Graph-Structured Declarative Computing (often shortened to “Grasp”) is a way of building systems where the graph is the system, not just a database. You declare what you want to exist, and the system resolves it by following structural relationships. Instead of writing step-by-step logic, you describe a target shape—fields, relationships, and outcomes—and allow resolvers to fill in missing parts only when queried. The graph becomes a living map of intent, and computation becomes traversal rather than a rigid instruction sequence.
Imagine that instead of calling functions, you ask the graph, “What should exist here?” The graph replies by revealing or generating the missing pieces—summaries, embeddings, derived metadata—without mutating the underlying structure unless you explicitly declare a mutation. Queries inquire; mutations commit. This boundary preserves stability: you can explore, simulate, and reason without accidentally changing the world.
Core Idea: Structure First, Execution Second
You start with outcomes, not procedures. If you want summaries for transcripts, you declare a `summary` field on `Transcript` and attach a resolver that knows how to generate it. When you query `summary`, it resolves. If you never ask, it never runs. Computation is lazy, localized, and tied to fields rather than global execution flows.This is a shift from time-based orchestration to shape-based resolution:
- You don’t plan a sequence of steps.
- You define the structure you want.
- The system finds the minimal transformations to make it true.
It’s the same mental posture as declarative UI: you describe the desired state, and the system reconciles toward it. The outcome becomes the source of gravity.
Nodes as Potential, Properties as Computation
In Grasp, nodes are containers of potential. Each property can resolve independently, and each field is its own computation “island.” If you request `content`, only content resolves. If you request `vector`, only vector resolves. Nothing else fires unless you ask for it.This yields practical benefits:
- Partial resolution: a missing field doesn’t break the node.
- Modularity: each resolver is isolated and swappable.
- Minimal waste: expensive computations run only when needed.
- AI-friendly interaction: an agent can probe fields selectively without fetching everything.
Queries vs. Mutations: A Cognitive Contract
A core boundary keeps the system clean:- Queries are reflective. They ask what would be true given current structure.
- Mutations are declarative commits. They make changes real.
Because queries do not write, you can explore safely. AI can test hypotheses, inspect missing data, and simulate outcomes without side effects. When you want to persist, you issue a mutation explicitly.
GraphQL as the Framework, Not the Layer
In Grasp, GraphQL is not just an API surface; it becomes the programming framework. The schema is the system. Types are semantic anchors. Resolvers are behavioral extension points. Queries and mutations are the programming interface. You don’t build a system and expose it via GraphQL—you build the system in GraphQL, and the graph is the runtime.This produces a compact, legible architecture:
- Schema is documentation.
- Queries are declarations of intent.
- Resolvers are the minimal code to fulfill a field.
- The graph holds causal history and structure.
Declarative Causality Without Explicit Events
Grasp resembles event-driven architecture but removes explicit events and messages. Data presence is the signal. When a node of a certain shape exists, any process that consumes that shape can activate. The graph itself is the scheduler. This gives you the modularity of event systems without brittle message plumbing.Shape-Driven Compatibility
Instead of wiring processes together manually, you declare what shapes they consume and produce. A process that consumes `MarkdownFileShape` and produces `ParsedMarkdownShape` will activate whenever the right shape appears. This decouples producers from consumers and makes the graph self-orienting.Structural Introspection
Because structure is explicit, you can query for latent potential:- “Which data nodes match a shape but haven’t been consumed?”
- “Which processes produce a shape that nothing uses?”
- “Where are the gaps between intended and realized structure?”
This is not just debugging; it’s a way to surface unfulfilled intent. The graph becomes a self-describing map of what is possible and what remains to be done.
AI as Collaborative Traverser
AI thrives in Grasp because the graph provides orientation. Instead of guessing how code flows, an agent can read shapes, declared affordances, and relationships. It can propose new processes, spot missing links, and safely explore via queries. AI stops being a code generator and becomes a structural collaborator.Where It Changes Your Daily Work
- You declare an outcome first, then fill in missing resolvers only as needed.
- You query for what should exist and let the system stabilize.
- You build reusable resolvers that are bound to fields, not global flows.
- You reason about systems by walking the graph rather than tracing call stacks.
The result is a system that scales without compounding complexity: more nodes don’t mean more global entanglement. Each node remains locally understandable, and each resolver stays small and explicit.
Going Deeper
- Intent vs. Mutation Boundaries - A deep look at why queries are reflective and mutations are explicit, and how this keeps systems stable while still enabling discovery.
- Field-Level Computation and Lazy Resolution - How properties become atomic computation units and why this enables partial resolution, modularity, and zero-waste execution.
- Shape-Driven Orchestration and Declarative Causality - How processes activate through shape compatibility, replacing explicit control flow with structural resonance.
- Graph Introspection as Debugging and Discovery - Using structural queries to reveal missing work, emergent patterns, and system health without tracing code paths.
- AI Collaboration in Graph-Native Systems - Why a graph-first architecture lets AI reason, explore, and extend systems without guessing at hidden control flow.