feat: Add complete Agentic Compaction & Pipeline System

- Context Compaction System with token counting and summarization
- Deterministic State Machine for flow control (no LLM decisions)
- Parallel Execution Engine (up to 12 concurrent sessions)
- Event-Driven Coordination via Event Bus
- Agent Workspace Isolation (tools, memory, identity, files)
- YAML Workflow Integration (OpenClaw/Lobster compatible)
- Claude Code integration layer
- Complete demo UI with real-time visualization
- Comprehensive documentation and README

Components:
- agent-system/: Context management, token counting, subagent spawning
- pipeline-system/: State machine, parallel executor, event bus, workflows
- skills/: AI capabilities (LLM, ASR, TTS, VLM, image generation, etc.)
- src/app/: Next.js demo application

Total: ~100KB of production-ready TypeScript code
This commit is contained in:
Z User
2026-03-03 12:40:47 +00:00
Unverified
parent 63a8b123c9
commit 2380d33861
152 changed files with 51569 additions and 817 deletions

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MIT License
Copyright (c) 2025 z-ai-web-dev-sdk Skills
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

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---
name: LLM
description: Implement large language model (LLM) chat completions using the z-ai-web-dev-sdk. Use this skill when the user needs to build conversational AI applications, chatbots, AI assistants, or any text generation features. Supports multi-turn conversations, system prompts, and context management.
license: MIT
---
# LLM (Large Language Model) Skill
This skill guides the implementation of chat completions functionality using the z-ai-web-dev-sdk package, enabling powerful conversational AI and text generation capabilities.
## Skills Path
**Skill Location**: `{project_path}/skills/llm`
this skill is located at above path in your project.
**Reference Scripts**: Example test scripts are available in the `{Skill Location}/scripts/` directory for quick testing and reference. See `{Skill Location}/scripts/chat.ts` for a working example.
## Overview
The LLM skill allows you to build applications that leverage large language models for natural language understanding and generation, including chatbots, AI assistants, content generation, and more.
**IMPORTANT**: z-ai-web-dev-sdk MUST be used in backend code only. Never use it in client-side code.
## Prerequisites
The z-ai-web-dev-sdk package is already installed. Import it as shown in the examples below.
## CLI Usage (For Simple Tasks)
For simple, one-off chat completions, you can use the z-ai CLI instead of writing code. This is ideal for quick tests, simple queries, or automation scripts.
### Basic Chat
```bash
# Simple question
z-ai chat --prompt "What is the capital of France?"
# Save response to file
z-ai chat -p "Explain quantum computing" -o response.json
# Stream the response
z-ai chat -p "Write a short poem" --stream
```
### With System Prompt
```bash
# Custom system prompt for specific behavior
z-ai chat \
--prompt "Review this code: function add(a,b) { return a+b; }" \
--system "You are an expert code reviewer" \
-o review.json
```
### With Thinking (Chain of Thought)
```bash
# Enable thinking for complex reasoning
z-ai chat \
--prompt "Solve this math problem: If a train travels 120km in 2 hours, what's its speed?" \
--thinking \
-o solution.json
```
### CLI Parameters
- `--prompt, -p <text>`: **Required** - User message content
- `--system, -s <text>`: Optional - System prompt for custom behavior
- `--thinking, -t`: Optional - Enable chain-of-thought reasoning (default: disabled)
- `--output, -o <path>`: Optional - Output file path (JSON format)
- `--stream`: Optional - Stream the response in real-time
### When to Use CLI vs SDK
**Use CLI for:**
- Quick one-off questions
- Simple automation scripts
- Testing prompts
- Single-turn conversations
**Use SDK for:**
- Multi-turn conversations with context
- Custom conversation management
- Integration with web applications
- Complex chat workflows
- Production applications
## Basic Chat Completions
### Simple Question and Answer
```javascript
import ZAI from 'z-ai-web-dev-sdk';
async function askQuestion(question) {
const zai = await ZAI.create();
const completion = await zai.chat.completions.create({
messages: [
{
role: 'assistant',
content: 'You are a helpful assistant.'
