Tencent Hunyuan has introduced the second version of its memory framework for AI agents, Hy Memory 2.0, and made all three levels of the stack open source. These levels include a specialized memory model, the framework itself, and a local memory application called MemHub. Users can apply these components either individually or in combination.
Tencent reports that Hy Memory is already being used by internal teams in areas such as finance, news agencies, knowledge management, office agents, social entertainment, and gaming.
The framework is responsible for storing, updating, and developing three types of memory: chat, working, and code. Developers integrate it into their agents via an SDK available on PyPI under the name hy-memory 2.0.2. The new features in version 2.0 are working and code memory. They record the task goal, decisions made, intermediate results, and useful lessons, as well as the conditions applicable to these lessons. Before resuming a long-term task, the system first determines the current task, then retrieves relevant decisions, operational experience, and previous work history.
Chat memory now groups disparate facts into events, indicating time, location, and participants, merges duplicates, and resolves contradictions before passing context to the model.
Framework Testing Results
Tencent stated that it ranked first among open-source frameworks, including Mem0, MemOS, and EverOS, in four different tests. According to the company, working memory showed an average score of 55.31% in trip planning and shopping tasks on the MemoryArena platform. Code memory achieved 64.2% accuracy in question answering and 35.6% task resolution on CodeMemoryBench, as well as 28.3% task resolution on SWE-ContextBench. Chat memory demonstrated 72.6% accuracy on LoCoMo while reducing the number of tokens per question from 1159 to 703, representing a 39.3% decrease.
The layer model, named Hy-Memory-30B-A3B, is a 'mixture of experts' type model with approximately 3 billion active parameters, trained for memory retrieval and conflict resolution. This model is evaluated in a technical report on a fixed sample of 20% of benchmarks across all four memory systems. On this sample, it showed a macro average of 55.49, slightly exceeding the scores of Claude Opus 5 (54.62) and DeepSeek V4.1 Flash (53.64). The model weights are available on Hugging Face.
The MemHub application is a locally oriented tool that includes a command line and a Mac client. After authorizing connected tools, the user imports past sessions into a unified local memory store and supports synchronization of new data. Tencent notes that MemHub is currently integrating with 10 AI tools, including WorkBuddy, Cursor, Codex, Qwen Code, OpenCode, and DeepSeek Harness.
The repository license file on GitHub states that Hy-Memory is released under the Apache 2.0 license.
