ZipengWu365 / TOMC

Cumulative clones · recorded since 2026-10-02 · updated 2026-10-22 UTC. Method and updates 
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 TOMC Memory 
 Context compression and task memory for Codex / Claude Code / Cursor / TRAE / WorkBuddy 
 Compress and compile long histories, and recall saved task notes on CPU. 
 Use with your API · Install TOMC Memory · Switch assistants · See the demo · Paper explained · Paper results · English | 简体中文 
 CPU preparation · No training or extra model service · Plain-text output · Research preview 
Official GitHub home · Plugin downloads · Installation guide
This is the official repository for **TOMC Memory**, with source code, plugin downloads and installation guides.
Inspect recorded examples · API, shared-memory and evaluation guides
## Part 1 · Use TOMC Memory
**TOMC Memory** is an open-source tool built on Task-Oriented Memory Compilation (TOMC). It compresses and compiles context for AI assistants and provides task memory through explicitly saved notes. It is available through an MCP server and a Python API. Give it a history, the next task and a memory budget. It selects source text and computes supported state, relation or count records, so your application can send the prepared context in place of the full history. Preparation runs on CPU, with no training or extra model service.
Use task memory while making videos, organizing materials, handling files or developing code: save the brief, decisions, constraints, completed steps and remaining work with `remember_memory`, then use `recall_memory` to prepare those notes for the next task. These are examples of how to use saved notes. The recorded measurements below cover synthetic project histories and the paper benchmarks, with their own tasks and protocols; they do not establish performance for every task in these workflows.
Add the Python API to your own application, install the plugin in Codex or Claude Code, or connect Cursor, TRAE, Tencent WorkBuddy and Claude Desktop through local MCP. Savings depend on the history and task; short histories can stay intact. The default keeps about 80% of a long history. Compare 40% or 60% budgets on your own tasks when you need smaller requests; see the separate Codex and Claude results.
Reuse saved task notes in a new chat with the same assistant or another assistant. With both clients using the same TOMC database on one computer, you can reuse saved decisions, constraints and next steps. Try the Codex → Claude Code → Codex handoff.
This is a preview, version **0.1.0**. Download the plugins from the MIT-licensed assistant preview. There is no PyPI package or public marketplace listing.
### Video overview
Full video (1:37, with sound) · Chinese video · Media and test scope
API integrations call TOMC before the next request and send the prepared context. Codex and Claude Code use tools to save or recall task notes. The numbers in the video come from specific tests with their own protocols