pi-coding-agent

name	pi-coding-agent
description	Build sandboxed autonomous agent workflows with @earendil-works/pi-coding-agent and @earendil-works/pi-ai
license	Apache-2.0
metadata	{"author":"David East","version":"2.0"}

Pi SDK: Under the Hood

Comprehensive technical documentation for building sandboxed autonomous agent workflows with the @earendil-works/pi-coding-agent, @earendil-works/pi-agent-core, and @earendil-works/pi-ai SDK packages.

---

Documentation Map

Quadrant	Purpose
🎓 Tutorial	Acquire competence — build a working headless agent from scratch
🔧 How-to Guides	Apply skills — task-oriented recipes for sandboxing, steering, and extending
📖 Reference	Look up API details — exhaustive, austere, factual
💡 Explanation	Understand the system — architecture, design philosophy, lifecycle

---

🎓 Tutorial: Build Your First Autonomous Agent

We are going to build a headless agent that sends a prompt to an LLM, streams the response token-by-token, executes tool calls, and logs every lifecycle event to a JSONL file. By the end, you will have a working autonomous agent script you can extend for any sandboxed workflow.

Prerequisites

We will use the following:

• Node.js 20+
• TypeScript with ESM ("type": "module" in package.json)
• An API key for a supported provider (we will use Google Gemini)

Step 1: Set up the project

Create a new directory and initialize it.

mkdir pi-agent-sandbox && cd pi-agent-sandbox
npm init -y
npm install @earendil-works/pi-coding-agent @earendil-works/pi-ai

Add "type": "module" to your package.json. We will write our agent in index.ts.

Step 2: Import the SDK primitives

Open index.ts. We need four things from the SDK: credential storage, a model registry, a session manager, and the session factory.

import {
  AuthStorage,
  createAgentSession,
  ModelRegistry,
  SessionManager,
} from "@earendil-works/pi-coding-agent";
import { getModel } from "@earendil-works/pi-ai";

Notice that we import getModel from pi-ai — this is the model catalog. Everything else comes from pi-coding-agent, which is the orchestration layer.

Step 3: Initialize the runtime

We need to set up the credential pipeline and select a model. AuthStorage manages API keys, and ModelRegistry resolves them at call time.

const authStorage = AuthStorage.create();
const modelRegistry = ModelRegistry.create(authStorage);
const model = getModel("google", "gemini-3-flash-preview");

Now we create the session. SessionManager.inMemory() gives us a session that lives only in process memory — no files on disk, perfect for ephemeral sandbox runs.

const { session } = await createAgentSession({
  sessionManager: SessionManager.inMemory(),
  authStorage,
  modelRegistry,
  model,
});

The createAgentSession factory returns an object containing the session (an AgentSession instance). This is the primary handle for all agent operations.

Step 4: Subscribe to lifecycle events

Before we send a prompt, we subscribe to the event stream. This is how we observe everything the agent does.

import { appendFile } from "node:fs/promises";
import { mkdirSync, existsSync } from "node:fs";

const logsDir = "./logs";
if (!existsSync(logsDir)) mkdirSync(logsDir);

session.subscribe(async (event) => {
  // Log every event to a JSONL file
  await appendFile(
    `${logsDir}/${session.sessionId}.jsonl`,
    JSON.stringify(event) + "\n"
  );

  // Stream text deltas to stdout
  if (
    event.type === "message_update" &&
    event.assistantMessageEvent.type === "text_delta"
  ) {
    process.stdout.write(event.assistantMessageEvent.delta);
  }
});

Every agent event flows through this subscription — agent_start, turn_start, message_start, message_update, tool_execution_start, tool_execution_end, turn_end, agent_end. We will explore these in detail in the Explanation: Agent Lifecycle section.

Step 5: Send a prompt

await session.prompt("List the files in the current directory and describe what you see.");
console.log("\n--- Agent finished ---");

Run it:

npx tsx index.ts

You will see the LLM response streaming to your terminal. If the agent has tools enabled (it does by default — bash, read, edit, write, grep, find, ls), it will execute an ls or bash command and describe the output.

Step 6: Inspect the log

Open the JSONL file in ./logs/. Each line is a single AgentEvent. You will see the full lifecycle: the agent started, a turn began, the LLM generated a message with tool calls, the tools executed, and the agent ended.

