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After the C7 milestone, the LLM helpers live in praisonai-code — no wrapper import required. 
from praisonaiagents import Agent
from praisonai_code.llm.catalogue import ModelCatalogue
from praisonai_code.llm.credentials import is_configured

catalogue = ModelCatalogue()
models = catalogue.list_models(provider="openai")

if is_configured():
    agent = Agent(
        name="LLM Info Agent",
        instructions=f"You have access to {len(models)} OpenAI models.",
    )
    agent.start("Which OpenAI model supports vision and tool calling?")
The user asks about models; catalogue and credential helpers from praisonai-code answer without wrapper imports.

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How It Works

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Module Map

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Quick Start

1

Simple Usage

from praisonaiagents import Agent
from praisonai_code.llm.catalogue import ModelCatalogue

catalogue = ModelCatalogue()
models = catalogue.list_models(provider="openai")

agent = Agent(
    name="LLM Info Agent",
    instructions=f"You have access to {len(models)} OpenAI models.",
)
agent.start("Which OpenAI model supports vision and tool calling?")
2

With Credential Gate

from praisonaiagents import Agent
from praisonai_code.llm.credentials import is_configured, inject_credentials_into_env

if not is_configured():
    raise SystemExit("Run: praisonai setup  (or set OPENAI_API_KEY)")

inject_credentials_into_env()

agent = Agent(
    name="Secure Agent",
    instructions="You are a helpful assistant.",
)
agent.start("What models are available for my account?")
3

Discover Available Models

from praisonai_code.llm.catalogue import ModelCatalogue

catalogue = ModelCatalogue()
models = catalogue.list_models(provider="openai")
print(f"{len(models)} OpenAI models available")
4

Check Credentials and Resolve Endpoint

from praisonai_code.llm.credentials import is_configured, inject_credentials_into_env
from praisonai_code.llm.env import resolve_llm_endpoint

if is_configured():
    inject_credentials_into_env()
    ep = resolve_llm_endpoint()
    print(f"Using: {ep.model} at {ep.base_url}")

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How It Works

The user asks about models; the agent queries the catalogue and credential helpers, then answers.

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The Four Modules

ModulePublic exportsPurpose
praisonai_code.llm.catalogueModelCatalogue, ModelInfoDiscover and validate LLM models
praisonai_code.llm.configbuild_config_listAutoGen-style [{model, base_url, api_key, api_type}]
praisonai_code.llm.envLLMEndpoint, resolve_llm_endpoint, default_model_for_available_providerResolve which model/endpoint to hit
praisonai_code.llm.credentialsis_configured, inject_credentials_into_env, resolve_llm_endpoint_with_credentialsCredential gate for CLI startup

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How It Works

The user asks about models; the catalogue and credential gate resolve an endpoint, then the agent answers.

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Which Import Path Should I Use?

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Backward Compatibility Guarantee

All four praisonai.llm.* module paths still resolve — a sys.modules alias in the wrapper points at praisonai_code.llm.*. The unit test test_c5_backward_compat.py::test_module_identity asserts:
  • praisonai.llm.catalogue is praisonai_code.llm.catalogue
  • praisonai.llm.config is praisonai_code.llm.config
  • praisonai.llm.env is praisonai_code.llm.env
  • praisonai.llm.credentials is praisonai_code.llm.credentials
You do not need to migrate existing code — both paths point at the same object.

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Standalone Smoke Test

This example runs in a venv with only praisonai-agents + praisonai-code installed (no wrapper): 
from praisonai_code.cli.configuration.resolver import ConfigResolver
from praisonai_code._version import get_package_version
from praisonai_code.llm.credentials import is_configured
from praisonai_code.llm.catalogue import ModelCatalogue

assert ModelCatalogue().list_models(provider="openai")   # 132 models

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Module Reference

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praisonai_code.llm.catalogue

from praisonai_code.llm.catalogue import ModelCatalogue, ModelInfo

catalogue = ModelCatalogue(
    cache_dir=None,   # defaults to ~/.praison/cache
    cache_ttl=3600,   # 1 hour
)

# List all models, optionally filter
models = catalogue.list_models(provider="openai", search="gpt-4")

# Get full metadata for one model
info = catalogue.describe_model("gpt-4o")

# Validate a model ID — raises ValueError with suggestions on mismatch
try:
    valid_id = catalogue.validate_model("gpt-4o-mni")
except ValueError as e:
    print(e)  # "Unknown model 'gpt-4o-mni'. Did you mean: gpt-4o-mini, gpt-4o"

# Quick boolean check
ok = catalogue.is_valid_model("gpt-4o")        # True

# Fuzzy suggestions
suggestions = catalogue.get_suggestions("gpt4o")  # ['gpt-4o', 'gpt-4o-mini']
Cache file: ~/.praison/cache/models.json (TTL 3600s). Falls back to 14 curated models (OpenAI 5, Anthropic 3, Google 3, Groq 2, Ollama 1) when litellm is unavailable.

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praisonai_code.llm.config

from praisonai_code.llm.config import build_config_list

config = build_config_list(include_api_type=True)
# → [{'model': 'gpt-4o-mini', 'base_url': 'https://api.openai.com/v1',
#     'api_key': 'sk-...', 'api_type': 'openai'}]

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praisonai_code.llm.env

from praisonai_code.llm.env import (
    LLMEndpoint,
    resolve_llm_endpoint,
    default_model_for_available_provider,
)

ep = resolve_llm_endpoint()
print(ep.model, ep.base_url, ep.api_key)

model = default_model_for_available_provider()

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praisonai_code.llm.credentials

from praisonai_code.llm.credentials import (
    is_configured,
    inject_credentials_into_env,
    resolve_llm_endpoint_with_credentials,
)

if is_configured():
    inject_credentials_into_env()
    ep = resolve_llm_endpoint_with_credentials()

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Best Practices

New scripts that target the praisonai-code package should import from praisonai_code.llm.* to avoid taking a dependency on the wrapper:
from praisonai_code.llm.catalogue import ModelCatalogue
from praisonai_code.llm.env import resolve_llm_endpoint
Existing code using from praisonai.llm.catalogue import ModelCatalogue works without change — the shim guarantees module identity. Migration has no functional benefit.
Use is_configured() as a fast gate before running an agent — it checks env vars and the credential store without making any network calls:
from praisonai_code.llm.credentials import is_configured, inject_credentials_into_env

if not is_configured():
    print("Run: praisonai setup  (or set OPENAI_API_KEY)")
    raise SystemExit(1)

inject_credentials_into_env()

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Configuration Options

ModelCatalogue Python Reference

Python configuration options for ModelCatalogue

LLMEndpoint Python Reference

Python configuration options for LLMEndpoint

Model Catalogue

CLI commands for models list, describe, validate

AutoGen Config List

Build AutoGen config_list from the resolved endpoint

LLM Endpoint Config

Endpoint precedence and provider routing

PraisonAI Code CLI

The standalone CLI runtime these modules power

Package Boundaries (C7.1)

Three-tier ownership and the _wrapper_bridge pattern