# @earendil-works/pi-agent-core

Stateful agent with tool execution and event streaming. Built on `@earendil-works/pi-ai`.

## Installation

```bash
npm install @earendil-works/pi-agent-core
```

### SQLite session backends

The SQLite session backend and the `node:sqlite` adapter live in a separate package, `@earendil-works/pi-session-backend-sqlite-node`, so the core package does not pull in runtime builtins or native SQLite dependencies by default. The backend accepts a runtime-specific SQLite factory, allowing other session backends to ship as their own packages in the future.

## Quick Start

```typescript
import { Agent } from "@earendil-works/pi-agent-core";
import { createModels } from "@earendil-works/pi-ai";
import { anthropicProvider } from "@earendil-works/pi-ai/providers/anthropic";

const models = createModels();
models.setProvider(anthropicProvider());
const model = models.getModel("anthropic", "claude-sonnet-4-6");
if (!model) throw new Error("Model not found");

const agent = new Agent({
  initialState: {
    systemPrompt: "You are a helpful assistant.",
    model,
  },
  streamFn: models.streamSimple.bind(models),
});

agent.subscribe((event) => {
  if (event.type === "message_update" && event.assistantMessageEvent.type === "text_delta") {
    // Stream just the new text chunk
    process.stdout.write(event.assistantMessageEvent.delta);
  }
});

await agent.prompt("Hello!");
```

## Core Concepts

### AgentMessage vs LLM Message

The agent works with `AgentMessage`, a flexible type that can include:
- Standard LLM messages (`user`, `assistant`, `toolResult`)
- Custom app-specific message types via declaration merging

LLMs only understand `user`, `assistant`, and `toolResult`. The `convertToLlm` function bridges this gap by filtering and transforming messages before each LLM call.

### Message Flow

```
AgentMessage[] → transformContext() → AgentMessage[] → convertToLlm() → Message[] → LLM
                    (optional)                           (required)
```

1. **transformContext**: Prune old messages, inject external context
2. **convertToLlm**: Filter out UI-only messages, convert custom types to LLM format

## Event Flow

The agent emits events for UI updates. Understanding the event sequence helps build responsive interfaces.

### prompt() Event Sequence

When you call `prompt("Hello")`:

```
prompt("Hello")
├─ agent_start
├─ turn_start
├─ message_start   { message: userMessage }      // Your prompt
├─ message_end     { message: userMessage }
├─ message_start   { message: assistantMessage } // LLM starts responding
├─ message_update  { message: partial... }       // Streaming chunks
├─ message_update  { message: partial... }
├─ message_end     { message: assistantMessage } // Complete response
├─ turn_end        { message, toolResults: [] }
└─ agent_end       { messages: [...] }
```

### With Tool Calls

If the assistant calls tools, the loop continues:

```
prompt("Read config.json")
├─ agent_start
├─ turn_start
├─ message_start/end  { userMessage }
├─ message_start      { assistantMessage with toolCall }
├─ message_update...
├─ message_end        { assistantMessage }
├─ tool_execution_start  { toolCallId, toolName, args }
├─ tool_execution_update { partialResult }           // If tool streams
├─ tool_execution_end    { toolCallId, result }
├─ message_start/end  { toolResultMessage }
├─ turn_end           { message, toolResults: [toolResult] }
│
├─ turn_start                                        // Next turn
├─ message_start      { assistantMessage }           // LLM responds to tool result
├─ message_update...
├─ message_end
├─ turn_end
└─ agent_end
```

Tool execution mode is configurable:

- `parallel` (default): preflight tool calls sequentially, execute allowed tools concurrently, emit `tool_execution_end` as soon as each tool is finalized, then emit toolResult messages and `turn_end.toolResults` in assistant source order
- `sequential`: execute tool calls one by one, matching the historical behavior

In parallel mode, tool completion events follow tool completion order, but persisted toolResult messages still follow assistant source order.

The mode can be set globally via `toolExecution` in the agent config, or per-tool via `executionMode` on `AgentTool`. If any tool call in a batch targets a tool with `executionMode: "sequential"`, the entire batch executes sequentially regardless of the global setting.

