OPEN SOURCE // MIT, AND HONEST ABOUT THE EDGES

The clients are yours.
The engine is not.

Everything you need to call Voxell from your own stack is MIT and on the public registries. The embedding engine and the models are not, and that line is drawn on purpose.

Clients and integrations

Call Forge from the tool you already use. Each of these is a thin, readable client you can fork.

forge-gov0.1.0 · MIT

Go client, native gRPC

Speaks protobuf over mutual TLS rather than HTTP and JSON. Vectors travel as packed binary at four bytes per float, and the client authenticates with a certificate instead of putting a bearer token on the wire.

Read the source →
forge-mcpv0.1.5 · MIT

MCP server for coding agents

Gives Claude Code, Codex, Cursor and any stdio MCP client a retrieval tool backed by your own corpus. The most used thing on this page: 481 installs in the last thirty days.

npx -y @voxell/forge-mcp →
langchain-voxell0.1.1 · MIT

LangChain embeddings

Sets Forge's asymmetric input type for you, so queries embed as queries and documents embed as documents. Mixing those two is a quiet way to lose recall.

pip install langchain-voxell →
llama-index-embeddings-forge0.1.0 · MIT

LlamaIndex embeddings

The same asymmetric handling, wired into LlamaIndex. Matryoshka dimensions are real here: a truncated vector is re-normalized, so a shorter dimension is a usable prefix rather than a broken one.

pip install llama-index-embeddings-forge →
forge-ai-provider0.1.0 · MIT

Vercel AI SDK provider

Drop-in for the AI SDK's embedding interface. The OpenAI-compatible path preserves your existing width, so a swap does not force you to rebuild an index you already have.

npm i @voxell/forge-ai-provider →
n8n-nodes-forgeSOURCE ONLY

n8n embedding node

An AiEmbedding sub-node for n8n workflows. Complete and readable, but not published to npm yet, so today it is source you clone rather than a package you install. The README says otherwise and is wrong.

Read the source →

What is not open

Worth saying plainly, because it is the first thing worth checking.

THE ENGINE

Forge itself is closed

The CUDA embedding engine, the serving stack and the models are private. What is open is every path into them, which is the part that decides whether you can leave.

THE MODELS

Weights are not published

The research model has a public model card and a demo endpoint, but the weights behind the commercial tiers are not downloadable. If that is a hard requirement, say so early and we will tell you honestly whether this fits.

THE SORT

MASH is verifiable, not reproducible

You can re-run the benchmarks and check the integrity proofs. You cannot read the algorithm. That is a real limit on what independent verification means here, and it is stated rather than left to be discovered.

Fork it, or just call it.

The Turbo tier is free forever and needs no card, so the fastest way to evaluate any of this is to run a real call before you read a line of the source.