyright statement to Your modifications and
-      may provide additional or different license terms and conditions
-      for use, reproduction, or distribution of Your modifications, or
-      for any such Derivative Works as a whole, provided Your use,
-      reproduction, and distribution of the Work otherwise complies with
-      the conditions stated in this License.
-
-   5. Submission of Contributions. Unless You explicitly state otherwise,
-      any Contribution intentionally submitted for inclusion in the Work
-      by You to the Licensor shall be under the terms and conditions of
-      this License, without any additional terms or conditions.
-      Notwithstanding the above, nothing herein shall supersede or modify
-      the terms of any separate license agreement you may have executed
-      with Licensor regarding such Contributions.
-
-   6. Trademarks. This License does not grant permission to use the trade
-      names, trademarks, service marks, or product names of the Licensor,
-      except as required for reasonable and customary use in describing the
-      origin of the Work and reproducing the content of the NOTICE file.
-
-   7. Disclaimer of Warranty. Unless required by applicable law or
-      agreed to in writing, Licensor provides the Work (and each
-      Contributor provides its Contributions) on an "AS IS" BASIS,
-      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
-      implied, including, without limitation, any warranties or conditions
-      of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
-      PARTICULAR PURPOSE. You are solely responsible for determining the
-      appropriateness of using or redistributing the Work and assume any
-      risks associated with Your exercise of permissions under this License.
-
-   8. Limitation of Liability. In no event and under no legal theory,
-      whether in tort (including negligence), contract, or otherwise,
-      unless required by applicable law (such as deliberate and grossly
-      negligent acts) or agreed to in writing, shall any Contributor be
-      liable to You for damages, including any direct, indirect, special,
-      incidental, or consequential damages of any character arising as a
-      result of this License or out of the use or inability to use the
-      Work (including but not limited to damages for loss of goodwill,
-      work stoppage, computer failure or malfunction, or any and all
-      other commercial damages or losses), even if such Contributor
-      has been advised of the possibility of such damages.
-
-   9. Accepting Warranty or Additional Liability. While redistributing
-      the Work or Derivative Works thereof, You may choose to offer,
-      and charge a fee for, acceptance of support, warranty, indemnity,
-      or other liability obligations and/or rights consistent with this
-      License. However, in accepting such obligations, You may act only
-      on Your own behalf and on Your sole responsibility, not on behalf
-      of any other Contributor, and only if You agree to indemnify,
-      defend, and hold each Contributor harmless for any liability
-      incurred by, or claims asserted against, such Contributor by reason
-      of your accepting any such warranty or additional liability.
-
-   END OF TERMS AND CONDITIONS
-
-   APPENDIX: How to apply the Apache License to your work.
-
-      To apply the Apache License to your work, attach the following
-      boilerplate notice, with the fields enclosed by brackets "[]"
-      replaced with your own identifying information. (Don't include
-      the brackets!)  The text should be enclosed in the appropriate
-      comment syntax for the file format. We also recommend that a
-      file or class name and description of purpose be included on the
-      same "printed page" as the copyright notice for easier
-      identification within third-party archives.
-
-   Copyright [yyyy] [name of copyright owner]
-
-   Licensed under the Apache License, Version 2.0 (the "License");
-   you may not use this file except in compliance with the License.
-   You may obtain a copy of the License at
-
-       http://www.apache.org/licenses/LICENSE-2.0
-
-   Unless required by applicable law or agreed to in writing, software
-   distributed under the License is distributed on an "AS IS" BASIS,
-   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   See the License for the specific language governing permissions and
-   limitations under the License.

README.md

@@ -12,102 +12,9 @@ short_description: Find a surf spot near you
 license: apache-2.0
 ---
 
+# Welcome to Streamlit!
 
-<p align="center">
-  <picture>
-    <!-- When the user prefers dark mode, show the white logo -->
-    <source media="(prefers-color-scheme: dark)" srcset="./images/Blueprint-logo-white.png">
-    <!-- When the user prefers light mode, show the black logo -->
-    <source media="(prefers-color-scheme: light)" srcset="./images/Blueprint-logo-black.png">
-    <!-- Fallback: default to the black logo -->
-    <img src="./images/Blueprint-logo-black.png" width="35%" alt="Project logo"/>
-  </picture>
-</p>
+Edit `/src/streamlit_app.py` to customize this app to your heart's desire. :heart:
 
-<div align="center">
-  <p><a href="https://huggingface.co/spaces/mozilla-ai/surf-spot-finder" target="_blank">Huggingface Demo 🤗</a></p>
-
-</div>
-
-
-
-Many Large Language Model (LLM) capabilities are unlocked when they are given access to tools and given control of their
-own runtime and execution path. However, it's important that as they are given greater capabilities, they are properly
-evaluated and controlled.
-
-In this Blueprint, we demonstrate an AI agent designed for an extremely specific task (some refer to this as a "Vertical Agent")
-that is given the web and searching access it needs to find an answer the same way you would find the answer as a human.
-
-This agent is designed for help in finding the next great surf spot near you: the agent is provided with a location, a distance,
-a timestamp, and it's able to independently search and browse the web to recommend the best spot to you along with the
-relevant information!
-
-Although this exact use-case may not be useful to you directly, the framework we provide here is intended to be easily
-adapted to the Agent use case you have in mind.
-
-This implementation uses the [smolagents](https://huggingface.co/docs/smolagents/index) library for Agentic capabilities, alongside
-of the increasingly Model Context Protocol (MCP) which allows for a standard access communication standard for a large number of tools.
-
-📘 To explore this project further and discover other Blueprints, visit the [**Blueprints Hub**](https://developer-hub.mozilla.ai/).
-
-### Built with
-![Python](https://img.shields.io/badge/Python-3.10%2B-blue)
-[![smolagents](https://img.shields.io/badge/Smolagents-%F0%9F%A4%97-yellow)](https://huggingface.co/docs/smolagents/index)
-
-
-## 🚀 Quick Start
-
-### 1️⃣ Clone the Project
-```bash
-git clone https://github.com/mozilla-ai/surf-spot-finder.git
-cd surf-spot-finder
-```
-
-### 2️⃣ Update submodule and install dependencies
-```bash
-pip install -e .  # Install root project dependencies
-```
-
-### 3️⃣ Run
-
-```bash
-surf-spot-finder examples/single_agent_with_tools.yaml
-```
-
-## How it Works
-
-
-## Pre-requisites
-
-- **System requirements**:
-  - OS: Windows, macOS, or Linux
-  - Python 3.10 or higher
-  - Minimum RAM:
-  - Disk space:
-
-- **Dependencies**:
-  - Docker
-  - Dependencies listed in `pyproject.toml`
-
-## Run Tests
-
-```bash
-pip install -e .[tests]
-```
-
-### Unit Tests
-
-```bash
-pytest
-```
-
-## Troubleshooting
-
-
-## License
-
-This project is licensed under the Apache 2.0 License. See the [LICENSE](LICENSE) file for details.
-
-## Contributing
-
-Contributions are welcome! To get started, you can check out the [CONTRIBUTING.md](CONTRIBUTING.md) file.
+If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
+forums](https://discuss.streamlit.io).

