@earendil-works/pi-coding-agent
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Coding agent CLI with read, bash, edit, write tools and session management
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JavaScript
/**
* Context compaction for long sessions.
*
* Pure functions for compaction logic. The session manager handles I/O,
* and after compaction the session is reloaded.
*/
import { contentText, retryAssistantCall, uuidv7 } from "@earendil-works/pi-ai";
import { completeSimple } from "@earendil-works/pi-ai/compat";
import { convertToLlm } from "../messages.js";
import { buildSessionContext, sessionEntryToContextMessages, } from "../session-manager.js";
import { computeFileLists, createFileOps, extractFileOpsFromMessage, formatFileOperations, SUMMARIZATION_SYSTEM_PROMPT, serializeConversation, } from "./utils.js";
/**
* Extract file operations from messages and previous compaction entries.
*/
function extractFileOperations(messages, entries, prevCompactionIndex) {
const fileOps = createFileOps();
// Collect from previous compaction's details (if pi-generated)
if (prevCompactionIndex >= 0) {
const prevCompaction = entries[prevCompactionIndex];
if (!prevCompaction.fromHook && prevCompaction.details) {
// fromHook field kept for session file compatibility
const details = prevCompaction.details;
if (Array.isArray(details.readFiles)) {
for (const f of details.readFiles)
fileOps.read.add(f);
}
if (Array.isArray(details.modifiedFiles)) {
for (const f of details.modifiedFiles)
fileOps.edited.add(f);
}
}
}
// Extract from tool calls in messages
for (const msg of messages) {
extractFileOpsFromMessage(msg, fileOps);
}
return fileOps;
}
// ============================================================================
// Message Extraction
// ============================================================================
/**
* Extract AgentMessage from an entry if it produces one.
* Returns undefined for entries that don't contribute to LLM context.
*/
function getMessageFromEntryForCompaction(entry) {
if (entry.type === "compaction") {
return undefined;
}
return sessionEntryToContextMessages(entry)[0];
}
function combineUsage(first, second) {
return {
input: first.input + second.input,
output: first.output + second.output,
cacheRead: first.cacheRead + second.cacheRead,
cacheWrite: first.cacheWrite + second.cacheWrite,
...(first.cacheWrite1h !== undefined || second.cacheWrite1h !== undefined
? { cacheWrite1h: (first.cacheWrite1h ?? 0) + (second.cacheWrite1h ?? 0) }
: {}),
...(first.reasoning !== undefined || second.reasoning !== undefined
? { reasoning: (first.reasoning ?? 0) + (second.reasoning ?? 0) }
: {}),
totalTokens: first.totalTokens + second.totalTokens,
cost: {
input: first.cost.input + second.cost.input,
output: first.cost.output + second.cost.output,
cacheRead: first.cost.cacheRead + second.cost.cacheRead,
cacheWrite: first.cost.cacheWrite + second.cost.cacheWrite,
total: first.cost.total + second.cost.total,
},
};
}
export const DEFAULT_COMPACTION_SETTINGS = {
enabled: true,
reserveTokens: 16384,
keepRecentTokens: 20000,
};
// ============================================================================
// Token calculation
// ============================================================================
/**
* Calculate total context tokens from usage.
* Uses the native totalTokens field when available, falls back to computing from components.
*/
export function calculateContextTokens(usage) {
return usage.totalTokens || usage.input + usage.output + usage.cacheRead + usage.cacheWrite;
}
/**
* Get usage from an assistant message if available.
* Skips aborted, error, and all-zero usage messages as they don't have valid usage data.
*/
function getAssistantUsage(msg) {
if (msg.role === "assistant" && "usage" in msg) {
const assistantMsg = msg;
if (assistantMsg.stopReason !== "aborted" &&
assistantMsg.stopReason !== "error" &&
assistantMsg.usage &&
calculateContextTokens(assistantMsg.usage) > 0) {
return assistantMsg.usage;
}
}
return undefined;
}
/**
* Find the last valid assistant message usage from session entries.
