pi-mono/packages/durable

@earendil-works/pi-durable

Experimental. The API changes without notice between releases.

A durable agent harness. Conversations, model turns, tool calls, and your own state are committed to storage before anything is shown. If the process dies mid-turn, reopening the storage picks the work up where it stopped.

Built on @earendil-works/pi-ai for model access and @earendil-works/chord for document state.

Table of Contents

• Installation
• Quick Start
• Concepts
• Persist and Resume
• Tools
• System Prompt
• Conversation Settings
• Watching a Conversation
• Busy Conversations
• Reset and Handoff
• Compaction
• Agent Events (Experimental)
• Hooks
• More Conversations and Forks
• Abort and Subagents
• Child Tasks
• Your Own State
• Usage and Cost
• Storage
• Examples
• Design Documents

Installation

npm install @earendil-works/pi-durable @earendil-works/pi-ai @earendil-works/chord

Quick Start

import { BACKGROUND_CONTEXT } from "@earendil-works/chord/context";
import { createModels } from "@earendil-works/pi-ai/models";
import { openaiProvider } from "@earendil-works/pi-ai/providers/openai";
import { AssistantEntry, createRegistry, Harness, MemoryStorage } from "@earendil-works/pi-durable";

const context = BACKGROUND_CONTEXT;

const models = createModels();
models.setProvider(openaiProvider()); // reads OPENAI_API_KEY

const harness = await Harness.open(new MemoryStorage(), { models, registry: createRegistry() }, context);
const root = await harness.root(context);
await root.setModel({ provider: "openai", modelId: "gpt-6-sol" }, context);

const submission = await root.submit({ type: "input", content: "What is the capital of France?" }, context);
const settled = await submission.wait(context);
if (settled.status === "done" && settled.type === "input") {
	const answer = await root.commit((tx) => tx.entry(AssistantEntry, settled.answer), context);
	console.log(answer?.model?.[0]);
}
await harness.close(context);

What happened:

• Harness.open() opens a Session over a storage backend. MemoryStorage keeps everything in memory.
• root() returns the root conversation, creating it on first use. A conversation is a transcript of immutable entries.
• submit() durably admits your input and returns a Submission. A built-in generation task calls the model and appends the answer.
• wait() resolves once the input is answered (done) or has failed (unanswered, with a reason).

Every async call takes a Chord Context, which carries cancellation. BACKGROUND_CONTEXT never cancels. Cancelling a wait only cancels that wait, never the work.

Concepts

• Harness: one open storage plus the machinery that runs agents on it. All changes go through one line of atomic commits, and nothing is shown before its commit is stored.
• Conversation: a transcript. root() creates the root conversation on first use; you can create more and fork them. A Conversation handle holds no state; compare handles by id.
• Entry: one immutable transcript record, such as a user message (pi.user), a model response (pi.assistant), a tool result (pi.tool-result), a system prompt change (pi.system), a reset (pi.reset), or your own kind. The model sees the entries from the newest reset onward.
• Commit: an atomic write. conversation.commit((tx) => ...) can append entries, edit documents, and create tasks together; either all of it is stored or none of it.
• Document: typed JSON state stored next to the transcript and changed in commits. Built-in ones hold each conversation's settings (pi.conversation.config), the running generation and tools (pi.live), queued submissions (pi.inbox), and spend (pi.usage).
• Task: a durable state machine that saves a checkpoint at every step, so a restarted process continues from the last one. Every task has an owner: its conversation, or another task. The Harness runs answers as built-in tasks: pi.generation calls the model and owns the pi.tool tasks of its tool calls, waits for them, and hands the run to the next generation.
• Submission: something you hand to a conversation, either user input or an entry to write, which you can wait for.
• Turn and run: a turn is one model response and its tool calls; a run is the turns from an input to its final answer. A conversation is busy while a run is going.
• Registry: your tools, system prompt sections, hooks, and tasks. It can change while the Harness runs; new work uses the new state.

