diff --git a/.github/workflows/deploy-website-mintlify.yml b/.github/workflows/deploy-website-mintlify.yml new file mode 100644 index 0000000000..91a31aebec --- /dev/null +++ b/.github/workflows/deploy-website-mintlify.yml @@ -0,0 +1,119 @@ +name: mintlify docs + +on: + pull_request: + branches: [main] + paths: + - "autogen/**" + - "website/**" + - ".github/workflows/deploy-website-mintlify.yml" + push: + branches: [main] + paths: + - "autogen/**" + - "website/**" + - ".github/workflows/deploy-website-mintlify.yml" + workflow_dispatch: + merge_group: + types: [checks_requested] +permissions: + contents: write +jobs: + checks: + if: github.event_name != 'push' + runs-on: ubuntu-latest + defaults: + run: + working-directory: website + steps: + - uses: actions/checkout@v4 + with: + lfs: true + fetch-depth: 0 + - uses: actions/setup-node@v4 + with: + node-version: 18.x + - name: setup python + uses: actions/setup-python@v5 + with: + python-version: "3.8" + - name: Install Python dependencies + run: | + python -m pip install --upgrade pip + pip install pydoc-markdown pyyaml termcolor nbconvert + # Pin databind packages as version 4.5.0 is not compatible with pydoc-markdown. + pip install databind.core==4.4.2 databind.json==4.4.2 + + - name: Install quarto + uses: quarto-dev/quarto-actions/setup@v2 + + - name: Generate API documentation + run: | + python ./process_api_reference.py + working-directory: website + + - name: Convert Jupyter notebooks to MDX + run: | + python ./process_notebooks.py render + working-directory: website + + - name: Prepare website content + run: | + # Create a temporary directory for filtered content + mkdir -p temp_website + + # Copy files except .ipynb, node_modules, .quarto, .gitignore, and temp_website + find . -type f -not -path "*/node_modules/*" -not -path "*/.quarto/*" -not -path "./temp_website/*" -not -name "*.ipynb" -not -name ".gitignore" -exec cp --parents {} temp_website/ \; + + mintlify-release: + if: github.event_name != 'pull_request' + runs-on: ubuntu-latest + defaults: + run: + working-directory: website + steps: + - uses: actions/checkout@v4 + with: + lfs: true + fetch-depth: 0 + - uses: actions/setup-node@v4 + with: + node-version: 18.x + - name: setup python + uses: actions/setup-python@v5 + with: + python-version: "3.8" + - name: Install Python dependencies + run: | + python -m pip install --upgrade pip + pip install pydoc-markdown pyyaml termcolor nbconvert + # Pin databind packages as version 4.5.0 is not compatible with pydoc-markdown. + pip install databind.core==4.4.2 databind.json==4.4.2 + + - name: Install quarto + uses: quarto-dev/quarto-actions/setup@v2 + + - name: Generate API documentation + run: | + python ./process_api_reference.py + working-directory: website + + - name: Convert Jupyter notebooks to MDX + run: | + python ./process_notebooks.py render + working-directory: website + + - name: Prepare website content + run: | + # Create a temporary directory for filtered content + mkdir -p temp_website + + # Copy files except .ipynb, node_modules, .quarto, .gitignore, and temp_website + find . -type f -not -path "*/node_modules/*" -not -path "*/.quarto/*" -not -path "./temp_website/*" -not -name "*.ipynb" -not -name ".gitignore" -exec cp --parents {} temp_website/ \; + + - name: Deploy to Mintlify + uses: peaceiris/actions-gh-pages@v3 + with: + github_token: ${{ secrets.GITHUB_TOKEN }} + publish_branch: mintlify-pages + publish_dir: ./website/temp_website diff --git a/.github/workflows/deploy-website.yml b/.github/workflows/deploy-website.yml index 8ff5a1b536..cf0f89e6a7 100644 --- a/.github/workflows/deploy-website.yml +++ b/.github/workflows/deploy-website.yml @@ -1,121 +1,47 @@ -name: docs +name: Deploy Redirect Page on: - pull_request: - branches: [main] - path: - - "autogen/*" - - "website/*" - - ".github/workflows/deploy-website.yml" push: branches: [main] - path: - - "autogen/*" - - "website/*" + paths: - ".github/workflows/deploy-website.yml" workflow_dispatch: - merge_group: - types: [checks_requested] permissions: contents: write jobs: - checks: - if: github.event_name != 'push' - runs-on: ubuntu-latest - defaults: - run: - working-directory: website - steps: - - uses: actions/checkout@v4 - with: - lfs: true - - uses: actions/setup-node@v4 - with: - node-version: 18.x - - name: setup python - uses: actions/setup-python@v5 - with: - python-version: "3.12" - - name: pydoc-markdown install - run: | - python -m pip install --upgrade pip - pip install pydoc-markdown pyyaml termcolor - # Pin databind packages as version 4.5.0 is not compatible with pydoc-markdown. - pip install databind.core==4.4.2 databind.json==4.4.2 - - name: pydoc-markdown run - run: | - pydoc-markdown - - name: quarto install - working-directory: ${{ runner.temp }} - run: | - wget -q https://github.com/quarto-dev/quarto-cli/releases/download/v1.5.23/quarto-1.5.23-linux-amd64.tar.gz - tar -xzf quarto-1.5.23-linux-amd64.tar.gz - echo "$(pwd)/quarto-1.5.23/bin/" >> $GITHUB_PATH - - name: Process notebooks - run: | - python process_notebooks.py render - - name: Test Build - run: | - if [ -e yarn.lock ]; then - yarn install --frozen-lockfile --ignore-engines - yarn build - elif [ -e package-lock.json ]; then - npm ci - npm run build - else - npm i --legacy-peer-deps - npm run build - fi - gh-release: - if: github.event_name != 'pull_request' + deploy: runs-on: ubuntu-latest - defaults: - run: - working-directory: website steps: - - uses: actions/checkout@v4 - with: - lfs: true - - uses: actions/setup-node@v4 - with: - node-version: 18.x - - name: setup python - uses: actions/setup-python@v5 - with: - python-version: "3.12" - - name: pydoc-markdown install - run: | - python -m pip install --upgrade pip - pip install pydoc-markdown pyyaml termcolor - # Pin databind packages as version 4.5.0 is not compatible with pydoc-markdown. - pip install databind.core==4.4.2 databind.json==4.4.2 - - name: pydoc-markdown run - run: | - pydoc-markdown - - name: quarto install - working-directory: ${{ runner.temp }} - run: | - wget -q https://github.com/quarto-dev/quarto-cli/releases/download/v1.5.23/quarto-1.5.23-linux-amd64.tar.gz - tar -xzf quarto-1.5.23-linux-amd64.tar.gz - echo "$(pwd)/quarto-1.5.23/bin/" >> $GITHUB_PATH - - name: Process notebooks - run: | - python process_notebooks.py render - - name: Build website - run: | - if [ -e yarn.lock ]; then - yarn install --frozen-lockfile --ignore-engines - yarn build - elif [ -e package-lock.json ]; then - npm ci - npm run build - else - npm i --legacy-peer-deps - npm run build - fi + # Step 1: Check out the repository + - name: Checkout repository + uses: actions/checkout@v4 + + # Step 2: Create a fresh redirect HTML file + - name: Create redirect HTML + run: | + mkdir -p dist + cat > dist/index.html << 'EOF' + + + + + + + Page Redirection + + + If you are not redirected automatically, follow this link to the new documentation. + + + EOF + + # Step 3: Deploy to gh-pages branch - name: Deploy to GitHub Pages uses: peaceiris/actions-gh-pages@v3 with: github_token: ${{ secrets.GITHUB_TOKEN }} - # Build output to publish to the `gh-pages` branch: - publish_dir: ./website/build + publish_dir: ./dist + force_orphan: true + user_name: 'github-actions[bot]' + user_email: 'github-actions[bot]@users.noreply.github.com' + commit_message: 'Deploy redirect page' diff --git a/autogen/oai/anthropic.py b/autogen/oai/anthropic.py index 3d9e1ed8fc..44dc7bd60d 100644 --- a/autogen/oai/anthropic.py +++ b/autogen/oai/anthropic.py @@ -11,6 +11,7 @@ Install the `anthropic` package by running `pip install --upgrade anthropic`. - https://docs.anthropic.com/en/docs/quickstart-guide +```python import autogen config_list = [ @@ -22,12 +23,14 @@ ] assistant = autogen.AssistantAgent("assistant", llm_config={"config_list": config_list}) +``` Example usage for Anthropic Bedrock: Install the `anthropic` package by running `pip install --upgrade anthropic`. - https://docs.anthropic.com/en/docs/quickstart-guide +```python import autogen config_list = [ @@ -42,7 +45,7 @@ ] assistant = autogen.AssistantAgent("assistant", llm_config={"config_list": config_list}) - +``` """ from __future__ import annotations diff --git a/autogen/oai/bedrock.py b/autogen/oai/bedrock.py index 808aad5d76..5d8f34fe51 100644 --- a/autogen/oai/bedrock.py +++ b/autogen/oai/bedrock.py @@ -11,6 +11,7 @@ Install the `boto3` package by running `pip install --upgrade boto3`. - https://docs.aws.amazon.com/bedrock/latest/userguide/conversation-inference.html +```python import autogen config_list = [ @@ -25,7 +26,7 @@ ] assistant = autogen.AssistantAgent("assistant", llm_config={"config_list": config_list}) - +``` """ from __future__ import annotations diff --git a/autogen/retrieve_utils.py b/autogen/retrieve_utils.py index e20fec9656..774e5e57d3 100644 --- a/autogen/retrieve_utils.py +++ b/autogen/retrieve_utils.py @@ -52,6 +52,7 @@ "yaml", "yml", "pdf", + "mdx", ] UNSTRUCTURED_FORMATS = [ "doc", diff --git a/notebook/agentchat_custom_model.ipynb b/notebook/agentchat_custom_model.ipynb index 354f1c2556..101c4b64a6 100644 --- a/notebook/agentchat_custom_model.ipynb +++ b/notebook/agentchat_custom_model.ipynb @@ -441,7 +441,7 @@ ], "metadata": { "front_matter": { - "description": "Define and laod a custom model", + "description": "Define and load a custom model", "tags": [ "integration", "custom model" diff --git a/test/test_retrieve_utils.py b/test/test_retrieve_utils.py index 18bb1ea23f..f9e8d71b74 100755 --- a/test/test_retrieve_utils.py +++ b/test/test_retrieve_utils.py @@ -226,7 +226,7 @@ def test_retrieve_utils(self): dir_path="./website/docs", client=client, collection_name="autogen-docs", - custom_text_types=["txt", "md", "rtf", "rst"], + custom_text_types=["txt", "md", "rtf", "rst", "mdx"], get_or_create=True, ) results = query_vector_db( diff --git a/website/.gitignore b/website/.gitignore index 21fd07b316..9cd41d9a23 100644 --- a/website/.gitignore +++ b/website/.gitignore @@ -9,7 +9,7 @@ package-lock.json .docusaurus .cache-loader docs/reference -/docs/notebooks +/notebooks docs/tutorial/*.mdx docs/tutorial/**/*.png diff --git a/website/README.md b/website/README.md index f65890830a..22a4e10d6a 100644 --- a/website/README.md +++ b/website/README.md @@ -1,22 +1,15 @@ # Website -This website is built using [Docusaurus 3](https://docusaurus.io/), a modern static website generator. +This website is built using [Mintlify](https://mintlify.com/docs/quickstart), a modern website generator. ## Prerequisites -To build and test documentation locally, begin by downloading and installing [Node.js](https://nodejs.org/en/download/), and then installing [Yarn](https://classic.yarnpkg.com/en/). -On Windows, you can install via the npm package manager (npm) which comes bundled with Node.js: - -```console -npm install --global yarn -``` +To build and test documentation locally, begin by downloading and installing [Node.js](https://nodejs.org/en/download/) and [Mintlify CLI](https://www.npmjs.com/package/mintlify) ## Installation ```console pip install pydoc-markdown pyyaml colored -cd website -yarn install ``` ### Install Quarto @@ -34,7 +27,13 @@ Navigate to the `website` folder and run: ```console pydoc-markdown python ./process_notebooks.py render -yarn start +npm install +``` + +Run the following command at the root of your documentation (where mint.json is) + +```console +npm run mintlify:dev ``` This command starts a local development server and opens up a browser window. Most changes are reflected live without having to restart the server. diff --git a/website/blog/2023-04-21-LLM-tuning-math/index.md b/website/blog/2023-04-21-LLM-tuning-math/index.mdx similarity index 100% rename from website/blog/2023-04-21-LLM-tuning-math/index.md rename to website/blog/2023-04-21-LLM-tuning-math/index.mdx diff --git a/website/blog/2023-07-14-Local-LLMs/index.md b/website/blog/2023-07-14-Local-LLMs/index.mdx similarity index 98% rename from website/blog/2023-07-14-Local-LLMs/index.md rename to website/blog/2023-07-14-Local-LLMs/index.mdx index 2747509885..e650c73988 100644 --- a/website/blog/2023-07-14-Local-LLMs/index.md +++ b/website/blog/2023-07-14-Local-LLMs/index.mdx @@ -64,7 +64,7 @@ class CompletionResponseStreamChoice(BaseModel): ``` -## Interact with model using `oai.Completion` (requires openai<1) +## Interact with model using `oai.Completion` (requires openai{'<'}1) Now the models can be directly accessed through openai-python library as well as `autogen.oai.Completion` and `autogen.oai.ChatCompletion`. diff --git a/website/blog/2024-02-02-AutoAnny/index.mdx b/website/blog/2024-02-02-AutoAnny/index.mdx index 0968a1feec..fb03d00964 100644 --- a/website/blog/2024-02-02-AutoAnny/index.mdx +++ b/website/blog/2024-02-02-AutoAnny/index.mdx @@ -5,10 +5,8 @@ authors: tags: [AutoGen] --- -import AutoAnnyLogo from './img/AutoAnnyLogo.jpg'; -
- AutoAnny Logo + AutoAnny Logo

Anny is a Discord bot powered by AutoGen to help AutoGen's Discord server.