*/
export function getLastAssistantUsage(entries) {
for (let i = entries.length - 1; i >= 0; i--) {
const entry = entries[i];
if (entry.type === "message") {
const usage = getAssistantUsage(entry.message);
if (usage)
return usage;
}
}
return undefined;
}
function getLastAssistantUsageInfo(messages) {
for (let i = messages.length - 1; i >= 0; i--) {
const usage = getAssistantUsage(messages[i]);
if (usage)
return { usage, index: i };
}
return undefined;
}
/**
* Estimate context tokens from messages, using the last assistant usage when available.
* If there are messages after the last usage, estimate their tokens with estimateTokens.
*/
export function estimateContextTokens(messages) {
const usageInfo = getLastAssistantUsageInfo(messages);
if (!usageInfo) {
let estimated = 0;
for (const message of messages) {
estimated += estimateTokens(message);
}
return {
tokens: estimated,
usageTokens: 0,
trailingTokens: estimated,
lastUsageIndex: null,
};
}
const usageTokens = calculateContextTokens(usageInfo.usage);
let trailingTokens = 0;
for (let i = usageInfo.index + 1; i < messages.length; i++) {
trailingTokens += estimateTokens(messages[i]);
}
return {
tokens: usageTokens + trailingTokens,
usageTokens,
trailingTokens,
lastUsageIndex: usageInfo.index,
};
}
/**
* Check if compaction should trigger based on context usage.
*/
export function shouldCompact(contextTokens, contextWindow, settings) {
if (!settings.enabled)
return false;
return contextTokens > contextWindow - settings.reserveTokens;
}
// ============================================================================
// Cut point detection
// ============================================================================
const ESTIMATED_IMAGE_CHARS = 4800;
function estimateTextAndImageContentChars(content) {
if (typeof content === "string") {
return content.length;
}
let chars = 0;
for (const block of content) {
if (block.type === "text" && block.text) {
chars += block.text.length;
}
else if (block.type === "image") {
chars += ESTIMATED_IMAGE_CHARS;
}
}
return chars;
}
/**
* Estimate token count for a message using chars/4 heuristic.
* This is conservative (overestimates tokens).
*/
export function estimateTokens(message) {
let chars = 0;
switch (message.role) {
case "user": {
chars = estimateTextAndImageContentChars(message.content);
return Math.ceil(chars / 4);
}
case "assistant": {
const assistant = message;
for (const block of assistant.content) {
if (block.type === "text") {
chars += block.text.length;
}
else if (block.type === "thinking") {
chars += block.thinking.length;
}
else if (block.type === "toolCall") {
chars += block.name.length + JSON.stringify(block.arguments).length;
}
}
return Math.ceil(chars / 4);
}
case "custom":
case "toolResult": {
chars = estimateTextAndImageContentChars(message.content);
return Math.ceil(chars / 4);
}
case "bashExecution": {
chars = message.command.length + message.output.length;
return Math.ceil(chars / 4);
}
case "branchSummary":
case "compactionSummary": {
chars = message.summary.length;
return Math.ceil(chars / 4);
}
}
return 0;
}
function isCutPointMessage(message) {
switch (message.role) {
case "user":
case "assistant":
case "bashExecution":
case "custom":
case "branchSummary":
case "compactionSummary":
return true;
case "toolResult":
return false;
}
return false;
}
function isTurnStartMessage(message) {
switch (message.role) {
case "user":
case "bashExecution":
case "custom":
case "branchSummary":
case "compactionSummary":
return true;
case "assistant":
case "toolResult":
return false;
}
return false;
}
function isTurnStartEntry(entry) {
if (entry.type === "compaction") {
return false;
}
return sessionEntryToContextMessages(entry).some(isTurnStartMessage);
}
/**
* Find valid cut points: indices of context-visible user-like or assistant messages.
* Never cut at tool results (they must follow their tool call).
* When we cut at an assistant message with tool calls, its tool results follow it
* and will be kept.
