Question 1

What is SynapseKit?

Accepted Answer

SynapseKit is an async-native, open-source Python framework for building LLM-powered applications. It provides RAG pipelines, ReAct agents, graph workflows, and AgentFederation with only 2 hard dependencies (numpy and rank-bm25). It supports 33 LLM providers, 53 document loaders, and 11 vector stores out of the box.

Question 2

Is SynapseKit a LangChain alternative?

Accepted Answer

Yes. SynapseKit is designed as a lightweight, async-native alternative to LangChain. It achieves the same capabilities — RAG, agents, tool use, graph workflows — with only 2 hard dependencies instead of LangChain's large dependency tree. It avoids hidden abstractions and gives developers full control over their LLM pipelines.

Question 3

Does SynapseKit support async Python?

Accepted Answer

Yes, SynapseKit is async-native throughout. Every LLM call, loader, retriever, and agent supports async/await. This makes it suitable for production web applications, APIs, and high-throughput pipelines where blocking I/O would be a bottleneck.

Question 4

How do I install SynapseKit?

Accepted Answer

Install SynapseKit with pip: `pip install synapsekit`. For optional extras like specific vector stores or loaders, use extras such as `pip install synapsekit[chroma]`. Full installation docs are at https://synapsekit.github.io/synapsekit-docs/getting-started/installation.

Question 5

Which LLM providers does SynapseKit support?

Accepted Answer

SynapseKit supports 33 LLM providers including OpenAI, Anthropic Claude, Google Gemini, Mistral, Cohere, Ollama, LM Studio, xAI Grok, Groq, AWS Bedrock, Azure OpenAI, Together AI, Replicate, HuggingFace, and many more. Provider switching requires changing one line of code.

Question 6

What is RAG and how does SynapseKit support it?

Accepted Answer

RAG (Retrieval-Augmented Generation) lets LLMs answer questions using your documents. SynapseKit provides a complete RAG pipeline with 53 document loaders (PDF, web, S3, databases, APIs), 11 vector stores (Chroma, Pinecone, Weaviate, Qdrant, pgvector, etc.), hybrid BM25+vector search, and reranking — all composable with a few lines of Python.

Question 7

What are ReAct agents in SynapseKit?

Accepted Answer

ReAct agents in SynapseKit follow the Reason+Act pattern: the LLM reasons about a task, selects a tool, observes the result, and iterates. SynapseKit ships 47+ built-in tools (web search, code execution, file I/O, APIs) and makes it easy to write custom tools as plain Python functions.

Question 8

What are Graph Workflows?

Accepted Answer

Graph Workflows let you define LLM pipelines as directed graphs with nodes (processing steps) and edges (transitions). Unlike linear chains, graphs support branching, loops, conditional routing, and parallel execution. SynapseKit's graph runtime also supports recursive subgraphs for complex multi-agent coordination.

Question 9

Does SynapseKit have evaluation support?

Accepted Answer

Yes. SynapseKit includes EvalDataset, EvalRecord, and a PromptOptimizer for scoring prompt variants. There is also EvalCI — a free GitHub Action (github.com/SynapseKit/evalci) that runs your eval suite on every pull request, so regressions are caught before merging.

Question 10

What license is SynapseKit released under?

Accepted Answer

SynapseKit is released under the Apache License 2.0. It is free to use, modify, and distribute for personal, academic, and commercial projects. There is no SaaS version, no paid tier, and no telemetry.

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