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Architecture

SynapseKit is structured as a set of loosely-coupled, composable layers. You can use each layer independently or compose them together.

Layer overview​

SynapseKit layer architecture

Core abstractions​

BaseLLM​

All 23 LLM providers implement BaseLLM. The interface is:

class BaseLLM:
    async def generate(self, prompt: str, **kwargs) -> str: ...
    async def stream(self, prompt: str, **kwargs) -> AsyncIterator[str]: ...
    async def call_with_tools(self, messages, tools, **kwargs) -> dict: ...

RAGPipeline​

Composes embeddings, a vector store, a retriever, and an LLM into a single aquery() call with streaming support.

StateGraph / CompiledGraph​

A DAG of async node functions with typed state, conditional edges, parallel fan-out, checkpointing, and human-in-the-loop support.

BaseAgent → ReActAgent / FunctionCallingAgent​

Tool-using agents. ReAct uses chain-of-thought. FunctionCallingAgent uses native provider function calling.

Optional dependency model​

SynapseKit has 2 required dependencies (numpy, rank-bm25). Everything else is opt-in:

pip install synapsekit[openai]         # OpenAI LLM
pip install synapsekit[chroma]         # ChromaDB vector store
pip install synapsekit[serve]          # CLI serve command (FastAPI)
pip install synapsekit[redis]          # Redis memory + checkpointer
pip install synapsekit[postgres]       # Postgres checkpointer
pip install synapsekit[all]            # Everything

Internals use lazy imports — uninstalled extras raise ImportError with a helpful message only when accessed.

Async model​

Every public API is async def. Sync wrappers (e.g., rag.query()) call asyncio.run() for convenience in scripts and notebooks. Never mix sync wrappers with an existing event loop.

Data flow: RAG query​

RAG pipeline data flow

Data flow: Graph execution​

Graph execution flow