co-mono/packages/agent/README.md
Mario Zechner fa22595f25 Update READMEs: remove agent section from pi-ai, rewrite pi-agent-core
- 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
2025-12-30 22:42:20 +01:00

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# @mariozechner/pi-agent-core
Stateful agent with tool execution, event streaming, and extensible message types. Built on `@mariozechner/pi-ai`.
## Installation
```bash
npm install @mariozechner/pi-agent-core
```
## Quick Start
```typescript
import { Agent } from '@mariozechner/pi-agent-core';
import { getModel } from '@mariozechner/pi-ai';
const agent = new Agent({
initialState: {
systemPrompt: 'You are a helpful assistant.',
model: getModel('anthropic', 'claude-sonnet-4-20250514'),
thinkingLevel: 'medium',
tools: []
}
});
// Subscribe to events for reactive UI updates
agent.subscribe((event) => {
switch (event.type) {
case 'message_update':
for (const block of event.message.content) {
if (block.type === 'text') process.stdout.write(block.text);
}
break;
case 'tool_execution_start':
console.log(`Calling ${event.toolName}...`);
break;
case 'tool_execution_end':
console.log(`Result:`, event.result.content);
break;
}
});
await agent.prompt('Hello, world!');
console.log(agent.state.messages);
```
## Agent Options
```typescript
interface AgentOptions {
initialState?: Partial<AgentState>;
// Converts AgentMessage[] to LLM-compatible Message[] before each call.
// Default: filters to user/assistant/toolResult and converts attachments.
convertToLlm?: (messages: AgentMessage[]) => Message[] | Promise<Message[]>;
// Transform context before convertToLlm (for pruning, injecting context, etc.)
transformContext?: (messages: AgentMessage[], signal?: AbortSignal) => Promise<AgentMessage[]>;
// Queue mode: 'all' sends all queued messages, 'one-at-a-time' sends one per turn
queueMode?: 'all' | 'one-at-a-time';
// Custom stream function (for proxy backends). Default: streamSimple from pi-ai
streamFn?: StreamFn;
// Dynamic API key resolution (useful for expiring OAuth tokens)
getApiKey?: (provider: string) => Promise<string | undefined> | string | undefined;
}
```
## Agent State
```typescript
interface AgentState {
systemPrompt: string;
model: Model<any>;
thinkingLevel: ThinkingLevel; // 'off' | 'minimal' | 'low' | 'medium' | 'high' | 'xhigh'
tools: AgentTool<any>[];
messages: AgentMessage[];
isStreaming: boolean;
streamMessage: AgentMessage | null;
pendingToolCalls: Set<string>;
error?: string;
}
```
## Events
Events provide fine-grained lifecycle information for building reactive UIs:
| Event | Description |
|-------|-------------|
| `agent_start` | Agent begins processing |
| `agent_end` | Agent completes, contains all generated messages |
| `turn_start` | New turn begins (one LLM response + tool executions) |
| `turn_end` | Turn completes with assistant message and tool results |
| `message_start` | Message begins (user, assistant, or toolResult) |
| `message_update` | Assistant message streaming update |
| `message_end` | Message completes |
| `tool_execution_start` | Tool begins execution |
| `tool_execution_update` | Tool streams progress |
| `tool_execution_end` | Tool completes with result |
## Custom Message Types
Extend `AgentMessage` for app-specific messages via declaration merging:
```typescript
declare module '@mariozechner/pi-agent-core' {
interface CustomMessages {
artifact: { role: 'artifact'; code: string; language: string; timestamp: number };
notification: { role: 'notification'; text: string; timestamp: number };
}
}
// AgentMessage now includes your custom types
const msg: AgentMessage = { role: 'artifact', code: '...', language: 'typescript', timestamp: Date.now() };
```
Custom messages are stored in state but filtered out by the default `convertToLlm`. Provide your own converter to handle them:
```typescript
const agent = new Agent({
convertToLlm: (messages) => {
return messages
.filter(m => m.role === 'user' || m.role === 'assistant' || m.role === 'toolResult')
.map(m => {
// Convert custom types or pass through
if (m.role === 'artifact') {
return { role: 'user', content: `[Artifact: ${m.language}]\n${m.code}`, timestamp: m.timestamp };
}
return m;
});
}
});
```
## Message Queue
Queue messages to inject at the next turn:
```typescript
agent.setQueueMode('one-at-a-time');
// Queue while agent is streaming
agent.queueMessage({
role: 'user',
content: 'Stop what you are doing and focus on this instead.',
timestamp: Date.now()
});
```
When queued messages are detected after a tool call, remaining tool calls are skipped with error results.
## Attachments
User messages can include attachments (images, documents):
```typescript
await agent.prompt('What is in this image?', [{
id: 'img1',
type: 'image',
fileName: 'photo.jpg',
mimeType: 'image/jpeg',
size: 102400,
content: base64ImageData // base64 without data URL prefix
}]);
```
## Proxy Usage
For browser apps that need to proxy through a backend, use `streamProxy`:
```typescript
import { Agent, streamProxy } from '@mariozechner/pi-agent-core';
const agent = new Agent({
streamFn: (model, context, options) => streamProxy(
'/api/agent',
model,
context,
options,
{ 'Authorization': 'Bearer ...' }
)
});
```
## Low-Level API
For more control, use `agentLoop` and `agentLoopContinue` directly:
```typescript
import { agentLoop, agentLoopContinue, AgentLoopContext, AgentLoopConfig } from '@mariozechner/pi-agent-core';
import { getModel, streamSimple } from '@mariozechner/pi-ai';
const context: AgentLoopContext = {
systemPrompt: 'You are helpful.',
messages: [],
tools: [myTool]
};
const config: AgentLoopConfig = {
model: getModel('openai', 'gpt-4o-mini'),
convertToLlm: (msgs) => msgs.filter(m => ['user', 'assistant', 'toolResult'].includes(m.role))
};
const userMessage = { role: 'user', content: 'Hello', timestamp: Date.now() };
for await (const event of agentLoop(userMessage, context, config, undefined, streamSimple)) {
console.log(event.type);
}
// Continue from existing context (e.g., after overflow recovery)
for await (const event of agentLoopContinue(context, config, undefined, streamSimple)) {
console.log(event.type);
}
```
## API Reference
### Agent Methods
| Method | Description |
|--------|-------------|
| `prompt(text, attachments?)` | Send a user prompt |
| `prompt(message)` | Send an AgentMessage directly |
| `continue()` | Continue from current context |
| `abort()` | Abort current operation |
| `waitForIdle()` | Promise that resolves when agent is idle |
| `reset()` | Clear all messages and state |
| `subscribe(fn)` | Subscribe to events, returns unsubscribe function |
| `queueMessage(msg)` | Queue message for next turn |
| `clearMessageQueue()` | Clear queued messages |
### State Mutators
| Method | Description |
|--------|-------------|
| `setSystemPrompt(v)` | Update system prompt |
| `setModel(m)` | Switch model |
| `setThinkingLevel(l)` | Set reasoning level |
| `setQueueMode(m)` | Set queue mode |
| `setTools(t)` | Update available tools |
| `replaceMessages(ms)` | Replace all messages |
| `appendMessage(m)` | Append a message |
| `clearMessages()` | Clear all messages |
## License
MIT