@earendil-works/pi-agent-core

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

Installation

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

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!");

Experimental facet services

Transport-neutral facet-service primitives live in @earendil-works/chord. The agent core does not export the service runtime.

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.

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.

Request preparation and turn finalization

prepareRequest runs immediately before every conversational provider request, including the first. Use it to install canonical persisted context after pending input has been emitted:

agent.prepareRequest = async ({ context }) => ({
  context: { ...context, messages: await session.loadModelContext() },
});

prepareRequest does not poll queues. Steering queued while it runs waits for the next normal steering poll.

finishTurn runs after the assistant and all tool results are finalized, but before turn_end. It runs for normal, error, and aborted responses:

agent.finishTurn = async ({ message }) => {
  if (message.stopReason === "error" || message.stopReason === "aborted") return;
  if (shouldEndRun(message)) return { action: "end" };
  return needsAnotherResponse(message) ? { action: "continue" } : undefined;
};

Returning undefined preserves normal scheduling. { action: "end" } stops immediately after turn_end, before polling steering or follow-up queues or preparing another request. On a normal response, { action: "continue" } ensures one next provider request. If tool results, steering, or a follow-up already cause that request, they satisfy the decision and no additional request is made; otherwise the loop makes one context-only request. Error and aborted responses remain hard exits, so their decisions are ignored. finishTurn runs again after the next request, so returning { action: "continue" } unconditionally creates an endless loop.

To migrate from the removed shouldStopAfterTurn, return { action: "end" }. Guard error and aborted responses to preserve the old hook's normal-response-only invocation, especially when the predicate has side effects or assumes a successful response:

finishTurn: async (turn, signal) => {
  if (turn.message.stopReason === "error" || turn.message.stopReason === "aborted") return;
  return (await shouldStop(turn, signal)) ? { action: "end" } : undefined;
},

Each provider turn follows this lifecycle:

selected input events
→ prepareRequest
→ provider response
→ tool results
→ finishTurn
→ turn_end
→ existing continuation scheduling or agent_end

continue() and queued input

continue() retains its existing queue behavior. Empty and system-only transcripts reject without consuming queues. A non-assistant tail continues from existing context: steering is polled at startup, while follow-up input waits until the response naturally stops.

agent.followUp({ role: "user", content: "After the retry", timestamp: Date.now() });
await agent.continue(); // The first request retries the existing user/toolResult tail.

An assistant tail cannot be sent directly, so continue() falls back to one queued steering batch, then one queued follow-up batch. Queue mode still controls whether that selected batch contains one message or all messages:

agent.steer({ role: "user", content: "Continue from here", timestamp: Date.now() });
await agent.continue(); // Uses the queued message only because the tail is 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

const agent = new Agent({
  // Initial state. systemPrompt and tools become the leading system message
  // unless messages already starts with one.
  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. Receives a TranscriptContext: the prompt and tools
  // are in the transcript's system messages, not on the context.
  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 } };
    }
  },

  // Rebuild finalized context immediately before every provider request.
  prepareRequest: async ({ context }, signal) => {
    return { context: { ...context, messages: await loadCanonicalMessages(signal) } };
  },

  // Finalize a completed turn before turn_end is emitted.
  // `continue` ensures one next request; existing tool/queue scheduling can satisfy it.
  // `end` ends this run after turn_end without polling queues.
  finishTurn: async ({ message, toolResults }, signal) => {
    return shouldContinue(message, toolResults) ? { action: "continue" } : undefined;
  },

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

Agent State

interface AgentState {
  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.

The transcript owns the system prompt and tool declarations: the leading system message is the prompt, later system messages patch it (see SystemMessage in pi-ai). agent.state.systemPrompt is read-only and replays the transcript. agent.state.tools is the executable loadout; before every request the loop diffs it against the tools the transcript declares and, if they differ, announces the change in a system message (merged into a pending system message when one exists). pi-ai's getCurrentSystemMessage(messages) returns the replayed head, including declared tools, for any message array, including agent transcripts with custom message roles.

To change the prompt mid-conversation, append a system message with content (added instructions) or sections (named replacements):

await agent.prompt([
  { role: "system", content: "", sections: { skills: "<skills>...</skills>" }, timestamp: Date.now() },
  { role: "user", content: "Continue", timestamp: Date.now() },
]);

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

// 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 existing non-assistant input; an assistant tail may use queued input as fallback
await agent.continue();

State Management

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.prepareRequest = async ({ context }) => ({
  context: { ...context, messages: await loadCanonicalMessages() },
});
agent.finishTurn = async () => undefined;
agent.state.messages = newMessages; // top-level array is copied
agent.state.messages.push(message);
const nextQueuedMessages = agent.peekQueuedMessages(); // respects queue modes; does not consume
agent.reset();

Session and Thinking Budgets

agent.sessionId = "session-123";

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

Control

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

Events

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.

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:

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:

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:

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.

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.

MCP and Codemode

@earendil-works/pi-mcp connects to MCP servers and @earendil-works/pi-codemode runs model-written JavaScript that calls tools. examples/mcp-codemode wraps both as AgentTools: one tool per MCP tool, and a codemode tool whose scripts call the agent's tools through runToolCall(), so beforeToolCall and afterToolCall apply to those calls too.

Proxy Usage

For browser apps that proxy through a backend:

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:

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

const context: AgentContext = {
  messages: [{ role: "system", content: "You are helpful.", timestamp: Date.now() }],
  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