Introduction

flow-state-dev gives you blocks, flows, state, and streaming. Those are execution primitives. They don't have opinions about how an agent should think.

Thought Fabric is the cognitive layer. It's a separate framework built on top of flow-state-dev that models how agents manage attention, develop identity, perceive their environment, and reason about problems. Where flow-state-dev handles the "how does this run" question, Thought Fabric handles "how does this think."

The separation is deliberate. Not every flow needs cognition. A data pipeline that validates, transforms, and stores doesn't need salience scoring or a perspective model. But an agent that prioritizes what matters and behaves consistently across interactions does. Thought Fabric is for that second case.

The vision

Thought Fabric maps cognitive science concepts onto composable building blocks. The full architecture spans several domains:

Domain	What it models	Status
Attention	What to focus on. Relevance filtering and salience scoring.	Shipped
Identity	How to interpret. Perspective (viewpoint/expertise) with evolving observations and positions.	Shipped (partial)
Perception	How to interpret input. Sensory processing, context framing, signal extraction.	Coming soon
Reasoning	How to think. Structured deliberation, chain-of-thought, planning strategies.	Coming soon
Metacognition	How to self-monitor. Bias detection, sycophancy scoring, counter-argument generation.	Shipped (partial)
Learning	How to improve. Pattern extraction, skill acquisition, feedback integration.	Planned

Each domain exports blocks, helpers, and resource definitions that compose with flow-state-dev primitives. A Thought Fabric block is a standard flow-state-dev block. You use it in sequencers, pass it as a tool, register it in flows. No special runtime, no separate execution model.

The goal isn't to simulate human cognition. It's to give agent builders a structured vocabulary for the cognitive behaviors they're already implementing ad-hoc. Instead of hand-rolling salience heuristics or bolting bias checks onto prompt templates, you compose purpose-built blocks that handle these concerns with tested, configurable implementations.

Memory used to live here. Cross-turn memory shipped from Thought Fabric in earlier waves. It now lives in @flow-state-dev/memory so it can ship as part of the open framework. Thought Fabric will host specialized cognitive memory variants on top of that contract when they're ready — until then it doesn't address memory at all. See the framework docs at Ecosystem → Memory.

What's shipped today

Attention ships two blocks. filterRelevance does deterministic keyword-based relevance filtering: fast, no LLM, good for cutting noise before expensive operations. scoreSalience uses an LLM to score items along configurable dimensions (goal relevance, recency, novelty, emotional weight). Use them together: filter first, then score the survivors. See Attention.

Identity ships perspective() — a structured viewpoint model that shapes how an agent interprets information. Perspectives accumulate observations and positions over a session via resource-backed state. The system() factory bundles blocks, a capability, and a capture pipeline. A second primitive, constitution() (values and behavioral constraints), is planned. See Identity.

Metacognition ships bias and sycophancy detection. The biasAnalyzer sequencer takes a user input and AI response, detects agreement bias across four dimensions, classifies six cognitive bias types, computes a composite sycophancy score, and generates counter-arguments when the score warrants it. All five internal blocks are exported individually for custom pipelines. See Metacognition.

Import paths

Use subpath exports for tree-shaking:

import { filterRelevance, scoreSalience } from '@thought-fabric/core/attention'
import { perspective, system } from '@thought-fabric/core/identity'
import { biasAnalyzer } from '@thought-fabric/core/metacognition'

Or import domain namespaces from the main package:

import { attention, identity, metacognition } from '@thought-fabric/core'
// attention.filterRelevance, identity.perspective, metacognition.biasAnalyzer

The package depends on @flow-state-dev/core. Build core first if you hit type resolution issues.

Naming convention

Word order encodes the category:

Pattern	Category	Example
[domain][Verb]	Block or item	perspectiveObserve, constitutionAuditor
[verb][Domain]	Helper (verb first)	addPerspective, summarizePerspective

A noun-first identifier is a block you compose in a pipeline. A verb-first identifier is a helper you call on a resource ref. The inversion tells you which is which without checking docs.