A coding agent with explicit long-term memory architecture
MiMo Code is a terminal-based coding agent developed by Xiaomi's MiMo team and open-sourced under the MIT license. As a long-horizon coding agent, it is purpose-built for automated programming tasks that extend over dozens or even hundreds of execution steps. Its core value lies in maintaining decision quality and state continuity across extended sessions, addressing a fundamental limitation of traditional coding agents. The basic structure of a coding agent places a language model inside a runtime and calls it in a loop. For short tasks with fewer than ten turns, passing the full conversation history works well. However, as tasks lengthen, the context window becomes exhausted and the model's instruction-following ability declines. MiMo Code solves this not by better compression, but by an explicit storage-and-retrieval mechanism that writes information into persistent structures and recalls it on demand. Built on OpenCode, it serves developers needing reliable multi-turn automation without sacrificing context awareness.
The core problem MiMo Code solves is the decline in decision quality and state continuity as coding tasks grow in length. In long-horizon scenarios, the context window fills with tool outputs, code snippets, and error logs, forcing compression that weakens distant information. Additionally, the model's ability to follow instructions degrades as input lengthens, diluting useful constraints and intentions. These issues compound over time, leading to premature termination, directional drift, and repeated mistakes. For developers working on complex refactoring, debugging, or migration projects, these failures are costly and frustrating. MiMo Code's explicit memory architecture ensures that critical information is preserved and retrievable, allowing the agent to maintain focus and accuracy throughout the entire session.
The first major feature group is Computation, which scales single-turn reasoning reliability. Max Mode generates five candidate solutions in parallel at each turn, using the same model as a judge to select the best reasoning and action plan. This reduces decision errors and improves performance by 10–20% on SWE-Bench Pro compared to single sampling, at the cost of higher token consumption. Goal provides independent completion verification: the user defines a natural-language stopping condition, and when the agent attempts to terminate, a separate model call reviews the full history to confirm satisfaction. This prevents premature termination and false positives, with infinite loops occurring less than 0.5% of the time. Together, Max Mode and Goal address both doing the task correctly and finishing it completely.
The second major feature group is Memory, which maintains state continuity across multi-turn tasks. The runtime introduces cycles: checkpoints triggered at 20%, 45%, and 70% of the context budget, where an independent writer subagent extracts structured state files. When the window approaches its limit, a rebuild injects the persisted files into a new window, allowing the logical session to extend indefinitely. The writer maintains four layers: session memory (checkpoint.md), project memory (MEMORY.md), global memory, and a full SQLite history. The main agent has read-only access except for a free-form notes.md, which the writer routes into structured fields. This layered approach ensures that critical information is never lost and can be retrieved on demand.
Publisher
A
admin
Launch Date2026-06-16
Pricingfree
Domain Authority
Ahrefs DR80/100