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docs: trim ai-and-agents how-to-choose list to 12 items

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The list had 22 items, which in the upcoming layout sits above the entry table and would push it 4-5 phone screens down; it now has one item per README subcategory, in README order, reusing the intro's own wording.

Co-Authored-By: Claude <noreply@anthropic.com>
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Vinta Chen
2026-09-26 22:14:15 +08:00
co-authored by Claude
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website/data/category_intros/ai-and-agents.md
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@@ -2,28 +2,18 @@ LangChain is the place to start among Python libraries for AI agents, and LangGr
How to choose: How to choose:
- A first agent, or a prebuilt tool-calling loop: LangChain - Skills for your coding agent: Django AI Skills, Sentry Skills, or Trail of Bits Skills
- Long-running, stateful agents that mix fixed steps with LLM-driven ones: LangGraph - A first agent: LangChain, or LangGraph to control every step
- Typed agents whose outputs are validated: Pydantic AI
- A team of role-playing agents: CrewAI
- An agent built on one vendor's platform: OpenAI Agents SDK or Claude Agent SDK - An agent built on one vendor's platform: OpenAI Agents SDK or Claude Agent SDK
- Structured data from an LLM, without an agent framework: Instructor - A ready-made personal assistant: Hermes Agent, or AstrBot for chat apps
- Prompts tuned against a metric instead of by hand: DSPy - Prompts tuned against a metric instead of by hand: DSPy
- RAG over your own documents: LlamaIndex - Structured output, RAG, or agent memory: Instructor, LlamaIndex, or Mem0
- Memory that survives across sessions: Mem0
- Agent context you can browse and edit like files: OpenViking
- A knowledge graph with provenance for regulated domains: Semantica
- Running pre-trained models: Transformers - Running pre-trained models: Transformers
- Serving a model on GPUs: vLLM, or SGLang when requests share long prompts - Serving a model: vLLM, or MLX LM on Apple silicon
- Running a model on Apple silicon: MLX LM
- One API for many LLM providers: LiteLLM - One API for many LLM providers: LiteLLM
- Image and video generation: Diffusers - Image and video generation: Diffusers
- Fine-tuning: PEFT for adapters, Unsloth for fast low-memory training, Axolotl for YAML-configured runs across GPUs - Fine-tuning: PEFT, Unsloth, or Axolotl
- Speech to text: Whisper, or FunASR for streaming and edge deployment - Speech: Whisper for speech to text, Kitten TTS for text to speech
- Text to speech: Kitten TTS on CPU, gTTS for a quick online voice
- Speech research: VibeVoice
- A ready-made personal assistant: Hermes Agent, or AstrBot for chat apps like Telegram, Slack, and QQ
- Skills for your coding agent: Django AI Skills for Django, Sentry Skills for code review, Trail of Bits Skills for security work
New to agents? LangGraph's own docs [recommend LangChain's prebuilt agents](https://docs.langchain.com/oss/python/langgraph/overview), which run on LangGraph: give an agent a model, tools, and a prompt, and the loop is handled for you. Drop down to LangGraph for [needs that combine deterministic and agentic workflows](https://docs.langchain.com/oss/python/langchain/overview). You don't need LangChain to use LangGraph. New to agents? LangGraph's own docs [recommend LangChain's prebuilt agents](https://docs.langchain.com/oss/python/langgraph/overview), which run on LangGraph: give an agent a model, tools, and a prompt, and the loop is handled for you. Drop down to LangGraph for [needs that combine deterministic and agentic workflows](https://docs.langchain.com/oss/python/langchain/overview). You don't need LangChain to use LangGraph.