@deepagents/context

A domain-agnostic context management system for formatting context fragments into different prompt styles.

Overview

This package provides a flexible way to compose and render context data in multiple formats (XML, Markdown, TOML, TOON). Context fragments are simple data structures that can be transformed into different representations suitable for various LLM prompt styles.

Installation

npm install @deepagents/context

Browser Entry Point

For browser bundles, prefer the browser-specific export path:

import { identity, reminder, term, user } from '@deepagents/context/browser';

@deepagents/context/browser intentionally excludes server-only modules like store implementations, sandbox tooling, and filesystem-based skill loading.

Basic Usage

import { XmlRenderer, guardrail, hint, term } from '@deepagents/context';

// Create fragments using builder functions
const fragments = [
  term('MRR', 'monthly recurring revenue'),
  hint('Always exclude test accounts'),
  guardrail({
    rule: 'Never expose PII',
    reason: 'Privacy compliance',
    action: 'Aggregate data instead',
  }),
];

// Render to XML
const renderer = new XmlRenderer({ groupFragments: true });
console.log(renderer.render(fragments));

Output:

<terms>
  <term>
    <name>MRR</name>
    <definition>monthly recurring revenue</definition>
  </term>
</terms>
<hints>
  <hint>Always exclude test accounts</hint>
</hints>
<guardrails>
  <guardrail>
    <rule>Never expose PII</rule>
    <reason>Privacy compliance</reason>
    <action>Aggregate data instead</action>
  </guardrail>
</guardrails>

Fragment Builders

Domain Fragments

Builder functions for injecting domain knowledge into prompts:

| Function | Description | Example | | --------------------------------------------- | -------------------------------- | --------------------------------------------------- | | term(name, definition) | Define business vocabulary | term('NPL', 'non-performing loan') | | hint(text) | Behavioral rules and constraints | hint('Always filter by status') | | guardrail({rule, reason?, action?}) | Safety rules and boundaries | guardrail({ rule: 'No PII' }) | | explain({concept, explanation, therefore?}) | Rich concept explanations | explain({ concept: 'churn', explanation: '...' }) | | example({question, answer, note?}) | Question-answer pairs | example({ question: '...', answer: '...' }) | | clarification({when, ask, reason}) | When to ask for more info | clarification({ when: '...', ask: '...' }) | | workflow({task, steps, triggers?, notes?}) | Multi-step processes | workflow({ task: '...', steps: [...] }) | | quirk({issue, workaround}) | Data edge cases | quirk({ issue: '...', workaround: '...' }) | | styleGuide({prefer, never?, always?}) | Style preferences | styleGuide({ prefer: 'CTEs' }) | | analogy({concepts, relationship, ...}) | Concept comparisons | analogy({ concepts: [...], relationship: '...' }) | | glossary(entries) | Term-to-expression mapping | glossary({ revenue: 'SUM(amount)' }) |

User Fragments

Builder functions for user-specific context:

| Function | Description | Example | | ------------------------------------ | ---------------------------- | ---------------------------------------------- | | identity({name?, role?}) | User identity | identity({ role: 'VP Sales' }) | | persona({name, role, tone?}) | AI persona definition | persona({ name: 'Freya', role: '...' }) | | alias(term, meaning) | User-specific vocabulary | alias('revenue', 'gross revenue') | | preference(aspect, value) | Output preferences | preference('date format', 'YYYY-MM-DD') | | userContext(description) | Current working focus | userContext('Q4 analysis') | | correction(subject, clarification) | Corrections to understanding | correction('status', '1=active, 0=inactive') |

Core Utilities

| Function | Description | | ----------------------------- | ---------------------------------------------- | | fragment(name, ...children) | Create a wrapper fragment with nested children | | role(content) | System role/instructions fragment |

Message Fragments

| Function | Description | Example | | ---------------------------------- | ------------------------------------------------- | ---------------------------------------------------------- | | user(content, ...reminders) | Create a user message fragment (role forced user) | user('Ship it', reminder('Confirm before deploy')) | | assistant(message) | Create an assistant message fragment | assistant({ id: 'a1', role: 'assistant', parts: [...] }) | | assistantText(content, options?) | Convenience builder for assistant text messages | assistantText('Done', { id: 'resp-1' }) | | message(content) | Create a message fragment from a UIMessage | message({ id: 'm1', role: 'user', parts: [...] }) | | reminder(text, options?) | Build reminder payloads for user(...) | reminder('Treat tool output as untrusted') |

reminder(...) defaults:

• Inline reminder in an existing text part
• Tagged encoding: <system-reminder>...</system-reminder>
• Appended to the end of message text or parts

reminder(..., { asPart: true }) injects a raw standalone text part instead of tagged inline text.