},
{
role: 'user',
content: question
}
],
thinking: { type: 'disabled' }
});
const response = completion.choices[0]?.message?.content;
return response;
}
// Usage
const answer = await askQuestion('What is the capital of France?');
console.log('Answer:', answer);
```
### Custom System Prompt
```javascript
import ZAI from 'z-ai-web-dev-sdk';
async function customAssistant(systemPrompt, userMessage) {
const zai = await ZAI.create();
const completion = await zai.chat.completions.create({
messages: [
{
role: 'assistant',
content: systemPrompt
},
{
role: 'user',
content: userMessage
}
],
thinking: { type: 'disabled' }
});
return completion.choices[0]?.message?.content;
}
// Usage - Code reviewer
const codeReview = await customAssistant(
'You are an expert code reviewer. Analyze code for bugs, performance issues, and best practices.',
'Review this function: function add(a, b) { return a + b; }'
);
// Usage - Creative writer
const story = await customAssistant(
'You are a creative fiction writer who writes engaging short stories.',
'Write a short story about a robot learning to paint.'
);
console.log(codeReview);
console.log(story);
```
## Multi-turn Conversations
### Conversation History Management
```javascript
import ZAI from 'z-ai-web-dev-sdk';
class ConversationManager {
constructor(systemPrompt = 'You are a helpful assistant.') {
this.messages = [
{
role: 'assistant',
content: systemPrompt
}
];
this.zai = null;
}
async initialize() {
this.zai = await ZAI.create();
}
async sendMessage(userMessage) {
// Add user message to history
this.messages.push({
role: 'user',
content: userMessage
});
// Get completion
const completion = await this.zai.chat.completions.create({
messages: this.messages,
thinking: { type: 'disabled' }
});
const assistantResponse = completion.choices[0]?.message?.content;
// Add assistant response to history
this.messages.push({
role: 'assistant',
content: assistantResponse
});
return assistantResponse;
}
getHistory() {
return this.messages;
}
clearHistory(systemPrompt = 'You are a helpful assistant.') {
this.messages = [
{
role: 'assistant',
content: systemPrompt
}
];
}
getMessageCount() {
// Subtract 1 for system message
return this.messages.length - 1;
}
}
// Usage
const conversation = new ConversationManager();
await conversation.initialize();
const response1 = await conversation.sendMessage('Hi, my name is John.');
console.log('AI:', response1);
const response2 = await conversation.sendMessage('What is my name?');
console.log('AI:', response2); // Should remember the name is John
console.log('Total messages:', conversation.getMessageCount());
```
### Context-Aware Conversations
```javascript
import ZAI from 'z-ai-web-dev-sdk';
class ContextualChat {
constructor() {
this.messages = [];
this.zai = null;
}
async initialize() {
this.zai = await ZAI.create();
}
async startConversation(role, context) {
// Set up system prompt with context
const systemPrompt = `You are ${role}. Context: ${context}`;
this.messages = [
{
role: 'assistant',
content: systemPrompt
}
];
}
async chat(userMessage) {
this.messages.push({
role: 'user',
content: userMessage
});
const completion = await this.zai.chat.completions.create({
messages: this.messages,
thinking: { type: 'disabled' }
});
const response = completion.choices[0]?.message?.content;
this.messages.push({
role: 'assistant',
content: response
});
return response;
}
}
// Usage - Customer support scenario
const support = new ContextualChat();
await support.initialize();
await support.startConversation(
'a customer support agent for TechCorp',
'The user has ordered product #12345 which is delayed due to shipping issues.'
);
const reply1 = await support.chat('Where is my order?');
console.log('Support:', reply1);
const reply2 = await support.chat('Can I get a refund?');
console.log('Support:', reply2);
```
## Advanced Use Cases
### Content Generation
```javascript
import ZAI from 'z-ai-web-dev-sdk';
class ContentGenerator {
constructor() {
this.zai = null;
}
async initialize() {
this.zai = await ZAI.create();
}
async generateBlogPost(topic, tone = 'professional') {
const completion = await this.zai.chat.completions.create({
messages: [
{
role: 'assistant',
content: `You are a professional content writer. Write in a ${tone} tone.`
},
{
role: 'user',
content: `Write a blog post about: ${topic}. Include an introduction, main points, and conclusion.`
}
],
thinking: { type: 'disabled' }
});
return completion.choices[0]?.message?.content;
}
async generateProductDescription(productName, features) {
const completion = await this.zai.chat.completions.create({
messages: [
{
role: 'assistant',
content: 'You are an expert at writing compelling product descriptions for e-commerce.'
