Reading
This article introduces the open-source AgentChat project—a self-hosted agent chat framework designed specifically for multi-runtime Codex-style workflows. It combines the Next.js, Convex, and Bun tech stack, allowing developers to have full control over local states.
Section 01
AgentChat is an open-source self-hosted agent chat framework designed specifically for multi-runtime Codex-style workflows. It combines the Next.js, Convex, and Bun tech stack, enabling developers to have full control over local states and addressing the data privacy, internal resource access restrictions, and cost issues of Codex cloud services.
Section 02
OpenAI Codex pioneered a new paradigm for AI-assisted programming, but as a cloud service, it faces issues such as data needing to leave the local environment, inability to access internal resources, and costs constrained by API pricing. Enterprises and developers that prioritize data privacy, need to integrate internal toolchains, or aim to reduce long-term costs have an urgent demand for self-hosted Codex-style workflows—leading to the emergence of AgentChat.
Section 03
Tech Stack Selection: Frontend Next.js provides smooth interaction, backend Convex enables real-time synchronization, and the runtime relies on Bun for high performance; Multi-Runtime Support: Compatible with Bun (fast response), Node.js (rich npm ecosystem), and edge runtime (lightweight tasks); Convex Real-Time Management: Supports real-time collaboration, optimistic updates, and transaction security; Next.js Frontend: Stream rendering, server components, file system routing; Local State: Self-hosted deployment, local data storage, and auditability.
Section 04
Conversation-Driven Workflow: Natural language code generation, file browsing/editing, terminal command execution; Extensible Tool System: Custom tool integration with internal APIs, permission configuration, and automated tool combination; Multi-Agent Collaboration: Role division (architect/developer/tester), context transfer, and human-machine collaboration intervention.
Section 05
One-Click Deployment: Containerization solutions support Docker Compose and Kubernetes, with pre-configured images, environment variable setup, and health checks; Security Recommendations: Network isolation, least privilege principle, audit logs; Performance Optimization: Connection pool configuration, caching strategy, horizontal scaling.
Section 06
AgentChat competes with Continue and Supermaven in the self-hosted agent framework space. Its differentiated advantages include a complete tech stack (integrated frontend-backend), modern runtime support (early Bun adaptation), and Convex-native real-time collaboration capabilities.
Section 07
AgentChat represents the decentralization trend of AI-assisted development tools, meeting enterprises' needs for data privacy and cost control. Its open-source release provides the community with a high-quality reference implementation, and its technical architecture offers insights for similar projects. It will play a key role in the implementation of enterprise-level AI applications in the future.
Keep going with more reads from the same topic.
Splinter is a minimalist, high-performance key-value (KV) and vector storage system enabling zero-latency inter-process communication via shared memory and atomic operations. With only 766 lines of core code, it supports millions of operations per second and 768-dimensional vector storage, offering a new architectural approach for local LLM inference and data-intensive applications.
Folkering OS is the world's first AI-native bare-metal operating system, entirely written in Rust no_std without relying on Linux, POSIX, or libc. It can generate commands from scratch, compile them into WASM, and run them in 10 seconds, achieving true self-evolution.
This project explores how to use Large Language Models (LLMs) to detect logical vulnerabilities in Ethereum smart contracts, especially business logic flaws that are difficult for traditional static analysis tools to capture, such as economic manipulation and execution order errors.
This article introduces an open-source project for building modern large language models from scratch. The project evolves from a GPT-2-style basic architecture to a Llama 2/3-style production-level implementation, covering complete tutorial implementations of key technologies such as RMSNorm, RoPE, SwiGLU, GQA, and MoE.