diff --git a/website/blog/2024-03-03-AutoGen-Update/index.mdx b/website/blog/2024-03-03-AutoGen-Update/index.mdx index d8dc0eefcd..11e7b7301b 100644 --- a/website/blog/2024-03-03-AutoGen-Update/index.mdx +++ b/website/blog/2024-03-03-AutoGen-Update/index.mdx @@ -38,10 +38,10 @@ Many users have deep understanding of the value in different dimensions, such as > The same reason autogen is significant is the same reason OOP is a good idea. Autogen packages up all that complexity into an agent I can create in one line, or modify with another. - +*/} Over time, more and more users share their experiences in using or contributing to autogen. diff --git a/website/blog/2024-03-11-AutoDefense/Defending LLMs Against Jailbreak Attacks with AutoDefense.mdx b/website/blog/2024-03-11-AutoDefense/index.mdx similarity index 100% rename from website/blog/2024-03-11-AutoDefense/Defending LLMs Against Jailbreak Attacks with AutoDefense.mdx rename to website/blog/2024-03-11-AutoDefense/index.mdx diff --git a/website/docs/Examples.md b/website/docs/Examples.mdx similarity index 97% rename from website/docs/Examples.md rename to website/docs/Examples.mdx index e7084d1284..aa92f534a7 100644 --- a/website/docs/Examples.md +++ b/website/docs/Examples.mdx @@ -1,4 +1,6 @@ -# Examples +--- +title: Examples by Category +--- ## Automated Multi Agent Chat @@ -43,7 +45,7 @@ Links to notebook examples: ### Applications - Automated Continual Learning from New Data - [View Notebook](/docs/notebooks/agentchat_stream) - +{/* - [OptiGuide](https://github.com/microsoft/optiguide) - Coding, Tool Using, Safeguarding & Question Answering for Supply Chain Optimization */} - [AutoAnny](https://github.com/ag2ai/build-with-ag2/tree/main/samples/apps/auto-anny) - A Discord bot built using AutoGen ### RAG @@ -99,7 +101,7 @@ Links to notebook examples: ### Long Context Handling - +{/* - Conversations with Chat History Compression Enabled - [View Notebook](https://github.com/ag2ai/ag2/blob/main/notebook/agentchat_compression.ipynb) */} - Long Context Handling as A Capability - [View Notebook](/docs/notebooks/agentchat_transform_messages) ### Evaluation and Assessment diff --git a/website/docs/FAQ.mdx b/website/docs/FAQ.mdx index 5d6152bcc8..eda56e5133 100644 --- a/website/docs/FAQ.mdx +++ b/website/docs/FAQ.mdx @@ -1,8 +1,7 @@ -import TOCInline from "@theme/TOCInline"; - -# Frequently Asked Questions - - +--- +title: Frequently Asked Questions +sidebarTitle: FAQ +--- ## Install the correct package - `autogen` @@ -34,8 +33,8 @@ In version >=1, OpenAI renamed their `api_base` parameter to `base_url`. So for Yes. You currently have two options: -- Autogen can work with any API endpoint which complies with OpenAI-compatible RESTful APIs - e.g. serving local LLM via FastChat or LM Studio. Please check https://ag2ai.github.io/ag2/blog/2023/07/14/Local-LLMs for an example. -- You can supply your own custom model implementation and use it with Autogen. Please check https://ag2ai.github.io/ag2/blog/2024/01/26/Custom-Models for more information. +- Autogen can work with any API endpoint which complies with OpenAI-compatible RESTful APIs - e.g. serving local LLM via FastChat or LM Studio. Please check [here](/blog/2023-07-14-Local-LLMs) for an example. +- You can supply your own custom model implementation and use it with Autogen. Please check [here](/blog/2024-01-26-Custom-Models) for more information. ## Handle Rate Limit Error and Timeout Error @@ -52,9 +51,9 @@ When you call `initiate_chat` the conversation restarts by default. You can use ## `max_consecutive_auto_reply` vs `max_turn` vs `max_round` -- [`max_consecutive_auto_reply`](https://ag2ai.github.io/ag2/docs/reference/agentchat/conversable_agent#max_consecutive_auto_reply) the maximum number of consecutive auto replie (a reply from an agent without human input is considered an auto reply). It plays a role when `human_input_mode` is not "ALWAYS". -- [`max_turns` in `ConversableAgent.initiate_chat`](https://ag2ai.github.io/ag2/docs/reference/agentchat/conversable_agent#initiate_chat) limits the number of conversation turns between two conversable agents (without differentiating auto-reply and reply/input from human) -- [`max_round` in GroupChat](https://ag2ai.github.io/ag2/docs/reference/agentchat/groupchat#groupchat-objects) specifies the maximum number of rounds in a group chat session. +- [`max_consecutive_auto_reply`](/docs/reference/agentchat/conversable_agent#max_consecutive_auto_reply) the maximum number of consecutive auto replie (a reply from an agent without human input is considered an auto reply). It plays a role when `human_input_mode` is not "ALWAYS". +- [`max_turns` in `ConversableAgent.initiate_chat`](/docs/reference/agentchat/conversable_agent#initiate_chat) limits the number of conversation turns between two conversable agents (without differentiating auto-reply and reply/input from human) +- [`max_round` in GroupChat](/docs/reference/agentchat/groupchat#groupchat-objects) specifies the maximum number of rounds in a group chat session. ## How do we decide what LLM is used for each agent? How many agents can be used? How do we decide how many agents in the group? @@ -109,7 +108,7 @@ You can also develop your AutoGen application in a docker container. For example, when developing in [GitHub codespace](https://codespaces.new/ag2ai/ag2?quickstart=1), AutoGen runs in a docker container. If you are not developing in GitHub Codespaces, -follow instructions [here](installation/Docker.md#option-1-install-and-run-autogen-in-docker) +follow instructions [here](/docs/installation/Docker#option-1-install-and-run-autogen-in-docker) to install and run AutoGen in docker. ## Agents keep thanking each other when using `gpt-3.5-turbo` @@ -159,7 +158,7 @@ Explanation: Per [this gist](https://gist.github.com/defulmere/8b9695e415a442710 (from [issue #478](https://github.com/microsoft/autogen/issues/478)) -See here https://ag2ai.github.io/ag2/docs/reference/agentchat/conversable_agent/#register_reply +See here /docs/reference/agentchat/conversable_agent/#register_reply For example, you can register a reply function that