References
References
LangGraph
- Build a custom workflow
- LangGraph の(LLM なし)Human-in-the-loop を試してみた
- 🤖 LangGraph Multi-Agent Supervisor
- Software Design 誌「実践 LLM アプリケーション開発」第 24 回サンプルコード
- Streamlit
- LangChain MCP Adapters
- Research Agent with MCP Integration.
- Command: A new tool for building multi-agent architectures in LangGraph
- Combine control flow and state updates with Command
- Command: a new tool for building multi-agent architectures in LangGraph
- masamasa59/genai-agent-advanced-book > chapter6
- langchain-ai/deepagents
- Custom UI for Deep Agents
- How to deploy self-hosted standalone server
- 「現場で活用するための AI エージェント実践入門」リポジトリ
- Add and manage memory
- Persistence
- Chatbot with message summarization & external DB memory
- LangGraph の会話履歴を SQLite に保持しよう
LangChain
Azure AI Foundry
- Quickstart: Get started with Azure AI Foundry
- azure-rest-api-specs/specification/ai/data-plane/Azure.AI.Agents
- How to use the Deep Research tool
Vision
Services
Observability
- OpenTelemetry
- Python / Getting Started
- Python / Cookbook
- OpenTelemetry Collector
- OpenTelemetry Collector / Quick Start
- zPages / Exposed zPages routes
- Jaeger
- Jaeger / Minimal deployment example (Elasticsearch backend)
- Prometheus
- Prometheus / Getting Started
- Trace with LangGraph
n8n
Audio
- Python の sounddevice を改めて試す
- How To Install libportaudio2 on Ubuntu 22.04:
sudo apt-get -y install libportaudio2 - python-sounddevice
- python-soundfile
OpenAI
Realtime API
- August 2025 / Realtime API audio model GA
- Global Standard model availability
- specification/cognitiveservices/data-plane/AzureOpenAI/inference/preview/2025-04-01-preview/inference.json
- Realtime API with WebSocket
- GPT-4o Realtime API for speech and audio
- OpenAI Python API library > examples/realtime
- How to use the GPT-4o Realtime API via WebRTC
Responses API
- OpenAI / New tools for building agents
- OpenAI / Responses
- Azure OpenAI Responses API
- LangChain / Responses API
Hugging Face
DSPy
- DSPy (Declarative Self-improving Python)
- Language Models
- Language Models / v3.0.3
- Software Design 誌「実践 LLM アプリケーション開発」第 25 回サンプルコード
MLflow
LiteLLM
Langfuse
Codex CLI
- Azure OpenAI で Codex CLI を使う: Codex Azure OpenAI Integration: Fast & Secure Code Development
- OpenAI Codex CLI のクイックスタート
# Install Codex CLI
npm install -g @openai/codex
# Generate shell completion scripts
codex completion zsh
# Dump configurations
cat ~/.codex/config.toml
# Set up environment variables
export AZURE_OPENAI_API_KEY="<your-api-key>"
# MCP server management: https://qiita.com/tomada/items/2eb8d5b5173a4d70b287
## Add a global MCP server entry
codex mcp add context7 -- npx -y @upstash/context7-mcp
codex mcp add playwright -- npx -y @playwright/mcp@latest
codex mcp add mslearn -- npx -y mcp-remote "https://learn.microsoft.com/api/mcp" # ref. https://zenn.dev/yanskun/articles/codex-remote-mcp
## Remove MCP server
codex mcp remove context7
## List MCP servers
codex mcp list
# Run Codex non-interactively
codex exec "Playwright MCP を使って Yahoo リアルタイム検索の上位キーワードをまとめて"
Model Context Protocol (MCP)
# Running the Inspector
npx @modelcontextprotocol/inspector
# Run the Codex MCP server (stdio transport)
codex mcp-server --help
# Run the Inspector with the Codex MCP server
npx @modelcontextprotocol/inspector -- codex mcp-server