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Overview

Pi is a modular AI agent toolkit framework built as a monorepo with clear separation of concerns. The architecture follows a layered approach where higher-level packages build on foundational abstractions.

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Package Structure

packages/
├── ai/              # LLM provider abstraction
├── agent/           # Core agent loop and types
├── coding-agent/    # Agent runtime with tools and sessions
└── tui/             # Terminal UI components

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Core Layers

The framework is organized into three main layers: 
┌─────────────────────────────────────┐
│      coding-agent (Runtime)         │
│  - AgentSession                     │
│  - Tools (bash, read, edit, write)  │
│  - SessionManager                   │
│  - ExtensionSystem                  │
│  - Compaction                       │
└─────────────────────────────────────┘
           ↓ uses
┌─────────────────────────────────────┐
│        agent (Loop Logic)           │
│  - Agent class                      │
│  - agentLoop / agentLoopContinue    │
│  - Message types                    │
│  - Tool execution                   │
└─────────────────────────────────────┘
           ↓ uses
┌─────────────────────────────────────┐
│      ai (LLM Providers)             │
│  - streamSimple                     │
│  - Provider implementations         │
│  - Model registry                   │
│  - Token counting                   │
└─────────────────────────────────────┘

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Key Classes

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Agent (@mariozechner/pi-agent-core)

The Agent class orchestrates LLM interactions with tool execution: 
import { Agent } from "@mariozechner/pi-agent-core";
import { getModel } from "@mariozechner/pi-ai";

const agent = new Agent({
  initialState: {
    model: getModel("anthropic", "claude-sonnet-4-20250514"),
    systemPrompt: "You are a helpful coding assistant.",
    tools: [readTool, bashTool, editTool, writeTool],
    thinkingLevel: "medium",
  },
  convertToLlm: (messages) => messages.filter(
    m => m.role === "user" || m.role === "assistant" || m.role === "toolResult"
  ),
});

// Subscribe to events
agent.subscribe((event) => {
  if (event.type === "message_update") {
    console.log("Token:", event.assistantMessageEvent);
  } else if (event.type === "tool_execution_start") {
    console.log("Tool call:", event.toolName, event.args);
  }
});

// Send prompts
await agent.prompt("List all TypeScript files");
Key responsibilities:
  • Execute the agent loop (LLM → tool calls → tool results → LLM)
  • Manage streaming state
  • Handle tool execution
  • Support steering (interrupt mid-run) and follow-up messages
  • Provider-agnostic LLM interaction
Location: packages/agent/src/agent.ts:96

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AgentSession (@mariozechner/pi-coding-agent)

Builds on Agent to add session persistence, compaction, and extension support: 
import { AgentSession } from "@mariozechner/pi-coding-agent";
import { SessionManager } from "@mariozechner/pi-coding-agent";

const sessionManager = SessionManager.create(process.cwd());
const agentSession = new AgentSession({
  agent,
  sessionManager,
  settingsManager,
  cwd: process.cwd(),
  resourceLoader,
  modelRegistry,
});

// Prompt with automatic session persistence
await agentSession.prompt("Read package.json");

// Manual compaction
const result = await agentSession.compact();

// Switch to a different session file
await agentSession.switchSession("path/to/session.jsonl");
Key responsibilities:
  • Session lifecycle (new, resume, fork, branch)
  • Automatic message persistence to JSONL
  • Context compaction when approaching context limits
  • Extension system integration
  • Model/thinking level management
  • Resource loading (skills, prompts, themes)
Location: packages/coding-agent/src/core/agent-session.ts:213

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SessionManager (@mariozechner/pi-coding-agent)

Manages append-only session trees stored as JSONL: 
import { SessionManager } from "@mariozechner/pi-coding-agent";

// Create new session
const session = SessionManager.create(process.cwd());

// Append messages
session.appendMessage({
  role: "user",
  content: [{ type: "text", text: "Hello" }],
  timestamp: Date.now(),
});

// Branch from earlier entry
session.branch(entryId);

// Build context for LLM
const { messages, thinkingLevel, model } = session.buildSessionContext();
Session structure:
  • Append-only JSONL file
  • Tree structure via id/parentId fields
  • Leaf pointer tracks current position
  • Supports branching without modifying history
Location: packages/coding-agent/src/core/session-manager.ts:663

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Message Flow

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User Input → Agent Response

1. User sends prompt
   ↓
2. AgentSession.prompt()
   - Expands templates/skills
   - Validates model & API key
   - Calls Agent.prompt()
   ↓
3. Agent.prompt()
   - Queues message(s)
   - Starts agent loop
   ↓
4. agentLoop()
   - Converts messages via convertToLlm
   - Calls streamSimple (LLM provider)
   - Streams response tokens
   ↓
5. Tool calls extracted
   - Agent executes tools sequentially
   - Each tool returns AgentToolResult
   - Results appended as toolResult messages
   ↓
6. Loop continues if more tool calls
   - Or stops if no tools / max iterations
   ↓
7. SessionManager persists messages
   - Appends to JSONL file
   - Updates tree structure

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Event System

The framework emits events at multiple levels:

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Agent Events

agent.subscribe((event) => {
  switch (event.type) {
    case "agent_start":
      // Agent loop started
      break;
    case "turn_start":
      // New turn (LLM call) started
      break;
    case "message_start":
      // Message started (user, assistant, or toolResult)
      break;
    case "message_update":
      // Token streamed (assistant only)
      break;
    case "message_end":
      // Message completed
      break;
    case "tool_execution_start":
      // Tool started executing
      break;
    case "tool_execution_end":
      // Tool completed
      break;
    case "turn_end":
      // Turn completed
      break;
    case "agent_end":
      // Agent loop finished
      break;
  }
});
Location: packages/agent/src/types.ts:177

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Extension Events

Extensions can hook into additional lifecycle events: 
pi.on("session_start", async (event, ctx) => {
  // Initialize extension state
});

pi.on("before_agent_start", async (event, ctx) => {
  // Inject context before LLM call
  return {
    message: { customType: "context", content: "...", display: true },
  };
});

pi.on("tool_call", async (event, ctx) => {
  // Intercept tool calls
  if (event.toolName === "bash" && event.input.command.includes("rm -rf")) {
    return { block: true, reason: "Dangerous command" };
  }
});
Location: packages/coding-agent/src/core/extensions/types.ts:797

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Dependency Flow

Packages depend on each other as follows: 
coding-agent
  ├─> @mariozechner/pi-agent-core
  ├─> @mariozechner/pi-ai
  └─> @mariozechner/pi-tui

agent
  └─> @mariozechner/pi-ai

ai
  └─> (no internal dependencies)

tui
  └─> (no internal dependencies)
This ensures:
  • ai package is provider-agnostic and reusable
  • agent package has pure agent loop logic
  • coding-agent brings it all together with tools, sessions, and UI
  • tui provides terminal components for interactive mode

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Configuration

Configuration flows from multiple sources: 
CLI flags
  ↓
Environment variables (.env)
  ↓
Settings file (~/.pi/agent/settings.jsonl)
  ↓
Defaults
Each layer overrides the previous. Extensions can register custom CLI flags: 
pi.registerFlag("my-flag", {
  type: "string",
  description: "Custom flag for my extension",
  default: "value",
});

const value = pi.getFlag("my-flag");
Location: packages/coding-agent/src/core/settings-manager.ts

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Next Steps

Tools

Learn about the tool system

Extensions

Build custom extensions

Sessions

Session management and branching

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