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SynapseKit/SynapseKit

SynapseKit

SynapseKit is a Python framework for building production-grade LLM applications. Built async-native and streaming-first from day one — not retrofitted. Two hard dependencies. Every abstraction is composable, transparent, and replaceable: plain Python you can read, debug, and extend. No magic. No hidden chains. No lock-in.


⚡ Async-native

Every API is async/await first.
Sync wrappers for scripts and notebooks.
No event loop surprises.

🌊 Streaming-first

Token-level streaming is the default,
not an afterthought.
Works across all providers.

🪶 Minimal footprint

2 hard dependencies: numpy + rank-bm25.
Everything else is optional.
Install only what you use.

🔌 One interface

13 LLM providers and 5 vector stores
behind the same API.
Swap without rewriting.

🧩 Composable

RAG pipelines, agents, and graph nodes
are interchangeable.
Wrap anything as anything.

🔍 Transparent

No hidden chains.
Every step is plain Python
you can read and override.

Who is it for?

SynapseKit is for Python developers who want to ship LLM features without fighting their framework.

  • Backend engineers adding AI features to existing Python services
  • ML engineers building RAG or agent pipelines who need full control over retrieval, prompting, and tool use
  • Researchers and hackers who want a clean, readable codebase they can understand and extend
  • Teams who need something they can actually debug and maintain in production

What it covers

🗂 RAG Pipelines
Retrieval-augmented generation with streaming, BM25 reranking, conversation memory, and token tracing. Load from PDFs, URLs, CSVs, HTML, directories, and more.

🤖 Agents
ReAct loop (any LLM) and native function calling (OpenAI / Anthropic / Gemini / Mistral). 19 built-in tools including calculator, Python REPL, web search, SQL, HTTP, shell, summarization, sentiment analysis, and translation. Fully extensible.

🔀 Graph Workflows
DAG-based async pipelines. Nodes run in waves — parallel nodes execute concurrently. Conditional routing, typed state with reducers, fan-out/fan-in, SSE streaming, event callbacks, human-in-the-loop, checkpointing, and Mermaid export.

🧠 LLM Providers
OpenAI, Anthropic, Ollama, Gemini, Cohere, Mistral, Bedrock, Azure OpenAI, Groq, DeepSeek, OpenRouter, Together, Fireworks — all behind one interface. Auto-detected from the model name. Swap without rewriting.

🗄 Vector Stores
InMemory (built-in, .npz persistence), ChromaDB, FAISS, Qdrant, Pinecone. One interface for all backends.

🔧 Utilities
Output parsers (JSON, Pydantic, List), prompt templates (standard, chat, few-shot), token tracing with cost estimation.


Install

pip

pip install synapsekit[openai]       # OpenAI
pip install synapsekit[anthropic]    # Anthropic
pip install synapsekit[ollama]       # Ollama (local)
pip install synapsekit[all]          # Everything

uv

uv add synapsekit[openai]
uv add synapsekit[all]

Poetry

poetry add synapsekit[openai]
poetry add "synapsekit[all]"

Full installation options → docs


Documentation

Everything you need to get started and go deep is in the docs.

🚀 Quickstart Up and running in 5 minutes
🗂 RAG Pipelines, loaders, retrieval, vector stores
🤖 Agents ReAct, function calling, tools, executor
🔀 Graph Workflows DAG pipelines, conditional routing, parallel execution
🧠 LLM Providers All 13 providers with examples
📖 API Reference Full class and method reference

Development

git clone https://github.com/SynapseKit/SynapseKit
cd SynapseKit
uv sync --group dev
uv run pytest tests/ -q

Contributing

Contributions are welcome — bug reports, documentation fixes, new providers, new features.

Read CONTRIBUTING.md to get started. Look for issues tagged good first issue if you're new.


Community


Contributors

Nautiverse
Nautiverse

💻 📖 🚧
Gordienko Andrey
Gordienko Andrey

💻
Deepak singh
Deepak singh

💻
by22Jy
by22Jy

💻
Arjun Kundapur
Arjun Kundapur

💻
Harshit Gupta
Harshit Gupta

📖

License

MIT