*/
function findValidCutPoints(entries, startIndex, endIndex) {
const cutPoints = [];
for (let i = startIndex; i < endIndex; i++) {
const entry = entries[i];
if (entry.type === "compaction") {
continue;
}
if (sessionEntryToContextMessages(entry).some(isCutPointMessage)) {
cutPoints.push(i);
}
}
return cutPoints;
}
/**
* Find the context-visible user-role message that starts the turn containing the given entry index.
* Returns -1 if no turn start found before the index.
*/
export function findTurnStartIndex(entries, entryIndex, startIndex) {
for (let i = entryIndex; i >= startIndex; i--) {
if (isTurnStartEntry(entries[i])) {
return i;
}
}
return -1;
}
/**
* Find the cut point in session entries that keeps approximately `keepRecentTokens`.
*
* Algorithm: Walk backwards from newest, accumulating estimated message sizes.
* Stop when we've accumulated >= keepRecentTokens. Cut at that point.
*
* Can cut at user OR assistant messages (never tool results). When cutting at an
* assistant message with tool calls, its tool results come after and will be kept.
*
* Returns CutPointResult with:
* - firstKeptEntryIndex: the entry index to start keeping from
* - turnStartIndex: if cutting mid-turn, the user message that started that turn
* - isSplitTurn: whether we're cutting in the middle of a turn
*
* Only considers entries between `startIndex` and `endIndex` (exclusive).
*/
export function findCutPoint(entries, startIndex, endIndex, keepRecentTokens) {
const cutPoints = findValidCutPoints(entries, startIndex, endIndex);
if (cutPoints.length === 0) {
return { firstKeptEntryIndex: startIndex, turnStartIndex: -1, isSplitTurn: false };
}
// Walk backwards from newest, accumulating estimated message sizes
let accumulatedTokens = 0;
let cutIndex = cutPoints[0]; // Default: keep from first message (not header)
for (let i = endIndex - 1; i >= startIndex; i--) {
const entry = entries[i];
const messageTokens = sessionEntryToContextMessages(entry).reduce((sum, message) => sum + estimateTokens(message), 0);
if (messageTokens === 0)
continue;
accumulatedTokens += messageTokens;
// Check if we've exceeded the budget
if (accumulatedTokens >= keepRecentTokens) {
// Find the closest valid cut point at or after this entry
for (let c = 0; c < cutPoints.length; c++) {
if (cutPoints[c] >= i) {
cutIndex = cutPoints[c];
break;
}
}
break;
}
}
// Scan backwards from cutIndex to include adjacent metadata entries that do not affect context.
while (cutIndex > startIndex) {
const prevEntry = entries[cutIndex - 1];
// Stop at compaction boundaries or context-visible entries.
if (prevEntry.type === "compaction" || sessionEntryToContextMessages(prevEntry).length > 0) {
break;
}
cutIndex--;
}
// Determine if this is a split turn
const cutEntry = entries[cutIndex];
const startsTurn = isTurnStartEntry(cutEntry);
const turnStartIndex = startsTurn ? -1 : findTurnStartIndex(entries, cutIndex, startIndex);
return {
firstKeptEntryIndex: cutIndex,
turnStartIndex,
isSplitTurn: !startsTurn && turnStartIndex !== -1,
};
}
// ============================================================================
// Summarization
// ============================================================================
const SUMMARIZATION_PROMPT = `The messages above are a conversation to summarize. Create a structured context checkpoint summary that another LLM will use to continue the work.
Use this EXACT format:
## Goal
[What is the user trying to accomplish? Can be multiple items if the session covers different tasks.]
