hexastack-ai

AI engine, LLM provider integration (LiteLLM, Instructor, PydanticAI), and CQRS agent tool reflection for the Hexastack hexagonal architecture framework.

1. Overview

hexastack-ai integrates an agnostic, production-grade AI stack directly into the Hexastack architecture:

• LiteLLM: Unified driver abstraction across 100+ LLM providers (OpenAI, Anthropic Claude, Google Gemini, Ollama, Groq, Bedrock).
• Instructor: Self-correcting structured output validation returning strongly typed Pydantic models.
• PydanticAI: Type-safe agent loop orchestration and tool execution.
• CQRS Tool Reflection: Automatically turns Hexastack CQRS Commands and Queries into callable AI Agent tools.

2. Architecture & Relationships

graph TD
    subgraph Core ["hexastack-core"]
        PORT["LlmProviderPort"]
        MEM["InMemoryLlmProvider"]
    end

    subgraph AI ["hexastack-ai"]
        BOOT["AiBootstrapper (order=18)"]
        ADAPTER["LiteLlmAdapter"]
        TOOLS["create_cqrs_agent / create_tool_for_message"]
        AGENT_ADAPTER["PydanticAiAgentAdapter"]
    end

    subgraph CQRS ["hexastack-cqrs"]
        PIPELINE["ExecutionPipeline"]
        CMDS["Commands & Queries"]
    end

    subgraph UpstreamAI ["Agnostic AI Stack"]
        LITE["LiteLLM (100+ Providers)"]
        INST["Instructor (Schema Validation)"]
        PY_AI["PydanticAI (Agent Execution)"]
    end

    BOOT -->|binds into DI| PORT
    ADAPTER -. implements .-> PORT
    ADAPTER --> LITE
    ADAPTER --> INST
    TOOLS --> PIPELINE
    TOOLS --> PY_AI
    AGENT_ADAPTER --> PY_AI

3. Installation

# Standalone install
pip install hexastack-ai

# Via umbrella package
pip install "hexastack[ai]"

4. Configuration Reference

[hexastack.ai]
# Provider: "memory" (default for testing), "litellm", "openai", "anthropic", "gemini", "ollama"
provider = "litellm"
model = "gpt-4o-mini"
temperature = 0.2
max_tokens = 2048
api_key = "sk-..." # Or set standard env var OPENAI_API_KEY / ANTHROPIC_API_KEY

# LiteLLM Dialect Settings
[hexastack.ai.litellm]
drop_params = true
num_retries = 3
timeout = 60.0
api_base = "http://localhost:4000" # Optional LiteLLM proxy URL

# Ollama Local Dialect Settings
[hexastack.ai.ollama]
base_url = "http://localhost:11434"

# PydanticAI Agent Settings
[hexastack.ai.agent]
max_turns = 10
system_prompt = "You are a helpful AI assistant."

5. Usage Examples

1. Structured Output Extraction

from pydantic import BaseModel
from hexastack_core.ports.ai import LlmProviderPort


class InvoiceDTO(BaseModel):
    customer_id: str
    total_amount: float
    items: list[str]


def extract_invoice(llm: LlmProviderPort, text: str) -> InvoiceDTO:
    return llm.generate_structured(
        prompt=f"Extract invoice details from: {text}",
        response_schema=InvoiceDTO,
    )

2. Auto-Reflecting CQRS Commands as Agent Tools

from hexastack_core.domain import Command, Query
from hexastack_ai.infra.tools import create_cqrs_agent


class CancelSubscriptionCommand(Command):
    user_id: str
    reason: str


class GetUserPlanQuery(Query[str]):
    user_id: str


# Create PydanticAI agent with CQRS handlers as native tools:
agent = create_cqrs_agent(
    pipeline=runtime.pipeline,
    messages=[CancelSubscriptionCommand, GetUserPlanQuery],
    model="anthropic/claude-3-5-sonnet",
    system_prompt="You are a customer support agent.",
)

# Agent selects appropriate tools and executes them via Hexastack ExecutionPipeline:
result = agent.run_sync("Cancel subscription for user 123 due to pricing.")