The `beforeToolCall` hook runs after `tool_execution_start` and validated argument parsing. It can block execution and attach `terminate: true` to the blocked result. The `afterToolCall` hook runs after tool execution finishes and before `tool_execution_end` and final tool result message events are emitted.

Tools, blocked `beforeToolCall` results, and `afterToolCall` overrides can return `terminate: true` to hint that the automatic follow-up LLM call should be skipped. The loop only stops early when every finalized tool result in that batch sets `terminate: true`. Mixed batches continue normally.

The `Agent` class accepts `shouldStopAfterTurn` in `AgentOptions`. Low-level loop callers can set the same hook in `AgentLoopConfig`:

```typescript
const stream = agentLoop(
  prompts,
  context,
  {
    model,
    convertToLlm,
    shouldStopAfterTurn: async ({ message, toolResults, context, newMessages }) => {
      return shouldCompactBeforeNextTurn(context.messages);
    },
  },
  undefined,
  models.streamSimple.bind(models),
);
```

`shouldStopAfterTurn` runs after `turn_end` is emitted and after the assistant response and any tool executions have completed normally. If it returns `true`, the loop emits `agent_end` and exits before polling steering or follow-up queues, and before starting another LLM call. It does not abort the provider stream, does not cancel running tools, and does not alter the assistant message stop reason. The `AgentOptions` callback also receives the active run's `AbortSignal` as its second argument.

When you use the `Agent` class, assistant `message_end` processing is treated as a barrier before tool preflight begins. That means `beforeToolCall` sees agent state that already includes the assistant message that requested the tool call.

### continue() Event Sequence

`continue()` resumes from existing context without adding a new message. Use it for retries after errors.

```typescript
// After an error, retry from current state
await agent.continue();
```

The last message in context must be `user` or `toolResult` (not `assistant`).

### Event Types

| Event | Description |
|-------|-------------|
| `agent_start` | Agent begins processing |
| `agent_end` | Final event for the run. Awaited subscribers for this event still count toward settlement |
| `turn_start` | New turn begins (one LLM call + tool executions) |
| `turn_end` | Turn completes with assistant message and tool results |
| `message_start` | Any message begins (user, assistant, toolResult) |
| `message_update` | **Assistant only.** Includes `assistantMessageEvent` with delta |
| `message_end` | Message completes |
| `tool_execution_start` | Tool begins |
| `tool_execution_update` | Tool streams progress |
| `tool_execution_end` | Tool completes |

`Agent.subscribe()` listeners are awaited in registration order. `agent_end` means no more loop events will be emitted, but `await agent.waitForIdle()` and `await agent.prompt(...)` only settle after awaited `agent_end` listeners finish.

## Agent Options

```typescript
const agent = new Agent({
  // Initial state
  initialState: {
    systemPrompt: string,
    model: Model<any>,
    thinkingLevel: "off" | "minimal" | "low" | "medium" | "high" | "xhigh" | "max",
    tools: AgentTool<any>[],
    messages: AgentMessage[],
  },

  // Convert AgentMessage[] to LLM Message[] (required for custom message types)
  convertToLlm: (messages) => messages.filter(...),

  // Transform context before convertToLlm (for pruning, compaction)
  transformContext: async (messages, signal) => pruneOldMessages(messages),

  // Steering mode: "one-at-a-time" (default) or "all"
  steeringMode: "one-at-a-time",

  // Follow-up mode: "one-at-a-time" (default) or "all"
  followUpMode: "one-at-a-time",

  // Required stream function
  streamFn: models.streamSimple.bind(models),

  // Session ID for provider caching
  sessionId: "session-123",

  // Dynamic API key resolution (for expiring OAuth tokens)
  getApiKey: async (provider) => refreshToken(),

  // Tool execution mode: "parallel" (default) or "sequential"
  toolExecution: "parallel",

  // Preflight each tool call after args are validated. Can block execution.
  beforeToolCall: async ({ toolCall, args, context }) => {
    if (toolCall.name === "bash") {
      return { block: true, reason: "bash is disabled", terminate: true };
    }
  },

  // Postprocess each tool result before final tool events are emitted.
  afterToolCall: async ({ toolCall, result, isError, context }) => {
    if (toolCall.name === "notify_done" && !isError) {
      return { terminate: true };
    }
    if (!isError) {
      return { details: { ...result.details, audited: true } };
    }
  },

  // Stop gracefully after a completed turn, before queued messages are polled.
  shouldStopAfterTurn: async ({ context }, signal) => {
    return shouldCompactBeforeNextTurn(context.messages, signal);
  },

  // Custom thinking budgets for token-based providers
  thinkingBudgets: {
    minimal: 128,
    low: 512,
    medium: 1024,
    high: 2048,
  },
});
```

