🧵 ContextWeaver

> Stop getting "Token Limit Exceeded" errors in your AI chatbot. Smart context management that just works.

![npm version](https://www.npmjs.com/package/context-weaver)
![License: MIT](https://opensource.org/licenses/MIT)
![TypeScript](https://www.typescriptlang.org/)

v1.0.0 - Production Ready • Zero Dependencies • 253 Tests • TypeScript

---

⚡ Quick Start (30 seconds)

bash

npm install context-weaver

typescript

import { ContextWeaver } from 'context-weaver';

import OpenAI from 'openai';



const openai = new OpenAI();

const memory = new ContextWeaver({ tokenLimit: 4000 });



// Add messages

await memory.add('user-123', 'user', 'My budget is $500');

await memory.add('user-123', 'assistant', 'I can help you find options!');



// Get context (always fits token limit!)

const { messages } = await memory.getContext('user-123');



// Send to OpenAI

const response = await openai.chat.completions.create({

  model: 'gpt-4',

  messages // ✅ Never crashes. Always fits.

});





That's it! No token counting. No manual trimming. No crashes. 🎉



---



❌ The Problem



Every AI developer hits this wall:

javascript

// Your chatbot code

const history = await getMessages(userId);

const response = await openai.chat.completions.create({

  model: 'gpt-4',

  messages: history // 💥 Error: "maximum context length exceeded"

});





What goes wrong:

1. 🔥 Token limit crashes - Conversation gets too long, app crashes

2. 🧠 Lost context - Slicing messages loses user's name, goals, preferences

3. 🍝 Complex code - Token counting, Redis management, manual cleanup

4. ⏰ Wastes dev time - Spend days building what should take minutes



---



✅ The Solution



ContextWeaver manages conversation history automatically:

javascript

import { ContextWeaver } from 'context-weaver';



const memory = new ContextWeaver({ tokenLimit: 4000 });



// Add messages - simple!

await memory.add('user-123', 'user', 'My name is Alice');

await memory.add('user-123', 'user', 'My budget is $500');

// ... 50 more messages later ...



// Get context - always fits!

const { messages } = await memory.getContext('user-123');



// Send to AI - never crashes!

const response = await openai.chat.completions.create({

  model: 'gpt-4',

  messages // ✅ Guaranteed to fit. Keeps important stuff.

});





What it does:

- ✅ Auto token management - Always fits your limit

- ✅ Smart selection - Keeps important messages (name, budget, goals)

- ✅ Drops filler - Removes "ok", "thanks", empty messages

- ✅ Zero dependencies - No heavy frameworks

- ✅ Production ready - Sessions, TTL, Redis, Postgres



---



🎯 Features



| What You Get | Why It Matters |

|--------------|----------------|

| 🧠 Smart Context | Keeps important messages, drops filler automatically |

| 🎯 Token Budgeting | Never exceed your token limit, no math needed |

| 💬 Conversation Pairs | Q&A stay together ("explain step 2" just works) |

| 🔌 Storage Adapters | Redis, Postgres, or in-memory - your choice |

| 📝 Streaming Support | OpenAI, Anthropic, Google - all handled |

| 🔄 Session Management | Auto-cleanup, TTL, background jobs included |

| 📊 Observability | Hooks for logging, metrics, monitoring |

| 🪶 Lightweight | Zero dependencies, 347 KB package |



---



📦 Installation

bash

npm install context-weaver





---



🚀 Usage Examples



$3

typescript

import { ContextWeaver } from 'context-weaver';

import OpenAI from 'openai';



const openai = new OpenAI();

const memory = new ContextWeaver({ tokenLimit: 4000 });



async function chat(userId: string, message: string) {

  // 1. Add user message

  await memory.add(userId, 'user', message);

  

  // 2. Get optimized context (always fits!)

  const { messages } = await memory.getContext(userId);

  

  // 3. Send to AI

  const response = await openai.chat.completions.create({

    model: 'gpt-4',

    messages

  });

  

  const reply = response.choices[0].message.content;

  

  // 4. Save AI response

  await memory.add(userId, 'assistant', reply);

  

  return reply;

}



// Use it

await chat('user-123', 'Hello! My budget is $500');

await chat('user-123', 'Show me laptops');





---



$3

typescript

import { SmartContextWeaver } from 'context-weaver/smart';



const memory = new SmartContextWeaver(); // Zero config!