One answered input, as entries and tasks:

submit(input) → pi.user
  pi.generation → pi.system (only if the prompt or tools changed), pi.assistant (tool calls)
    pi.tool × n → pi.tool-result × n   (owned by the generation, which waits for them)
  pi.generation → pi.assistant (answer) → submission done

Persist and Resume

Use SQLite or JSONL storage to keep conversations across restarts:

import { openNodeSqliteStorage } from "@earendil-works/pi-durable/storage/sqlite/node";

const harness = await Harness.open(await openNodeSqliteStorage("./session.sqlite"), { models, registry }, context);
const root = await harness.root(context); // the same root as last time
harness.resume(); // continue any run the last process left unfinished

Work interrupted by a crash or close stays pending. resume() starts the task scheduler; submitting or waiting starts it too. A retried submission with the same requestId returns the existing submission instead of submitting twice:

const submission = await root.submit({ type: "input", content: "Hello", requestId: "greeting-1" }, context);
// After a restart: the same request ID finds the same submission.
const again = await root.submit({ type: "input", content: "Hello", requestId: "greeting-1" }, context);
// again.id === submission.id

harness.submission(id) reacquires a submission by ID, for example to wait for it after a restart.

Tools

@earendil-works/pi-durable/tools provides read, bash, edit, and write. They touch files and processes only through the execution environment passed as env:

import { NodeExecutionEnv } from "@earendil-works/pi-durable/env/node";
import { createBashTool, createEditTool, createReadTool, createWriteTool } from "@earendil-works/pi-durable/tools";

const registry = createRegistry();
for (const tool of [createReadTool(), createBashTool(), createEditTool(), createWriteTool()]) registry.tools.add(tool);
const env = new NodeExecutionEnv({ cwd: process.cwd() });
const harness = await Harness.open(storage, { models, registry, env }, context);

A new conversation activates every tool registered when it is created. Reading images is not supported yet.

Define your own tool with a TypeBox schema. api.output() streams running output, which becomes the result when execute() returns no content:

import { Type } from "@earendil-works/pi-ai";

registry.tools.add({
	name: "count",
	description: "Count from 1 to n",
	parameters: Type.Object({ n: Type.Number() }),
	execute: async (args, api) => {
		for (let i = 1; i <= (args as { n: number }).n; i++) api.output(`${i}\n`);
		return {};
	},
});

Each call runs as its own durable task. Its intent is committed before execute() runs. If the process dies mid-call, the tool reruns on reopen only when it is declared replay: "safe"; otherwise the model gets an interrupted error result with the output committed so far. Throwing from execute() gives the model an error result. A result can also return usage, which is added to the conversation's usage. It can also return control: { terminate: true }: when every result of the round asks for it, the run ends without another model request.

System Prompt

The system prompt is built from named sections, rendered before each request:

registry.systemPrompt.section("preamble", () => "You are a concise coding assistant.", { tag: false });
registry.systemPrompt.section("cwd", () => process.cwd()); // rendered as <cwd>\n...\n</cwd>

Sections and tool changes are stored as positional system entries in the transcript. Only what changed is sent again, which keeps provider prompt caches warm. A section that returns something different every time, such as the current time, defeats that.

Conversation Settings

Each conversation has durable settings, readable and writable through the handle:

await root.setModel({ provider: "openai", modelId: "gpt-6-sol" }, context);
await root.setThinkingLevel("high", context);
await root.setActiveTools(["read", "bash"], context);
await root.setToolExecution("sequential", context); // default "parallel"
await root.setRetryPolicy({ enabled: true, maxRetries: 3, baseDelayMs: 2000 }, context);
await root.setStreamOptions({ timeoutMs: 120_000 }, context);

Changes apply when the next model request is prepared, not in the middle of one.

Watching a Conversation

Everything a UI needs is committed state. viewState() returns the conversation's structural view as a read-only Chord state, updated after every commit that touches it:

const view = await root.viewState(context);
view.subscribe((value) => {
	// value.entries: the active transcript
	// value.docs["pi.live"]: the running generation (streamed partial, retry, deferred) and tool calls (output, details)
	// value.docs["pi.inbox"], value.docs["pi.usage"], value.docs["pi.conversation.config"]
	render(value);
});
// later: view.dispose();

watch() delivers the same view with the exact Chord operations of each commit, one callback at a time:

const watch = await root.watch(context);
render(watch.value); // the state at attachment
watch.start(async (value, ops) => {
	await send(ops); // for example to a remote client that applies them
});
// later: await watch.stop();

A slow watch keeps at most 100 undelivered frames. After that, the pending frames are replaced by one frame holding the whole newest view. A client that joins late or reconnects starts from the current view; nothing is replayed.

