Default Model Selection

The CLI auto-selects the best available model from your credentials — no more hardcoded gpt-4o-mini

When you don’t specify a model, the CLI ranks every model your credential unlocks by capability and picks the best one. A one-line notice tells you what was chosen and why. The same resolver drives the SDK Agent(llm=None) path — construct an Agent with no llm= and it picks from your credentials too.

import os
from praisonaiagents import Agent

os.environ["OPENAI_API_KEY"] = "sk-..."

# No llm= and only OPENAI_API_KEY set — picks the best stable, chat-callable
# OpenAI model in the catalogue (not gpt-5-pro, not gpt-5-2025-08-07).
agent = Agent(instructions="Summarise the latest release notes.")
agent.start("Give me the top three changes in the last PR.")

from praisonaiagents import Agent

agent = Agent(
    name="Assistant",
    instructions="Help with tasks",
)

agent.start("Summarise the key points from this week")

The user omits --model; the CLI picks the best credential-backed model and prints what it chose.

How It Works

Resolution Order

How the ranking works

When only a credential is set, the CLI ranks every model that credential unlocks by capability (tool-use, context, reasoning, vision) and uses the best-available one — not a fixed cheap default.

• Prefers tool-use (essential for agentic work).
• Excludes models whose LiteLLM mode is not chat (e.g. OpenAI Responses-API-only ids like gpt-5-pro are filtered out — the Chat Completions path can’t call them). Entries with no mode are kept, so older cached catalogues still work.
• De-prioritises unstable preview / experimental / -auto ids.
• De-prioritises dated snapshot ids matching -YYYY-MM-DD or -YYYYMMDD (e.g. gpt-5-2025-08-07, claude-3-5-haiku-20241022) so the stable alias wins.
• De-prioritises -mini / nano / small / lite / haiku / flash / 3.5-turbo class ids.
• Then larger max_context, then reasoning, then vision.
• Non-chat ids by name (embeddings, rerankers, speech, image, moderation, vision-encoder, clip) are also excluded from the chat default.
• Google/Gemini aliasing: a GEMINI_API_KEY-only run still ranks litellm’s google-labelled Gemini models (and vice-versa).

The ranking returns a bare id, but the provider prefix is preserved — anthropic/<id>, gemini/<id>, cohere/<id>, openrouter/openai/<id>, etc. keep their prefix so runtime routing is unchanged. If ranking yields nothing — including when every candidate was filtered out by the mode or name gates — best_available() returns None and selection degrades to the historical fixed representative (gpt-4o-mini for OpenAI).

Quick Start

1. Auto-pick model from credentials

   Set your API key and run any command — no --model needed:

   export ANTHROPIC_API_KEY=sk-ant-...
   praisonai chat "Tell me a joke"

   No model set; using claude-sonnet-4-5 because ANTHROPIC_API_KEY is present.

   The one-line notice only appears when a provider-inferred default is used (not when using the MRU or an explicit override).

2. Override for a single run

   praisonai chat --model gpt-4o "Explain quantum entanglement"

   This model is saved as the most-recently-used and will be the default for the next run.

3. Pin the model for the project

   Add a .praison.json to your project root:

   {
     "model": "anthropic/claude-3-5-sonnet-20241022"
   }

   Every praisonai command in this directory now uses this model.

4. Check with praisonai doctor

   praisonai doctor

   Shows which credentials are present and which model would be selected on the next zero-config run.

Resolution Order

Provider-inferred defaults (priority 4) and the gpt-4o-mini fallback (priority 5) are intentionally not persisted as MRU. Persisting them would let a stale default win over fresh credential-based inference on a later run when your available providers have changed.

MRU State

The most-recently-used model is stored at:

~/.praisonai/state/model.json

This honours PRAISONAI_HOME ($PRAISONAI_HOME/state/model.json when set). The legacy ~/.praison/state/model.json is still read as a fallback.

Content:

{"model": "claude-sonnet-4-5"}

To reset it (force credential-based re-inference):

rm ~/.praisonai/state/model.json

Or simply run with an explicit --model — that overwrites the MRU automatically.

Best Practices

- name: Run agent task
  env:
    ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
    MODEL_NAME: "anthropic/claude-3-5-sonnet-20241022"
  run: praisonai run task.yaml

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