Congratulations. You now have a functioning autonomous agent. The next sections show you how to sandbox it, steer it mid-run, and extend it with custom tools.

---

🔧 How-to Guides

How to Build a Sandboxed Agent Pipeline

Restrict the working directory

To confine all file operations to a specific directory, set the cwd option. All built-in tools (bash, read, edit, write, grep, find, ls) resolve paths relative to this directory.

const { session } = await createAgentSession({
  sessionManager: SessionManager.inMemory(),
  authStorage,
  modelRegistry,
  model,
  cwd: "/tmp/sandbox-workspace",
});

Restrict the tool set

To limit which tools the agent can use, pass tools or readOnlyTools:

import { createReadOnlyTools } from "@earendil-works/pi-coding-agent";

const { session } = await createAgentSession({
  sessionManager: SessionManager.inMemory(),
  authStorage,
  modelRegistry,
  model,
  cwd: "/tmp/sandbox-workspace",
  readOnlyTools: true,  // Only read, grep, find, ls — no bash, edit, write
});

For fine-grained control, provide explicit customTools:

import { createToolDefinition } from "@earendil-works/pi-coding-agent";

const { session } = await createAgentSession({
  // ...
  customTools: [
    createToolDefinition("read", "/tmp/sandbox-workspace"),
    createToolDefinition("grep", "/tmp/sandbox-workspace"),
  ],
});

Use file-backed sessions for persistence

Replace SessionManager.inMemory() with a file-backed session to persist conversation state across process restarts:

const sessionManager = SessionManager.create("/path/to/sessions-dir");

const { session } = await createAgentSession({
  sessionManager,
  // ...
});

The session manager writes an append-only JSONL file. On restart, it replays entries to reconstruct state.

Inject a custom system prompt

const { session } = await createAgentSession({
  // ...
  systemPrompt: "You are a security auditor. Only analyze code for vulnerabilities.",
  appendSystemPrompt: ["Never execute destructive commands."],
});

systemPrompt replaces the default. appendSystemPrompt adds additional rules after the default system prompt.

---

How to Steer and Follow Up During a Run

Steer the agent mid-turn

Steering messages are injected after the current assistant turn finishes, before the next LLM call. Use this to redirect the agent while it is working.

// While the agent is streaming...
session.steer("Focus on the authentication module, not the UI.");

Queue a follow-up

Follow-up messages are delivered only after the agent would otherwise stop (no more tool calls, no steering messages). Use this for chained tasks.

session.followUp("Now write unit tests for the code you just created.");

Control queue draining

By default, all queued messages drain at once. To process them one at a time:

session.steeringMode = "one-at-a-time";
session.followUpMode = "one-at-a-time";

Abort a running agent

session.abort();

This sends an abort signal to the active run. Tool executions receive the signal and should terminate gracefully. The agent emits agent_end after abort.

Wait for the agent to finish

await session.waitForIdle();
// Safe to read state, send new prompts, or shut down

---

How to Register Custom Tools and Extensions

Register a tool via extension factory

If using createAgentSession with extensions enabled, provide an ExtensionFactory:

import { Type } from "typebox";
import type { ExtensionFactory } from "@earendil-works/pi-coding-agent";

const myExtension: ExtensionFactory = (pi) => {
  pi.registerTool({
    name: "count_lines",
    label: "Count Lines",
    description: "Counts lines in a file",
    parameters: Type.Object({
      filePath: Type.String({ description: "Path to the file" }),
    }),
    async execute(toolCallId, params, signal) {
      const { execSync } = await import("node:child_process");
      const count = execSync(`wc -l < "${params.filePath}"`)
        .toString()
        .trim();
      return {
        content: [{ type: "text", text: `${count} lines` }],
        details: { lineCount: parseInt(count) },
      };
    },
  });
};

Hook into lifecycle events

const auditExtension: ExtensionFactory = (pi) => {
  pi.on("tool_call", async (event, ctx) => {
    if (event.toolName === "bash" && event.input.command.includes("rm ")) {
      return { block: true, reason: "Destructive commands are blocked." };
    }
  });

  pi.on("agent_end", async (event, ctx) => {
    console.log(`Agent completed. ${event.messages.length} messages produced.`);
  });
};

Send messages from extensions

const notifierExtension: ExtensionFactory = (pi) => {
  pi.on("tool_execution_end", async (event, ctx) => {
    if (event.isError) {
      pi.sendMessage({
        customType: "error_notification",
        content: `Tool ${event.toolName} failed.`,
        display: `⚠️ ${event.toolName} error`,
      });
    }
  });
};

---

📖 Reference

Core API Reference

createAgentSession(options)

Factory function. Returns { session: AgentSession }.