gets called when `generate_reply` is called for an agent. @@ -188,11 +187,11 @@ In the above, we register a `print_messages` function that is called each time t ## How to get last message ? -Refer to https://ag2ai.github.io/ag2/docs/reference/agentchat/conversable_agent/#last_message +Refer to /docs/reference/agentchat/conversable_agent/#last_message ## How to get each agent message ? -Please refer to https://ag2ai.github.io/ag2/docs/reference/agentchat/conversable_agent#chat_messages +Please refer to /docs/reference/agentchat/conversable_agent#chat_messages ## When using autogen docker, is it always necessary to reinstall modules? diff --git a/website/docs/Gallery.mdx b/website/docs/Gallery.mdx index 8a5cd334ba..cfb19a6e99 100644 --- a/website/docs/Gallery.mdx +++ b/website/docs/Gallery.mdx @@ -1,18 +1,18 @@ --- -hide_table_of_contents: true +title: Application Gallery +mode: "wide" --- -import GalleryPage from '../src/components/GalleryPage'; -import galleryData from "../src/data/gallery.json"; - -# Gallery +import { GalleryPage } from "/snippets/components/GalleryPage.mdx"; +import { galleryItems } from "/snippets/data/GalleryItems.mdx"; +import { ClientSideComponent } from "/snippets/components/ClientSideComponent.mdx"; This page contains a list of demos that use AutoGen in various applications from the community. **Contribution guide:** Built something interesting with AutoGen? Submit a PR to add it to the list! See the [Contribution Guide below](#contributing) for more details. - + ## Contributing diff --git a/website/docs/Getting-Started.mdx b/website/docs/Getting-Started.mdx index 4a75431597..d3fbb6d6ec 100644 --- a/website/docs/Getting-Started.mdx +++ b/website/docs/Getting-Started.mdx @@ -1,7 +1,6 @@ -import Tabs from "@theme/Tabs"; -import TabItem from "@theme/TabItem"; - -# Getting Started +--- +title: "Getting Started" +--- AG2 (formerly AutoGen) is an open-source programming framework for building AI agents and facilitating cooperation among multiple agents to solve tasks. AG2 aims to provide an easy-to-use @@ -10,7 +9,7 @@ like PyTorch for Deep Learning. It offers features such as agents that can conve with other agents, LLM and tool use support, autonomous and human-in-the-loop workflows, and multi-agent conversation patterns. -![AG2 Overview](/img/autogen_agentchat.png) +![AG2 Overview](/static/img/autogen_agentchat.png) ### Main Features @@ -37,12 +36,15 @@ Microsoft, Penn State University, and University of Washington. ```sh pip install autogen ``` -:::tip -You can also install with different [optional dependencies](/docs/installation/Optional-Dependencies). -::: +
+ + You can also install with different [optional + dependencies](/docs/installation/Optional-Dependencies). + +
- + ```python import os @@ -59,12 +61,14 @@ user_proxy.initiate_chat( ) ``` - - + + -:::warning -When asked, be sure to check the generated code before continuing to ensure it is safe to run. -::: +
+ + When asked, be sure to check the generated code before continuing to ensure it is safe to run. + +
```python import os @@ -85,8 +89,8 @@ user_proxy.initiate_chat( ) ``` - - + + ```python import os @@ -110,13 +114,16 @@ with autogen.coding.DockerCommandLineCodeExecutor(work_dir="coding") as code_exe Open `coding/plot.png` to see the generated plot. - + -:::tip -Learn more about configuring LLMs for agents [here](/docs/topics/llm_configuration). -::: +
+ + Learn more about configuring LLMs for agents + [here](/docs/topics/llm_configuration). + +
#### Multi-Agent Conversation Framework @@ -125,7 +132,7 @@ By automating chat among multiple capable agents, one can easily make them colle The figure below shows an example conversation flow with AG2. -![Agent Chat Example](/img/chat_example.png) +![Agent Chat Example](/static/img/chat_example.png) ### Where to Go Next? @@ -141,7 +148,7 @@ The figure below shows an example conversation flow with AG2. If you like our project, please give it a [star](https://github.com/ag2ai/ag2) on GitHub. If you are interested in contributing, please read [Contributor's Guide](/docs/contributor-guide/contributing). + + {resource.linkText} + + + + ))} + + + ); +}; diff --git a/website/snippets/data/GalleryItems.mdx b/website/snippets/data/GalleryItems.mdx new file mode 100644 index 0000000000..f6ea6f2c78 --- /dev/null +++ b/website/snippets/data/GalleryItems.mdx @@ -0,0 +1,239 @@ +export const galleryItems = [ + { + "title": "AutoTx - Crypto Transactions Agent", + "link": "https://www.agentcoin.org/blog/autotx", + "description": "Generates on-chain transactions, which are submitted to a smart account so users can easily approve & execute them.", + "image": "autotx.png", + "tags": ["tools", "groupchat", "app", "blockchain"] + }, + { + "title": "AutoGen Virtual Focus Group", + "link": "https://github.com/msamylea/autogen_focus_group", + "description": "A virtual consumer focus group with multiple custom personas, product details, and final analysis created with AutoGen, Ollama/Llama3, and Streamlit.", + "image": "default.png", + "tags": ["ui", "groupchat"] + }, + { + "title": "Function Generator & Validator", + "link": "https://github.com/abhaymathur21/TensionCode", + "description": "A platform where user-required code is generated and simultaneously validated against sample data by AutoGen.", + "image": "TensionCode.png", + "tags": ["app", "ui"] + }, + { + "title": "Autogen Robot", + "link": "https://github.com/AaronWard/generative-ai-workbook/tree/main/personal_projects/19.autogen-robot", + "description": "This project showcases how agent's can act as a brain to control a physical robot.", + "image": "robot.jpg", + "tags": ["robotics"] + }, + { + "title": "AutoGen Group Chat Playground", + "link": "https://huggingface.co/spaces/thinkall/AutoGen_Playground", + "description": "A huggingface space to explore AutoGen group chat, build agents with zero-code, and access source code for reuse.", + "tags": ["ui"] + }, + { + "title": "A Stateful Dev Environment Powered by Jupyter Notebook", + "link": "https://github.com/olimoz/AI_Teams_AutoGen/blob/main/JupyterNotebooksForAutoGen.ipynb", + "description": "An