Provider Prefix Validation

PraisonAIModel rejects unknown provider/model strings instead of silently routing to OpenAI

Point a CrewAI or AutoGen YAML at mistral/mistral-small without registering the provider, and PraisonAI tells you at startup instead of returning a confusing 404 from OpenAI.

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Quick Start

1. Built-in prefix works

   agents.yaml

   framework: crewai
   topic: Research
   roles:
     researcher:
       role: Researcher
       goal: Summarise a topic
       backstory: You are a concise researcher.
       llm:
         model: groq/llama-3.3-70b-versatile
       tasks:
         summary:
           description: Summarise the history of the transistor.
           expected_output: Three sentences.
   
2. Unknown prefix without an endpoint fails loudly

   Change the model to mistral/mistral-small with no endpoint configured:

   ValueError: Unknown LLM provider prefix 'mistral' in model 'mistral/mistral-small'.
   Use one of ['anthropic', 'cohere', 'google', 'groq', 'ollama', 'openai', 'openrouter'],
   set base_url/OPENAI_API_BASE for an OpenAI-compatible endpoint, or register it via
   praisonai.llm.register_llm_provider().
   
3. Send it to an OpenAI-compatible endpoint

   Setting a custom endpoint passes slashed ids through unchanged — ideal for LiteLLM proxies, vLLM, and gateways.

   export OPENAI_API_BASE=http://localhost:4000/v1
   praisonai agents.yaml
   

   Or per role in YAML:

       llm:
         model: meta-llama/Llama-3.1-8B
         base_url: http://localhost:8000/v1
   
4. Or register the provider

   from praisonai.llm import register_llm_provider
   
   class MistralProvider:
       provider_id = "mistral"
   
       def __init__(self, model_id, config=None):
           self.model_id = model_id
           self.config = config or {}
   
   register_llm_provider("mistral", MistralProvider)
   

   See Custom Provider Registry for a full provider implementation.

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When does the error fire?

All three must be true:

1. The model string contains / — plain OpenAI ids such as gpt-4o-mini still default to OpenAI.
2. No custom endpoint is set — base_url, OPENAI_API_BASE, and OPENAI_BASE_URL are all unset.
3. The prefix is not built-in and not registered with register_llm_provider(...).

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Built-in prefixes

Prefix	Example
openai	openai/gpt-4o-mini
groq	groq/llama-3.3-70b-versatile
cohere	cohere/command-r
ollama	ollama/llama3.2
anthropic	anthropic/claude-3-5-sonnet
google	google/gemini-2.0-flash
openrouter	openrouter/meta-llama/llama-3.1-8b-instruct

Prefixes already in the praisonai.llm registry (for example vllm or lm_studio) count as registered.

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Who this affects

Entry point	Affected
framework: crewai YAML llm.model	✅ Yes
framework: autogen YAML llm.model	✅ Yes
Direct PraisonAIModel(model=...) in Python	✅ Yes
praisonaiagents.Agent(llm="mistral/...")	❌ No — routed through LiteLLM directly

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Best Practices

Gateways: set the endpoint once

If every model goes through one LiteLLM proxy, export OPENAI_API_BASE and keep the slashed ids as-is.

Native providers: register them

For a provider with its own SDK (Bedrock, Mistral, Cloudflare), call register_llm_provider before running the YAML.

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Related

Custom Provider Registry

Register your own LLM providers

CrewAI

Run CrewAI from PraisonAI YAML