Parameter	Type	Required	Description
sessionManager	SessionManager	Yes	Session persistence backend. Use SessionManager.inMemory() or SessionManager.create(dir).
authStorage	AuthStorage	Yes	Credential storage. Created via AuthStorage.create().
modelRegistry	ModelRegistry	Yes	Model resolution registry. Created via ModelRegistry.create(authStorage).
model	Model<any>	Yes	Initial model. Obtained via getModel(provider, modelId) from @earendil-works/pi-ai.
cwd	string	No	Working directory for tool execution. Defaults to process.cwd().
systemPrompt	string	No	Replaces the default system prompt.
appendSystemPrompt	string[]	No	Additional system prompt sections appended after the default.
customTools	ToolDefinition[]	No	Explicit tool set. Overrides default tools when provided.
readOnlyTools	boolean	No	When true, registers only read, grep, find, ls.
extensionFactories	ExtensionFactory[]	No	Extension factory functions to load at session creation.
settingsManager	SettingsManager	No	Settings backend. Created via SettingsManager.inMemory() or SettingsManager.create(cwd).
resourceLoader	ResourceLoader	No	Custom resource loader for skills, prompts, themes, and extensions.

---

AgentSession

Primary interface for controlling the agent. Extends Agent from @earendil-works/pi-agent-core.

Properties

Property	Type	Description
sessionId	string	Unique session identifier.
state	AgentState	Current agent state (system prompt, model, tools, messages, streaming status).
state.isStreaming	boolean	true while the agent is processing a prompt or continuation.
state.messages	AgentMessage[]	Current conversation transcript.
state.tools	AgentTool[]	Currently registered tools.
state.pendingToolCalls	ReadonlySet<string>	Tool call IDs currently executing.
state.errorMessage	string | undefined	Error from the most recent failed assistant turn.
signal	AbortSignal | undefined	Active abort signal for the current run.

Methods

Method	Signature	Description
prompt	(message: AgentMessage | AgentMessage[] | string, images?: ImageContent[]) => Promise<void>	Start a new agent turn. Accepts a string, single message, or batch.
continue	() => Promise<void>	Continue from the current transcript. Last message must be user or tool-result.
subscribe	(listener: (event: AgentEvent, signal: AbortSignal) => Promise<void> | void) => () => void	Subscribe to lifecycle events. Returns unsubscribe function.
steer	(message: AgentMessage) => void	Queue a message for injection after the current turn.
followUp	(message: AgentMessage) => void	Queue a message that runs after the agent would otherwise stop.
abort	() => void	Abort the current run.
waitForIdle	() => Promise<void>	Resolve when the current run and all event listeners settle.
reset	() => void	Clear transcript, runtime state, and queued messages.
clearSteeringQueue	() => void	Remove all queued steering messages.
clearFollowUpQueue	() => void	Remove all queued follow-up messages.
clearAllQueues	() => void	Remove all queued messages.
hasQueuedMessages	() => boolean	true when either queue has pending messages.
cycleModel	() => void	Cycle to the next model in the scoped model set.

Queue Modes

Property	Type	Default	Description
steeringMode	"all" | "one-at-a-time"	"all"	Controls how steering messages drain.
followUpMode	"all" | "one-at-a-time"	"all"	Controls how follow-up messages drain.

---

AgentState

Field	Type	Description
systemPrompt	string	System prompt sent with each model request.
model	Model<any>	Active model.
thinkingLevel	ThinkingLevel	Requested reasoning level ("off", "minimal", "low", "medium", "high", "xhigh").
tools	AgentTool[]	Available tools. Assigning copies the array.
messages	AgentMessage[]	Conversation transcript. Assigning copies the array.
isStreaming	boolean (readonly)	true during prompt processing.
streamingMessage	AgentMessage | undefined (readonly)	Partial assistant message during streaming.
pendingToolCalls	ReadonlySet<string> (readonly)	IDs of currently executing tool calls.
errorMessage	string | undefined (readonly)	Error from last failed turn.