// Add messages - it figures out what's important

await memory.add('user-123', 'user', 'My name is Bob and my budget is $1000');

await memory.add('user-123', 'user', 'ok cool');

await memory.add('user-123', 'user', 'Show me gaming laptops');



// Get smart context

const { messages } = await memory.getContext('user-123', {

  currentQuery: 'Show me gaming laptops'

});

// ✅ Keeps: name, budget

// ✅ Drops: "ok cool"





---



$3

typescript

import { ContextWeaver, RedisAdapter } from 'context-weaver';

import Redis from 'ioredis';



const redis = new Redis(process.env.REDIS_URL);



const memory = new ContextWeaver({

  storage: new RedisAdapter(redis, { 

    ttl: 86400 // 24 hours

  }),

  tokenLimit: 8000

});



// Now all messages stored in Redis with auto-expiry!

await memory.add('user-456', 'user', 'Hello');





---



$3

typescript

const stream = await openai.chat.completions.create({

  model: 'gpt-4',

  messages,

  stream: true

});



// One line - handles all streaming complexity!

await memory.addStream('user-789', 'assistant', stream);





---



Pinning Important Messages



Some messages should never be dropped, like system instructions or key user info:

typescript

// Pin system instructions

const systemId = await memory.add(

  sessionId, 

  'system', 

  'You are a helpful travel assistant. The user prefers budget-friendly options.',

  { pinned: true }

);



// Or pin later

const msgId = await memory.add(sessionId, 'user', 'My name is Alice and I\'m vegetarian.');

await memory.pin(sessionId, msgId);



// Pinned messages are ALWAYS included in context, regardless of token limits

const { messages, pinnedCount } = await memory.getContext(sessionId);

console.log(

Included ${pinnedCount} pinned messages

);





Smart Summarization



When conversations get long, summarize older messages to preserve context:

typescript

import OpenAI from 'openai';

import { ContextWeaver } from 'context-weaver';



const openai = new OpenAI();



const memory = new ContextWeaver({

  tokenLimit: 4000,

  summarizeThreshold: 8000, // Auto-summarize when tokens exceed this

  

  // Provide your own summarizer

  summarizer: async (messages) => {

    const response = await openai.chat.completions.create({

      model: 'gpt-3.5-turbo',

      messages: [

        { 

          role: 'system', 

          content: 'Summarize this conversation in 2-3 sentences, preserving key facts and user preferences.' 

        },

        ...messages.map(m => ({ role: m.role, content: m.content }))

      ]

    });

    return response.choices[0].message.content!;

  }

});



// Messages will be auto-summarized when threshold is exceeded

// Or trigger manually:

await memory.summarize(sessionId);





Custom Storage Adapters



$3

typescript

import { ContextWeaver, InMemoryAdapter } from 'context-weaver';



const memory = new ContextWeaver({

  storage: new InMemoryAdapter() // Default, good for development

});

$3

typescript

import { ContextWeaver, StorageAdapter, Message } from 'context-weaver';

import Redis from 'ioredis';



class RedisAdapter implements StorageAdapter {

  private redis: Redis;

  

  constructor(redis: Redis) {

    this.redis = redis;

  }

  

  async getMessages(sessionId: string): Promise {

    const data = await this.redis.get(

context:${sessionId}:messages

);

    return data ? JSON.parse(data) : [];

  }

  

  async addMessage(sessionId: string, message: Message): Promise {

    const messages = await this.getMessages(sessionId);

    messages.push(message);

    await this.redis.set(

context:${sessionId}:messages

, JSON.stringify(messages));

