feat: upgrade MiniMax default model to M3#1859
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## Summary - add an OpenClaw runtime lock to block duplicate plugin instances before tools/hooks register - fail startup on viewer port conflicts and clean up partial runtime state - keep lightweight local memories searchable/listable without an LLM final filter, while preserving full-mode self-evolution boundaries - cover runtime locking, duplicate startup, lightweight retrieval, delayed agent_end recovery, and partial migration behavior ## Tests - npm test -- --run tests/unit - npm run lint - npm run build - git diff --check --cached
MemTensor#1807) Automated PR from mem-agent-0520-niu to mem-agent-0520.
## Description Please include a summary of the change, the problem it solves, the implementation approach, and relevant context. List any dependencies required for this change. Related Issue (Required): Fixes #issue_number ## Type of change Please delete options that are not relevant. - [ ] Bug fix (non-breaking change which fixes an issue) - [ ] New feature (non-breaking change which adds functionality) - [ ] Breaking change (fix or feature that would cause existing functionality to not work as expected) - [ ] Refactor (does not change functionality, e.g. code style improvements, linting) - [ ] Documentation update ## How Has This Been Tested? Please describe the tests that you ran to verify your changes. Provide instructions so we can reproduce. Please also list any relevant details for your test configuration - [ ] Unit Test - [ ] Test Script Or Test Steps (please provide) - [ ] Pipeline Automated API Test (please provide) ## Checklist - [ ] I have performed a self-review of my own code | 我已自行检查了自己的代码 - [ ] I have commented my code in hard-to-understand areas | 我已在难以理解的地方对代码进行了注释 - [ ] I have added tests that prove my fix is effective or that my feature works | 我已添加测试以证明我的修复有效或功能正常 - [ ] I have created related documentation issue/PR in [MemOS-Docs](https://github.com/MemTensor/MemOS-Docs) (if applicable) | 我已在 [MemOS-Docs](https://github.com/MemTensor/MemOS-Docs) 中创建了相关的文档 issue/PR（如果适用） - [ ] I have linked the issue to this PR (if applicable) | 我已将 issue 链接到此 PR（如果适用） - [ ] I have mentioned the person who will review this PR | 我已提及将审查此 PR 的人 ## Reviewer Checklist - [ ] closes #xxxx (Replace xxxx with the GitHub issue number) - [ ] Made sure Checks passed - [ ] Tests have been provided
- Set MiniMax-M3 as the new default in the API config - Update example to use MiniMax-M3 and refresh the available models list - Retain MiniMax-M2.7 and MiniMax-M2.7-highspeed; drop the deprecated MiniMax-M2.5 and MiniMax-M2.5-highspeed entries from the example MiniMax-M3 is the new flagship model with 512K context window, 128K max output, and image input support across both OpenAI-compatible and Anthropic-compatible interfaces. Co-Authored-By: Octopus <liyuan851277048@icloud.com>
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Summary
Upgrade MiniMax model configuration to use the latest M3 as the new default, following the same convention as PR #1291 (M2.6 → M2.7).
Changes
src/memos/api/config.py: SetMiniMax-M3as the default chat model whenMOS_CHAT_MODELenv var is unsetexamples/basic_modules/llm.py: Update the MiniMax example to useMiniMax-M3and refresh the "Available models" commentMiniMax-M3(flagship, default)MiniMax-M2.7andMiniMax-M2.7-highspeed(low-latency)MiniMax-M2.5/MiniMax-M2.5-highspeedfrom the example listWhy
MiniMax-M3 is the new flagship model with a 512K context window, 128K max output tokens, and image input support — across both the OpenAI-compatible and Anthropic-compatible interfaces exposed by
https://api.minimax.io. M3 is now the recommended default; M2.7 / M2.7-highspeed stay supported via the samemodel_name_or_pathfield.Scope
Pure config/default change. No API URL changes, no provider wiring changes, no dependency bumps. Existing
MinimaxLLMandMinimaxLLMConfigclasses are untouched, so users who pin a specific model viaMOS_CHAT_MODELare unaffected.Testing
tests/llms/test_minimax.pyandtests/configs/test_llm.pycontinue to pass (they pin specific models — M2.7 / M2.7-highspeed — which remain valid)MiniMax-M3is reachable via the OpenAI-compatible endpoint athttps://api.minimax.io/v1