## Agent State

```typescript
interface AgentState {
  systemPrompt: string;
  model: Model<any>;
  thinkingLevel: ThinkingLevel;
  tools: AgentTool<any>[];
  messages: AgentMessage[];
  readonly isStreaming: boolean;
  readonly streamingMessage?: AgentMessage;
  readonly pendingToolCalls: ReadonlySet<string>;
  readonly errorMessage?: string;
}
```

Access state via `agent.state`.

Assigning `agent.state.tools = [...]` or `agent.state.messages = [...]` copies the top-level array before storing it. Mutating the returned array mutates the current agent state.

During streaming, `agent.state.streamingMessage` contains the current partial assistant message.

`agent.state.isStreaming` remains `true` until the run fully settles, including awaited `agent_end` subscribers.

## Methods

### Prompting

```typescript
// Text prompt
await agent.prompt("Hello");

// With images
await agent.prompt("What's in this image?", [
  { type: "image", data: base64Data, mimeType: "image/jpeg" }
]);

// AgentMessage directly
await agent.prompt({ role: "user", content: "Hello", timestamp: Date.now() });

// Continue from current context (last message must be user or toolResult)
await agent.continue();
```

### State Management

```typescript
agent.state.systemPrompt = "New prompt";
agent.state.model = getModel("openai", "gpt-4o");
agent.state.thinkingLevel = "medium";
agent.state.tools = [myTool];
agent.toolExecution = "sequential";
agent.beforeToolCall = async ({ toolCall }) => undefined;
agent.afterToolCall = async ({ toolCall, result }) => undefined;
agent.shouldStopAfterTurn = async ({ context }) => shouldCompactBeforeNextTurn(context.messages);
agent.state.messages = newMessages; // top-level array is copied
agent.state.messages.push(message);
agent.reset();
```

### Session and Thinking Budgets

```typescript
agent.sessionId = "session-123";

agent.thinkingBudgets = {
  minimal: 128,
  low: 512,
  medium: 1024,
  high: 2048,
};
```

### Control

```typescript
agent.abort();           // Cancel current operation
await agent.waitForIdle(); // Wait for completion
```

### Events

```typescript
const unsubscribe = agent.subscribe(async (event, signal) => {
  if (event.type === "agent_end") {
    // Final barrier work for the run
    await flushSessionState(signal);
  }
});
unsubscribe();
```

## Steering and Follow-up

Steering messages let you interrupt the agent while tools are running. Follow-up messages let you queue work after the agent would otherwise stop.

```typescript
agent.steeringMode = "one-at-a-time";
agent.followUpMode = "one-at-a-time";

// While agent is running tools
agent.steer({
  role: "user",
  content: "Stop! Do this instead.",
  timestamp: Date.now(),
});

// After the agent finishes its current work
agent.followUp({
  role: "user",
  content: "Also summarize the result.",
  timestamp: Date.now(),
});

const steeringMode = agent.steeringMode;
const followUpMode = agent.followUpMode;

agent.clearSteeringQueue();
agent.clearFollowUpQueue();
agent.clearAllQueues();
```

Use clearSteeringQueue, clearFollowUpQueue, or clearAllQueues to drop queued messages.

When steering messages are detected after a turn completes:
1. All tool calls from the current assistant message have already finished
2. Steering messages are injected
3. The LLM responds on the next turn

Follow-up messages are checked only when there are no more tool calls and no steering messages. If any are queued, they are injected and another turn runs.