demo/Dockerfile

@@ -1,21 +0,0 @@
-FROM python:3.12-slim
-
-WORKDIR /app
-
-RUN apt-get update && apt-get install -y \
-    build-essential \
-    curl \
-    software-properties-common \
-    git \
-    && rm -rf /var/lib/apt/lists/*
-
-COPY requirements.txt ./
-COPY . ./demo/
-
-RUN pip3 install -r requirements.txt
-
-EXPOSE 8501
-
-HEALTHCHECK CMD curl --fail http://localhost:8501/_stcore/health
-
-ENTRYPOINT ["streamlit", "run", "demo/streamlit_app.py", "--server.port=8501", "--server.address=0.0.0.0"]

demo/README.md

@@ -1,20 +0,0 @@
----
-title: Surf Spot Finder
-emoji: 🚀
-colorFrom: red
-colorTo: red
-sdk: docker
-app_port: 8501
-tags:
-- streamlit
-pinned: false
-short_description: Find a surf spot near you
-license: apache-2.0
----
-
-# Welcome to Streamlit!
-
-Edit `/src/streamlit_app.py` to customize this app to your heart's desire. :heart:
-
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).

docs/api.md

@@ -1,13 +0,0 @@
-# API Reference
-
-::: surf_spot_finder.cli
-
-::: surf_spot_finder.config.Config
-
-::: surf_spot_finder.no_framework
-
-## Tools
-
-::: surf_spot_finder.tools.openmeteo
-
-::: surf_spot_finder.tools.openstreetmap

docs/customization.md

@@ -1,22 +0,0 @@
-# 🎨 **Customization Guide**
-
-This Blueprint is designed to be flexible and easily adaptable to your specific needs. This guide will walk you through some key areas you can customize to make the Blueprint your own.
-
----
-
-## 🧠 **Changing the Model**
-
-
-
-## 📝 **Modifying the system Prompt**
-
-
-
-## 💡 Other Customization Ideas
-
-- other ideas..
-
-
-## 🤝 **Contributing to the Blueprint**
-
-Want to help improve or extend this Blueprint? Check out the **[Future Features & Contributions Guide](future-features-contributions.md)** to see how you can contribute your ideas, code, or feedback to make this Blueprint even better!

docs/future-features-contributions.md

@@ -1,28 +0,0 @@
-# 🚀 **Future Features & Contributions**
-
-This Blueprint is an evolving project designed to grow with the help of the open-source community. Whether you’re an experienced developer or just starting, there are many ways you can contribute and help shape the future of this tool.
-
----
-
-## 🌟 **How You Can Contribute**
-
-### 🛠️ **Enhance the Blueprint**
-- Check the [Issues](https://github.com/mozilla-ai/surf-spot-finder/issues) page to see if there are feature requests you'd like to implement
-- Refer to our [Contribution Guide](https://github.com/mozilla-ai/surf-spot-finder/blob/main/CONTRIBUTING.md) for more details on contributions
-
-### 🎨 **Extensibility Ideas**
-
-This Blueprint is designed to be a foundation you can build upon. By extending its capabilities, you can open the door to new applications, improve user experience, and adapt the Blueprint to address other use cases. Here are a few ideas for how you can expand its potential:
-
-
-We’d love to see how you can enhance this Blueprint! If you create improvements or extend its capabilities, consider contributing them back to the project so others in the community can benefit from your work. Check out our [Contributions Guide](https://github.com/mozilla-ai/surf-spot-finder/blob/main/CONTRIBUTING.md) to get started!
-
-### 💡 **Share Your Ideas**
-Got an idea for how this Blueprint could be improved? You can share your suggestions through [GitHub Discussions](https://github.com/mozilla-ai/surf-spot-finder/discussions).
-
-### 🌍 **Build New Blueprints**
-This project is part of a larger initiative to create a collection of reusable starter code solutions that use open-source AI tools. If you’re inspired to create your own Blueprint, you can use the [surf-spot-finder](https://github.com/new?template_name=surf-spot-finder&template_owner=mozilla-ai) to get started.
-
----
-
-Your contributions help make this Blueprint better for everyone 🎉

docs/getting-started.md

@@ -1,9 +0,0 @@
-Get started with this Blueprint using one of the options below:
-
----
-
-### **Option 1:**
-
-
-
-###  **Option 2:**

docs/index.md

@@ -1,41 +0,0 @@
-# **Welcome to the Blueprint docs**
-
-Blueprints are customizable workflows that help developers build AI applications using open-source tools and models
-
-These docs are your companion to mastering this Blueprint.
-
-### Built with
-- Python 3.10+
-- Tool 1
-- Tool 2
-
-
----
-
-### 🚀 **Get Started Quickly**
-#### _Start building the Blueprint in minutes:_
-- **[Getting Started](getting-started.md):** Quick setup and installation instructions.
-
-### 🔍 **Understand the System**
-#### _Dive deeper into how the Blueprint works:_
-- **[Step-by-Step Guide](step-by-step-guide.md):** A detailed breakdown of the system’s design and workflow.
-- **[API Reference](api.md):** Explore the technical details of the core modules.
-
-### 🎨 **Make It Yours**
-#### _Customize the Blueprint to fit your needs:_
-- **[Customization Guide](customization.md):** Tailor project parameters to fit your needs
-
-### 🌟 **Join the Community**
-#### _Help shape the future of Blueprints:_
-- **[Future Features & Contributions](future-features-contributions.md):** Learn about exciting upcoming features and how to contribute to the project.
-
-
-Have more questions? Reach out to us on Discord and we'll see how we can help:
-
-<a target="_blank" href="https://discord.gg/gazz5XDU"><img src="https://dcbadge.limes.pink/api/server/gazz5XDU" alt="" /></a>
-
----
-
-## **Why Blueprints?**
-
-Blueprints are more than starter code—they’re your gateway to building AI-powered solutions with confidence. With step-by-step guidance, modular design, and open-source tools, we make AI accessible for developers of all skill levels.

docs/step-by-step-guide.md

@@ -1,27 +0,0 @@
-# **Step-by-Step Guide: How this Blueprint Works**
-
-
----
-
-## **Overview**
-
-
----
-
-## **Step 1**
-
-
-## **Step 2**
-
-
-## **Step 3**
-
-
-
-## 🎨 **Customizing the Blueprint**
-
-To better understand how you can tailor this Blueprint to suit your specific needs, please visit the **[Customization Guide](customization.md)**.
-
-## 🤝 **Contributing to the Blueprint**
-
-Want to help improve or extend this Blueprint? Check out the **[Future Features & Contributions Guide](future-features-contributions.md)** to see how you can contribute your ideas, code, or feedback to make this Blueprint even better!

examples/multi_agent.yaml

@@ -1,31 +0,0 @@
-main_agent:
-  name: main_agent
-  model_id: # optional, will prompt for it if not provided
-  instructions: >
-    # System context\n
-    You are part of a multi-agent system, designed to make agent coordination and execution easy.
-    Agents uses two primary abstraction: **Agents** and **Handoffs**.
-    An agent encompasses instructions and tools and can hand off a conversation to another agent when appropriate.
-    Handoffs are achieved by calling a handoff function, generally named `transfer_to_<agent_name>`.
-    Transfers between agents are handled seamlessly in the background; do not mention or draw attention to these transfers in your conversation with the user.\n
-
-managed_agents:
-
-  - name: user-verification-agent
-    model_id: # optional, will prompt for it if not provided
-    instructions: Ask users to verify a step, plan or answer.
-    tools:
-      - any_agent.tools.ask_user_verification
-
-  - name: general-web-search-agent
-    model_id: # optional, will prompt for it if not provided
-    instructions: Search the web and visit webpages to find answers.
-    tools:
-      - any_agent.tools.search_web
-      - any_agent.tools.visit_webpage
-
-  - name: user-communication-agent
-    model_id: # optional, will prompt for it if not provided
-    instructions: Communicates to the user
-    tools:
-      - any_agent.tools.show_final_output