  }

  

  async updateMessage(sessionId: string, messageId: string, updates: Partial): Promise {

    const messages = await this.getMessages(sessionId);

    const index = messages.findIndex(m => m.id === messageId);

    if (index !== -1) {

      messages[index] = { ...messages[index], ...updates };

      await this.redis.set(

context:${sessionId}:messages

, JSON.stringify(messages));

    }

  }

  

  async deleteMessage(sessionId: string, messageId: string): Promise {

    const messages = await this.getMessages(sessionId);

    const filtered = messages.filter(m => m.id !== messageId);

    await this.redis.set(

context:${sessionId}:messages

, JSON.stringify(filtered));

  }

  

  async getSummary(sessionId: string): Promise {

    return this.redis.get(

context:${sessionId}:summary

);

  }

  

  async setSummary(sessionId: string, summary: string): Promise {

    await this.redis.set(

context:${sessionId}:summary

, summary);

  }

  

  async clearSession(sessionId: string): Promise {

    await this.redis.del(

context:${sessionId}:messages, context:${sessionId}:summary

);

  }

  

  async hasSession(sessionId: string): Promise {

    return (await this.redis.exists(

context:${sessionId}:messages

)) === 1;

  }

}



// Use it

const redis = new Redis();

const memory = new ContextWeaver({

  storage: new RedisAdapter(redis)

});





Accurate Token Counting



For production, use tiktoken for accurate counts:

typescript

import { ContextWeaver, createTiktokenCounter } from 'context-weaver';

import { encoding_for_model } from 'tiktoken';



const encoder = encoding_for_model('gpt-4');

const tokenCounter = createTiktokenCounter(encoder);



const memory = new ContextWeaver({

  tokenLimit: 4000,

  tokenCounter, // Now uses tiktoken for accurate counts

});





API Reference



$3



#### Constructor Options



| Option | Type | Default | Description |

|--------|------|---------|-------------|

|

tokenLimit | number | 4000

 | Maximum tokens for context output |

|

recentMessageCount | number | 10

 | Minimum recent messages to keep |

|

tokenCounter | (text: string) => number

 | Built-in | Custom token counter function |

|

summarizer | (messages: Message[]) => Promise | undefined

 | Custom summarizer function |

|

storage | StorageAdapter | InMemoryAdapter

 | Storage backend |

|

summarizeThreshold | number | tokenLimit * 2

 | Auto-summarize threshold |



#### Methods



| Method | Description |

|--------|-------------|

|

add(sessionId, role, content, options?)

 | Add a message |

|

getContext(sessionId, options?)

 | Get optimized context |

|

pin(sessionId, messageId)

 | Pin a message |

|

unpin(sessionId, messageId)

 | Unpin a message |

|

summarize(sessionId, keepRecent?)

 | Manually trigger summarization |

|

getMessages(sessionId)

 | Get all messages |

|

getStats(sessionId)

 | Get session statistics |

|

clear(sessionId)

 | Clear a session |

|

hasSession(sessionId)

 | Check if session exists |



$3



The smart, zero-config context manager with auto-importance detection.

typescript

import { SmartContextWeaver } from 'context-weaver/smart';



const memory = new SmartContextWeaver({

  tokenLimit: 4000,            // Optional: default 4000

  enableSemantic: true,        // Optional: enable semantic search

  enableAutoImportance: true,  // Optional: auto-detect important messages

  enableLocalSummary: true,    // Optional: local summarization

  enableConversationPairs: true, // NEW v0.5: Keep Q&A together

  minRecentPairs: 3,           // NEW v0.5: Min pairs to always keep

});





#### Conversation Pairs (v0.5+)



When users say things like "explain step 2" or "go with option B", the AI needs the original context. Conversation Pairs ensures Q&A stay together:

typescript

import { 

  SmartContextWeaver,

  ConversationPairManager,

  hasConversationReference 

} from 'context-weaver/smart';