---

AgentEvent (Union)

Events emitted by Agent.subscribe().

Event Type	Key Fields	Timing
agent_start	—	Agent loop begins.
agent_end	messages: AgentMessage[]	Agent loop ends. Last event emitted per run.
turn_start	—	Individual LLM turn begins.
turn_end	message: AgentMessage, toolResults: ToolResultMessage[]	LLM turn ends.
message_start	message: AgentMessage	Message begins (user, assistant, or tool result).
message_update	message: AgentMessage, assistantMessageEvent: AssistantMessageEvent	Token-by-token streaming delta.
message_end	message: AgentMessage	Message finishes.
tool_execution_start	toolCallId, toolName, args	Tool begins executing.
tool_execution_update	toolCallId, toolName, args, partialResult	Partial/streaming tool output.
tool_execution_end	toolCallId, toolName, result, isError	Tool finishes.

---

SessionManager

Manages append-only conversation history as a tree of entries.

Static Constructors

Method	Description
SessionManager.create(sessionDir)	File-backed session manager. Writes JSONL to sessionDir.
SessionManager.inMemory()	In-memory session manager. No file I/O.

Key Methods

Method	Signature	Description
appendEntry	(entry) => void	Append a session entry to the current branch.
getCurrentBranch	() => SessionEntry[]	Get the linear history of the active branch.
getTree	() => SessionEntry[]	Get all entries across all branches.
navigateTo	(entryId) => void	Navigate to a specific entry in the tree.
fork	(entryId, position?) => string	Create a branch from a specific entry. Returns new session file path.
exportToHtml	() => string	Export current branch to HTML.
exportToJsonl	() => string	Export current branch to JSONL.

Entry Types

Type	Description
UserEntry	User message.
AssistantEntry	Assistant response.
ToolCallEntry	Tool call from assistant.
ToolResultEntry	Result from tool execution.
CompactionEntry	Summary created by context compaction.
BranchSummaryEntry	Summary created when navigating branches.
CustomEntry	Extension-defined custom data.

---

AgentSessionRuntime

Orchestrates session transitions and CWD-bound services.

Method	Signature	Description
switchSession	(sessionPath, options?) => Promise<{cancelled}>	Switch to a different session file.
forkSession	(entryId, options?) => Promise<{cancelled}>	Fork from a specific entry.
newSession	(options?) => Promise<{cancelled}>	Start a new session.
navigateTree	(targetId, options?) => Promise<{cancelled}>	Navigate to a point in the session tree.
shutdown	() => void	Gracefully shut down the runtime.

---

ModelRegistry

Resolves API keys and manages provider configurations.

Method	Description
ModelRegistry.create(authStorage)	Create a registry bound to credential storage.
getApiKey(provider)	Resolve the API key for a provider.
getModel(provider, modelId)	Get a model instance.

---

AuthStorage

Persistent credential storage.

Method	Description
AuthStorage.create()	Create default credential storage (typically ~/.pi/auth).

---

SettingsManager

Manages global and project-level settings.

Constructor	Description
SettingsManager.create(cwd, agentDir?)	File-backed settings from .pi/settings.json.
SettingsManager.inMemory(settings?)	In-memory settings (no file I/O).
SettingsManager.fromStorage(storage)	Custom storage backend.

Key settings categories: compaction, retry, transport, thinking budgets, terminal display, model filtering, shell configuration.

---

Extension System Reference

ExtensionFactory

type ExtensionFactory = (pi: ExtensionAPI) => void | Promise<void>;

A factory function that receives the ExtensionAPI and registers handlers, tools, commands, and shortcuts.

---

ExtensionAPI

The registration surface available inside ExtensionFactory.