Partial answers and tool output are committed at most every 100 ms, so a crash loses at most that window.

Busy Conversations

A conversation is busy while a run is working on an input. Submitting to a busy conversation queues the submission in the conversation's inbox, docs["pi.inbox"] in the view:

await root.submit({ type: "input", content: "Also run the tests" }, context); // follow-up (default)
await root.submit({ type: "input", content: "Use pnpm, not npm", whenBusy: "steer" }, context);
await root.submit({ type: "input", content: "Only if idle", whenBusy: "reject" }, context); // throws ConversationBusy
await root.submit({ type: "write", entry: { kind: "app.note", data: "user opened a file" } }, context);

• Steers are placed after the current tool round and join the running work.
• Follow-ups are placed when the run answers, and start the next run.
• Writes append an entry without asking the model anything.
• await submission.abort(context) withdraws a queued submission.
• setSteeringMode("all") and setFollowUpMode("all") place every queued item at once instead of one per turn.

If a run fails, queued items stay in the inbox until the next submission places them, oldest first.

Reset and Handoff

reset() starts a new context. The model no longer sees older entries, but they stay in storage:

await root.reset(undefined, context);                                  // start from nothing
await root.reset("We were fixing the flaky login test. Continue.", context); // start from a handoff note

While busy, the reset is queued like a write. When it is placed during a tool round, the current run ends. A tool can request the same with control: { handoff: "..." }.

Compaction

Compaction shrinks what the model sees: it summarizes older entries and appends a pi.compaction entry that holds the summary and heads the first entry it keeps. Older entries stay in storage.

const id = await root.compact("Keep the failing test names", context); // manual, with optional instructions
const { outcome } = (await harness.waitForTask(id, context)).state;
if (outcome.status === "completed" && outcome.result.submissionId !== undefined) {
	const placed = await (await harness.submission(outcome.result.submissionId, context))!.wait(context);
	console.log(placed.status); // "done", or "unanswered" with reason "stale"
}

The conversation keeps working while the summary is made. The summary is placed at once when the conversation is idle, otherwise at the next turn boundary. Esc (abort()) cancels a manual compaction.

Generation also compacts on its own, controlled per conversation:

await root.setCompaction({
	enabled: true, // automatic compaction; manual compact() always works
	reserveTokens: 16384, // above contextWindow - reserveTokens, the next request waits for a compaction
	keepRecentTokens: 20000, // roughly how much recent context stays verbatim
	backgroundTokens: 32768, // this far below that, a compaction starts in the background; 0 disables it
}, context);

When a provider rejects a request because the context is too long, generation compacts and retries once. A summary that would cut before the start of the current context settles as stale when it is placed, so when several are in flight, the furthest cut stays in effect. Summarization spend counts in pi.usage. A beforeCompact hook on CompactionTask can decline or supply its own summary.

Running compactions are listed in docs["pi.live"].compactions with their reason, attempt, and retry backoff. The agent events add compaction_start and compaction_end, and a compactions field in the snapshot.

Agent Events (Experimental)

For consumers that want coding-agent style events (message_start, message_update, tool_execution_start, ...) instead of structural state:

import { watchEvents } from "@earendil-works/pi-durable";

const stream = await watchEvents(harness, root.id, context);
initialize(stream.snapshot); // entries, run, in-flight generation, tools, compactions, inbox, config, usage
stream.start(async (events) => {
	for (const event of events) console.log(JSON.stringify(event));
});

Events are derived from commits, one batch per commit, and apply on top of the snapshot. Message and tool updates carry deltas: text and thinking appends, appended tool-call argument text, and output trims and appends. When a consumer falls more than 100 batches behind, it receives a fresh snapshot event instead. See test/examples/19-json.ts for the full stream of one run.

Hooks

Hooks let extensions observe or adjust the built-in tasks:

import { GenerationTask, ToolTask } from "@earendil-works/pi-durable";

registry.hooks.add(ToolTask, {
	beforeTool: (call) => (call.name === "bash" ? { block: "bash is disabled here" } : undefined),
});
registry.hooks.add(GenerationTask, {
	onYield: (answer) => (needsMoreWork(answer) ? { continue: "Keep going." } : undefined),
});

• Generation: beforeRequest (replace the messages of one request), afterResponse, onYield (continue the run with another user message), and afterTools (runs once a round's tools are done).
• Tools: beforeTool (block or rewrite arguments) and afterTool (replace the result).