examples/single_agent_user_confirmation.yaml

@@ -1,32 +0,0 @@
-
-input_prompt_template: |
-  According to the forecast, what will be the best spot to surf around {LOCATION},
-  in a {MAX_DRIVING_HOURS} hour driving radius, at {DATE}?
-  Find a few options and then discuss it with a friend using the send_console_message any_agent.tools.ask_user_verification
-  You should recommend some choices to them and then confirm the final selection with him.
-  Once he gives the final selection, save a detailed description of the weather at the chosen location into a file
-  named "final_answer.txt". Also save a file called "history.txt" which has a list of your thought process in the choice.
-
-main_agent:
-  model_id: # optional, will prompt for it if not provided
-  tools:
-  - "surf_spot_finder.tools.driving_hours_to_meters"
-  - "surf_spot_finder.tools.get_area_lat_lon"
-  - "surf_spot_finder.tools.get_surfing_spots"
-  - "surf_spot_finder.tools.get_wave_forecast"
-  - "surf_spot_finder.tools.get_wind_forecast"
-  - "any_agent.tools.send_console_message"
-  - command: "docker"
-    args:
-      - "run"
-      - "-i"
-      - "--rm"
-      - "--mount"
-      - "type=bind,src={{ path_variable }},dst=/projects" # this is the custom part where for demo purposes we will check this in config.py
-      - "mcp/filesystem"
-      - "/projects"
-    tools:
-      - "read_file"
-      - "write_file"
-      - "directory_tree"
-      - "list_allowed_directories"

examples/single_agent_with_tools.yaml

@@ -1,31 +0,0 @@
-main_agent:
-  model_id: # optional, will prompt for it if not provided
-  tools:
-  - "surf_spot_finder.tools.driving_hours_to_meters"
-  - "surf_spot_finder.tools.get_area_lat_lon"
-  - "surf_spot_finder.tools.get_surfing_spots"
-  - "surf_spot_finder.tools.get_wave_forecast"
-  - "surf_spot_finder.tools.get_wind_forecast"
-  - "any_agent.tools.search_web"
-  - "any_agent.tools.visit_webpage"
-
-
-evaluation_cases:
-  - llm_judge: openai/gpt-4.1-mini
-    checkpoints:
-      - criteria: "Check if the agent used the get_surfing_spots tool and it succeeded, and that the tool was used before the get_wave_forecast and get_wind_forecast tools"
-        points: 1
-      - criteria: "Check if the agent used the get_wave_forecast tool and it succeeded"
-        points: 1
-      - criteria: "Check if the agent used the get_wind_forecast tool and it succeeded"
-        points: 1
-      - criteria: "Check if the agent used the get_area_lat_lon tool and it succeeded"
-        points: 1
-      - criteria: "Check if the agent used the driving_hours_to_meters tool to convert the driving hours to meters and it succeeded"
-        points: 1
-      - criteria: "Check if the final answer contains any description about the weather at the chosen location"
-        points: 1
-      - criteria: "Check if the final answer contains one of the surf spots found by a call of the get_surfing_spots tool"
-        points: 1
-      - criteria: "Check that the agent completed in fewer than 10 steps"
-        points: 1

mkdocs.yml

@@ -1,53 +0,0 @@
-site_name: Surf Spot Finder
-
-repo_url: https://github.com/mozilla-ai/surf-spot-finder
-repo_name: surf-spot-finder
-
-nav:
-  - Home: index.md
-  - Getting Started: getting-started.md
-  - Step-by-Step Guide: step-by-step-guide.md
-  - Customization Guide: customization.md
-  - API Reference: api.md
-  - Future Features & Contributions: future-features-contributions.md
-
-theme:
-  name: material
-  palette:
-    - scheme: default
-      primary: black
-      toggle:
-        icon: material/lightbulb
-        name: Switch to dark mode
-    - scheme: slate
-      primary: grey
-      toggle:
-        icon: material/lightbulb-outline
-        name: Switch to light mode
-  logo: images/Blueprint-logo-white.png
-  favicon: images/Blueprint-logo-black-flavicon.png
-  extra_css:
-    - assets/custom.css
-  features:
-    - content.code.copy
-    - content.tabs.link
-
-markdown_extensions:
-  - pymdownx.highlight:
-      anchor_linenums: true
-      line_spans: __span
-      pygments_lang_class: true
-  - pymdownx.inlinehilite
-  - pymdownx.snippets
-  - pymdownx.superfences
-  - pymdownx.tabbed:
-      alternate_style: true
-
-plugins:
-- search
-- mkdocstrings:
-    handlers:
-      python:
-        options:
-          show_root_heading: true
-          heading_level: 3

pyproject.toml

@@ -1,61 +0,0 @@
-[build-system]
-requires = ["setuptools>=48", "setuptools_scm[toml]>=6.3.1"]
-build-backend = "setuptools.build_meta"
-
-[project]
-name = "surf-spot-finder"
-readme = "README.md"
-license = {text = "Apache-2.0"}
-requires-python = ">=3.11"
-dynamic = ["version"]
-dependencies = [
-  "any-agent[all]>=0.12.2",
-  "fire",
-  "pydantic",
-  "pyyaml",
-  "litellm",
-  "geocoder>=1.38.1",
-  "rich>=14.0.0",
-]
-
-[project.optional-dependencies]
-demo = [
-  "gradio",
-  "spaces"
-]
-
-docs = [
-  "mkdocs",
-  "mkdocs-material",
-  "mkdocstrings-python",
-]
-
-tests = [
-  "pytest>=8,<9",
-  "pytest-sugar>=0.9.6",
-  "debugpy>=1.8.13",
-  "evaluate>=0.4.3",
-]
-
-dev = [
-  "pre-commit>=4.1.0",
-  "ipykernel>=6.29.5",
-  "jupyter>=1.1.1",
-]
-
-[project.urls]
-Documentation = "https://mozilla-ai.github.io/surf-spot-finder/"
-Issues = "https://github.com/mozilla-ai/surf-spot-finder/issues"
-Source = "https://github.com/mozilla-ai/surf-spot-finder"
-
-[tool.setuptools.packages.find]
-exclude = ["tests", "tests.*"]
-where = ["src"]
-namespaces = false
-
-[tool.setuptools_scm]
-
-[project.scripts]
-surf-spot-finder = "surf_spot_finder.cli:main"
-surf-spot-finder-no-framework = "surf_spot_finder.no_framework:main"
-surf-spot-finder-evaluate = "surf_spot_finder.evaluation.main:main"