Event Registration

pi.on(eventName, handler)

Event Name	Event Type	Result Type	Description
session_start	SessionStartEvent	void	Session started, loaded, or reloaded. reason: "startup", "reload", "new", "resume", "fork".
session_shutdown	SessionShutdownEvent	void	Extension runtime tearing down. reason: "quit", "reload", "new", "resume", "fork".
session_before_switch	SessionBeforeSwitchEvent	SessionBeforeSwitchResult	Before switching sessions. Return { cancel: true } to block.
session_before_fork	SessionBeforeForkEvent	SessionBeforeForkResult	Before forking. Return { cancel: true } to block.
session_before_compact	SessionBeforeCompactEvent	SessionBeforeCompactResult	Before context compaction. Return { cancel: true } or provide custom compaction.
session_before_tree	SessionBeforeTreeEvent	SessionBeforeTreeResult	Before tree navigation. Return { cancel: true } to block.
session_compact	SessionCompactEvent	void	After compaction completes.
session_tree	SessionTreeEvent	void	After tree navigation completes.
context	ContextEvent	ContextEventResult	Before each LLM call. Can modify messages.
before_agent_start	BeforeAgentStartEvent	BeforeAgentStartEventResult	After prompt submitted, before agent loop. Can replace system prompt.
before_provider_request	BeforeProviderRequestEvent	Payload	Before provider HTTP request. Can replace payload.
after_provider_response	AfterProviderResponseEvent	void	After provider HTTP response.
agent_start	AgentStartEvent	void	Agent loop starts.
agent_end	AgentEndEvent	void	Agent loop ends.
turn_start	TurnStartEvent	void	LLM turn starts.
turn_end	TurnEndEvent	void	LLM turn ends.
message_start	MessageStartEvent	void	Message begins.
message_update	MessageUpdateEvent	void	Token streaming delta.
message_end	MessageEndEvent	MessageEndEventResult	Message ends. Can replace finalized message.
tool_execution_start	ToolExecutionStartEvent	void	Tool begins executing.
tool_execution_update	ToolExecutionUpdateEvent	void	Partial tool output.
tool_execution_end	ToolExecutionEndEvent	void	Tool finishes.
tool_call	ToolCallEvent	ToolCallEventResult	Before tool executes. Can block. Input is mutable.
tool_result	ToolResultEvent	ToolResultEventResult	After tool executes. Can modify result.
input	InputEvent	InputEventResult	User input received. Can transform or handle.
user_bash	UserBashEvent	UserBashEventResult	User executes ! command. Can intercept.
model_select	ModelSelectEvent	void	Model changed.
thinking_level_select	ThinkingLevelSelectEvent	void	Thinking level changed.
resources_discover	ResourcesDiscoverEvent	ResourcesDiscoverResult	Discovery phase. Return additional skill/prompt/theme paths.

Tool Registration

pi.registerTool(toolDefinition)

Command & Shortcut Registration

pi.registerCommand(name, { description?, handler, getArgumentCompletions? })
pi.registerShortcut(keyId, { description?, handler })
pi.registerFlag(name, { description?, type, default? })

Messaging

pi.sendMessage(message, options?)    // Custom message
pi.sendUserMessage(content, options?) // User message (triggers turn)
pi.appendEntry(customType, data?)     // Persistence-only entry

Provider Registration

pi.registerProvider(name, config)
pi.unregisterProvider(name)

State Queries

pi.getFlag(name)              // Get CLI flag value
pi.getActiveTools()           // Get active tool names
pi.getAllTools()               // Get all tools with metadata
pi.setActiveTools(toolNames)  // Set active tools
pi.getCommands()              // Get slash commands
pi.setModel(model)            // Set current model
pi.getThinkingLevel()         // Get thinking level
pi.setThinkingLevel(level)    // Set thinking level

---

ExtensionContext

Context passed to event handlers. Provides read access to session state and control methods.

Field / Method	Type	Description
ui	ExtensionUIContext	UI interaction methods (select, confirm, input, notify).
hasUI	boolean	false in headless/RPC mode.
cwd	string	Current working directory.
sessionManager	ReadonlySessionManager	Read-only session manager.
modelRegistry	ModelRegistry	Model registry for key resolution.
model	Model<any> | undefined	Current model.
isIdle()	() => boolean	true when not streaming.
signal	AbortSignal | undefined	Active abort signal.
abort()	() => void	Abort current operation.
hasPendingMessages()	() => boolean	true when messages are queued.
shutdown()	() => void	Graceful shutdown.
getContextUsage()	() => ContextUsage | undefined	Token usage: { tokens, contextWindow, percent }.
compact(options?)	() => void	Trigger context compaction.
getSystemPrompt()	() => string	Current effective system prompt.

---

ToolDefinition<TParams, TDetails, TState>

Schema for registering a custom LLM-callable tool.