// The manager builds pairs from messages

const pairManager = new ConversationPairManager();

const pairs = pairManager.buildPairs(messages);



// Check if a query references previous content

if (hasConversationReference('explain step 2')) {

  // Automatically finds and includes the pair with steps

}



// Or just use SmartContextWeaver with pairs enabled

const memory = new SmartContextWeaver({

  enableConversationPairs: true

});





Reference patterns detected:

- Numbered: "step 2", "option 1", "point 3"

- Ordinal: "the first one", "the last thing"

- Back-reference: "you mentioned", "as you said before"

- Continuation: "tell me more", "explain that"

- Demonstrative: "that approach", "this method"



#### Smart Utilities



| Import | Description |

|--------|-------------|

|

LRUCache

 | O(1) LRU cache with TTL |

|

TokenCache

 | Specialized token count cache |

|

BloomFilter

 | Fast probabilistic set |

|

AutoImportance

 | Importance detection |

|

SemanticIndex

 | TF-IDF semantic search |

|

LocalSummarizer

 | API-free summarization |



Why ContextWeaver?



| Feature | ContextWeaver | LangChain Memory | Raw Arrays |

|---------|--------------|------------------|------------|

| Zero Dependencies | ✅ | ❌ | ✅ |

| Token Budgeting | ✅ | ⚠️ Limited | ❌ |

| Auto-Importance | ✅ | ❌ | ❌ |

| Semantic Search | ✅ | ⚠️ Needs VectorDB | ❌ |

| Local Summarization | ✅ | ❌ | ❌ |

| Pluggable Storage | ✅ | ✅ | ❌ |

| TypeScript Native | ✅ | ⚠️ | N/A |

| Framework Lock-in | ❌ | ✅ | ❌ |

| Production Ready | ✅ | ✅ | ❌ |



Contributing



Contributions are welcome! Please read our Contributing Guide first.

bash

Clone the repo

git clone https://github.com/srikrishna/context-weaver.git

cd context-weaver



Install dependencies

npm install



Run tests

npm test



Build

npm run build

License

MIT © Srikrishna

---

Built with ❤️ for the AI engineering community

⭐ Star us on GitHub

🧵 ContextWeaver

⚡ Quick Start (30 seconds)

bash

npm install context-weaver

typescript

import { ContextWeaver } from 'context-weaver';

import OpenAI from 'openai';



const openai = new OpenAI();

const memory = new ContextWeaver({ tokenLimit: 4000 });



// Add messages

await memory.add('user-123', 'user', 'My budget is $500');

await memory.add('user-123', 'assistant', 'I can help you find options!');



// Get context (always fits token limit!)

const { messages } = await memory.getContext('user-123');



// Send to OpenAI

const response = await openai.chat.completions.create({

  model: 'gpt-4',

  messages // ✅ Never crashes. Always fits.

});





That's it! No token counting. No manual trimming. No crashes. 🎉



---



❌ The Problem



Every AI developer hits this wall:

javascript

// Your chatbot code

const history = await getMessages(userId);

const response = await openai.chat.completions.create({

  model: 'gpt-4',

  messages: history // 💥 Error: "maximum context length exceeded"

});





What goes wrong:

1. 🔥 Token limit crashes - Conversation gets too long, app crashes

2. 🧠 Lost context - Slicing messages loses user's name, goals, preferences

3. 🍝 Complex code - Token counting, Redis management, manual cleanup

4. ⏰ Wastes dev time - Spend days building what should take minutes



---



✅ The Solution



ContextWeaver manages conversation history automatically:

javascript

import { ContextWeaver } from 'context-weaver';



const memory = new ContextWeaver({ tokenLimit: 4000 });



// Add messages - simple!

await memory.add('user-123', 'user', 'My name is Alice');

await memory.add('user-123', 'user', 'My budget is $500');

// ... 50 more messages later ...



