Joseph Dolivo and Igor Fil write about Pizza Bot, an open source application that runs background AI agents and provides an email-like inbox for their outputs. The design pattern moves agent interaction away from live chat sessions toward asynchronous threads, allowing users to delegate tasks that require multiple steps or human approval without having to monitor a terminal in real time. Because it is fully self-hosted and supports local models, it allows users to maintain strict data boundaries while utilizing tools they already own through MCP servers and agent skills.
- The architecture is built on DeepAgents and LangGraph, using SQLite for local persistence and checkpointing.
- It features an "Action" queue specifically for tasks paused on human approval or decision-making.
- Cron schedules run once after a machine wakes from sleep rather than replaying every missed occurrence.
- The project name references Amazon's two-pizza team methodology.
- It currently ships with a browser-automation skill and a documentation guide skill.
- The architecture is built on DeepAgents and LangGraph, using SQLite for local persistence and checkpointing.
- It features an "Action" queue specifically for tasks paused on human approval or decision-making.
- Cron schedules run once after a machine wakes from sleep rather than replaying every missed occurrence.
- The project name references Amazon's two-pizza team methodology.
- It currently ships with a browser-automation skill and a documentation guide skill.
Paul Sawers writes that Amazon Web Services (AWS) has released an open-source application called Pizza Bot, which provides developers with an email-inspired inbox to manage autonomous AI agents. Designed specifically for tasks that continue after a user has finished their session, the tool moves away from chat interfaces toward an asynchronous model where completed jobs arrive as threads and urgent decisions are surfaced for human triage.
- The project is now a standalone community project rather than an AWS service.
- It supports multiple models including Anthropic, Amazon Bedrock, Google Gemini, OpenAI, OpenRouter, or local models via Ollama.
- Built using LangGraph and DeepAgents to enable stateful agent execution and persistence through checkpoints.
- Available as a desktop app for macOS, Windows, and Linux, with browser and terminal clients also available.
- The project is now a standalone community project rather than an AWS service.
- It supports multiple models including Anthropic, Amazon Bedrock, Google Gemini, OpenAI, OpenRouter, or local models via Ollama.
- Built using LangGraph and DeepAgents to enable stateful agent execution and persistence through checkpoints.
- Available as a desktop app for macOS, Windows, and Linux, with browser and terminal clients also available.
Clearwing is an autonomous offensive security tool built on LangGraph, designed to emulate advanced vulnerability scanning capabilities using accessible AI models. It functions as a dual-mode system featuring a network pentest agent for live target scanning and service detection, alongside a source-code hunter that utilizes agent-driven pipelines to identify, verify, and potentially patch vulnerabilities in codebases.
Key features include:
* Dual-mode operation covering both network penetration testing and source-code analysis.
* A ReAct-loop network agent equipped with 63 bind-tools for scanning and exploitation attempts.
* An automated source-code hunter that uses adversarial verification and sanitizer crashes as ground truth.
* Comprehensive reporting capabilities including SARIF, markdown, and JSON formats.
* Support for various AI providers such as Anthropic, OpenAI, and local LLM endpoints via OpenRouter or Ollama.
Key features include:
* Dual-mode operation covering both network penetration testing and source-code analysis.
* A ReAct-loop network agent equipped with 63 bind-tools for scanning and exploitation attempts.
* An automated source-code hunter that uses adversarial verification and sanitizer crashes as ground truth.
* Comprehensive reporting capabilities including SARIF, markdown, and JSON formats.
* Support for various AI providers such as Anthropic, OpenAI, and local LLM endpoints via OpenRouter or Ollama.
This article details how to use Ollama to run large language models locally, protecting sensitive data by keeping it on your machine. It covers installation, usage with Python, LangChain, and LangGraph, and provides a practical example with FinanceGPT, while also discussing the tradeoffs of using local LLMs.
Examples for common OpenSandbox use cases. Each subdirectory contains runnable code and documentation. Integrations and sandboxes are available for various tools and services like AI models, desktop environments, and web scraping.
This blog post details how to build a natural language Bash agent using NVIDIA Nemotron Nano v2, requiring roughly 200 lines of Python code. It covers the core components, safety considerations, and offers both a from-scratch implementation and a simplified approach using LangGraph.
This article details how to implement Generative User Interfaces (Generative UI) using LangGraph, specifically focusing on integrating React components with LangGraph graphs to create dynamic and interactive applications.
An analysis of the tradeoffs between building bespoke code-based agents and using major agent frameworks such as LangGraph and LlamaIndex Workflows for creating autonomous AI systems.