AutoGen Teams Powered by Jupyter notebook.", + "tags": ["extension", "tools"] + }, + { + "title": "Solving Security Vulnerabilities with AutoGen", + "link": "https://www.linkedin.com/pulse/solving-security-vulnerabilities-llms-society-mind-model-leah-bonser-kcswc?trk=public_post_feed-article-content", + "description": "An article discussing the use of AutoGen to tackle security vulnerabilities.", + "image": "default.png", + "tags": ["app"] + }, + { + "title": "Research Agents via AutoGen", + "link": "https://youtu.be/AVInhYBUnKs?feature=shared", + "description": "A guide to building a team of AI research agents using AutoGen.", + "image": "default.png", + "tags": ["groupchat", "tools"] + }, + { + "title": "AutoGen with Ollama and LiteLLM", + "link": "https://youtu.be/y7wMTwJN7rA", + "description": "A demonstration of integrating Ollama, LiteLLM, and AutoGen.", + "image": "default.png", + "tags": ["extension"] + }, + { + "title": "AutoGen Engineer", + "link": "https://chat.openai.com/g/g-Y50TY4F35-AutoGen-engineer", + "description": "Join the AutoGen Engineer group chat to collaborate and build with others.", + "image": "default.png", + "tags": ["groupchat", "app"] + }, + { + "title": "AutoGen with Obsidian", + "link": "https://youtu.be/iWdVAr4xMkg", + "description": "Learn how to integrate AutoGen with Obsidian for note-taking and management.", + "image": "default.png", + "tags": ["tools", "app"] + }, + { + "title": "AutoGen Builder GPT", + "link": "https://chat.openai.com/g/g-EwugVj4zq-AutoGen-builder", + "description": "A platform for building conversational AI agents with AutoGen Builder GPT.", + "image": "default.png", + "tags": ["groupchat", "ui"] + }, + { + "title": "AutoGen Multi-Round Human Interaction Chatbot with Gradio 4.0", + "link": "https://huggingface.co/spaces/thinkall/AutoGen-human-input-demo", + "description": "Experience a multi-round human interaction chatbot built with AutoGen and Gradio 4.0.", + "image": "default.png", + "tags": ["ui", "app"] + }, + { + "title": "Agentcloud.dev (UI for AutoGen)", + "link": "https://github.com/rnadigital/agentcloud", + "description": "Agentcloud.dev provides a user interface for managing and collaborating with AutoGen agents.", + "image": "default.png", + "tags": ["ui"] + }, + { + "title": "Next.js + FASTAPI Based UI for AutoGen", + "link": "https://github.com/victordibia/AutoGen-ui", + "description": "A project featuring a UI for AutoGen built with Next.js and FastAPI.", + "image": "default.png", + "tags": ["ui"] + }, + { + "title": "Full Function UI for AutoGen Powered by Panel", + "link": "https://youtu.be/9lSaRP9GLCY?si=HihULAe3FFyteFHY", + "description": "A UI allows users to directly interact with AI agents in real-time during a group chat scenario", + "tags": ["ui"] + }, + { + "title": "AutoGen Monitoring and Observability", + "link": "https://docs.arize.com/phoenix/quickstart/llm-traces/autogen-support", + "description": "Documentation on monitoring and observability features for AutoGen.", + "image": "default.png", + "tags": ["extension"] + }, + { + "title": "Postgres Data Analytics AI Agent with AutoGen", + "link": "https://www.youtube.com/playlist?list=PLS_o2ayVCKvDzj2YxeFqMq9UbR1PkPEh0", + "description": "Utilizing AutoGen to speak directly to Postgres Database.", + "image": "default.png", + "tags": ["tools", "app"] + }, + { + "title": "AutoGen with Local LLMs", + "link": "https://hackernoon.com/beep-beep-bop-bop-how-to-deploy-multiple-ai-agents-using-local-llms", + "description": "An article on deploying multiple AI agents using local LLMs with AutoGen.", + "image": "default.png", + "tags": ["extension"] + }, + { + "title": "AutoGen with FastApi backend and React Frontend", + "link": "https://github.com/bonadio/AutoGenwebdemo", + "description": "A demonstration of using AutoGen with a FastAPI backend and React frontend.", + "image": "default.png", + "tags": ["ui"] + }, + { + "title": "Talk to AutoGen Agents Using Whisper and Gradio", + "link": "https://youtu.be/WysBjwJoulo", + "description": "Interact with AutoGen agents using Whisper and Gradio interfaces.", + "image": "default.png", + "tags": ["ui"] + }, + { + "title": "AutoGen + LangChain + ChromaDB = You Super AI Assistant", + "link": "https://www.youtube.com/watch?v=fd9fcRhYoFQ", + "description": "Create a super AI assistant combining AutoGen, LangChain, and ChromaDB.", + "image": "default.png", + "tags": ["app"] + }, + { + "title": "AutoGen + Flowise = Super AI Agents on No-Code Platform", + "link": "https://github.com/sugarforever/LangChain-Advanced/blob/main/Integrations/AutoGen/autogen_flowise_ai_agent.ipynb", + "description": "Build super AI agents on a no-code platform using AutoGen and Flowise.", + "image": "default.png", + "tags": ["app"] + }, + { + "title": "AutoGen with RunPod and TextGen WebUI", + "link": "https://youtu.be/FHXmiAvloUg", + "description": "Learn how to use AutoGen with RunPod and TextGen WebUI for enhanced AI agent integration.", + "image": "default.png", + "tags": ["ui", "extension"] + }, + { + "title": "Jarvis Collaborates with AutoGen for Tweet Analysis", + "link": "https://github.com/ngaut/jarvis#demo", + "description": "Explore how Jarvis collaborates with AutoGen for tweet analysis.", + "image": "default.png", + "tags": ["tools", "app"] + }, + { + "title": "AutoGen + LangChain + PlayHT = Super AI Agent that Speaks", + "link": "https://www.youtube.com/watch?v=zo2ft4Qje1Y", + "description": "Combine AutoGen, LangChain, and PlayHT to create a speaking super AI agent.", + "image": "default.png", + "tags": ["tools", "app"] + }, + { + "title": "AutoGen Flow with FastAPI and Nextjs", + "link": "https://github.com/jaemil/agentsflow", + "description": "A development flow using AutoGen with FastAPI and Next.js.", + "image": "default.png", + "tags": ["ui"] + }, + { + "title": "Build Vision-Enabled AI Agents with AutoGen + Llava", + "link": "https://youtu.be/JgVb8A6OJwM", + "description": "Tutorial on building vision-enabled AI agents using AutoGen and llava.", + "image": "default.png", + "tags": ["tools", "app"] + }, + { + "title": "AutoGen + Chainlit chat interface with