## Custom Message Types

Extend `AgentMessage` via declaration merging:

```typescript
declare module "@earendil-works/pi-agent-core" {
  interface CustomAgentMessages {
    notification: { role: "notification"; text: string; timestamp: number };
  }
}

// Now valid
const msg: AgentMessage = { role: "notification", text: "Info", timestamp: Date.now() };
```

Handle custom types in `convertToLlm`:

```typescript
const agent = new Agent({
  streamFn: models.streamSimple.bind(models),
  convertToLlm: (messages) => messages.flatMap(m => {
    if (m.role === "notification") return []; // Filter out
    return [m];
  }),
});
```

## Tools

Define tools using `AgentTool`:

```typescript
import { Type } from "typebox";

const readFileTool: AgentTool = {
  name: "read_file",
  label: "Read File",  // For UI display
  description: "Read a file's contents",
  parameters: Type.Object({
    path: Type.String({ description: "File path" }),
  }),
  // Override execution mode for this tool (optional).
  // "sequential" forces the entire batch to run one at a time.
  // "parallel" allows concurrent execution with other tool calls.
  // If omitted, the global toolExecution config applies.
  executionMode: "sequential",
  execute: async (toolCallId, params, signal, onUpdate) => {
    const content = await fs.readFile(params.path, "utf-8");

    // Optional: stream progress
    onUpdate?.({ content: [{ type: "text", text: "Reading..." }], details: {} });

    // Optional: add `terminate: true` here to skip the automatic follow-up LLM call
    // when every finalized tool result in the batch does the same.
    return {
      content: [{ type: "text", text: content }],
      details: { path: params.path, size: content.length },
    };
  },
};

agent.state.tools = [readFileTool];
```

### Error Handling

**Throw an error** when a tool fails. Do not return error messages as content.

```typescript
execute: async (toolCallId, params, signal, onUpdate) => {
  if (!fs.existsSync(params.path)) {
    throw new Error(`File not found: ${params.path}`);
  }
  // Return content only on success
  return { content: [{ type: "text", text: "..." }] };
}
```

Thrown errors are caught by the agent and reported to the LLM as tool errors with `isError: true`.

Return `terminate: true` from `execute()`, a blocked `beforeToolCall`, or `afterToolCall` to hint that the agent should stop after the current tool batch. This only takes effect when every finalized tool result in the batch is terminating. The hint is runtime-only; emitted `toolResult` transcript messages remain standard LLM tool results.

## Proxy Usage

For browser apps that proxy through a backend:

```typescript
import { Agent, streamProxy } from "@earendil-works/pi-agent-core";

const agent = new Agent({
  streamFn: (model, context, options) =>
    streamProxy(model, context, {
      ...options,
      authToken: "...",
      proxyUrl: "https://your-server.com",
    }),
});
```

## Low-Level API

For direct control without the Agent class:

```typescript
import { agentLoop, agentLoopContinue } from "@earendil-works/pi-agent-core";

const context: AgentContext = {
  systemPrompt: "You are helpful.",
  messages: [],
  tools: [],
};

const config: AgentLoopConfig = {
  model: getModel("openai", "gpt-4o"),
  convertToLlm: (msgs) => msgs.filter(m => ["user", "assistant", "toolResult"].includes(m.role)),
  toolExecution: "parallel",  // overridden by per-tool executionMode if set
  beforeToolCall: async ({ toolCall, args, context }) => undefined,
  afterToolCall: async ({ toolCall, result, isError, context }) => undefined,
};

const userMessage = { role: "user", content: "Hello", timestamp: Date.now() };

const streamFn = models.streamSimple.bind(models);
for await (const event of agentLoop([userMessage], context, config, undefined, streamFn)) {
  console.log(event.type);
}

// Continue from existing context
for await (const event of agentLoopContinue(context, config, undefined, streamFn)) {
  console.log(event.type);
}
```

These low-level streams are observational. They preserve event order, but they do not wait for your async event handling to settle before later producer phases continue. If you need message processing to act as a barrier before tool preflight, use the `Agent` class instead of raw `agentLoop()` or `agentLoopContinue()`.

## License

MIT
