@diyor28/context

Context management library for LLM conversations with deterministic block ordering, token budgeting, and multi-provider support.

Originally part of foundry, now extracted as a standalone package for use in any LLM application.

Features

• Deterministic block ordering: KIND_ORDER ensures consistent context compilation
• Content-addressed blocks: Stable hashing for deduplication and caching
• Token budgeting: First-class token estimation and overflow handling
• Multi-provider support: Anthropic Claude, OpenAI GPT, Google Gemini
• Codec system: Extensible block rendering with validation
• Automatic compaction: Tool output pruning and history summarization
• Sensitivity filtering: Fine-grained content control for sensitive data
• Attachment management: Budget-aware file selection

Installation

# npm
npm install @diyor28/context

# pnpm
pnpm add @diyor28/context

# yarn
yarn add @diyor28/context

Quick Start

import {
  ContextBuilder,
  DEFAULT_POLICY,
  BUILT_IN_CODECS,
  AnthropicTokenEstimator,
  AnthropicCompiler,
} from '@diyor28/context';

// 1. Create a context builder with default policy
const builder = new ContextBuilder({
  policy: DEFAULT_POLICY,
  codecs: BUILT_IN_CODECS,
  tokenEstimator: new AnthropicTokenEstimator('claude-sonnet-4-5'),
});

// 2. Add context blocks
builder.addBlock({
  kind: 'pinned',
  codecId: 'system-rules',
  payload: {
    rules: ['Be helpful and concise', 'Always validate inputs'],
  },
  sensitivity: 'public',
});

builder.addBlock({
  kind: 'history',
  codecId: 'conversation-history',
  payload: {
    messages: [
      { role: 'user', content: 'Hello!' },
      { role: 'assistant', content: 'Hi! How can I help you?' },
    ],
  },
  sensitivity: 'public',
});

// 3. Compile to provider-specific format
const compiled = AnthropicCompiler.compile(builder.getGraph());

// 4. Send to LLM
const response = await anthropic.messages.create({
  model: 'claude-sonnet-4-5',
  max_tokens: 8000,
  system: compiled.system,
  messages: compiled.messages,
});

Core Concepts

Context Blocks

Context blocks are the fundamental unit of context management. Each block has:

• kind: Determines ordering and priority (pinned, reference, memory, state, tool_output, history, turn)
• codecId: Defines how the block is rendered (e.g., system-rules, tool-schema, conversation-history)
• payload: The actual content (codec-specific structure)
• sensitivity: Access level (public, internal, confidential, secret)

Block Ordering (KIND_ORDER)

Blocks are automatically ordered by kind for deterministic compilation:

1. pinned - System rules, core instructions (always at top)
2. reference - Documentation, schemas, guidelines
3. memory - Long-term facts, user preferences
4. state - Workflow state, execution context
5. tool_output - Tool/function call results
6. history - Conversation history
7. turn - Current user message (always at bottom)

Token Budgeting

The library automatically manages token budgets:

const policy = {
  ...DEFAULT_POLICY,
  contextWindow: 200_000,
  completionReserve: 8_000,
  overflowStrategy: 'compact', // Auto-prune old tool outputs
};

Built-in Codecs

• system-rules - System instructions and rules
• tool-schema - Function/tool definitions
• structured-reference - Markdown documentation
• conversation-history - Chat messages
• tool-output - Function call results
• redacted-stub - Placeholder for sensitive content
• unsafe-text - Raw text (bypasses validation)

Custom Codecs

Implement the BlockCodec interface to create custom renderers:

import { BlockCodec, registerCodec } from '@diyor28/context';

const myCodec: BlockCodec = {
  codecId: 'my-custom-codec',

  encode(payload: unknown) {
    // Validate and return typed payload
    return payload as MyPayloadType;
  },

  render(payload: MyPayloadType) {
    // Return rendered text
    return `# Custom Content\n${payload.text}`;
  },

  estimateTokens(payload: MyPayloadType) {
    // Return estimated token count
    return payload.text.length / 4;
  },
};

registerCodec(myCodec);

Multi-Provider Support

Anthropic Claude

import { AnthropicTokenEstimator, AnthropicCompiler } from '@diyor28/context';

const estimator = new AnthropicTokenEstimator('claude-sonnet-4-5');
const compiled = AnthropicCompiler.compile(graph);

OpenAI GPT

import { OpenAITokenEstimator, OpenAICompiler } from '@diyor28/context';

const estimator = new OpenAITokenEstimator('gpt-5.2');
const compiled = OpenAICompiler.compile(graph);

Google Gemini

import { GeminiTokenEstimator, GeminiCompiler } from '@diyor28/context';

const estimator = new GeminiTokenEstimator('gemini-3-pro-preview');
const compiled = GeminiCompiler.compile(graph);

Advanced Features

Automatic Compaction

When token budget is exceeded, the library automatically:

1. Prunes old tool outputs (configurable age threshold)
2. Summarizes conversation history
3. Removes least-recently-used attachments

const policy = {
  ...DEFAULT_POLICY,
  compaction: {
    pruneToolOutputs: true,
    maxToolOutputAge: 3600, // 1 hour
    summarizeHistory: true,
    maxHistoryMessages: 20,
  },
};

Context Forking

Create isolated branches for parallel operations:

const fork = builder.fork({
  branchId: 'speculative-execution',
  sensitivity: {
    maxSensitivity: 'public', // Restrict to public data only
    redactRestricted: true,
  },
});

fork.addBlock({
  kind: 'turn',
  codecId: 'conversation-history',
  payload: { messages: [{ role: 'user', content: 'What if...?' }] },
  sensitivity: 'public',
});

// Original builder is unchanged

Attachment Management

Smart selection of attachments based on budget and priority:

import { AttachmentSelector } from '@diyor28/context';

const selector = new AttachmentSelector({
  maxTokensTotal: 10_000,
  selectionStrategy: {
    rankBy: ['purpose', 'user_mention', 'recency'],
    purposePriority: {
      evidence: 1,    // Highest priority
      input: 2,
      context: 3,
      artifact: 4,    // Lowest priority
    },
  },
});

const selected = selector.select(attachments, tokenEstimator);

Testing

# Run all tests
npm test

# Run with coverage
npm run test:coverage

# Watch mode
npm run test:watch

Contributing

Contributions are welcome! Please:

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Run tests: npm test
5. Run linting: npm run lint
6. Submit a pull request

Issues

Found a bug? Have a feature request? Open an issue on GitHub.

Changelog

See CHANGELOG.md for version history and breaking changes.

License

MIT - See LICENSE for details