mirror of
https://github.com/getcompanion-ai/co-mono.git
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- Removed Agent API section from pi-ai README (moved to agent package) - Rewrote agent package README for new architecture: - No more transports (ProviderTransport, AppTransport removed) - Uses streamFn directly with streamProxy for proxy usage - Documents convertToLlm and transformContext - Documents low-level agentLoop/agentLoopContinue API - Updated custom message types documentation
250 lines
7 KiB
Markdown
250 lines
7 KiB
Markdown
# @mariozechner/pi-agent-core
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Stateful agent with tool execution, event streaming, and extensible message types. Built on `@mariozechner/pi-ai`.
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## Installation
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```bash
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npm install @mariozechner/pi-agent-core
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```
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## Quick Start
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```typescript
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import { Agent } from '@mariozechner/pi-agent-core';
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import { getModel } from '@mariozechner/pi-ai';
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const agent = new Agent({
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initialState: {
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systemPrompt: 'You are a helpful assistant.',
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model: getModel('anthropic', 'claude-sonnet-4-20250514'),
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thinkingLevel: 'medium',
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tools: []
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}
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});
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// Subscribe to events for reactive UI updates
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agent.subscribe((event) => {
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switch (event.type) {
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case 'message_update':
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for (const block of event.message.content) {
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if (block.type === 'text') process.stdout.write(block.text);
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}
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break;
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case 'tool_execution_start':
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console.log(`Calling ${event.toolName}...`);
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break;
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case 'tool_execution_end':
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console.log(`Result:`, event.result.content);
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break;
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}
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});
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await agent.prompt('Hello, world!');
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console.log(agent.state.messages);
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```
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## Agent Options
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```typescript
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interface AgentOptions {
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initialState?: Partial<AgentState>;
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// Converts AgentMessage[] to LLM-compatible Message[] before each call.
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// Default: filters to user/assistant/toolResult and converts attachments.
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convertToLlm?: (messages: AgentMessage[]) => Message[] | Promise<Message[]>;
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// Transform context before convertToLlm (for pruning, injecting context, etc.)
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transformContext?: (messages: AgentMessage[], signal?: AbortSignal) => Promise<AgentMessage[]>;
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// Queue mode: 'all' sends all queued messages, 'one-at-a-time' sends one per turn
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queueMode?: 'all' | 'one-at-a-time';
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// Custom stream function (for proxy backends). Default: streamSimple from pi-ai
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streamFn?: StreamFn;
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// Dynamic API key resolution (useful for expiring OAuth tokens)
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getApiKey?: (provider: string) => Promise<string | undefined> | string | undefined;
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}
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```
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## Agent State
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```typescript
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interface AgentState {
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systemPrompt: string;
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model: Model<any>;
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thinkingLevel: ThinkingLevel; // 'off' | 'minimal' | 'low' | 'medium' | 'high' | 'xhigh'
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tools: AgentTool<any>[];
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messages: AgentMessage[];
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isStreaming: boolean;
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streamMessage: AgentMessage | null;
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pendingToolCalls: Set<string>;
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error?: string;
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}
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```
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## Events
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Events provide fine-grained lifecycle information for building reactive UIs:
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| Event | Description |
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|-------|-------------|
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| `agent_start` | Agent begins processing |
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| `agent_end` | Agent completes, contains all generated messages |
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| `turn_start` | New turn begins (one LLM response + tool executions) |
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| `turn_end` | Turn completes with assistant message and tool results |
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| `message_start` | Message begins (user, assistant, or toolResult) |
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| `message_update` | Assistant message streaming update |
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| `message_end` | Message completes |
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| `tool_execution_start` | Tool begins execution |
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| `tool_execution_update` | Tool streams progress |
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| `tool_execution_end` | Tool completes with result |
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## Custom Message Types
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Extend `AgentMessage` for app-specific messages via declaration merging:
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```typescript
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declare module '@mariozechner/pi-agent-core' {
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interface CustomMessages {
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artifact: { role: 'artifact'; code: string; language: string; timestamp: number };
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notification: { role: 'notification'; text: string; timestamp: number };
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}
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}
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// AgentMessage now includes your custom types
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const msg: AgentMessage = { role: 'artifact', code: '...', language: 'typescript', timestamp: Date.now() };
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```
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Custom messages are stored in state but filtered out by the default `convertToLlm`. Provide your own converter to handle them:
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```typescript
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const agent = new Agent({
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convertToLlm: (messages) => {
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return messages
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.filter(m => m.role === 'user' || m.role === 'assistant' || m.role === 'toolResult')
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.map(m => {
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// Convert custom types or pass through
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if (m.role === 'artifact') {
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return { role: 'user', content: `[Artifact: ${m.language}]\n${m.code}`, timestamp: m.timestamp };
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}
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return m;
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});
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}
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});
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```
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## Message Queue
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Queue messages to inject at the next turn:
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```typescript
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agent.setQueueMode('one-at-a-time');
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// Queue while agent is streaming
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agent.queueMessage({
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role: 'user',
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content: 'Stop what you are doing and focus on this instead.',
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timestamp: Date.now()
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});
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```
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When queued messages are detected after a tool call, remaining tool calls are skipped with error results.