multi-agent conversation", + "link": "https://github.com/antoineross/Autogen-UI/tree/main", + "description": "Chainlit chat interface with multi-agent conversation between agents to complete a tasks", + "image": "default.png", + "tags": ["ui"] + }, + { + "title": "XForce IDE: Build AutoGen based workforces from a drag and drop UI", + "link": "https://ide.x-force.ai", + "description": "X-Force IDE is a low-code, agent-as-a-service UI framework that lets you create AutoGen-based workforces from a drag-and-drop-like user interface.", + "image": "x-force-ide-ui.png", + "tags": ["ui"] + }, + { + "title": "Multimodal Webagent created with AutoGen and OpenAI's Assistants API", + "link": "https://github.com/schauppi/MultimodalWebAgent", + "description": "A multimodal webbrowsing agent that autonomously browses the web.", + "image": "webagent.jpg", + "tags": ["tools", "app"] + }, + { + "title": "Create Issues from Code Commits - using Autogen", + "link": "https://blog.composio.dev/automating-task-creation-with-openai-assistant-from-code-comments-to-linear-issues/", + "description": "Automatically creating linear tasks and assigning them to the right person, project, and team from GitHub commits using AutoGen Agents.", + "image": "composio-autogen.png", + "tags": ["tools"] + }, + { + "title": "Agentok - Autogen Visualized", + "link": "https://github.com/hughlv/agentok", + "description": "Offering intuitive visual tools that streamline the creation and management of complex AutoGen workflows.", + "image": "agentok.png", + "tags": [ + "tools", "ui", "app" + ] + }, + { + "title": "Expense Tracker - using Autogen", + "link": "https://github.com/Kirushikesh/Personal-Finance-Agent", + "description": "Tracks personal finance income and expenses then helps the user to analyse it using AutoGen agents.", + "image": "default.png", + "tags": ["tools", "app"] + } +] diff --git a/website/snippets/data/NotebooksMetadata.mdx b/website/snippets/data/NotebooksMetadata.mdx new file mode 100644 index 0000000000..d5c69dcf3a --- /dev/null +++ b/website/snippets/data/NotebooksMetadata.mdx @@ -0,0 +1,1007 @@ +{/* +Auto-generated file - DO NOT EDIT +Please edit the add_front_matter_to_metadata_mdx function in process_notebooks.py +*/} + +export const notebooksMetadata = [ + { + "title": "Using RetrieveChat Powered by MongoDB Atlas for Retrieve Augmented Code Generation and Question Answering", + "link": "/notebooks/agentchat_RetrieveChat_mongodb", + "description": "Explore the use of AutoGen's RetrieveChat for tasks like code generation from docstrings, answering complex questions with human feedback, and exploiting features like Update Context, custom prompts, and few-shot learning.", + "image": null, + "tags": [ + "MongoDB", + "integration", + "RAG" + ], + "source": "/notebook/agentchat_RetrieveChat_mongodb.ipynb" + }, + { + "title": "Using RetrieveChat Powered by PGVector for Retrieve Augmented Code Generation and Question Answering", + "link": "/notebooks/agentchat_RetrieveChat_pgvector", + "description": "Explore the use of AutoGen's RetrieveChat for tasks like code generation from docstrings, answering complex questions with human feedback, and exploiting features like Update Context, custom prompts, and few-shot learning.", + "image": null, + "tags": [ + "PGVector", + "integration", + "RAG" + ], + "source": "/notebook/agentchat_RetrieveChat_pgvector.ipynb" + }, + { + "title": "Using RetrieveChat with Qdrant for Retrieve Augmented Code Generation and Question Answering", + "link": "/notebooks/agentchat_RetrieveChat_qdrant", + "description": "This notebook demonstrates the usage of QdrantRetrieveUserProxyAgent for RAG.", + "image": null, + "tags": [ + "Qdrant", + "integration", + "RAG" + ], + "source": "/notebook/agentchat_RetrieveChat_qdrant.ipynb" + }, + { + "title": "Agent Tracking with AgentOps", + "link": "/notebooks/agentchat_agentops", + "description": "Use AgentOps to simplify the development process and monitor your agents in production.", + "image": null, + "tags": [ + "integration", + "monitoring", + "debugging" + ], + "source": "/notebook/agentchat_agentops.ipynb" + }, + { + "title": "AgentOptimizer: An Agentic Way to Train Your LLM Agent", + "link": "/notebooks/agentchat_agentoptimizer", + "description": "AgentOptimizer is able to prompt LLMs to iteratively optimize function/skills of AutoGen agents according to the historical conversation and performance.", + "image": null, + "tags": [ + "optimization", + "tool/function" + ], + "source": "/notebook/agentchat_agentoptimizer.ipynb" + }, + { + "title": "Task Solving with Code Generation, Execution and Debugging", + "link": "/notebooks/agentchat_auto_feedback_from_code_execution", + "description": "Use conversable language learning model agents to solve tasks and provide automatic feedback through a comprehensive example of writing, executing, and debugging Python code to compare stock price changes.", + "image": null, + "tags": [ + "code generation" + ], + "source": "/notebook/agentchat_auto_feedback_from_code_execution.ipynb" + }, + { + "title": "Assistants with Azure Cognitive Search and Azure Identity", + "link": "/notebooks/agentchat_azr_ai_search", + "description": "This notebook demonstrates the use of Assistant Agents in conjunction with Azure Cognitive Search and Azure Identity", + "image": null, + "tags": [ + "integration", + "RAG", + "Azure Identity", + "Azure AI Search" + ], + "source": "/notebook/agentchat_azr_ai_search.ipynb" + }, + { + "title": "CaptainAgent", + "link": "/notebooks/agentchat_captainagent", + "description": "Introducing CaptainAgent, a powerful agent that can manage and orchestrate other agents and tools to solve complex tasks.", + "image": null, + "tags": [ + "autobuild", + "CaptainAgent" + ], + "source": "/notebook/agentchat_captainagent.ipynb" + }, + { + "title": "Usage tracking with AutoGen", + "link": "/notebooks/agentchat_cost_token_tracking", + "description": "cost calculation", + "image": null, + "tags": [ + "cost" + ], + "source": "/notebook/agentchat_cost_token_tracking.ipynb" + }, + { + "title": "Agent Chat with custom model