Lean Install (litellm-free)

Use PraisonAI without litellm — capability detection still works via a conservative static heuristic

On lean installs (no litellm), PraisonAI still detects what each model can do — structured outputs, function calling, prompt caching, and web search — via a conservative built-in heuristic.

You installed PraisonAI without extra dependencies, and structured outputs still work.

Quick Start

1. Install lean (no litellm)

   pip install praisonaiagents

   from praisonaiagents import Agent
   from pydantic import BaseModel
   
   class Recipe(BaseModel):
       name: str
       steps: list[str]
   
   agent = Agent(name="Chef", instructions="Return a simple recipe.", llm="gpt-4o")
   result = agent.chat("Give me a recipe for pancakes", output_pydantic=Recipe)
   print(result)

   Capability gating stays usable — gpt-4o still reports structured-output support without litellm.

2. Add litellm for authoritative detection

   pip install litellm

   from praisonaiagents import Agent
   from pydantic import BaseModel
   
   class Recipe(BaseModel):
       name: str
       steps: list[str]
   
   agent = Agent(name="Chef", instructions="Return a simple recipe.", llm="gpt-4o")
   result = agent.chat("Give me a recipe for pancakes", output_pydantic=Recipe)
   print(result)

   Same code, now backed by litellm’s live catalogue — recommended for production and for newly-released models.

How It Works

Each supports_* helper checks for litellm first, then falls back to the static heuristic only when litellm is absent.

litellm stays authoritative whenever it is installed — even when a helper is missing or raises, the result is False rather than the heuristic, so an unsupported param never reaches a provider request. The static heuristic runs only when litellm is None.

What each model gets on a lean install

These are the exact patterns from the static heuristic — names are lowercased and any provider/ prefix is stripped before matching.

¹ Only *-search-preview variants (e.g. gpt-4o-search-preview) report native web search. ² Claude uses web_fetch instead of native web search, so it is intentionally excluded from the web-search heuristic — this is expected, not a bug. ³ Mistral / Mixtral / Llama / Grok report serial function calling but not parallel — the parallel heuristic is deliberately narrower. ⁴ Function calling is disabled by design for embedding, whisper, tts, and dall-e model names.

Lean or full install?

When to install litellm anyway

Add litellm when a wrong True would break a real request.

• Newly released models with no matching pattern — the heuristic returns False until you add litellm.
• Non-standard model IDs — custom deployments, self-hosted proxies, or renamed models.
• Production workloads where a wrong True would send an unsupported response_format or web_search_options param and fail the call.

Best Practices

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