src/surf_spot_finder/cli.py

@@ -1,106 +0,0 @@
-import datetime
-import os
-from pathlib import Path
-
-from any_agent import AgentFramework, AnyAgent, TracingConfig
-from any_agent.evaluation.schemas import TraceEvaluationResult
-from fire import Fire
-from any_agent.logging import logger
-from any_agent.evaluation import evaluate
-
-from surf_spot_finder.config import (
-    Config,
-)
-
-from surf_spot_finder.instructions.openai import SINGLE_AGENT_SYSTEM_PROMPT
-from surf_spot_finder.instructions.smolagents import SYSTEM_PROMPT
-
-
-async def find_surf_spot(
-    config_file: str | None = None,
-) -> str:
-    """Find the best surf spot based on the given criteria.
-
-    Args:
-        config_file: Path to a YAML config file.
-            See [Config][surf_spot_finder.config.Config]
-
-    """
-    if config_file is None:
-        config = Config.from_dict({})
-    else:
-        logger.info("Loading %s", config_file)
-        config = Config.from_yaml(config_file)
-
-    if not config.main_agent.instructions:
-        if config.framework == AgentFramework.SMOLAGENTS:
-            config.main_agent.instructions = SYSTEM_PROMPT
-        elif config.framework == AgentFramework.OPENAI:
-            config.main_agent.instructions = SINGLE_AGENT_SYSTEM_PROMPT
-
-    logger.info("Loading %s agent", config.framework)
-    logger.info("Managed agents: %s", config.managed_agents)
-    agent = await AnyAgent.create_async(
-        agent_framework=config.framework,
-        agent_config=config.main_agent,
-        managed_agents=config.managed_agents,
-        tracing=TracingConfig(console=True, cost_info=True),
-    )
-
-    query = config.input_prompt_template.format(
-        LOCATION=config.location,
-        MAX_DRIVING_HOURS=config.max_driving_hours,
-        DATE=config.date,
-    )
-    logger.info("Running agent with query:\n%s", query)
-    agent_trace = await agent.run_async(query)
-
-    logger.info("Final output from agent:\n%s", agent_trace.final_output)
-
-    # dump the trace in the "output" directory
-    output_dir = "output"
-    os.makedirs(output_dir, exist_ok=True)
-    timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
-    file_path = Path(output_dir) / f"{timestamp}_trace.json"
-    with open(file_path, "w") as f:
-        f.write(agent_trace.model_dump_json(indent=2))
-
-    if config.evaluation_cases is not None:
-        results = []
-        logger.info("Found evaluation cases, running trace evaluation")
-        for i, case in enumerate(config.evaluation_cases):
-            logger.info("Evaluating case: %s", case)
-            result: TraceEvaluationResult = evaluate(
-                evaluation_case=case,
-                trace=agent_trace,
-                agent_framework=config.framework,
-            )
-            for list_of_checkpoints in [
-                result.checkpoint_results,
-                result.direct_results,
-                result.hypothesis_answer_results,
-            ]:
-                for checkpoint in list_of_checkpoints:
-                    msg = (
-                        f"Checkpoint: {checkpoint.criteria}\n"
-                        f"\tPassed: {checkpoint.passed}\n"
-                        f"\tReason: {checkpoint.reason}\n"
-                        f"\tScore: {'%d/%d' % (checkpoint.points, checkpoint.points) if checkpoint.passed else '0/%d' % checkpoint.points}"
-                    )
-                    logger.info(msg)
-            logger.info("==========================")
-            logger.info("Overall Score: %d%%", 100 * result.score)
-            logger.info("==========================")
-            results.append(result)
-        timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
-        file_path = Path(output_dir) / f"{timestamp}_eval_case_{i}.json"
-        with open(file_path, "w") as f:
-            f.write(result.model_dump_json(indent=2))
-
-
-def main():
-    Fire(find_surf_spot)
-
-
-if __name__ == "__main__":
-    main()

src/surf_spot_finder/config.py

@@ -1,233 +0,0 @@
-import os
-import tempfile
-from typing import Annotated
-from datetime import datetime, timedelta
-from any_agent import AgentFramework
-from any_agent.config import AgentConfig
-from pydantic import AfterValidator, BaseModel, ConfigDict, FutureDatetime, PositiveInt
-import yaml
-from rich.prompt import Prompt
-from any_agent.logging import logger
-from any_agent.evaluation import EvaluationCase
-import geocoder
-from litellm.litellm_core_utils.get_llm_provider_logic import (
-    get_llm_provider,
-)
-
-INPUT_PROMPT_TEMPLATE = """
-According to the forecast, what will be the best spot to surf around {LOCATION},
-in a {MAX_DRIVING_HOURS} hour driving radius,
-at {DATE}?"
-""".strip()
-
-
-def validate_prompt(value) -> str:
-    for placeholder in ("{LOCATION}", "{MAX_DRIVING_HOURS}", "{DATE}"):
-        if placeholder not in value:
-            raise ValueError(f"prompt must contain {placeholder}")
-    return value
-
-
-def ask_framework() -> AgentFramework:
-    """
-    Ask the user which framework they would like to use. They must select one of the Agent Frameworks
-    """
-    frameworks = [framework.name for framework in AgentFramework]
-    frameworks_str = "\n".join(
-        [f"{i}: {framework}" for i, framework in enumerate(frameworks)]
-    )
-    prompt = f"Select the agent framework to use:\n{frameworks_str}\n"
-    choice = Prompt.ask(prompt, default="0")
-    try:
-        choice = int(choice)
-        if choice < 0 or choice >= len(frameworks):
-            raise ValueError("Invalid choice")
-        return AgentFramework[frameworks[choice]]
-    except ValueError:
-        raise ValueError("Invalid choice")
-
-
-def date_picker() -> FutureDatetime:
-    """
-    Ask the user to select a date in the future. The date must be at least 1 day in the future.
-    """
-    prompt = "Select a date in the future (YYYY-MM-DD-HH)"
-    # the default should be the current date + 1 day
-    now = datetime.now()
-    default_val = (now + timedelta(days=1)).strftime("%Y-%m-%d-%H")
-    date_str = Prompt.ask(prompt, default=default_val)
-    try:
-        year, month, day, hour = map(int, date_str.split("-"))
-        date = datetime(year, month, day, hour)
-        return date
-    except ValueError:
-        raise ValueError("Invalid date format. Please use YYYY-MM-DD-HH.")
-
-
-def location_picker() -> str:
-    """
-    Ask the user to input a location. By default use the current location based on the IP address.
-    """
-    prompt = "Enter a location"
-    g = geocoder.ip("me")
-    default_val = f"{g.city} {g.state}, {g.country}"
-    location = Prompt.ask(prompt, default=default_val)
-    if not location:
-        raise ValueError("location cannot be empty")
-    return location
-
-
-def max_driving_hours_picker() -> int:
-    """
-    Ask the user to input the maximum driving hours. The default is 2 hours.
-    """
-    prompt = "Enter the maximum driving hours"
-    default_val = str(2)
-    max_driving_hours = Prompt.ask(prompt, default=default_val)
-    try:
-        max_driving_hours = int(max_driving_hours)
-        if max_driving_hours <= 0:
-            raise ValueError("Invalid choice")
-        return max_driving_hours
-    except ValueError:
-        raise ValueError("Invalid choice")
-
-
-def get_litellm_model_id(agent_name) -> str:
-    """
-    Ask the user to input a model_id string. Validate it using the litellm.validate_environment function
-    """
-    from litellm.utils import validate_environment
-
-    prompt = f"Enter a valid model_id for agent {agent_name} using LiteLLM syntax"
-    default_val = "openai/gpt-4o"
-    model_id = Prompt.ask(prompt, default=default_val)
-    # make a call to validate the model id: this will throw an error if the model id is not valid
-    get_llm_provider(model=model_id)
-    # make a call to validate that the environment is correct for the model id
-    env_check = validate_environment(model_id)
-    if not env_check["keys_in_environment"]:
-        msg = f"{env_check['missing_keys']} needed for {model_id}"
-        raise ValueError(msg)
-    return model_id
-
-
-def set_mcp_settings(tool):
-    logger.info(
-        f"This MCP uses {tool['command']}. If you don't have this set up this will not work"
-    )
-    if "mcp/filesystem" not in tool["args"]:
-        msg = "The only MCP that this demo supports is the filesystem MCP"
-        raise ValueError(msg)
-    if not any("{{ path_variable }}" in arg for arg in tool["args"]):
-        msg = "The filesystem MCP must have { path_variable } in the args list"
-        raise ValueError(msg)
-    for idx, item in enumerate(tool["args"]):
-        if "{{ path_variable }}" in item:
-            default_val = os.path.join(tempfile.gettempdir(), "surf_spot_finder")
-            answer = Prompt.ask(
-                "Please enter the path you'd like the Filesystem MCP to access",
-                default=default_val,
-            )
-            os.makedirs(answer, exist_ok=True)
-            tool["args"][idx] = item.replace("{{ path_variable }}", answer)
-    return tool
-
-
-class Config(BaseModel):
-    model_config = ConfigDict(extra="forbid")
-
-    location: str
-    max_driving_hours: PositiveInt
-    date: FutureDatetime
-    input_prompt_template: Annotated[str, AfterValidator(validate_prompt)] = (
-        INPUT_PROMPT_TEMPLATE
-    )
-
-    framework: AgentFramework
-
-    main_agent: AgentConfig
-    managed_agents: list[AgentConfig] | None = None
-
-    evaluation_cases: list[EvaluationCase] | None = None
-
-    @classmethod
-    def from_dict(cls, data: dict) -> "Config":
-        """
-        Create a Config instance from a dictionary.
-        Args:
-            data (dict): A dictionary containing the configuration data.
-
-        Returns:
-            Config: A new Config instance populated with values from the dictionary.
-        """
-        # for each tool listed in main_agent.tools, use import lib to import it and replace the str with the callable
-        callables = []
-        if data.get("main_agent") is None:
-            data["main_agent"] = {}
-        if not data["main_agent"].get("model_id"):
-            data["main_agent"]["model_id"] = get_litellm_model_id("main_agent")
-        else:
-            logger.info(f"Main agent using model_id {data['main_agent']['model_id']}")
-        for tool in data["main_agent"].get("tools", []):
-            if isinstance(tool, str):
-                module_name, func_name = tool.rsplit(".", 1)
-                module = __import__(module_name, fromlist=[func_name])
-                callables.append(getattr(module, func_name))
-            else:
-                # this means it must be an MCPStdioParams
-                # For the purposes of this demo, currently we just look for the filesystem MCP which we have a placeholder
-                # for the path variable (which controls which dirs the MCP will have access to).
-                mcp_tool = set_mcp_settings(tool)
-                callables.append(mcp_tool)
-        data["main_agent"]["tools"] = callables
-        for agent in data.get("managed_agents", []):
-            if agent.get("model_id") is None:
-                agent["model_id"] = get_litellm_model_id(
-                    agent.get("name", "managed_agent")
-                )
-            else:
-                logger.info(f"Agent {agent['name']} using model_id {agent['model_id']}")
-            callables = []
-            for tool in agent.get("tools", []):
-                if isinstance(tool, str):
-                    module_name, func_name = tool.rsplit(".", 1)
-                    module = __import__(module_name, fromlist=[func_name])
-                    callables.append(getattr(module, func_name))
-                else:
-                    # this means it must be an MCPStdioParams
-                    mcp_tool = set_mcp_settings(tool)
-                    callables.append(mcp_tool)
-            agent["tools"] = callables
-        if not data.get("framework"):
-            data["framework"] = ask_framework()
-        else:
-            logger.info(f"Using framework {data['framework']}")
-        if not data.get("location"):
-            data["location"] = location_picker()
-        else:
-            logger.info(f"Using location {data['location']}")
-        if not data.get("max_driving_hours"):
-            data["max_driving_hours"] = max_driving_hours_picker()
-        else:
-            logger.info(f"Using max driving hours {data['max_driving_hours']}")
-        if not data.get("date"):
-            data["date"] = date_picker()
-        else:
-            logger.info(f"Using date {data['date']}")
-
-        return cls(**data)
-
-    @classmethod
-    def from_yaml(cls, yaml_path: str) -> "Config":
-        """
-        with open(yaml_path, "r") as f:
-            data = yaml.safe_load(f)
-        return cls(**data)    yaml_path: Path to the YAML configuration file
-
-        Returns:
-            Config: A new Config instance populated with values from the YAML file
-        """
-        with open(yaml_path, "r") as f:
-            data = yaml.safe_load(f)
-        return cls.from_dict(data)