loading", + "link": "/notebooks/agentchat_custom_model", + "description": "Define and load a custom model", + "image": null, + "tags": [ + "integration", + "custom model" + ], + "source": "/notebook/agentchat_custom_model.ipynb" + }, + { + "title": "Agent Chat with Multimodal Models: DALLE and GPT-4V", + "link": "/notebooks/agentchat_dalle_and_gpt4v", + "description": "Multimodal agent chat with DALL-E and GPT-4v.", + "image": null, + "tags": [ + "multimodal", + "gpt-4v" + ], + "source": "/notebook/agentchat_dalle_and_gpt4v.ipynb" + }, + { + "title": "Use AutoGen in Databricks with DBRX", + "link": "/notebooks/agentchat_databricks_dbrx", + "description": "Use Databricks DBRX and Foundation Model APIs to build AutoGen applications backed by open-source LLMs.", + "image": null, + "tags": [ + "integration", + "code generation", + "dbrx", + "databricks", + "open source", + "lakehouse", + "custom model", + "data intelligence" + ], + "source": "/notebook/agentchat_databricks_dbrx.ipynb" + }, + { + "title": "Auto Generated Agent Chat: Task Solving with Provided Tools as Functions", + "link": "/notebooks/agentchat_function_call", + "description": "Register function calls using AssistantAgent and UserProxyAgent to execute python or shell code in customized ways. Demonstrating two ways of registering functions.", + "image": null, + "tags": [ + "code generation", + "tool/function" + ], + "source": "/notebook/agentchat_function_call.ipynb" + }, + { + "title": "Task Solving with Provided Tools as Functions (Asynchronous Function Calls)", + "link": "/notebooks/agentchat_function_call_async", + "description": "Learn how to implement both synchronous and asynchronous function calls using AssistantAgent and UserProxyAgent in AutoGen, with examples of their application in individual and group chat settings for task execution with language models.", + "image": null, + "tags": [ + "tool/function", + "async" + ], + "source": "/notebook/agentchat_function_call_async.ipynb" + }, + { + "title": "Writing a software application using function calls", + "link": "/notebooks/agentchat_function_call_code_writing", + "description": "Equip your agent with functions that can efficiently implement features into your software application.", + "image": null, + "tags": [ + "code generation", + "tool/function", + "fastapi", + "software engineering" + ], + "source": "/notebook/agentchat_function_call_code_writing.ipynb" + }, + { + "title": "Currency Calculator: Task Solving with Provided Tools as Functions", + "link": "/notebooks/agentchat_function_call_currency_calculator", + "description": "Learn how to register function calls using AssistantAgent and UserProxyAgent.", + "image": null, + "tags": [ + "tool/function" + ], + "source": "/notebook/agentchat_function_call_currency_calculator.ipynb" + }, + { + "title": "Groupchat with Llamaindex agents", + "link": "/notebooks/agentchat_group_chat_with_llamaindex_agents", + "description": "Integrate llamaindex agents with Autogen.", + "image": null, + "tags": [ + "react", + "llamaindex", + "integration", + "group chat", + "software engineering" + ], + "source": "/notebook/agentchat_group_chat_with_llamaindex_agents.ipynb" + }, + { + "title": "Group Chat", + "link": "/notebooks/agentchat_groupchat", + "description": "Explore the utilization of large language models in automated group chat scenarios, where agents perform tasks collectively, demonstrating how they can be configured, interact with each other, and retrieve specific information from external resources.", + "image": null, + "tags": [ + "orchestration", + "group chat", + "code generation" + ], + "source": "/notebook/agentchat_groupchat.ipynb" + }, + { + "title": "Group Chat with Retrieval Augmented Generation", + "link": "/notebooks/agentchat_groupchat_RAG", + "description": "Implement and manage a multi-agent chat system using AutoGen, where AI assistants retrieve information, generate code, and interact collaboratively to solve complex tasks, especially in areas not covered by their training data.", + "image": null, + "tags": [ + "group chat", + "orchestration", + "RAG" + ], + "source": "/notebook/agentchat_groupchat_RAG.ipynb" + }, + { + "title": "Group Chat with Customized Speaker Selection Method", + "link": "/notebooks/agentchat_groupchat_customized", + "description": "Introduce group chat with customized speaker selection method.", + "image": null, + "tags": [ + "orchestration", + "group chat" + ], + "source": "/notebook/agentchat_groupchat_customized.ipynb" + }, + { + "title": "FSM - User can input speaker transition constraints", + "link": "/notebooks/agentchat_groupchat_finite_state_machine", + "description": "Explore the demonstration of the Finite State Machine implementation, which allows the user to input speaker transition constraints.", + "image": null, + "tags": [ + "group chat", + "fsm", + "orchestration" + ], + "source": "/notebook/agentchat_groupchat_finite_state_machine.ipynb" + }, + { + "title": "Perform Research with Multi-Agent Group Chat", + "link": "/notebooks/agentchat_groupchat_research", + "description": "Perform research using a group chat with a number of specialized agents.", + "image": null, + "tags": [ + "group chat", + "planning", + "code generation" + ], + "source": "/notebook/agentchat_groupchat_research.ipynb" + }, + { + "title": "StateFlow: Build Workflows through State-Oriented Actions", + "link": "/notebooks/agentchat_groupchat_stateflow", + "description": "StateFlow: Build workflows through state-oriented actions.", + "image": null, + "tags": [ + "orchestration", + "group chat", + "stateflow", + "research" + ], + "source": "/notebook/agentchat_groupchat_stateflow.ipynb" + }, + { + "title": "Group Chat with Coder and Visualization Critic", + "link": "/notebooks/agentchat_groupchat_vis", + "description": "Explore a group chat example using agents such as a coder and visualization agent.", + "image": null, + "tags": [ + "group chat", + "code generation" + ], + "source": "/notebook/agentchat_groupchat_vis.ipynb" + }, + { + "title": "Using Guidance with AutoGen", + "link": "/notebooks/agentchat_guidance", + "description": "Constrained