Field	Type	Required	Description
name	string	Yes	Tool name used in LLM tool calls.
label	string	Yes	Human-readable label.
description	string	Yes	Description sent to the LLM.
parameters	TSchema	Yes	TypeBox parameter schema.
execute	(toolCallId, params, signal?, onUpdate?) => Promise<AgentToolResult>	Yes	Execution function. Throw on failure.
promptSnippet	string	No	One-line snippet for system prompt "Available tools" section.
promptGuidelines	string[]	No	Guideline bullets appended to system prompt.
executionMode	"sequential" | "parallel"	No	Per-tool execution mode override.
prepareArguments	(args) => Static<TParams>	No	Compatibility shim for raw args before validation.
renderCall	(args, theme, context) => Component	No	Custom rendering for tool call display.
renderResult	(result, options, theme, context) => Component	No	Custom rendering for tool result display.

---

AgentToolResult<T>

Return value from tool execution.

Field	Type	Description
content	(TextContent | ImageContent)[]	Content returned to the model.
details	T	Structured details for logs or UI rendering.
terminate	boolean?	Hint that the agent should stop after this tool batch. Only effective when all tools in the batch set this to true.

---

Tool System Reference

Built-in Tools

Tool Name	Factory	Description
bash	createBashTool(cwd, options?)	Execute shell commands.
read	createReadTool(cwd, options?)	Read file contents with line ranges.
edit	createEditTool(cwd, options?)	Apply targeted edits to files.
write	createWriteTool(cwd, options?)	Write or overwrite files.
grep	createGrepTool(cwd, options?)	Search file contents with regex.
find	createFindTool(cwd, options?)	Find files by name/pattern.
ls	createLsTool(cwd, options?)	List directory contents.

Tool Factory Functions

Function	Returns	Description
createCodingTools(cwd, options?)	Tool[]	All tools: bash, read, edit, write, grep, find, ls.
createReadOnlyTools(cwd, options?)	Tool[]	Read-only: read, grep, find, ls.
createAllTools(cwd, options?)	Record<ToolName, Tool>	All tools as a named record.
createToolDefinition(name, cwd, options?)	ToolDef	Single tool definition by name.
createCodingToolDefinitions(cwd, options?)	ToolDef[]	All coding tool definitions.
createReadOnlyToolDefinitions(cwd, options?)	ToolDef[]	Read-only tool definitions.

Tool Execution Modes

Mode	Behavior
"sequential"	Each tool call is prepared, executed, and finalized before the next starts.
"parallel"	Tool calls are prepared sequentially, then allowed tools execute concurrently. tool_execution_end emits in completion order. Tool-result message artifacts emit in assistant source order.

AgentLoopConfig Hooks

Hook	Signature	Description
convertToLlm	(messages: AgentMessage[]) => Message[]	Required. Transforms AgentMessage[] to LLM-compatible Message[] before each call.
transformContext	(messages, signal?) => Promise<AgentMessage[]>	Optional pre-convertToLlm transform (pruning, injection).
getApiKey	(provider) => string | undefined	Dynamic API key resolution per call.
shouldStopAfterTurn	(context) => boolean	Graceful stop after current turn.
getSteeringMessages	() => Promise<AgentMessage[]>	Return steering messages for injection.
getFollowUpMessages	() => Promise<AgentMessage[]>	Return follow-up messages for continuation.
beforeToolCall	(context, signal?) => Promise<BeforeToolCallResult>	Gate tool execution. Return { block: true } to prevent.
afterToolCall	(context, signal?) => Promise<AfterToolCallResult>	Modify tool results after execution.

---

ResourceLoader / DefaultResourceLoader

Loads extensions, skills, prompt templates, themes, and project context files.

Option	Type	Description
cwd	string	Project root directory.
agentDir	string	Agent configuration directory (e.g., .pi).
extensionFactories	ExtensionFactory[]	Programmatic extensions.
noExtensions	boolean	Disable extension loading.
noSkills	boolean	Disable skill loading.
noPromptTemplates	boolean	Disable prompt template loading.
noThemes	boolean	Disable theme loading.
noContextFiles	boolean	Disable context file loading.
systemPrompt	string	Override system prompt.
appendSystemPrompt	string[]	Append to system prompt.
extensionsOverride	(base) => LoadExtensionsResult	Transform loaded extensions.
skillsOverride	(base) => { skills, diagnostics }	Transform loaded skills.

