lexigram-ai-llm 0.1.1

LLM client layer for the Lexigram Framework — OpenAI, Anthropic, Ollama, Cohere, Groq, Mistral

lexigram-ai-llm

LLM client layer for the Lexigram Framework — OpenAI, Anthropic, Ollama, Cohere, Groq, Mistral

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Overview

LLM client layer for the Lexigram Framework. Provides typed, async-first clients for 18 providers, multi-provider routing, thinking/reasoning control, structured extraction, streaming, embeddings, and model management — all wired through the DI container via LLMModule. Zero-config usage starts with sensible defaults.

Full documentation: docs.lexigram.dev

Install

uv add lexigram-ai-llm
# Optional extras
uv add "lexigram-ai-llm[openai,anthropic,ollama]"

Quick Start

from lexigram import Application
from lexigram.di.module import Module, module

from lexigram.ai.llm import LLMModule
from lexigram.ai.llm.config import ClientConfig

@module(imports=[
    LLMModule.configure(
        ClientConfig(provider="anthropic", model="claude-sonnet-4-6")
    )
])
class AppModule(Module):
    pass

app = Application(modules=[AppModule])
if __name__ == "__main__":
    app.run()

Configuration

Zero-config usage: Call LLMModule.configure() with no arguments to use defaults.

Option 1 — YAML file

# application.yaml
ai_llm:
  provider: "anthropic"
  model: "claude-sonnet-4-6"
  api_key: "${LEX_AI_LLM__API_KEY}"
  temperature: 0.7
  max_tokens: null

Option 2 — Profiles + Environment Variables (recommended)

export LEX_AI_LLM__PROVIDER=anthropic
# Environment variables for each field

Option 3 — Python

from lexigram.ai.llm.config import ClientConfig
from lexigram.ai.llm import LLMModule

config = ClientConfig(
    provider="anthropic",
    model="claude-sonnet-4-6",
)
LLMModule.configure(config)

Config reference

Field	Default	Env var	Description
enabled	True	LEX_AI_LLM__ENABLED	Enable the LLM subsystem
provider	openai	LEX_AI_LLM__PROVIDER	LLM provider
model	gpt-4-turbo	LEX_AI_LLM__MODEL	Model name
api_key	None	LEX_AI_LLM__API_KEY	Provider API key
api_base	None	LEX_AI_LLM__API_BASE	Custom endpoint (Azure, local, proxy)
temperature	0.7	LEX_AI_LLM__TEMPERATURE	Sampling temperature (0.0–2.0)
max_tokens	None	LEX_AI_LLM__MAX_TOKENS	Response token limit
timeout	60.0	LEX_AI_LLM__TIMEOUT	Request timeout in seconds
enable_cache	False	LEX_AI_LLM__ENABLE_CACHE	Cache responses
cache_ttl	3600	LEX_AI_LLM__CACHE_TTL	Cache TTL in seconds
thinking	None	—	Reasoning/thinking control configuration

Module Factory Methods

Method	Description
LLMModule.configure(config)	Single-provider client
LLMModule.configure(routing=LLMConfig())	Multi-provider routing cascade
LLMModule.stub()	No-op client for tests

Key Features

• 18 providers: OpenAI, Anthropic, Google Gemini, Azure, Ollama, Groq, Mistral, Cohere, and more
• Multi-provider routing: Sequential, cost-optimized, and latency-optimized strategies
• Thinking/reasoning control: Extended thinking with token budget and suppression
• Structured extraction: JSON schema and Pydantic model extraction
• Streaming: Async streaming response support
• Embeddings: Text embedding client with same provider
• Caching: Response-level caching with configurable TTL

Testing

async with Application.boot(modules=[LLMModule.stub()]) as app:
    # your test code
    ...

Key Source Files

File	What it contains
src/lexigram/ai/llm/module.py	LLMModule.configure() and LLMModule.stub()
src/lexigram/ai/llm/config.py	ClientConfig
src/lexigram/ai/llm/routing/config.py	LLMConfig, ProviderConfig for routing
src/lexigram/ai/llm/di/provider.py	LLMProvider — registers and boots the client
src/lexigram/ai/llm/clients/	Provider implementations
src/lexigram/ai/llm/thinking/	ThinkingConfig handling and suppression
src/lexigram/ai/llm/exceptions.py	Full exception hierarchy