VoxCPM is a tokenizer-free, multilingual text-to-speech (TTS) system developed by OpenBMB. It supports voice cloning, voice design, and controllable speech generation across 30 languages and 9 Chinese dialects, outputting 48kHz audio. The architecture is a diffusion-autoregressive model built on MiniCPM-4, comprising AudioVAE, Local Encoder, Text-Semantic LM, Residual Acoustic LM, and Local DiT. The repository provides multiple interfaces: a Gradio web application (app.py) for interactive use, a command-line interface (voxcpm.cli) for batch processing, a fine-tuning web UI (lora_ft_webui.py) for LoRA and full-parameter training, and Python APIs for integration. It also includes utilities for audio enhancement (ZipEnhancer), timestamp alignment, and text normalization. While primarily an application, it contains research-grade model definitions and training pipelines, making it a hybrid project with a strong application focus.

ChatDev 2.0 (DevAll) is a zero-code multi-agent orchestration platform for building and executing graph-based AI workflows. Users define directed acyclic or cyclic graphs in YAML, composing agent nodes (with LLM providers, memory, tools, thinking modes), human-in-the-loop nodes, Python executors, subgraphs, and control-flow edges with conditions and data transformations. The system supports dynamic parallel execution patterns (Map/Tree), multiple memory stores (simple, file-based FAISS, cloud Mem0), and extensible tooling via local Python functions or MCP servers. The architecture is a full-stack application: a FastAPI backend with REST and WebSocket endpoints for workflow execution, batch runs, artifact streaming, and session management; a Vue 3 frontend with a visual workflow editor (VueFlow), dynamic schema-driven forms, and real-time execution monitoring; a CLI entry point (`run.py`); and a Python SDK (`runtime/sdk.py`) for programmatic use. The codebase is well-structured with registry patterns for extensibility, declarative configuration dataclasses with auto-generated UI forms, and a modular runtime with pluggable node executors, edge conditions, and memory stores. While primarily an application, the project has strong research roots (OpenBMB lab, Tsinghua NLP) and includes academic references (ChatDev, Co-Learning, MacNet). It serves as both a production-grade tool for multi-agent automation and a research platform for experimenting with agent architectures, memory systems, and collaborative workflows. The repository includes 20+ example workflows demonstrating diverse use cases from software development and data visualization to 3D scene generation and deep research.

The repository provides a comprehensive ecosystem for the MiniCPM-V and MiniCPM-o series of multimodal large language models (MLLMs). It enables efficient on-device inference and real-time full-duplex multimodal interaction (vision + audio + text) with low latency. The core contributions include the OmniLMM architecture with intra-ViT early compression for visual encoding, mixed 4x/16x visual token compression, and support for streaming audio/video inputs. The system includes evaluation frameworks for multimodal benchmarks (MMBench, Video-MME, OmniDocBench), fine-tuning utilities (LoRA and full model), and deployment tools for various platforms (iOS, Android, HarmonyOS, web) and inference backends (vLLM, SGLang, llama.cpp, Ollama). Key entry points are the model inference code in omnilmm/model/, evaluation scripts in eval_mm/vlmevalkit/, fine-tuning in finetune/, and web demos in web_demos/ demonstrating real-time chat with multimodal inputs.

This repository is the official release hub for the MiniCPM family of compact large language models (1B to 8B parameters) from OpenBMB. It provides model weights, deployment scripts for 10+ inference backends (vLLM, SGLang, llama.cpp, Ollama, etc.), fine-tuning recipes for 5 frameworks (TRL, LLaMA-Factory, unsloth, etc.), quantization tools (AWQ, GPTQ, BNB), and application demos (survey generation, MCP tool calling, code interpreter). The repo is primarily a model card and cookbook collection rather than a single integrated application; it serves as a developer toolkit for deploying and customizing MiniCPM models in production and edge scenarios. The repository also includes research artifacts such as training datasets (UltraData suite) and technical reports, but the dominant character is applied machine learning engineering.