Assistant Service Kit

Reusable Python package for building LangChain-based assistant services with FastAPI, auth, tenancy, tools, RAG, tracing, storage, and observability.

Install

Base runtime:

pip install assistant-service-kit

Provider-specific installs:

pip install "assistant-service-kit[rag,gemini]"
pip install "assistant-service-kit[rag,openai]"
pip install "assistant-service-kit[rag,anthropic]"

For local development from this package directory:

pip install -e ".[dev]"

Core Pieces

• Assistant: simple facade for building a FastAPI assistant quickly.
• AssistantDefinition: declares the assistant server contract.
• AssistantEndpoints / EndpointPolicy: declares enabled routes, custom paths, names, and endpoint permissions.
• AuthConnector: supports no-auth, user-only, gateway token, API key, and custom identity resolution.
• LLMProviderRegistry: Gemini, OpenAI, Anthropic, and fake chat model providers.
• EmbeddingProviderRegistry: Gemini, OpenAI, Anthropic, and fake embedding providers.
• ToolRegistry / ToolPolicy: exposes and validates tools by tenant, user, roles, and limits.
• SQLAlchemyTraceStore / InMemoryTraceStore: reusable chat trace persistence.
• PostgresRagStore / VectorStoreConnector: reusable RAG storage and vector-store integration points.
• create_assistant_sqlalchemy_models: standard SQLAlchemy trace and RAG models for the common case.
• answer_with_langchain_agent: LangChain tool-calling orchestrator with observability metadata.

Minimal Example

from assistant_service_kit import (
    Assistant,
    AssistantEndpoints,
)

assistant = Assistant(
    name="demo",
    title="Demo Assistant",
    base_path="/api/assistant",
    tenancy="none",
    prompt="Answer clearly.",
    endpoints=AssistantEndpoints(chat=False, conversations=False, messages=False, feedback=False),
)

assistant.with_no_auth()
assistant.with_llm(provider="fake", model="fake", extra={"response": "ok"})
assistant.with_memory_trace()

app = assistant.fastapi(create_schema=lambda: None, get_db=lambda: None)

For standard SQLAlchemy persistence:

from assistant_service_kit import create_assistant_sqlalchemy_models

models = create_assistant_sqlalchemy_models(Base)
assistant.with_sqlalchemy_storage(models=models)

For advanced integrations, build AssistantDefinition and connectors directly.

Status

The package is in alpha. APIs can change until the first stable release.