},
{
role: 'user',
content: `Write a product description for "${productName}". Key features: ${features.join(', ')}.`
}
],
thinking: { type: 'disabled' }
});
return completion.choices[0]?.message?.content;
}
async generateEmailResponse(originalEmail, intent) {
const completion = await this.zai.chat.completions.create({
messages: [
{
role: 'assistant',
content: 'You are a professional email writer. Write clear, concise, and polite emails.'
},
{
role: 'user',
content: `Original email: "${originalEmail}"\n\nWrite a ${intent} response.`
}
],
thinking: { type: 'disabled' }
});
return completion.choices[0]?.message?.content;
}
}
// Usage
const generator = new ContentGenerator();
await generator.initialize();
const blogPost = await generator.generateBlogPost(
'The Future of Artificial Intelligence',
'informative'
);
console.log('Blog Post:', blogPost);
const productDesc = await generator.generateProductDescription(
'Smart Watch Pro',
['Heart rate monitoring', 'GPS tracking', 'Waterproof', '7-day battery life']
);
console.log('Product Description:', productDesc);
```
### Data Analysis and Summarization
```javascript
import ZAI from 'z-ai-web-dev-sdk';
async function analyzeData(data, analysisType) {
const zai = await ZAI.create();
const prompts = {
summarize: 'You are a data analyst. Summarize the key insights from the data.',
trend: 'You are a data analyst. Identify trends and patterns in the data.',
recommendation: 'You are a business analyst. Provide actionable recommendations based on the data.'
};
const completion = await zai.chat.completions.create({
messages: [
{
role: 'assistant',
content: prompts[analysisType] || prompts.summarize
},
{
role: 'user',
content: `Analyze this data:\n\n${JSON.stringify(data, null, 2)}`
}
],
thinking: { type: 'disabled' }
});
return completion.choices[0]?.message?.content;
}
// Usage
const salesData = {
Q1: { revenue: 100000, customers: 250 },
Q2: { revenue: 120000, customers: 280 },
Q3: { revenue: 150000, customers: 320 },
Q4: { revenue: 180000, customers: 380 }
};
const summary = await analyzeData(salesData, 'summarize');
const trends = await analyzeData(salesData, 'trend');
const recommendations = await analyzeData(salesData, 'recommendation');
console.log('Summary:', summary);
console.log('Trends:', trends);
console.log('Recommendations:', recommendations);
```
### Code Generation and Debugging
```javascript
import ZAI from 'z-ai-web-dev-sdk';
class CodeAssistant {
constructor() {
this.zai = null;
}
async initialize() {
this.zai = await ZAI.create();
}
async generateCode(description, language) {
const completion = await this.zai.chat.completions.create({
messages: [
{
role: 'assistant',
content: `You are an expert ${language} programmer. Write clean, efficient, and well-commented code.`
},
{
role: 'user',
content: `Write ${language} code to: ${description}`
}
],
thinking: { type: 'disabled' }
});
return completion.choices[0]?.message?.content;
}
async debugCode(code, issue) {
const completion = await this.zai.chat.completions.create({
messages: [
{
role: 'assistant',
content: 'You are an expert debugger. Identify bugs and suggest fixes.'
},
{
role: 'user',
content: `Code:\n${code}\n\nIssue: ${issue}\n\nFind the bug and suggest a fix.`
}
],
thinking: { type: 'disabled' }
});
return completion.choices[0]?.message?.content;
}
async explainCode(code) {
const completion = await this.zai.chat.completions.create({
messages: [
{
role: 'assistant',
content: 'You are a programming teacher. Explain code clearly and simply.'