src/surf_spot_finder/instructions/openai.py

@@ -1,22 +0,0 @@
-SINGLE_AGENT_SYSTEM_PROMPT = """
-You will be asked to perform a task.
-
-Before solving the task, plan a sequence of actions using the available tools.
-Then, execute the sequence of actions using the tools.
-
-Prefer to use task-specific tools before relying on generic tools like web search.
-""".strip()
-
-MULTI_AGENT_SYSTEM_PROMPT = """
-You will be asked to perform a task.
-
-Always follow this steps:
-
-First, before solving the task, plan a sequence of actions using the available tools.
-Second, show the plan of actions and ask for user verification. If the user does not verify the plan, come up with a better plan.
-Third, execute the plan using the available tools, until you get a final answer.
-
-Once you get a final answer, show it and ask for user verification.  If the user does not verify the answer, come up with a better answer.
-
-Finally, use the available handoff tool (`transfer_to_<agent_name>`) to communicate it to the user.
-""".strip()

src/surf_spot_finder/instructions/smolagents.py

@@ -1,176 +0,0 @@
-# Copied from https://github.com/huggingface/smolagents/blob/main/src/smolagents/prompts/code_agent.yaml
-SYSTEM_PROMPT = """
-You are an expert assistant who can solve any task using code blobs. You will be given a task to solve as best you can.
-To do so, you have been given access to a list of tools: these tools are basically Python functions which you can call with code.
-To solve the task, you must plan forward to proceed in a series of steps, in a cycle of 'Thought:', 'Code:', and 'Observation:' sequences.
-
-At each step, in the 'Thought:' sequence, you should first explain your reasoning towards solving the task and the tools that you want to use.
-Then in the 'Code:' sequence, you should write the code in simple Python. The code sequence must end with '<end_code>' sequence.
-During each intermediate step, you can use 'print()' to save whatever important information you will then need.
-These print outputs will then appear in the 'Observation:' field, which will be available as input for the next step.
-In the end you have to return a final answer using the `final_answer` tool.
-
-Here are a few examples using notional tools:
----
-Task: "Generate an image of the oldest person in this document."
-
-Thought: I will proceed step by step and use the following tools: `document_qa` to find the oldest person in the document, then `image_generator` to generate an image according to the answer.
-Code:
-```py
-answer = document_qa(document=document, question="Who is the oldest person mentioned?")
-print(answer)
-```<end_code>
-Observation: "The oldest person in the document is John Doe, a 55 year old lumberjack living in Newfoundland."
-
-Thought: I will now generate an image showcasing the oldest person.
-Code:
-```py
-image = image_generator("A portrait of John Doe, a 55-year-old man living in Canada.")
-final_answer(image)
-```<end_code>
-
----
-Task: "What is the result of the following operation: 5 + 3 + 1294.678?"
-
-Thought: I will use python code to compute the result of the operation and then return the final answer using the `final_answer` tool
-Code:
-```py
-result = 5 + 3 + 1294.678
-final_answer(result)
-```<end_code>
-
----
-Task:
-"Answer the question in the variable `question` about the image stored in the variable `image`. The question is in French.
-You have been provided with these additional arguments, that you can access using the keys as variables in your python code:
-{'question': 'Quel est l'animal sur l'image?', 'image': 'path/to/image.jpg'}"
-
-Thought: I will use the following tools: `translator` to translate the question into English and then `image_qa` to answer the question on the input image.
-Code:
-```py
-translated_question = translator(question=question, src_lang="French", tgt_lang="English")
-print(f"The translated question is {translated_question}.")
-answer = image_qa(image=image, question=translated_question)
-final_answer(f"The answer is {answer}")
-```<end_code>
-
----
-Task:
-In a 1979 interview, Stanislaus Ulam discusses with Martin Sherwin about other great physicists of his time, including Oppenheimer.
-What does he say was the consequence of Einstein learning too much math on his creativity, in one word?
-
-Thought: I need to find and read the 1979 interview of Stanislaus Ulam with Martin Sherwin.
-Code:
-```py
-pages = search(query="1979 interview Stanislaus Ulam Martin Sherwin physicists Einstein")
-print(pages)
-```<end_code>
-Observation:
-No result found for query "1979 interview Stanislaus Ulam Martin Sherwin physicists Einstein".
-
-Thought: The query was maybe too restrictive and did not find any results. Let's try again with a broader query.
-Code:
-```py
-pages = search(query="1979 interview Stanislaus Ulam")
-print(pages)
-```<end_code>
-Observation:
-Found 6 pages:
-[Stanislaus Ulam 1979 interview](https://ahf.nuclearmuseum.org/voices/oral-histories/stanislaus-ulams-interview-1979/)
-
-[Ulam discusses Manhattan Project](https://ahf.nuclearmuseum.org/manhattan-project/ulam-manhattan-project/)
-
-(truncated)
-
-Thought: I will read the first 2 pages to know more.
-Code:
-```py
-for url in ["https://ahf.nuclearmuseum.org/voices/oral-histories/stanislaus-ulams-interview-1979/", "https://ahf.nuclearmuseum.org/manhattan-project/ulam-manhattan-project/"]:
-    whole_page = visit_webpage(url)
-    print(whole_page)
-    print("\n" + "="*80 + "\n")  # Print separator between pages
-```<end_code>
-Observation:
-Manhattan Project Locations:
-Los Alamos, NM
-Stanislaus Ulam was a Polish-American mathematician. He worked on the Manhattan Project at Los Alamos and later helped design the hydrogen bomb. In this interview, he discusses his work at
-(truncated)
-
-Thought: I now have the final answer: from the webpages visited, Stanislaus Ulam says of Einstein: "He learned too much mathematics and sort of diminished, it seems to me personally, it seems to me his purely physics creativity." Let's answer in one word.
-Code:
-```py
-final_answer("diminished")
-```<end_code>
-
----
-Task: "Which city has the highest population: Guangzhou or Shanghai?"
-
-Thought: I need to get the populations for both cities and compare them: I will use the tool `search` to get the population of both cities.
-Code:
-```py
-for city in ["Guangzhou", "Shanghai"]:
-    print(f"Population {city}:", search(f"{city} population")
-```<end_code>
-Observation:
-Population Guangzhou: ['Guangzhou has a population of 15 million inhabitants as of 2021.']
-Population Shanghai: '26 million (2019)'
-
-Thought: Now I know that Shanghai has the highest population.
-Code:
-```py
-final_answer("Shanghai")
-```<end_code>
-
----
-Task: "What is the current age of the pope, raised to the power 0.36?"
-
-Thought: I will use the tool `wiki` to get the age of the pope, and confirm that with a web search.