// Get context - always fits!

const { messages } = await memory.getContext('user-123');



// Send to AI - never crashes!

const response = await openai.chat.completions.create({

  model: 'gpt-4',

  messages // ✅ Guaranteed to fit. Keeps important stuff.

});





What it does:

- ✅ Auto token management - Always fits your limit

- ✅ Smart selection - Keeps important messages (name, budget, goals)

- ✅ Drops filler - Removes "ok", "thanks", empty messages

- ✅ Zero dependencies - No heavy frameworks

- ✅ Production ready - Sessions, TTL, Redis, Postgres



---



🎯 Features



| What You Get | Why It Matters |

|--------------|----------------|

| 🧠 Smart Context | Keeps important messages, drops filler automatically |

| 🎯 Token Budgeting | Never exceed your token limit, no math needed |

| 💬 Conversation Pairs | Q&A stay together ("explain step 2" just works) |

| 🔌 Storage Adapters | Redis, Postgres, or in-memory - your choice |

| 📝 Streaming Support | OpenAI, Anthropic, Google - all handled |

| 🔄 Session Management | Auto-cleanup, TTL, background jobs included |

| 📊 Observability | Hooks for logging, metrics, monitoring |

| 🪶 Lightweight | Zero dependencies, 347 KB package |



---



📦 Installation

bash

npm install context-weaver





---



🚀 Usage Examples



$3

typescript

import { ContextWeaver } from 'context-weaver';

import OpenAI from 'openai';



const openai = new OpenAI();

const memory = new ContextWeaver({ tokenLimit: 4000 });



async function chat(userId: string, message: string) {

  // 1. Add user message

  await memory.add(userId, 'user', message);

  

  // 2. Get optimized context (always fits!)

  const { messages } = await memory.getContext(userId);

  

  // 3. Send to AI

  const response = await openai.chat.completions.create({

    model: 'gpt-4',

    messages

  });

  

  const reply = response.choices[0].message.content;

  

  // 4. Save AI response

  await memory.add(userId, 'assistant', reply);

  

  return reply;

}



// Use it

await chat('user-123', 'Hello! My budget is $500');

await chat('user-123', 'Show me laptops');





---



$3

typescript

import { SmartContextWeaver } from 'context-weaver/smart';



const memory = new SmartContextWeaver(); // Zero config!



// Add messages - it figures out what's important

await memory.add('user-123', 'user', 'My name is Bob and my budget is $1000');

await memory.add('user-123', 'user', 'ok cool');

await memory.add('user-123', 'user', 'Show me gaming laptops');



// Get smart context

const { messages } = await memory.getContext('user-123', {

  currentQuery: 'Show me gaming laptops'

});

// ✅ Keeps: name, budget

// ✅ Drops: "ok cool"





---



$3

typescript

import { ContextWeaver, RedisAdapter } from 'context-weaver';

import Redis from 'ioredis';



const redis = new Redis(process.env.REDIS_URL);



const memory = new ContextWeaver({

  storage: new RedisAdapter(redis, { 

    ttl: 86400 // 24 hours

  }),

  tokenLimit: 8000

});



// Now all messages stored in Redis with auto-expiry!

await memory.add('user-456', 'user', 'Hello');





---



$3

typescript

const stream = await openai.chat.completions.create({

  model: 'gpt-4',

  messages,

  stream: true

});



// One line - handles all streaming complexity!

await memory.addStream('user-789', 'assistant', stream);





---



Pinning Important Messages



Some messages should never be dropped, like system instructions or key user info:

typescript

// Pin system instructions

const systemId = await memory.add(

  sessionId, 

  'system', 

  'You are a helpful travel assistant. The user prefers budget-friendly options.',

  { pinned: true }

);



// Or pin later

const msgId = await memory.add(sessionId, 'user', 'My name is Alice and I\'m vegetarian.');

await memory.pin(sessionId, msgId);



