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docs: trim ai-and-agents how-to-choose list to 12 items
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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@@ -2,28 +2,18 @@ LangChain is the place to start among Python libraries for AI agents, and LangGr
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How to choose:
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- A first agent, or a prebuilt tool-calling loop: LangChain
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- Long-running, stateful agents that mix fixed steps with LLM-driven ones: LangGraph
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- Typed agents whose outputs are validated: Pydantic AI
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- A team of role-playing agents: CrewAI
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- Skills for your coding agent: Django AI Skills, Sentry Skills, or Trail of Bits Skills
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- A first agent: LangChain, or LangGraph to control every step
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- An agent built on one vendor's platform: OpenAI Agents SDK or Claude Agent SDK
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- Structured data from an LLM, without an agent framework: Instructor
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- A ready-made personal assistant: Hermes Agent, or AstrBot for chat apps
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- Prompts tuned against a metric instead of by hand: DSPy
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- RAG over your own documents: LlamaIndex
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- Memory that survives across sessions: Mem0
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- Agent context you can browse and edit like files: OpenViking
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- A knowledge graph with provenance for regulated domains: Semantica
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- Structured output, RAG, or agent memory: Instructor, LlamaIndex, or Mem0
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- Running pre-trained models: Transformers
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- Serving a model on GPUs: vLLM, or SGLang when requests share long prompts
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- Running a model on Apple silicon: MLX LM
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- Serving a model: vLLM, or MLX LM on Apple silicon
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- One API for many LLM providers: LiteLLM
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- Image and video generation: Diffusers
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- Fine-tuning: PEFT for adapters, Unsloth for fast low-memory training, Axolotl for YAML-configured runs across GPUs
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- Speech to text: Whisper, or FunASR for streaming and edge deployment
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- Text to speech: Kitten TTS on CPU, gTTS for a quick online voice
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- Speech research: VibeVoice
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- A ready-made personal assistant: Hermes Agent, or AstrBot for chat apps like Telegram, Slack, and QQ
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- Skills for your coding agent: Django AI Skills for Django, Sentry Skills for code review, Trail of Bits Skills for security work
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- Fine-tuning: PEFT, Unsloth, or Axolotl
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- Speech: Whisper for speech to text, Kitten TTS for text to speech
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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.
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