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## Attachments
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User messages can include attachments (images, documents):
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```typescript
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await agent.prompt('What is in this image?', [{
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id: 'img1',
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type: 'image',
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fileName: 'photo.jpg',
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mimeType: 'image/jpeg',
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size: 102400,
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content: base64ImageData // base64 without data URL prefix
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}]);
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```
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## Proxy Usage
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For browser apps that need to proxy through a backend, use `streamProxy`:
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```typescript
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import { Agent, streamProxy } from '@mariozechner/pi-agent-core';
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const agent = new Agent({
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streamFn: (model, context, options) => streamProxy(
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'/api/agent',
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model,
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context,
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options,
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{ 'Authorization': 'Bearer ...' }
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)
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});
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```
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## Low-Level API
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For more control, use `agentLoop` and `agentLoopContinue` directly:
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```typescript
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import { agentLoop, agentLoopContinue, AgentLoopContext, AgentLoopConfig } from '@mariozechner/pi-agent-core';
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import { getModel, streamSimple } from '@mariozechner/pi-ai';
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const context: AgentLoopContext = {
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systemPrompt: 'You are helpful.',
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messages: [],
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tools: [myTool]
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};
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const config: AgentLoopConfig = {
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model: getModel('openai', 'gpt-4o-mini'),
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convertToLlm: (msgs) => msgs.filter(m => ['user', 'assistant', 'toolResult'].includes(m.role))
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};
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const userMessage = { role: 'user', content: 'Hello', timestamp: Date.now() };
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for await (const event of agentLoop(userMessage, context, config, undefined, streamSimple)) {
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console.log(event.type);
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}
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// Continue from existing context (e.g., after overflow recovery)
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for await (const event of agentLoopContinue(context, config, undefined, streamSimple)) {
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console.log(event.type);
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}
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```
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## API Reference
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### Agent Methods
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| Method | Description |
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|--------|-------------|
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| `prompt(text, attachments?)` | Send a user prompt |
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| `prompt(message)` | Send an AgentMessage directly |
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| `continue()` | Continue from current context |
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| `abort()` | Abort current operation |
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| `waitForIdle()` | Promise that resolves when agent is idle |
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| `reset()` | Clear all messages and state |
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| `subscribe(fn)` | Subscribe to events, returns unsubscribe function |
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| `queueMessage(msg)` | Queue message for next turn |
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| `clearMessageQueue()` | Clear queued messages |
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### State Mutators
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| Method | Description |
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|--------|-------------|
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| `setSystemPrompt(v)` | Update system prompt |
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| `setModel(m)` | Switch model |
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| `setThinkingLevel(l)` | Set reasoning level |
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| `setQueueMode(m)` | Set queue mode |
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| `setTools(t)` | Update available tools |
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| `replaceMessages(ms)` | Replace all messages |
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| `appendMessage(m)` | Append a message |
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| `clearMessages()` | Clear all messages |
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## License
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MIT
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