// Pinned messages are ALWAYS included in context, regardless of token limits

const { messages, pinnedCount } = await memory.getContext(sessionId);

console.log(

Included ${pinnedCount} pinned messages

);





Smart Summarization



When conversations get long, summarize older messages to preserve context:

typescript

import OpenAI from 'openai';

import { ContextWeaver } from 'context-weaver';



const openai = new OpenAI();



const memory = new ContextWeaver({

  tokenLimit: 4000,

  summarizeThreshold: 8000, // Auto-summarize when tokens exceed this

  

  // Provide your own summarizer

  summarizer: async (messages) => {

    const response = await openai.chat.completions.create({

      model: 'gpt-3.5-turbo',

      messages: [

        { 

          role: 'system', 

          content: 'Summarize this conversation in 2-3 sentences, preserving key facts and user preferences.' 

        },

        ...messages.map(m => ({ role: m.role, content: m.content }))

      ]

    });

    return response.choices[0].message.content!;

  }

});



// Messages will be auto-summarized when threshold is exceeded

// Or trigger manually:

await memory.summarize(sessionId);





Custom Storage Adapters



$3

typescript

import { ContextWeaver, InMemoryAdapter } from 'context-weaver';



const memory = new ContextWeaver({

  storage: new InMemoryAdapter() // Default, good for development

});

$3

typescript

import { ContextWeaver, StorageAdapter, Message } from 'context-weaver';

import Redis from 'ioredis';



class RedisAdapter implements StorageAdapter {

  private redis: Redis;

  

  constructor(redis: Redis) {

    this.redis = redis;

  }

  

  async getMessages(sessionId: string): Promise {

    const data = await this.redis.get(

context:${sessionId}:messages

);

    return data ? JSON.parse(data) : [];

  }

  

  async addMessage(sessionId: string, message: Message): Promise {

    const messages = await this.getMessages(sessionId);

    messages.push(message);

    await this.redis.set(

context:${sessionId}:messages

, JSON.stringify(messages));

  }

  

  async updateMessage(sessionId: string, messageId: string, updates: Partial): Promise {

    const messages = await this.getMessages(sessionId);

    const index = messages.findIndex(m => m.id === messageId);

    if (index !== -1) {

      messages[index] = { ...messages[index], ...updates };

      await this.redis.set(

context:${sessionId}:messages

, JSON.stringify(messages));

    }

  }

  

  async deleteMessage(sessionId: string, messageId: string): Promise {

    const messages = await this.getMessages(sessionId);

    const filtered = messages.filter(m => m.id !== messageId);

    await this.redis.set(

context:${sessionId}:messages

, JSON.stringify(filtered));

  }

  

  async getSummary(sessionId: string): Promise {

    return this.redis.get(

context:${sessionId}:summary

);

  }

  

  async setSummary(sessionId: string, summary: string): Promise {

    await this.redis.set(

context:${sessionId}:summary

, summary);

  }

  

  async clearSession(sessionId: string): Promise {

    await this.redis.del(

context:${sessionId}:messages, context:${sessionId}:summary

);

  }

  

  async hasSession(sessionId: string): Promise {

    return (await this.redis.exists(

context:${sessionId}:messages

)) === 1;

  }

}



// Use it

const redis = new Redis();

const memory = new ContextWeaver({

  storage: new RedisAdapter(redis)

});





Accurate Token Counting



For production, use tiktoken for accurate counts:

typescript

import { ContextWeaver, createTiktokenCounter } from 'context-weaver';

import { encoding_for_model } from 'tiktoken';



const encoder = encoding_for_model('gpt-4');

const tokenCounter = createTiktokenCounter(encoder);



const memory = new ContextWeaver({

  tokenLimit: 4000,

  tokenCounter, // Now uses tiktoken for accurate counts

});





