Edge
Identity, rate limits, request normalization, service classes.
Cognitive software architecture
Build a clear boundary for models, memory, tools, and neural data.
BrainAPI.com is an independent field guide for designing Brain API systems: agentic workflows, LLM model routing, durable memory, brain-computer interfaces, multimodal video and XR, analytics, mobile AI, and self-hosted operations.
Agentic systemsTools, memory, handoffs, approvals
Model infrastructureRouting, providers, self-hosting
Neural and spatial dataBCI, video, AR, and VR
Developer operationsLogs, analytics, Git review, QA
Definition
What does “Brain API” mean here?
It is a design pattern for an API layer that interprets goals, assembles context, chooses AI models, uses approved tools, applies policy, and returns a structured result.
The phrase is not one official standard and this site does not claim to operate a live model endpoint. BrainAPI.com explains the architecture behind cognitive services so developers can create stable product contracts while models and providers change underneath.
“Treat the model as one component. The product boundary is the complete loop: context, reasoning, tools, validation, state, and telemetry.”
Reference architecture
Six layers, one stable contract
Start in one process if needed, but keep responsibilities distinct so they can be tested, observed, and changed independently.
Context
Files, memory, database records, retrieval, provenance.
Reasoning
Prompt versions, model routing, multimodal inference.
Actions
Typed tools, permissions, proposals, idempotent execution.
Assurance
Guardrails, validation, human approval, safety policy.
Telemetry
Logs, traces, evaluations, cost, latency, analytics.
Explore the system
Architecture guides by layer
Use each page as a map to deeper implementation articles and practical release checklists.
AI agents
Design bounded agent loops with tools, durable state, specialist handoffs, guardrails, and approval.
Model routing
Choose AI models by task, capability, latency, context, privacy, cost, and measured quality.
Neural interfaces
Separate neural signals, features, decoded intent, consent, timing, and application control.
Developer tools
Connect CLI workflows, QA tests, Git review, observability, analytics, and safe deployment.
Multimodal systems
Align video, voice, gaze, spatial anchors, AR, VR, and structured model outputs.
Edge and self-hosted AI
Package local models, route between device and cloud, and operate private infrastructure.
Latest from the field guide
Practical articles for builders
Each guide includes a production checklist and exactly one contextual link to an authoritative source.
Self-Hosted Brain API: Deployment, Git Review, CLI, and QA Checklist
A production-oriented plan for self-hosting a Brain API with containers, gateways, model runtimes, queues, observability, CLI operations, Git review, and release QA.
On-Device Brain APIs for iOS, Android, and Mac Applications
An architecture guide to local AI inference, mobile and desktop model packaging, hybrid routing, privacy, updates, and test matrices for iOS, Android, and Mac apps.
Secure Prompt and Tool Pipelines: Threat Modeling a Brain API
A practical threat model for prompt injection, tool authorization, data exfiltration, secrets, sandboxing, output handling, and red-team tests in a Brain API.
Frequently asked questions
Brain API questions
Is Brain API a specific commercial API?
No. On this site, Brain API is a descriptive architecture pattern for coordinating models, context, tools, memory, validation, and telemetry. BrainAPI.com is an educational publication, not a claim of an active endpoint service.
How is a Brain API different from an LLM API?
An LLM API exposes model inference. A Brain API sits at the application layer and can route among models, retrieve data, call permitted tools, manage state, request approval, and return a stable task-oriented response.
Does a Brain API require multiple agents?
No. The strongest first version is usually one bounded workflow with one model and a small tool set. Multiple agents are useful only when responsibility, permissions, or evaluation criteria genuinely differ.
Can the architecture run on mobile devices?
Yes. A hybrid design can run selected models and private retrieval on iOS, Android, or Mac while routing larger tasks to a server. The client should make the execution path and data movement explicit.
Is the neural-interface content medical advice?
No. The BCI material is a software-architecture overview. High-impact or medical neural-interface work requires qualified clinical, regulatory, security, and human-factors expertise.
Build from a stable boundary
Begin with one measurable task and one complete loop.
Define the request, response, tool permissions, state, trace, and evaluation before expanding the feature surface.