## Constraints & Preferences
- [Any constraints, preferences, or requirements mentioned by user]
- [Or "(none)" if none were mentioned]
## Progress
### Done
- [x] [Completed tasks/changes]
### In Progress
- [ ] [Current work]
### Blocked
- [Issues preventing progress, if any]
## Key Decisions
- **[Decision]**: [Brief rationale]
## Next Steps
1. [Ordered list of what should happen next]
## Critical Context
- [Any data, examples, or references needed to continue]
- [Or "(none)" if not applicable]
Keep each section concise. Preserve exact file paths, function names, and error messages.`;
const UPDATE_SUMMARIZATION_PROMPT = `The messages above are NEW conversation messages to incorporate into the existing summary provided in <previous-summary> tags.
Update the existing structured summary with new information. RULES:
- PRESERVE all existing information from the previous summary
- ADD new progress, decisions, and context from the new messages
- UPDATE the Progress section: move items from "In Progress" to "Done" when completed
- UPDATE "Next Steps" based on what was accomplished
- PRESERVE exact file paths, function names, and error messages
- If something is no longer relevant, you may remove it
Use this EXACT format:
## Goal
[Preserve existing goals, add new ones if the task expanded]
## Constraints & Preferences
- [Preserve existing, add new ones discovered]
## Progress
### Done
- [x] [Include previously done items AND newly completed items]
### In Progress
- [ ] [Current work - update based on progress]
### Blocked
- [Current blockers - remove if resolved]
## Key Decisions
- **[Decision]**: [Brief rationale] (preserve all previous, add new)
## Next Steps
1. [Update based on current state]
## Critical Context
- [Preserve important context, add new if needed]
Keep each section concise. Preserve exact file paths, function names, and error messages.`;
function createSummarizationOptions(model, maxTokens, apiKey, headers, env, signal, thinkingLevel) {
const options = { maxTokens, signal, apiKey, headers, env };
if (model.reasoning && thinkingLevel && thinkingLevel !== "off") {
options.reasoning = thinkingLevel;
}
return options;
}
/**
* Shared choke point for every compaction/branch-summary summarization call. Wraps the
* single LLM call in {@link retryAssistantCall} so transient stream drops (e.g.
* `terminated`, socket close) honor the configured retry policy instead of failing
* the whole compaction on the first attempt. Deterministic errors and aborts return
* immediately (see {@link retryAssistantCall}).
*/
export async function completeSummarization(model, context, options, streamFn, retry, callbacks) {
// Summaries are standalone requests, so isolate routing and avoid cache writes that cannot be reused.
const requestOptions = {
...options,
cacheRetention: "none",
sessionId: uuidv7(),
};
const produce = async () => streamFn
? (await streamFn(model, context, requestOptions)).result()
: completeSimple(model, context, requestOptions);
return retryAssistantCall(produce, retry, requestOptions.signal, callbacks);
}
/**
* Generate a summary of the conversation using the LLM.
* If previousSummary is provided, uses the update prompt to merge.
*/
export async function generateSummary(currentMessages, model, reserveTokens, apiKey, headers, signal, customInstructions, previousSummary, thinkingLevel, streamFn, env, retry, callbacks) {
return (await generateSummaryWithUsage(currentMessages, model, reserveTokens, apiKey, headers, signal, customInstructions, previousSummary, thinkingLevel, streamFn, env, retry, callbacks)).text;
}
/** Generate or update a conversation summary and return its provider usage. */
export async function generateSummaryWithUsage(currentMessages, model, reserveTokens, apiKey, headers, signal, customInstructions, previousSummary, thinkingLevel, streamFn, env, retry, callbacks) {
const maxTokens = Math.min(Math.floor(0.8 * reserveTokens), model.maxTokens > 0 ? model.maxTokens : Number.POSITIVE_INFINITY);
// Use update prompt if we have a previous summary, otherwise initial prompt
let basePrompt = previousSummary ? UPDATE_SUMMARIZATION_PROMPT : SUMMARIZATION_PROMPT;
if (customInstructions) {
basePrompt = `${basePrompt}\n\nAdditional focus: ${customInstructions}`;
}
// Serialize conversation to text so model doesn't try to continue it
// Convert to LLM messages first (handles custom types like bashExecution, custom, etc.)