---

💡 Explanation

Explanation: The Agent Lifecycle

What happens when you call session.prompt()?

Because the Pi SDK is designed for autonomous operation, understanding the lifecycle is essential for building predictable, debuggable pipelines. The lifecycle is not a simple request-response — it is a multi-turn loop with interleaving tool execution, steering injection, and follow-up continuation.

flowchart TD
    A["prompt()"] --> B["agent_start"]
    B --> C["turn_start"]
    C --> D["LLM Call"]
    D --> E["message_start → message_update* → message_end"]
    E --> F{"Tool calls?"}
    F -->|Yes| G["tool_execution_start → tool_execution_end"]
    G --> H["turn_end"]
    H --> I{"shouldStopAfterTurn?"}
    I -->|Yes| J["agent_end"]
    I -->|No| K{"Steering messages?"}
    K -->|Yes| L["Inject steering → turn_start"]
    L --> D
    K -->|No| M{"Follow-up messages?"}
    M -->|Yes| N["Inject follow-up → turn_start"]
    N --> D
    M -->|No| J
    F -->|No| H

The turn loop

A single prompt() call can produce multiple turns. Each turn consists of one LLM call and zero or more tool executions. The agent continues looping until one of three exit conditions is met:

1. Natural stop: The LLM produces a response with no tool calls, and no messages are queued.
2. Graceful stop: shouldStopAfterTurn returns true.
3. Abort: The abort signal is triggered via session.abort().

Tool execution within a turn

When the LLM response contains tool calls, they are executed according to the toolExecution mode:

• Parallel (default): Tool calls are prepared sequentially (for argument validation and beforeToolCall hooks), then executed concurrently. Results are emitted in completion order for tool_execution_end, but tool-result messages are assembled back in the original assistant source order for the next LLM context.
• Sequential: Tool calls are fully processed one at a time.

Individual tools can override this via their executionMode field. A tool marked "sequential" in a parallel batch causes the entire batch to fall back to sequential mode.

Steering vs. follow-up: the two queues

The agent maintains two distinct message queues, and the distinction is critical for autonomous workflows:

• Steering queue: Drained between turns. If the agent just finished a turn and there are steering messages, they are injected into the context and the agent starts a new LLM call immediately. This is for redirecting the agent while it works.
• Follow-up queue: Drained only when the agent would otherwise stop. If there are no tool calls and no steering messages, the follow-up queue is checked. This is for chaining sequential tasks.

Both queues support "all" mode (drain everything at once) and "one-at-a-time" mode (drain one message, then re-enter the turn loop).

Event emission order

For a typical multi-tool turn, the precise emission order is:

agent_start
  turn_start
    message_start (assistant)
    message_update* (streaming deltas)
    message_end (assistant)
    tool_execution_start (tool A)
    tool_execution_start (tool B)   // parallel mode
    tool_execution_end (whichever finishes first)
    tool_execution_end (the other)
  turn_end
  // steering check → follow-up check
  turn_start   // if messages queued
    ...
  turn_end
agent_end

The agent_end contract

agent_end is always the last event. However, the agent does not become idle until all awaited agent_end listeners have settled. This means a subscriber can perform async cleanup in its agent_end handler and the caller's await session.prompt() or await session.waitForIdle() will wait for it.

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Explanation: Session Tree & Persistence

Why sessions are trees, not arrays

A naive conversation store would be a flat array of messages. The Pi SDK uses a tree for a reason: it enables branching. When a user (or an autonomous agent) realizes a conversation path was wrong, they can fork from any earlier point without losing the original history.

Each session entry has a unique ID and a parent ID. The "current conversation" is always a linear path from the root to the current leaf. Branching creates a sibling entry that starts a new path.

graph TD
    A["Root"] --> B["User: Analyze this file"]
    B --> C["Assistant: I see a bug in line 42"]
    C --> D["User: Fix it"]
    C --> E["User: Actually, focus on tests"]
    D --> F["Assistant: Fixed"]
    E --> G["Assistant: Writing tests..."]

In this tree, entries D/F and E/G are separate branches from the same parent C. The session manager tracks which leaf is active.

JSONL: the append-only format

Session files are JSONL (one JSON object per line). Every mutation is an append — entries are never modified or deleted. This makes the format:

1. Crash-safe: A partial write corrupts at most the last line.
2. Streamable: Entries can be tailed in real time.
3. Auditable: The full history is always available.

