Builderforce Memory MCP server

BuilderForce Agent Memory

A framework for giving any AI agent persistent, browser-trainable SSM memory. It is provider-neutral — swappable LLM bridges (Anthropic / OpenAI / Fetch), an inference router, online distillation, and a persistent memory store — so any agent or app can depend on it, not just BuilderForce.

BuilderForce.ai is the flagship deployment, not the boundary: it trains a custom SSM in the browser, exports a model, and pushes it to an agent runtime where it runs as Evermind — the model itself, not just memory bolted onto someone else's LLM. Evermind is the whole brain: its own shared-expert generator (the cortex that does reasoning and language), a write-through knowledge memory (the hippocampus), and a trainable affective layer (the limbic system). A request can be served by Evermind directly — evermind/ traffic is generated on-device, not forwarded to Claude/GPT — and external frontier models stay an optional routing choice rather than a hard dependency. The same framework is reusable by other agents off the shelf.

This monorepo consolidates two packages that previously lived in separate repos (mambacode.js and ssmjs) whose names described the technique rather than the role. They are renamed and unified here so that "what they do" is legible: they are Agent Memory.

Packages

The two-package split is deliberate: the engine is zero-dep and WebGPU-pure and can be consumed standalone; the runtime pulls in LLM-vendor bridges. Flattening them would force engine-only consumers to drag in vendor code and vice versa. They release in lockstep from one pipeline, which kills the publish-drift bug class that the separate-repo setup suffered (bumping one version without publishing + regenerating the consumer lockfile).

Cutting token cost

Agent Memory ships three layers that reduce LLM spend, in increasing power. All are portable — the same code runs in the browser (WebGPU SSM) and in Node (the agent's @webgpu/node SSM) because the embedder and storage are injected, never hard-wired.

The semantic cache is the real lever. It is read-through with two tiers, mirroring an L1-Map / L2-KV cache:

Shortened. The full README is on GitHub.