const llmMessages = convertToLlm(currentMessages);
const conversationText = serializeConversation(llmMessages);
// Build the prompt with conversation wrapped in tags
let promptText = `<conversation>\n${conversationText}\n</conversation>\n\n`;
if (previousSummary) {
promptText += `<previous-summary>\n${previousSummary}\n</previous-summary>\n\n`;
}
promptText += basePrompt;
const summarizationMessages = [
{
role: "user",
content: [{ type: "text", text: promptText }],
timestamp: Date.now(),
},
];
const completionOptions = createSummarizationOptions(model, maxTokens, apiKey, headers, env, signal, thinkingLevel);
const response = await completeSummarization(model, { systemPrompt: SUMMARIZATION_SYSTEM_PROMPT, messages: summarizationMessages }, completionOptions, streamFn, retry, callbacks);
if (response.stopReason === "error") {
throw new Error(`Summarization failed: ${response.errorMessage || "Unknown error"}`);
}
const textContent = contentText(response.content);
return { text: textContent, usage: response.usage };
}
export function prepareCompaction(pathEntries, settings) {
if (pathEntries.length > 0 && pathEntries[pathEntries.length - 1].type === "compaction") {
return undefined;
}
let prevCompactionIndex = -1;
for (let i = pathEntries.length - 1; i >= 0; i--) {
if (pathEntries[i].type === "compaction") {
prevCompactionIndex = i;
break;
}
}
let previousSummary;
let boundaryStart = 0;
if (prevCompactionIndex >= 0) {
const prevCompaction = pathEntries[prevCompactionIndex];
previousSummary = prevCompaction.summary;
const firstKeptEntryIndex = pathEntries.findIndex((entry) => entry.id === prevCompaction.firstKeptEntryId);
boundaryStart = firstKeptEntryIndex >= 0 ? firstKeptEntryIndex : prevCompactionIndex + 1;
}
const boundaryEnd = pathEntries.length;
const tokensBefore = estimateContextTokens(buildSessionContext(pathEntries).messages).tokens;
const cutPoint = findCutPoint(pathEntries, boundaryStart, boundaryEnd, settings.keepRecentTokens);
// Get UUID of first kept entry
const firstKeptEntry = pathEntries[cutPoint.firstKeptEntryIndex];
if (!firstKeptEntry?.id) {
return undefined; // Session needs migration
}
const firstKeptEntryId = firstKeptEntry.id;
const historyEnd = cutPoint.isSplitTurn ? cutPoint.turnStartIndex : cutPoint.firstKeptEntryIndex;
// Messages to summarize (will be discarded after summary)
const messagesToSummarize = [];
for (let i = boundaryStart; i < historyEnd; i++) {
const msg = getMessageFromEntryForCompaction(pathEntries[i]);
if (msg)
messagesToSummarize.push(msg);
}
// Messages for turn prefix summary (if splitting a turn)
const turnPrefixMessages = [];
if (cutPoint.isSplitTurn) {
for (let i = cutPoint.turnStartIndex; i < cutPoint.firstKeptEntryIndex; i++) {
const msg = getMessageFromEntryForCompaction(pathEntries[i]);
if (msg)
turnPrefixMessages.push(msg);
}
}
if (messagesToSummarize.length === 0 && turnPrefixMessages.length === 0) {
return undefined;
}
// Extract file operations from messages and previous compaction
const fileOps = extractFileOperations(messagesToSummarize, pathEntries, prevCompactionIndex);
// Also extract file ops from turn prefix if splitting
if (cutPoint.isSplitTurn) {
for (const msg of turnPrefixMessages) {
extractFileOpsFromMessage(msg, fileOps);
}
}
return {
firstKeptEntryId,
messagesToSummarize,
turnPrefixMessages,
isSplitTurn: cutPoint.isSplitTurn,
tokensBefore,
previousSummary,
fileOps,
settings,
};
}
// ============================================================================
// Main compaction function
// ============================================================================
const TURN_PREFIX_SUMMARIZATION_PROMPT = `This is the PREFIX of a turn that was too large to keep. The SUFFIX (recent work) is retained.