-Code:
-```py
-pope_age_wiki = wiki(query="current pope age")
-print("Pope age as per wikipedia:", pope_age_wiki)
-pope_age_search = web_search(query="current pope age")
-print("Pope age as per google search:", pope_age_search)
-```<end_code>
-Observation:
-Pope age: "The pope Francis is currently 88 years old."
-
-Thought: I know that the pope is 88 years old. Let's compute the result using python code.
-Code:
-```py
-pope_current_age = 88 ** 0.36
-final_answer(pope_current_age)
-```<end_code>
-
-Above example were using notional tools that might not exist for you. On top of performing computations in the Python code snippets that you create, you only have access to these tools:
-{%- for tool in tools.values() %}
-- {{ tool.name }}: {{ tool.description }}
-    Takes inputs: {{tool.inputs}}
-    Returns an output of type: {{tool.output_type}}
-{%- endfor %}
-
-{%- if managed_agents and managed_agents.values() | list %}
-You can also give tasks to team members.
-Calling a team member works the same as for calling a tool: simply, the only argument you can give in the call is 'task', a long string explaining your task.
-Given that this team member is a real human, you should be very verbose in your task.
-Here is a list of the team members that you can call:
-{%- for agent in managed_agents.values() %}
-- {{ agent.name }}: {{ agent.description }}
-{%- endfor %}
-{%- else %}
-{%- endif %}
-
-Here are the rules you should always follow to solve your task:
-1. Always provide a 'Thought:' sequence, and a 'Code:\n```py' sequence ending with '```<end_code>' sequence, else you will fail.
-2. Use only variables that you have defined!
-3. Always use the right arguments for the tools. DO NOT pass the arguments as a dict as in 'answer = wiki({'query': "What is the place where James Bond lives?"})', but use the arguments directly as in 'answer = wiki(query="What is the place where James Bond lives?")'.
-4. Take care to not chain too many sequential tool calls in the same code block, especially when the output format is unpredictable. For instance, a call to search has an unpredictable return format, so do not have another tool call that depends on its output in the same block: rather output results with print() to use them in the next block.
-5. Call a tool only when needed, and never re-do a tool call that you previously did with the exact same parameters.
-6. Don't name any new variable with the same name as a tool: for instance don't name a variable 'final_answer'.
-7. Never create any notional variables in our code, as having these in your logs will derail you from the true variables.
-8. You can use imports in your code, but only from the following list of modules: {{authorized_imports}}
-9. The state persists between code executions: so if in one step you've created variables or imported modules, these will all persist.
-10. Don't give up! You're in charge of solving the task, not providing directions to solve it.
-
-Now Begin! If you solve the task correctly, you will receive a reward of $1,000,000.
-""".strip()

src/surf_spot_finder/no_framework.py

@@ -1,109 +0,0 @@
-import re
-from datetime import datetime
-
-from fire import Fire
-from litellm import completion
-from any_agent.logging import logger
-from pydantic import BaseModel
-
-from any_agent.tools.web_browsing import search_web, visit_webpage
-from surf_spot_finder.tools.openmeteo import get_wave_forecast, get_wind_forecast
-from surf_spot_finder.tools.openstreetmap import (
-    driving_hours_to_meters,
-    get_area_lat_lon,
-    get_surfing_spots,
-)
-
-
-spot_info_pattern = r"\[.*?\]\((https:\/\/www\.surf-forecast\.com\/breaks\/[^)/]+)\)"
-
-
-class SpotScore(BaseModel):
-    score: int
-    reason: str
-
-
-def find_surf_spot_no_framework(
-    location: str, max_driving_hours: int, date: datetime, model_id: str
-) -> list[SpotScore]:
-    """Find the best surf spot based on the given `location` and `date`.
-
-    Uses the following tools:
-
-    - any_agent.tools.web_browsing
-    - [surf_spot_finder.tools.openmeteo][]
-    - [surf_spot_finder.tools.openstreetmap][]
-
-    To find nearby spots along with the forecast and
-    recommended conditions for the spot.
-
-    Then, uses `litellm` with the provided `model_id` to score
-    each spot based on the available information.
-
-    Args:
-        location: The place of interest.
-        max_driving_hours: Used to limit the surf spots based on
-            the distance to `location`.
-        date: Used to filter the forecast results.
-        model_id: Can be any of the [litellm providers](https://docs.litellm.ai/docs/providers).
-
-    Returns:
-        A list of spot scores and reasons for the value.
-    """
-    max_driving_meters = driving_hours_to_meters(max_driving_hours)
-    lat, lon = get_area_lat_lon(location)
-
-    logger.info(f"Getting surfing spots around {location}")
-    surf_spots = get_surfing_spots(lat, lon, max_driving_meters)
-
-    if not surf_spots:
-        logger.warning("No surfing spots found around {location}")
-        return None
-
-    spots_scores = []
-    for spot_name, (spot_lat, spot_lon) in surf_spots:
-        logger.info(f"Processing {spot_name}")
-        logger.debug("Getting wave forecast...")
-        wave_forecast = get_wave_forecast(spot_lat, spot_lon, date)
-        logger.debug("Getting wind forecast...")
-        wind_forecast = get_wind_forecast(spot_lat, spot_lon, date)
-
-        logger.debug("Searching web for spot information")
-        search_result = search_web(f"surf-forecast.com spot info {spot_name}")
-        match = re.search(spot_info_pattern, search_result)
-        if match:
-            extracted_url = match.group(1)
-            logger.debug(f"Visiting {extracted_url}")
-            spot_info = visit_webpage(extracted_url)
-        else:
-            logger.debug(f"Couldn't find spot info for {spot_name}")
-            continue
-
-        logger.debug("Scoring conditions with LLM")
-        response = completion(
-            model="openai/gpt-4o-mini",
-            messages=[
-                {
-                    "content": "Given the wind and wave forecast along with the spot information, "
-                    "rate from 1 to 5 the expected surfing conditions."
-                    f"Wind forecast:\n{wind_forecast}\n"
-                    f"Wave forecast:\n{wave_forecast}\n"
-                    f"Spot Information:\n{spot_info}",
-                    "role": "user",
-                }
-            ],
-            response_format=SpotScore,
-        )
-        spot_score = SpotScore.model_validate_json(response.choices[0].message.content)
-        logger.debug(spot_score)
-        spots_scores.append(spot_score)
-
-    return spots_scores
-
-
-def main():
-    Fire(find_surf_spot_no_framework)
-
-
-if __name__ == "__main__":
-    main()

