Standalone LLM Modules

Model catalogue, endpoint resolver, credentials gate, and AutoGen config-list — usable with pip install praisonai-code alone

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.

How It Works

Module Map

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.

---

The Four Modules

Module	Public exports	Purpose
praisonai_code.llm.catalogue	ModelCatalogue, ModelInfo	Discover and validate LLM models
praisonai_code.llm.config	build_config_list	AutoGen-style [{model, base_url, api_key, api_type}]
praisonai_code.llm.env	LLMEndpoint, resolve_llm_endpoint, default_model_for_available_provider	Resolve which model/endpoint to hit
praisonai_code.llm.credentials	is_configured, inject_credentials_into_env, resolve_llm_endpoint_with_credentials	Credential 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.

---

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

---

Module Reference

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.

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'}]

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()

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()

---

Best Practices

Use praisonai_code.llm.* for new standalone scripts

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

Do not migrate existing praisonai.llm.* imports

Existing code using from praisonai.llm.catalogue import ModelCatalogue works without change — the shim guarantees module identity. Migration has no functional benefit.

Check is_configured() before calling agents in CLI scripts

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

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Related

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