responses via guidance.", + "image": null, + "tags": [ + "guidance", + "integration", + "JSON" + ], + "source": "/notebook/agentchat_guidance.ipynb" + }, + { + "title": "Auto Generated Agent Chat: Task Solving with Code Generation, Execution, Debugging & Human Feedback", + "link": "/notebooks/agentchat_human_feedback", + "description": "Code generation, execution, debugging and human feedback.", + "image": null, + "tags": [ + "human", + "code generation" + ], + "source": "/notebook/agentchat_human_feedback.ipynb" + }, + { + "title": "Generate Dalle Images With Conversable Agents", + "link": "/notebooks/agentchat_image_generation_capability", + "description": "Generate images with conversable agents.", + "image": null, + "tags": [ + "capability", + "multimodal" + ], + "source": "/notebook/agentchat_image_generation_capability.ipynb" + }, + { + "title": "Auto Generated Agent Chat: Function Inception", + "link": "/notebooks/agentchat_inception_function", + "description": "Function Inception: Enable AutoGen agents to update/remove functions during conversations.", + "image": null, + "tags": [ + "function inception", + "tool/function" + ], + "source": "/notebook/agentchat_inception_function.ipynb" + }, + { + "title": "Auto Generated Agent Chat: Task Solving with Langchain Provided Tools as Functions", + "link": "/notebooks/agentchat_langchain", + "description": "Use Langchain tools.", + "image": null, + "tags": [ + "langchain", + "integration", + "tool/function" + ], + "source": "/notebook/agentchat_langchain.ipynb" + }, + { + "title": "Engaging with Multimodal Models: GPT-4V in AutoGen", + "link": "/notebooks/agentchat_lmm_gpt-4v", + "description": "Leveraging multimodal models through two different methodologies: MultimodalConversableAgent and VisionCapability.", + "image": null, + "tags": [ + "multimodal", + "gpt-4v" + ], + "source": "/notebook/agentchat_lmm_gpt-4v.ipynb" + }, + { + "title": "Agent Chat with Multimodal Models: LLaVA", + "link": "/notebooks/agentchat_lmm_llava", + "description": "Leveraging multimodal models like llava.", + "image": null, + "tags": [ + "multimodal", + "llava" + ], + "source": "/notebook/agentchat_lmm_llava.ipynb" + }, + { + "title": "Runtime Logging with AutoGen", + "link": "/notebooks/agentchat_logging", + "description": "Provide capabilities of runtime logging for debugging and performance analysis.", + "image": null, + "tags": [ + "logging", + "debugging" + ], + "source": "/notebook/agentchat_logging.ipynb" + }, + { + "title": "Agent with memory using Mem0", + "link": "/notebooks/agentchat_with_memory", + "description": "Use Mem0 to create agents with memory.", + "image": null, + "tags": [ + "mem0", + "integration", + "memory" + ], + "source": "/notebook/agentchat_with_memory.ipynb" + }, + { + "title": "Solving Multiple Tasks in a Sequence of Async Chats", + "link": "/notebooks/agentchat_multi_task_async_chats", + "description": "Use conversational agents to solve a set of tasks with a sequence of async chats.", + "image": null, + "tags": [ + "orchestration", + "async", + "sequential chats" + ], + "source": "/notebook/agentchat_multi_task_async_chats.ipynb" + }, + { + "title": "Solving Multiple Tasks in a Sequence of Chats", + "link": "/notebooks/agentchat_multi_task_chats", + "description": "Use conversational agents to solve a set of tasks with a sequence of chats.", + "image": null, + "tags": [ + "orchestration", + "sequential chats" + ], + "source": "/notebook/agentchat_multi_task_chats.ipynb" + }, + { + "title": "Nested Chats for Tool Use in Conversational Chess", + "link": "/notebooks/agentchat_nested_chats_chess", + "description": "LLM-backed agents playing chess with each other using nested chats.", + "image": null, + "tags": [ + "nested chat", + "tool/function", + "orchestration" + ], + "source": "/notebook/agentchat_nested_chats_chess.ipynb" + }, + { + "title": "Conversational Chess using non-OpenAI clients", + "link": "/notebooks/agentchat_nested_chats_chess_altmodels", + "description": "LLM-backed agents playing chess with each other using nested chats.", + "image": null, + "tags": [ + "nested chat", + "tool/function", + "orchestration" + ], + "source": "/notebook/agentchat_nested_chats_chess_altmodels.ipynb" + }, + { + "title": "Solving Complex Tasks with A Sequence of Nested Chats", + "link": "/notebooks/agentchat_nested_sequential_chats", + "description": "Solve complex tasks with one or more sequence chats nested as inner monologue.", + "image": null, + "tags": [ + "nested chat", + "sequential chats", + "orchestration" + ], + "source": "/notebook/agentchat_nested_sequential_chats.ipynb" + }, + { + "title": "Solving Complex Tasks with Nested Chats", + "link": "/notebooks/agentchat_nestedchat", + "description": "Solve complex tasks with a chat nested as inner monologue.", + "image": null, + "tags": [ + "nested chat", + "reflection", + "reasoning", + "orchestration" + ], + "source": "/notebook/agentchat_nestedchat.ipynb" + }, + { + "title": "OptiGuide with Nested Chats in AutoGen", + "link": "/notebooks/agentchat_nestedchat_optiguide", + "description": "This is a nested chat re-implementation of OptiGuide which is an LLM-based supply chain optimization framework.", + "image": null, + "tags": [ + "nested chat", + "hierarchical chat", + "code generation", + "orchestration" + ], + "source": "/notebook/agentchat_nestedchat_optiguide.ipynb" + }, + { + "title": "Chat with OpenAI Assistant using function call in AutoGen: OSS Insights for Advanced GitHub Data Analysis", + "link": "/notebooks/agentchat_oai_assistant_function_call", + "description": "This Jupyter Notebook demonstrates how to leverage OSS Insight (Open Source Software Insight) for advanced GitHub data analysis by defining `Function calls` in AutoGen for the OpenAI Assistant.", + "image": null, + "tags": [ + "OpenAI Assistant", + "tool/function" + ], + "source": "/notebook/agentchat_oai_assistant_function_call.ipynb" + }, + { + "title": "Auto Generated Agent Chat: Group Chat with GPTAssistantAgent", + "link": "/notebooks/agentchat_oai_assistant_groupchat", + 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