product → "has common issue" → issue), a sales pipeline (customer → order),
your infrastructure (service → runbook), or anything else. You define the
concepts; Open Index stores them, searches them, and draws the map.
Quickstart
Install, spin up the bundled example brain, and open the explorer in a few minutes.
Core concepts
doc_type, doc_schema, entity, connector — the four primitives a brain is built from.
Create a brain
Define doc_types, author entities, and load them into the search index.
Connect an agent
Expose the brain over MCP so agents can read context and keep it current.
What a brain is built from
A brain is built from four primitives:- doc_type — a concept you want to track and maintain (e.g.
service,customer,issue). - doc_schema — the fields stored for a given doc_type.
- entity — one instance of a doc_type, stored per its schema. Every entity can
link to others via
related_to(the target) +relationship_edge_meaning(free-text edge semantics). - connector — an optional source you extract entities from (e.g. an MCP server).
A context layer for domain-specialized agents
Open Index is designed to sit behind agents specialized for a domain — legal, marketing, customer support, sales, infrastructure, or a domain of your own. The MCP server gives those agents structured context and a validated way to keep that context current:- agent prompt — dynamic domain navigation is published through MCP server instructions so supporting hosts can inject it before the first turn.
- read —
navigation_guidelines()refreshes those instructions;search_brain()andget_entity()retrieve domain context. - write —
put_entity()(add/update an entity),create_doc_type()(define a concept).
--read-only when the agent should consume context without
mutating it.
Join the community
Ask questions, share the brains you’re modelling, or discuss an idea before you build it.