On load, the session manager replays all entries to reconstruct the tree in memory.

Context compaction

As conversations grow, they exceed the model's context window. The compaction system addresses this:

1. The session_before_compact event fires, giving extensions a chance to cancel or provide custom compaction.
2. Old messages are summarized into a single CompactionEntry.
3. The summary replaces the old messages in the active branch's context, but the original entries remain in the JSONL file.

Extensions can control compaction via settings: reserveTokens (how many tokens to keep free), keepRecentTokens (how many recent tokens to preserve verbatim).

Branch summaries

When navigating from one branch to another, the session can optionally produce a BranchSummaryEntry — a summary of the branch being left. This preserves context without carrying the full history into the new branch.

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Explanation: The Three-Package Stack

Why three packages?

The SDK is split into three NPM packages, each at a different abstraction level. Understanding this layering is essential for knowing which imports to use and where extension points live.

graph BT
    A["@earendil-works/pi-ai<br/><i>Model catalog, message protocol, streaming</i>"] --> B["@earendil-works/pi-agent-core<br/><i>Agent loop, state, events, tool execution</i>"]
    B --> C["@earendil-works/pi-coding-agent<br/><i>Session orchestration, tools, extensions, persistence</i>"]

@earendil-works/pi-ai — The foundation

This is the model-agnostic abstraction layer. It defines:

• Model<Api>: A model instance with provider, ID, context window size, cost, and capabilities.
• Message: The union of UserMessage, AssistantMessage, and ToolResultMessage — the canonical LLM message protocol.
• AssistantMessageEvent: Streaming events (text_delta, thinking_delta, tool_call_start, etc.).
• streamSimple: The low-level function that sends a context to a provider and returns an event stream.
• getModel(provider, id): Model catalog lookup.

This package knows nothing about agents, sessions, or tools. It is pure model interaction.

@earendil-works/pi-agent-core — The agent loop

This package adds autonomous behavior on top of pi-ai:

• Agent: The stateful wrapper that owns the transcript, executes tools, and emits lifecycle events.
• agentLoop / agentLoopContinue: The low-level loop that drives the LLM → tools → LLM cycle.
• AgentTool: The tool interface with execute(), parameter schemas, and execution mode.
• AgentEvent: The event union (agent_start, turn_start, message_update, tool_execution_end, etc.).
• Steering and follow-up queues: The two-queue message injection system.

This package has no concept of sessions, files, or extensions. It is a pure in-memory agent runtime.

@earendil-works/pi-coding-agent — The orchestration layer

This is the package you import in application code. It composes the lower layers into a complete system:

• createAgentSession: The factory that wires everything together — model registry, session manager, tools, extensions.
• AgentSession: Extends Agent with session management, model cycling, and runtime services.
• AgentSessionRuntime: Manages session transitions (switch, fork, new) and CWD-bound services.
• SessionManager: The append-only tree persistence layer.
• ExtensionRunner / ExtensionAPI: The extension system that allows dynamic tool registration, event hooks, and command definitions.
• Built-in tools: bash, read, edit, write, grep, find, ls — all CWD-scoped.
• ResourceLoader: Discovers and loads extensions, skills, prompts, and themes from the filesystem.
• SettingsManager: Global and project-level configuration.

The data flow

When session.prompt("Fix the bug") is called:

1. pi-coding-agent assembles the system prompt (from settings, skills, context files), converts the prompt string to an AgentMessage, and passes it to the Agent.
2. pi-agent-core enters the agent loop: it calls convertToLlm to transform AgentMessage[] to Message[], then invokes streamSimple.
3. pi-ai sends the HTTP request to the provider, streams back AssistantMessageEvent deltas.
4. pi-agent-core assembles the deltas into an AssistantMessage. If it contains tool calls, it executes them via AgentTool.execute().
5. pi-coding-agent provides the tool implementations (bash runs a shell command, read opens a file, etc.), fires extension events (tool_call, tool_result), and persists everything via SessionManager.
6. Control returns to step 2 for the next turn, or the loop exits.

This layering means you can use pi-ai alone for simple model calls, pi-agent-core for custom agent loops without the coding-agent opinion, or pi-coding-agent for the full batteries-included experience.