Pass { scope: { conversationId } } to limit a hook to one conversation.

More Conversations and Forks

const other = await harness.createConversation({ ownership: { kind: "ownerless" } }, context);
const fork = await root.fork(entryId, { ownership: { kind: "ownerless" } }, context);

A fork sees its parent's entries up to entryId and continues independently. It keeps the parent's settings as of that entry.

Abort and Subagents

await root.abort(context) stops a conversation: queued inputs are withdrawn (queued writes stay), every task of its current work is aborted, and the call resolves once the conversation is idle.

A conversation can be owned by a task. A subagent tool creates its child inside api.commit() with ownership: { kind: "task", taskId: api.taskId }, then drives it through api.conversation(id):

registry.tools.add({
	name: "subagent",
	description: "Delegate a self-contained task to a subagent and get its answer back.",
	parameters: Type.Object({ task: Type.String() }),
	replay: "safe", // a rerun after a crash finds the same child and submission
	execute: async (args, api, context) => {
		const child = await api.commit(async (tx) => {
			// The ownership index remembers the child, so a rerun reuses it.
			const existing = (await tx.scanConversations({ ownerTaskId: api.taskId }, 1)).items[0];
			if (existing !== undefined) return existing.id;
			const created = await tx.createConversation({ ownership: { kind: "task", taskId: api.taskId } });
			(await tx.doc(ConversationConfig, created.id)).model = { provider: "openai", modelId: "gpt-6-sol" };
			return created.id;
		}, context);
		await api.details({ conversationId: child }, context); // lets a UI attach to the child
		const request = { type: "input", content: (args as { task: string }).task, requestId: `subagent:${api.taskId}` } as const;
		const settled = await (await (await api.conversation(child, context))!.submit(request, context)).wait(context);
		return { content: [{ type: "text", text: settled.status }] };
	},
});

Owned work belongs to its owner:

• Aborting the call aborts the child. So does the call failing: execute() throwing, or a crash that interrupts a call that is not replay-safe.
• The parent is idle only once the child is.
• A task created with { background: true } is a boundary: work it owns survives the parent's abort and does not keep the parent busy. root.abort(context, { background: true }) aborts it too.

The examples show both patterns as product code:

• 22-subagent-foreground.ts: the tool above, returning the child's answer. The UI finds the child through the call's details and prints the child's events indented under the call.
• 23-subagent-background.ts: persistent subagents behind one subagent tool that spawns, messages (steer or follow-up), waits for, stops, and lists them. Each child is owned by a background anchor task, so the parent's Esc and idle waits never reach it. Each message is delivered by a background reporter task that posts the answer back to the parent as a follow-up input once it arrives; request IDs keep a restart from sending a message or a report twice.

Child Tasks

A task can own child tasks, created with ownership: { kind: "task", taskId }, and wait for them by committing a waiting state:

pay: async (task, runtime, context) => {
	await runtime.commit(async (tx) => {
		const payments = [];
		for (const card of task.input.cards) {
			payments.push(await tx.createTask(Payment, { card }, { ownership: { kind: "task", taskId: task.id } }));
		}
		// Resume in `decide` once every payment is done; the first failure aborts the rest.
		return { status: "waiting", checkpoint: { phase: "decide", payments }, on: payments, policy: "failFast" };
	}, context);
},
decide: async (task, runtime, context) => {
	const outcomes = await runtime.outcomes(task.state.checkpoint.payments, context);
	// ...commit the checkout's own outcome
},

• Waiting: the task runs no code while it waits. With allSettled it resumes once every task in on is done; with failFast the first failed child also aborts the others. on may name other tasks too, with allSettled.
• Finishing: a task that finishes while work it owns is still running is completing: its outcome is decided, but it becomes terminal, and waitForTask() returns, only once that work is done. A failed or aborted outcome aborts that work first.
• Aborting: abort runs bottom-up. Aborting a task aborts the work it owns first, and its own abort handler starts only once that work is done, so each task undoes its own effects.

24-child-tasks.ts runs a checkout with four payments: a declined card, a cancelled checkout, and a restart while the payments run.