API Reference



$3



#### Constructor Options



| Option | Type | Default | Description |

|--------|------|---------|-------------|

|

tokenLimit | number | 4000

 | Maximum tokens for context output |

|

recentMessageCount | number | 10

 | Minimum recent messages to keep |

|

tokenCounter | (text: string) => number

 | Built-in | Custom token counter function |

|

summarizer | (messages: Message[]) => Promise | undefined

 | Custom summarizer function |

|

storage | StorageAdapter | InMemoryAdapter

 | Storage backend |

|

summarizeThreshold | number | tokenLimit * 2

 | Auto-summarize threshold |



#### Methods



| Method | Description |

|--------|-------------|

|

add(sessionId, role, content, options?)

 | Add a message |

|

getContext(sessionId, options?)

 | Get optimized context |

|

pin(sessionId, messageId)

 | Pin a message |

|

unpin(sessionId, messageId)

 | Unpin a message |

|

summarize(sessionId, keepRecent?)

 | Manually trigger summarization |

|

getMessages(sessionId)

 | Get all messages |

|

getStats(sessionId)

 | Get session statistics |

|

clear(sessionId)

 | Clear a session |

|

hasSession(sessionId)

 | Check if session exists |



$3



The smart, zero-config context manager with auto-importance detection.

typescript

import { SmartContextWeaver } from 'context-weaver/smart';



const memory = new SmartContextWeaver({

  tokenLimit: 4000,            // Optional: default 4000

  enableSemantic: true,        // Optional: enable semantic search

  enableAutoImportance: true,  // Optional: auto-detect important messages

  enableLocalSummary: true,    // Optional: local summarization

  enableConversationPairs: true, // NEW v0.5: Keep Q&A together

  minRecentPairs: 3,           // NEW v0.5: Min pairs to always keep

});





#### Conversation Pairs (v0.5+)



When users say things like "explain step 2" or "go with option B", the AI needs the original context. Conversation Pairs ensures Q&A stay together:

typescript

import { 

  SmartContextWeaver,

  ConversationPairManager,

  hasConversationReference 

} from 'context-weaver/smart';



// The manager builds pairs from messages

const pairManager = new ConversationPairManager();

const pairs = pairManager.buildPairs(messages);



// Check if a query references previous content

if (hasConversationReference('explain step 2')) {

  // Automatically finds and includes the pair with steps

}



// Or just use SmartContextWeaver with pairs enabled

const memory = new SmartContextWeaver({

  enableConversationPairs: true

});





Reference patterns detected:

- Numbered: "step 2", "option 1", "point 3"

- Ordinal: "the first one", "the last thing"

- Back-reference: "you mentioned", "as you said before"

- Continuation: "tell me more", "explain that"

- Demonstrative: "that approach", "this method"



#### Smart Utilities



| Import | Description |

|--------|-------------|

|

LRUCache

 | O(1) LRU cache with TTL |

|

TokenCache

 | Specialized token count cache |

|

BloomFilter

 | Fast probabilistic set |

|

AutoImportance

 | Importance detection |

|

SemanticIndex

 | TF-IDF semantic search |

|

LocalSummarizer

 | API-free summarization |



Why ContextWeaver?



| Feature | ContextWeaver | LangChain Memory | Raw Arrays |

|---------|--------------|------------------|------------|

| Zero Dependencies | ✅ | ❌ | ✅ |

| Token Budgeting | ✅ | ⚠️ Limited | ❌ |

| Auto-Importance | ✅ | ❌ | ❌ |

| Semantic Search | ✅ | ⚠️ Needs VectorDB | ❌ |

| Local Summarization | ✅ | ❌ | ❌ |

| Pluggable Storage | ✅ | ✅ | ❌ |

| TypeScript Native | ✅ | ⚠️ | N/A |

| Framework Lock-in | ❌ | ✅ | ❌ |

| Production Ready | ✅ | ✅ | ❌ |



Contributing



Contributions are welcome! Please read our Contributing Guide first.

bash

Clone the repo

git clone https://github.com/srikrishna/context-weaver.git

cd context-weaver



Install dependencies

npm install



Run tests

npm test



Build

npm run build

License

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