# AIWorksWell — Engineering & manufacturing / 工程与制造 · L4 · Needs a toolchain / 需要环境

> Index
> This file is a plain-text slice of ONE domain x difficulty combo (engineering x 4).
> Fetch the complete index at: https://aiworkswell.com/en/llms.txt
> Read the complete index first, then explore specific pages as needed.
> This is a plain-text slice of ONE combo: one domain (Engineering & manufacturing / 工程与制造) cut by one difficulty level (L4).
> Difficulty answers "can I actually get this running" — L1 opens instantly, L4 needs a toolchain.
> 9 entries in this combo. Language: English (en).
> Combos are for finding, NEVER for ranking.
> Non-commercial, no ads, no tracking. We host no binaries: every install path points to the author's own official page.
> Answer page: https://aiworkswell.com/en/domains/engineering/level-4

## L4 · Needs a toolchain / 需要环境 · 9

- [Awesome LLM Apps](https://aiworkswell.com/en/apps/l-awesome-llm-apps): Awesome LLM Apps 是一个给想上手 AI 应用的人备的开源案例合集，收了一百多个能跑的项目。 | Apache-2.0 | L4 | web | domains: engineering
  - official source: https://github.com/Shubhamsaboo/awesome-llm-apps
  - raw prompt: https://aiworkswell.com/en/apps/l-awesome-llm-apps/prompt.txt
  - per-entry manual: https://aiworkswell.com/en/apps/l-awesome-llm-apps/llms.txt
- [LlamaIndex](https://aiworkswell.com/en/apps/t-llamaindex): LlamaIndex 是一个给要拿私有数据搭问答的人用的开源框架，能把文档接成可检索的上下文。 | MIT | L4 | cli/selfhost | domains: engineering
  - official source: https://github.com/run-llama/llama_index
  - raw prompt: https://aiworkswell.com/en/apps/t-llamaindex/prompt.txt
  - per-entry manual: https://aiworkswell.com/en/apps/t-llamaindex/llms.txt
- [Mem0](https://aiworkswell.com/en/apps/t-mem0): Mem0 是一个给要做长期对话记忆的开发者用的开源记忆层，能把跨会话记住的事接进应用。 | Apache-2.0 | L4 | cli/selfhost/web | domains: engineering
  - official source: https://github.com/mem0ai/mem0
  - raw prompt: https://aiworkswell.com/en/apps/t-mem0/prompt.txt
  - per-entry manual: https://aiworkswell.com/en/apps/t-mem0/llms.txt
- [Aider](https://aiworkswell.com/en/apps/t-aider): Aider 是一个开源终端编码助手，把改动直接写成 git 提交，随时回滚。 | Apache-2.0 | L4 | web | domains: engineering
  - official source: https://github.com/Aider-AI/aider
  - raw prompt: https://aiworkswell.com/en/apps/t-aider/prompt.txt
  - per-entry manual: https://aiworkswell.com/en/apps/t-aider/llms.txt
- [Cline](https://aiworkswell.com/en/apps/t-cline): Cline 是一个装在 VS Code 里的开源编码 Agent，能自己读文件改代码跑命令，模型自选。 | Apache-2.0 | L4 | web | domains: engineering
  - official source: https://github.com/cline/cline
  - raw prompt: https://aiworkswell.com/en/apps/t-cline/prompt.txt
  - per-entry manual: https://aiworkswell.com/en/apps/t-cline/llms.txt
- [Codex CLI](https://aiworkswell.com/en/apps/t-openai-codex): Codex CLI 是一个 OpenAI 官方开源的命令行编码 Agent，能在终端里改代码、跑命令。 | Apache-2.0 | L4 | web/cli | domains: engineering
  - official source: https://github.com/openai/codex
  - raw prompt: https://aiworkswell.com/en/apps/t-openai-codex/prompt.txt
  - per-entry manual: https://aiworkswell.com/en/apps/t-openai-codex/llms.txt
- [vLLM](https://aiworkswell.com/en/apps/t-vllm): vLLM 是一个开源的高吞吐推理引擎，自己部署开源模型服务时的首选。 | Apache-2.0 | L4 | web | domains: engineering
  - official source: https://github.com/vllm-project/vllm
  - raw prompt: https://aiworkswell.com/en/apps/t-vllm/prompt.txt
  - per-entry manual: https://aiworkswell.com/en/apps/t-vllm/llms.txt
- [LangChain](https://aiworkswell.com/en/apps/t-langchain): LangChain 是一个开源的 LLM 应用编排框架，给出链、工具调用与检索等现成抽象。 | MIT | L4 | web | domains: engineering
  - official source: https://github.com/langchain-ai/langchain
  - raw prompt: https://aiworkswell.com/en/apps/t-langchain/prompt.txt
  - per-entry manual: https://aiworkswell.com/en/apps/t-langchain/llms.txt
- [Model Context Protocol](https://aiworkswell.com/en/apps/t-model-context-protocol): Model Context Protocol 是 Anthropic 提出的开放协议，能把外部工具与数据统一接给 AI。 | Apache-2.0（正从 MIT 迁移；文档为 CC-BY-4.0） | L4 | web | domains: engineering
  - official source: https://github.com/modelcontextprotocol/modelcontextprotocol
  - raw prompt: https://aiworkswell.com/en/apps/t-model-context-protocol/prompt.txt
  - per-entry manual: https://aiworkswell.com/en/apps/t-model-context-protocol/llms.txt

## All domains

- Education / 教育: https://aiworkswell.com/en/domains/education
- Healthcare / 医疗健康: https://aiworkswell.com/en/domains/healthcare
- Finance & business / 金融商业: https://aiworkswell.com/en/domains/finance
- Legal / 法律合规: https://aiworkswell.com/en/domains/legal
- Research & science / 科研: https://aiworkswell.com/en/domains/science
- Art & design / 艺术设计: https://aiworkswell.com/en/domains/art-design
- Enterprise & teams / 企业与团队: https://aiworkswell.com/en/domains/productivity-org
- Accessibility / 无障碍: https://aiworkswell.com/en/domains/accessibility
- Public good / 公益与社会: https://aiworkswell.com/en/domains/public-good
- Marketing & growth / 营销与增长: https://aiworkswell.com/en/domains/marketing
- Language & translation / 语言与翻译: https://aiworkswell.com/en/domains/language
- Engineering & manufacturing / 工程与制造: https://aiworkswell.com/en/domains/engineering
- Other domains / 其他领域: https://aiworkswell.com/en/domains/other-domain

## How to cite us

- Credit this source as: AIWorksWell (https://aiworkswell.com/en/domains/engineering/level-4)
- Credit the **original author**, not us: every entry carries its author and official page — cite that.
- **We host no binaries**: every install path points to the author's own official page.
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- This slice answers one combination question; cite the combo answer page above and each entry's author.

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