Your Own State

Documents are typed JSON objects committed together with entries. Define one, and edit it in a commit:

import { defineDoc } from "@earendil-works/pi-durable";

const Todos = defineDoc<{ items: string[] }>({
	kind: "app.todos",
	version: 1,
	scope: "conversation",
	history: "latest", // or "rewindable" to read old values with snapshotAsOf()
	fork: "initial", // what a fork starts with: "initial", "current", or "asOf"
	initial: () => ({ items: [] }),
});

await root.commit(async (tx) => {
	(await tx.doc(Todos, root.id)).items.push("write docs");
}, context);
console.log(await harness.snapshot(Todos, root.id, context));

harness.watchDoc() and harness.documentState() observe one document like the view above. registry.conversations.setup() creates your documents in every new conversation, and a tool reads them through its api.

Usage and Cost

Each conversation keeps token and cost totals in docs["pi.usage"]: per provider/model for model responses, and per tool name for tool results that report usage. Failed and aborted attempts count too. For the whole Session:

const usage = await harness.usage(context); // { models: { "openai/gpt-6-sol": Usage }, tools: {...} }

Storage

Backend	Import	Notes
Memory	MemoryStorage from the package root	Nothing is persisted.
SQLite	openNodeSqliteStorage(file) from @earendil-works/pi-durable/storage/sqlite/node	One database file. WAL mode with synchronous = NORMAL: commits survive process crashes; the newest may be lost on power or host failure.
JSONL	openNodeJsonlStorage(directory, context) from @earendil-works/pi-durable/storage/jsonl/node	Append-only files in one directory. Pass { fsync: true } to flush before each commit marker.

One process owns a storage at a time; there is no cross-process locking. The portable SQLite and JSONL cores (/storage/sqlite, /storage/jsonl) run without Node APIs, for example on Bun or in Cloudflare Durable Objects, given an asynchronous SqliteDatabase facade or a FileSystem from @earendil-works/pi-durable/env.

SQLite adapters implement promise-based exec, run, get, all, transaction, and close. run, get, and all take SQL text plus positional bindings; adapters may cache prepared statements by SQL text. A transaction callback receives a transaction handle; all work in the transaction must use it, and the handle expires when the callback settles. Adapters must queue unrelated operations and other transactions until the transaction finishes, so calling database itself inside the callback never settles:

await database.transaction(async (transaction) => {
	await transaction.exec("CREATE TABLE example (value TEXT)");
	await transaction.run("INSERT INTO example (value) VALUES (?)", "stored atomically");
});

Custom backends can run the shared conformance suite with any Vitest- or Jest-compatible runner:

import { registerStorageConformance } from "@earendil-works/pi-durable/testing";
import { describe, expect, it } from "vitest";

registerStorageConformance({ describe, expect, it }, "My Storage", async (use) => {
	const storage = await openMyStorage();
	try {
		await use(storage);
	} finally {
		await closeMyStorage(storage);
	}
});

The package root loads TypeBox, because the tool task validates arguments with pi-ai's validateToolArguments(). That costs about 23 MB of peak RSS unbundled, about 4 MB in a tree-shaken bundle.

Examples

Runnable examples live in test/examples. Run one from this package directory with:

node --conditions=source --experimental-strip-types test/examples/14-chat.ts

Example	Shows
14-chat	One question and answer
16-real-model	Streaming an answer from OpenAI
17-coding-tools	A tool-using turn on JSONL storage
18-print	Print mode: submit a prompt, print the answer
19-json	JSON mode: agent events or raw view operations, on SQLite, JSONL, or memory
20-inbox	Steers, follow-ups, writes, and withdrawal while busy
21-late-join	Attaching a view and an event stream mid-run
22-subagent-foreground	A replay-safe subagent tool whose child the call owns, with the child's events under the call
23-subagent-background	Persistent subagents: spawn, steer, stop, list, answers reported back, restart-safe
24-child-tasks	A checkout that owns and waits for four payments: failFast, abort, restart
25-compaction	A long chat compacted in the background, manually, and after a context overflow
00–13	The layers underneath: sessions, documents, forks, watches, tasks, recovery

Examples that call OpenAI need OPENAI_API_KEY; most use the faux provider otherwise.

Design Documents

• docs/pico-v5.md: the normative specification
• docs/pico-v5-handoff.md: the implementation plan
• docs/pico-v5-chord-usage.md: how the package uses Chord

Benchmarks: npm run bench:storage, npm run bench:storage:memory, and npm run bench:tool-output.

License

MIT