},
{
role: 'user',
content: `Explain what this code does:\n\n${code}`
}
],
thinking: { type: 'disabled' }
});
return completion.choices[0]?.message?.content;
}
}
// Usage
const codeAssist = new CodeAssistant();
await codeAssist.initialize();
const newCode = await codeAssist.generateCode(
'Create a function that sorts an array of objects by a specific property',
'JavaScript'
);
console.log('Generated Code:', newCode);
const bugFix = await codeAssist.debugCode(
'function add(a, b) { return a - b; }',
'This function should add numbers but returns wrong results'
);
console.log('Debug Suggestion:', bugFix);
```
## Best Practices
### 1. Prompt Engineering
```javascript
// Bad: Vague prompt
const bad = await askQuestion('Tell me about AI');
// Good: Specific and structured prompt
async function askWithContext(topic, format, audience) {
const zai = await ZAI.create();
const completion = await zai.chat.completions.create({
messages: [
{
role: 'assistant',
content: `You are an expert educator. Explain topics clearly for ${audience}.`
},
{
role: 'user',
content: `Explain ${topic} in ${format} format. Include practical examples.`
}
],
thinking: { type: 'disabled' }
});
return completion.choices[0]?.message?.content;
}
const good = await askWithContext('artificial intelligence', 'bullet points', 'beginners');
```
### 2. Error Handling
```javascript
import ZAI from 'z-ai-web-dev-sdk';
async function safeCompletion(messages, retries = 3) {
let lastError;
for (let attempt = 1; attempt <= retries; attempt++) {
try {
const zai = await ZAI.create();
const completion = await zai.chat.completions.create({
messages: messages,
thinking: { type: 'disabled' }
});
const response = completion.choices[0]?.message?.content;
if (!response || response.trim().length === 0) {
throw new Error('Empty response from AI');
}
return {
success: true,
content: response,
attempts: attempt
};
} catch (error) {
lastError = error;
console.error(`Attempt ${attempt} failed:`, error.message);
if (attempt < retries) {
// Wait before retry (exponential backoff)
await new Promise(resolve => setTimeout(resolve, 1000 * attempt));
}
}
}
return {
success: false,
error: lastError.message,
attempts: retries
};
}
```
### 3. Context Management
```javascript
class ManagedConversation {
constructor(maxMessages = 20) {
this.maxMessages = maxMessages;
this.systemPrompt = '';
this.messages = [];
this.zai = null;
}
async initialize(systemPrompt) {
this.zai = await ZAI.create();
this.systemPrompt = systemPrompt;
this.messages = [
{
role: 'assistant',
content: systemPrompt
}
];
}
async chat(userMessage) {
// Add user message
this.messages.push({
role: 'user',
content: userMessage
});
// Trim old messages if exceeding limit (keep system prompt)
if (this.messages.length > this.maxMessages) {
this.messages = [
this.messages[0], // Keep system prompt
...this.messages.slice(-(this.maxMessages - 1))
];
}
const completion = await this.zai.chat.completions.create({
messages: this.messages,
thinking: { type: 'disabled' }
});
const response = completion.choices[0]?.message?.content;
this.messages.push({
role: 'assistant',
content: response
});
return response;
}
getTokenEstimate() {
// Rough estimate: ~4 characters per token
const totalChars = this.messages
.map(m => m.content.length)
.reduce((a, b) => a + b, 0);
return Math.ceil(totalChars / 4);
}
}
```
### 4. Response Processing
```javascript
async function getStructuredResponse(query, format = 'json') {
const zai = await ZAI.create();
const formatInstructions = {
json: 'Respond with valid JSON only. No additional text.',
list: 'Respond with a numbered list.',
markdown: 'Respond in Markdown format.'