Summarize the prefix to provide context for the retained suffix:
## Original Request
[What did the user ask for in this turn?]
## Early Progress
- [Key decisions and work done in the prefix]
## Context for Suffix
- [Information needed to understand the retained recent work]
Be concise. Focus on what's needed to understand the kept suffix.`;
/**
* Generate summaries for compaction using prepared data.
* Returns CompactionResult - SessionManager adds uuid/parentUuid when saving.
*
* @param preparation - Pre-calculated preparation from prepareCompaction()
* @param customInstructions - Optional custom focus for the summary
*/
export async function compact(preparation, model, apiKey, headers, customInstructions, signal, thinkingLevel, streamFn, env, retry, callbacks) {
const { firstKeptEntryId, messagesToSummarize, turnPrefixMessages, isSplitTurn, tokensBefore, previousSummary, fileOps, settings, } = preparation;
// Generate summaries and merge into one
let summary;
let summaryUsage;
if (isSplitTurn && turnPrefixMessages.length > 0) {
let historyText = "No prior history.";
let historyUsage;
if (messagesToSummarize.length > 0) {
const historyResult = await generateSummaryWithUsage(messagesToSummarize, model, settings.reserveTokens, apiKey, headers, signal, customInstructions, previousSummary, thinkingLevel, streamFn, env, retry, callbacks);
historyText = historyResult.text;
historyUsage = historyResult.usage;
}
const turnPrefixResult = await generateTurnPrefixSummary(turnPrefixMessages, model, settings.reserveTokens, apiKey, headers, env, signal, thinkingLevel, streamFn, retry, callbacks);
// Merge into single summary
summary = `${historyText}\n\n---\n\n**Turn Context (split turn):**\n\n${turnPrefixResult.text}`;
summaryUsage = historyUsage ? combineUsage(historyUsage, turnPrefixResult.usage) : turnPrefixResult.usage;
}
else {
// Just generate history summary
const result = await generateSummaryWithUsage(messagesToSummarize, model, settings.reserveTokens, apiKey, headers, signal, customInstructions, previousSummary, thinkingLevel, streamFn, env, retry, callbacks);
summary = result.text;
summaryUsage = result.usage;
}
// Compute file lists and append to summary
const { readFiles, modifiedFiles } = computeFileLists(fileOps);
summary += formatFileOperations(readFiles, modifiedFiles);
if (!firstKeptEntryId) {
throw new Error("First kept entry has no UUID - session may need migration");
}
return {
summary,
firstKeptEntryId,
tokensBefore,
usage: summaryUsage,
details: { readFiles, modifiedFiles },
};
}
/**
* Generate a summary for a turn prefix (when splitting a turn).
*/
async function generateTurnPrefixSummary(messages, model, reserveTokens, apiKey, headers, env, signal, thinkingLevel, streamFn, retry, callbacks) {
const maxTokens = Math.min(Math.floor(0.5 * reserveTokens), model.maxTokens > 0 ? model.maxTokens : Number.POSITIVE_INFINITY); // Smaller budget for turn prefix
const llmMessages = convertToLlm(messages);
const conversationText = serializeConversation(llmMessages);
const promptText = `<conversation>\n${conversationText}\n</conversation>\n\n${TURN_PREFIX_SUMMARIZATION_PROMPT}`;
const summarizationMessages = [
{
role: "user",
content: [{ type: "text", text: promptText }],
timestamp: Date.now(),
},
];
const response = await completeSummarization(model, { systemPrompt: SUMMARIZATION_SYSTEM_PROMPT, messages: summarizationMessages }, createSummarizationOptions(model, maxTokens, apiKey, headers, env, signal, thinkingLevel), streamFn, retry, callbacks);
if (response.stopReason === "error") {
throw new Error(`Turn prefix summarization failed: ${response.errorMessage || "Unknown error"}`);
}
return {
text: contentText(response.content),
usage: response.usage,
};
}
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