src/surf_spot_finder/tools/__init__.py

@@ -1,10 +0,0 @@
-from .openmeteo import get_wave_forecast, get_wind_forecast
-from .openstreetmap import driving_hours_to_meters, get_area_lat_lon, get_surfing_spots
-
-__all__ = [
-    "driving_hours_to_meters",
-    "get_area_lat_lon",
-    "get_surfing_spots",
-    "get_wave_forecast",
-    "get_wind_forecast",
-]

src/surf_spot_finder/tools/openmeteo.py

@@ -1,114 +0,0 @@
-import json
-from datetime import datetime, timedelta
-import requests
-
-
-def _extract_hourly_data(data: dict) -> list[dict]:
-    hourly_data = data["hourly"]
-    result = [
-        {k: v for k, v in zip(hourly_data.keys(), values)}
-        for values in zip(*hourly_data.values())
-    ]
-    return result
-
-
-def _filter_by_date(
-    date: datetime, hourly_data: list[dict], timedelta: timedelta = timedelta(hours=1)
-):
-    start_date = date - timedelta
-    end_date = date + timedelta
-    return [
-        item
-        for item in hourly_data
-        if start_date <= datetime.fromisoformat(item["time"]) <= end_date
-    ]
-
-
-def get_wave_forecast(lat: float, lon: float, date: str) -> list[dict]:
-    """Get wave forecast for given location.
-
-    Forecast will include:
-
-    - wave_direction (degrees)
-    - wave_height (meters)
-    - wave_period (seconds)
-    - sea_level_height_msl (meters)
-
-    Args:
-        lat: Latitude of the location.
-        lon: Longitude of the location.
-        date: Date to filter by in any valid ISO 8601 format.
-
-    Returns:
-        Hourly data for wave forecast.
-            Example output:
-
-            ```json
-            [
-                {'time': '2025-03-19T09:00', 'winddirection_10m': 140, 'windspeed_10m': 24.5}, {'time': '2025-03-19T10:00', 'winddirection_10m': 140, 'windspeed_10m': 27.1},
-                {'time': '2025-03-19T10:00', 'winddirection_10m': 140, 'windspeed_10m': 27.1}, {'time': '2025-03-19T11:00', 'winddirection_10m': 141, 'windspeed_10m': 29.2}
-            ]
-            ```
-    """
-    url = "https://marine-api.open-meteo.com/v1/marine"
-    params = {
-        "latitude": lat,
-        "longitude": lon,
-        "hourly": [
-            "wave_direction",
-            "wave_height",
-            "wave_period",
-            "sea_level_height_msl",
-        ],
-    }
-    response = requests.get(url, params=params)
-    response.raise_for_status()
-    data = json.loads(response.content.decode())
-    hourly_data = _extract_hourly_data(data)
-    if date is not None:
-        date = datetime.fromisoformat(date)
-        hourly_data = _filter_by_date(date, hourly_data)
-    if len(hourly_data) == 0:
-        raise ValueError("No data found for the given date")
-    return hourly_data
-
-
-def get_wind_forecast(lat: float, lon: float, date: str) -> list[dict]:
-    """Get wind forecast for given location.
-
-    Forecast will include:
-
-    - wind_direction (degrees)
-    - wind_speed (meters per second)
-
-    Args:
-        lat: Latitude of the location.
-        lon: Longitude of the location.
-        date: Date to filter by in any valid ISO 8601 format.
-
-    Returns:
-        Hourly data for wind forecast.
-            Example output:
-
-            ```json
-            [
-                {"time": "2025-03-18T22:00", "wind_direction": 196, "wind_speed": 9.6},
-                {"time": "2025-03-18T23:00", "wind_direction": 183, "wind_speed": 7.9},
-            ]
-            ```
-    """
-    url = "https://api.open-meteo.com/v1/forecast"
-    params = {
-        "latitude": lat,
-        "longitude": lon,
-        "hourly": ["winddirection_10m", "windspeed_10m"],
-    }
-    response = requests.get(url, params=params)
-    response.raise_for_status()
-    data = json.loads(response.content.decode())
-    hourly_data = _extract_hourly_data(data)
-    date = datetime.fromisoformat(date)
-    hourly_data = _filter_by_date(date, hourly_data)
-    if len(hourly_data) == 0:
-        raise ValueError("No data found for the given date")
-    return hourly_data

src/surf_spot_finder/tools/openstreetmap.py

@@ -1,92 +0,0 @@
-import requests
-import json
-
-
-def get_area_lat_lon(area_name: str) -> tuple[float, float]:
-    """Get the latitude and longitude of an area from Nominatim.
-
-    Uses the [Nominatim API](https://nominatim.org/release-docs/develop/api/Search/).
-
-    Args:
-        area_name: The name of the area.
-
-    Returns:
-        The area found.
-    """
-    response = requests.get(
-        f"https://nominatim.openstreetmap.org/search?q={area_name}&format=json",
-        headers={"User-Agent": "Mozilla/5.0"},
-    )
-    response.raise_for_status()
-    area = json.loads(response.content.decode())
-    return area[0]["lat"], area[0]["lon"]
-
-
-def driving_hours_to_meters(driving_hours: int) -> int:
-    """Convert driving hours to meters assuming a 70 km/h average speed.
-
-
-    Args:
-        driving_hours: The driving hours.
-
-    Returns:
-        The distance in meters.
-    """
-    return driving_hours * 70 * 1000
-
-
-def get_lat_lon_center(bounds: dict) -> tuple[float, float]:
-    """Get the latitude and longitude of the center of a bounding box.
-
-    Args:
-        bounds: The bounding box.
-
-            ```json
-            {
-                "minlat": float,
-                "minlon": float,
-                "maxlat": float,
-                "maxlon": float,
-            }
-            ```
-
-    Returns:
-        The latitude and longitude of the center.
-    """
-    return (
-        (bounds["minlat"] + bounds["maxlat"]) / 2,
-        (bounds["minlon"] + bounds["maxlon"]) / 2,
-    )
-
-
-def get_surfing_spots(
-    lat: float, lon: float, radius: int
-) -> list[tuple[str, tuple[float, float]]]:
-    """Get surfing spots around a given latitude and longitude.
-
-    Uses the [Overpass API](https://wiki.openstreetmap.org/wiki/Overpass_API).
-
-    Args:
-        lat: The latitude.
-        lon: The longitude.
-        radius: The radius in meters.
-
-    Returns:
-        The surfing places found.
-    """
-    overpass_url = "https://overpass-api.de/api/interpreter"
-    query = "[out:json];("
-    query += f'nwr["natural"="beach"](around:{radius},{lat},{lon});'
-    query += f'nwr["natural"="reef"](around:{radius},{lat},{lon});'
-    query += ");out body geom;"
-    params = {"data": query}
-    response = requests.get(
-        overpass_url, params=params, headers={"User-Agent": "Mozilla/5.0"}
-    )
-    response.raise_for_status()
-    elements = response.json()["elements"]
-    return [
-        (element.get("tags", {}).get("name", ""), get_lat_lon_center(element["bounds"]))
-        for element in elements
-        if "surfing" in element.get("tags", {}).get("sport", "") and "bounds" in element
-    ]