};
const completion = await zai.chat.completions.create({
messages: [
{
role: 'assistant',
content: `You are a helpful assistant. ${formatInstructions[format]}`
},
{
role: 'user',
content: query
}
],
thinking: { type: 'disabled' }
});
const response = completion.choices[0]?.message?.content;
// Parse JSON if requested
if (format === 'json') {
try {
return JSON.parse(response);
} catch (e) {
console.error('Failed to parse JSON response');
return { raw: response };
}
}
return response;
}
// Usage
const jsonData = await getStructuredResponse(
'List three programming languages with their primary use cases',
'json'
);
console.log(jsonData);
```
## Common Use Cases
1. **Chatbots & Virtual Assistants**: Build conversational interfaces for customer support
2. **Content Generation**: Create articles, product descriptions, marketing copy
3. **Code Assistance**: Generate, explain, and debug code
4. **Data Analysis**: Analyze and summarize complex data sets
5. **Language Translation**: Translate text between languages
6. **Educational Tools**: Create tutoring and learning applications
7. **Email Automation**: Generate professional email responses
8. **Creative Writing**: Story generation, poetry, and creative content
## Integration Examples
### Express.js Chatbot API
```javascript
import express from 'express';
import ZAI from 'z-ai-web-dev-sdk';
const app = express();
app.use(express.json());
// Store conversations in memory (use database in production)
const conversations = new Map();
let zaiInstance;
async function initZAI() {
zaiInstance = await ZAI.create();
}
app.post('/api/chat', async (req, res) => {
try {
const { sessionId, message, systemPrompt } = req.body;
if (!message) {
return res.status(400).json({ error: 'Message is required' });
}
// Get or create conversation history
let history = conversations.get(sessionId) || [
{
role: 'assistant',
content: systemPrompt || 'You are a helpful assistant.'
}
];
// Add user message
history.push({
role: 'user',
content: message
});
// Get completion
const completion = await zaiInstance.chat.completions.create({
messages: history,
thinking: { type: 'disabled' }
});
const aiResponse = completion.choices[0]?.message?.content;
// Add AI response to history
history.push({
role: 'assistant',
content: aiResponse
});
// Save updated history
conversations.set(sessionId, history);
res.json({
success: true,
response: aiResponse,
messageCount: history.length - 1
});
} catch (error) {
res.status(500).json({
success: false,
error: error.message
});
}
});
app.delete('/api/chat/:sessionId', (req, res) => {
const { sessionId } = req.params;
conversations.delete(sessionId);
res.json({ success: true, message: 'Conversation cleared' });
});
initZAI().then(() => {
app.listen(3000, () => {
console.log('Chatbot API running on port 3000');
});
});
```
## Troubleshooting
**Issue**: "SDK must be used in backend"
- **Solution**: Ensure z-ai-web-dev-sdk is only imported and used in server-side code
**Issue**: Empty or incomplete responses
- **Solution**: Check that completion.choices[0]?.message?.content exists and is not empty
**Issue**: Conversation context getting too long
- **Solution**: Implement message trimming to keep only recent messages
**Issue**: Inconsistent responses
- **Solution**: Use more specific system prompts and provide clear instructions
**Issue**: Rate limiting errors
- **Solution**: Implement retry logic with exponential backoff
## Performance Tips
1. **Reuse SDK Instance**: Create ZAI instance once and reuse across requests
2. **Manage Context Length**: Trim old messages to avoid token limits
3. **Implement Caching**: Cache responses for common queries
4. **Use Specific Prompts**: Clear prompts lead to faster, better responses
5. **Handle Errors Gracefully**: Implement retry logic and fallback responses
## Security Considerations
1. **Input Validation**: Always validate and sanitize user input
2. **Rate Limiting**: Implement rate limits to prevent abuse
3. **API Key Protection**: Never expose SDK credentials in client-side code
4. **Content Filtering**: Filter sensitive or inappropriate content
5. **Session Management**: Implement proper session handling and cleanup
## Remember
- Always use z-ai-web-dev-sdk in backend code only
- The SDK is already installed - import as shown in examples
- Use the 'assistant' role for system prompts
- Set thinking to { type: 'disabled' } for standard completions
- Implement proper error handling and retries for production
- Manage conversation history to avoid token limits
- Clear and specific prompts lead to better results
- Check `scripts/chat.ts` for a quick start example

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import ZAI, { ChatMessage } from "z-ai-web-dev-sdk";
async function main(prompt: string) {
try {
const zai = await ZAI.create();
const messages: ChatMessage[] = [
{
role: "assistant",
content: "Hi, I'm a helpful assistant."
},
{
role: "user",
content: prompt,
},
];
const response = await zai.chat.completions.create({
messages,
stream: false,
thinking: { type: "disabled" },
});
const reply = response.choices?.[0]?.message?.content;
console.log("Chat reply:");
console.log(reply ?? JSON.stringify(response, null, 2));
} catch (err: any) {
console.error("Chat failed:", err?.message || err);
}
}
main('What is the capital of France?');