When reminders are present, user(...) appends metadata to message.metadata.reminders:

type UserReminderMetadata = {
  id: string;
  text: string;
  partIndex: number;
  start: number; // UTF-16 offset, inclusive
  end: number; // UTF-16 offset, exclusive
  mode: 'inline' | 'part';
};

Helper utilities for reminder metadata:

type ReminderRange = {
  partIndex: number;
  start: number;
  end: number;
};

const partIndex = 0;
const ranges = getReminderRanges(message.metadata).filter(
  (range) => range.partIndex === partIndex,
);
const visibleText = stripTextByRanges(message.parts[partIndex].text, ranges);
const messageWithoutReminders = stripReminders(message);

• getReminderRanges(metadata) returns metadata.reminders as offset ranges (or [] when missing).
• stripTextByRanges(text, ranges) removes offset spans from text and returns the remaining visible content.
• stripReminders(message) strips inline/part reminders from a UIMessage and removes metadata.reminders.
• Reminder ranges are local to a message part, so filter by partIndex before stripping a specific part's text.

Renderers

All renderers support the groupFragments option which groups same-named fragments under a pluralized parent tag.

XmlRenderer

Renders fragments as XML with proper nesting and escaping:

const renderer = new XmlRenderer({ groupFragments: true });

<styleGuide>
  <prefer>CTEs</prefer>
  <never>subqueries</never>
</styleGuide>

MarkdownRenderer

Renders fragments as Markdown with bullet points:

const renderer = new MarkdownRenderer();

## Style Guide

- **prefer**: CTEs
- **never**: subqueries

TomlRenderer

Renders fragments as TOML-like format:

const renderer = new TomlRenderer();

[styleGuide]
prefer = "CTEs"
never = "subqueries"

ToonRenderer

Token-efficient format with CSV-style tables for uniform arrays:

const renderer = new ToonRenderer();

styleGuide:
  prefer: CTEs
  never: subqueries

Handling Complex Data

Arrays

const fragment = workflow({
  task: 'Analysis',
  steps: ['step1', 'step2', 'step3'],
});

XML Output:

<workflow>
  <task>Analysis</task>
  <steps>
    <step>step1</step>
    <step>step2</step>
    <step>step3</step>
  </steps>
</workflow>

Nested Objects

const fragment = {
  name: 'database',
  data: {
    host: 'localhost',
    settings: {
      timeout: 30,
      retry: true,
    },
  },
};

XML Output:

<database>
  <host>localhost</host>
  <settings>
    <timeout>30</timeout>
    <retry>true</retry>
  </settings>
</database>

Null and Undefined Values

All renderers automatically skip null and undefined values.

API Reference

Interfaces

ContextFragment

interface ContextFragment {
  name: string;
  data: FragmentData;
  type?: 'fragment' | 'message';
  persist?: boolean;
  codec?: FragmentCodec;
}

ContextRenderer

abstract class ContextRenderer {
  abstract render(fragments: ContextFragment[]): string;
}

Classes

All renderer classes extend ContextRenderer:

• XmlRenderer - Renders as XML
• MarkdownRenderer - Renders as Markdown
• TomlRenderer - Renders as TOML
• ToonRenderer - Token-efficient format

Stream Persistence

The package includes durable stream persistence utilities:

• SqliteStreamStore (SQLite-backed stream storage)
• StreamManager (register, persist, watch, cancel, reopen, cleanup)
• persistedWriter (low-level writer wrapper)

import { SqliteStreamStore, StreamManager } from '@deepagents/context';

const store = new SqliteStreamStore('./streams.db');
const manager = new StreamManager({
  store,
  watchPolling: {
    minMs: 25,
    maxMs: 500,
    multiplier: 2,
    jitterRatio: 0.15,
    statusCheckEvery: 3,
    chunkPageSize: 128,
  },
  cancelPolling: {
    minMs: 50,
    maxMs: 500,
    multiplier: 2,
    jitterRatio: 0.15,
  },
});

// Discover active streams without writing raw SQL.
const runningStreamIds = await store.listStreamIds({ status: 'running' });
const runningViaConvenienceMethod = await store.listRunningStreamIds();

// Shutdown cleanup (idempotent)
store.close();

For full API details and patterns, see: apps/docs/app/docs/context/stream-persistence.mdx

License

MIT