tests/unit/tools/test_unit_openmeteo.py

@@ -1,106 +0,0 @@
-import json
-from datetime import datetime, timedelta
-from unittest.mock import patch, MagicMock
-
-from surf_spot_finder.tools import openmeteo
-
-
-def test_extract_hourly_data():
-    data = {
-        "hourly": {
-            "time": ["2023-01-01T00:00", "2023-01-01T01:00"],
-            "wave_height": [1.5, 1.6],
-            "wave_period": [10, 11],
-        }
-    }
-    expected = [
-        {"time": "2023-01-01T00:00", "wave_height": 1.5, "wave_period": 10},
-        {"time": "2023-01-01T01:00", "wave_height": 1.6, "wave_period": 11},
-    ]
-    assert openmeteo._extract_hourly_data(data) == expected
-
-
-def test_filter_by_date():
-    hourly_data = [
-        {"time": "2023-01-01T00:00", "wave_height": 1.5},
-        {"time": "2023-01-01T01:00", "wave_height": 1.6},
-        {"time": "2023-01-01T02:00", "wave_height": 1.7},
-        {"time": "2023-01-01T03:00", "wave_height": 1.8},
-    ]
-    date = datetime.fromisoformat("2023-01-01T01:00")
-    expected = [
-        {"time": "2023-01-01T00:00", "wave_height": 1.5},
-        {"time": "2023-01-01T01:00", "wave_height": 1.6},
-        {"time": "2023-01-01T02:00", "wave_height": 1.7},
-    ]
-    assert openmeteo._filter_by_date(date, hourly_data) == expected
-
-    expected = [
-        {"time": "2023-01-01T01:00", "wave_height": 1.6},
-    ]
-    assert openmeteo._filter_by_date(date, hourly_data, timedelta(hours=0)) == expected
-
-
-def test_get_wave_forecast():
-    with patch("requests.get") as mock_get:
-        mock_response = MagicMock()
-        mock_response.status_code = 200
-        mock_response.content.decode.return_value = json.dumps(
-            {
-                "hourly": {
-                    "time": ["2023-02-02T00:00", "2023-02-02T01:00"],
-                    "wave_direction": [270, 280],
-                    "wave_height": [1.5, 1.6],
-                    "wave_period": [10, 11],
-                    "sea_level_height_msl": [0.5, 0.6],
-                }
-            }
-        )
-        mock_get.return_value = mock_response
-        result = openmeteo.get_wave_forecast(
-            lat=40.0, lon=-3.0, date="2023-02-02T01:00"
-        )
-
-        assert len(result) == 2
-        assert result[1]["time"] == "2023-02-02T01:00"
-        assert result[1]["wave_direction"] == 280
-        assert result[1]["wave_height"] == 1.6
-        assert result[1]["wave_period"] == 11
-        assert result[1]["sea_level_height_msl"] == 0.6
-
-        result_filtered = openmeteo.get_wave_forecast(
-            lat=40.0, lon=-3.0, date="2023-02-02T02:00"
-        )
-        assert len(result_filtered) == 1
-        assert result_filtered[0]["time"] == "2023-02-02T01:00"
-
-
-def test_get_wind_forecast():
-    with patch("requests.get") as mock_get:
-        mock_response = MagicMock()
-        mock_response.status_code = 200
-        mock_response.content.decode.return_value = json.dumps(
-            {
-                "hourly": {
-                    "time": ["2023-02-02T00:00", "2023-02-02T01:00"],
-                    "winddirection_10m": [270, 280],
-                    "windspeed_10m": [10, 11],
-                }
-            }
-        )
-        mock_get.return_value = mock_response
-
-        result = openmeteo.get_wind_forecast(
-            lat=40.0, lon=-3.0, date="2023-02-02T01:00"
-        )
-        assert len(result) == 2
-        assert result[1]["time"] == "2023-02-02T01:00"
-        assert result[1]["winddirection_10m"] == 280
-        assert result[1]["windspeed_10m"] == 11
-
-        result_filtered = openmeteo.get_wind_forecast(
-            lat=40.0, lon=-3.0, date="2023-02-02T02:00"
-        )
-        assert len(result_filtered) == 1
-        assert result_filtered[0]["time"] == "2023-02-02T01:00"
-        assert result[0]["windspeed_10m"] == 10

tests/unit/tools/test_unit_openstreetmap.py

@@ -1,74 +0,0 @@
-import json
-from unittest.mock import MagicMock, patch
-
-from surf_spot_finder.tools import openstreetmap
-
-
-def test_get_area_lat_lon():
-    with patch("requests.get") as mock_get:
-        mock_response = MagicMock()
-        mock_response.status_code = 200
-        mock_response.content.decode.return_value = json.dumps(
-            [{"lat": "40.0", "lon": "-3.0"}]
-        )
-        mock_get.return_value = mock_response
-
-        lat, lon = openstreetmap.get_area_lat_lon("Madrid")
-        assert lat == "40.0"
-        assert lon == "-3.0"
-
-
-def test_driving_hours_to_meters():
-    assert openstreetmap.driving_hours_to_meters(1) == 70000
-
-
-def test_get_lat_lon_center():
-    bounds = {"minlat": 40.0, "minlon": -3.0, "maxlat": 41.0, "maxlon": -2.0}
-    lat, lon = openstreetmap.get_lat_lon_center(bounds)
-    assert lat == 40.5
-    assert lon == -2.5
-
-
-def test_get_surfing_spots():
-    with patch("requests.get") as mock_get:
-        mock_response = MagicMock()
-        mock_response.status_code = 200
-        mock_response.json.return_value = {
-            "elements": [
-                {
-                    "tags": {"name": "Surf Spot 1", "sport": "surfing"},
-                    "bounds": {
-                        "minlat": 40.0,
-                        "minlon": -3.0,
-                        "maxlat": 40.1,
-                        "maxlon": -2.9,
-                    },
-                },
-                {
-                    "tags": {"name": "Beach 2", "sport": "swimming"},
-                    "bounds": {
-                        "minlat": 41.0,
-                        "minlon": -4.0,
-                        "maxlat": 41.1,
-                        "maxlon": -3.9,
-                    },
-                },
-                {
-                    "tags": {"name": "Surf Spot 3", "sport": "surfing"},
-                    "bounds": {
-                        "minlat": 42.0,
-                        "minlon": -5.0,
-                        "maxlat": 42.1,
-                        "maxlon": -4.9,
-                    },
-                },
-            ]
-        }
-        mock_get.return_value = mock_response
-
-        results = openstreetmap.get_surfing_spots(lat=40.5, lon=-3.5, radius=10000)
-        assert len(results) == 2
-        assert results[0][0] == "Surf Spot 1"
-        assert results[0][1] == (40.05, -2.95)
-        assert results[1][0] == "Surf Spot 3"
-        assert results[1][1] == (42.05, -4.95)