Compaction & Branch Summarization

LLMs have limited context windows. When conversations grow too long, Pi uses compaction to summarize older content while preserving recent work. This page covers both auto-compaction and branch summarization.

Source files (pi):

• packages/coding-agent/src/core/compaction/compaction.ts - Auto-compaction logic
• packages/coding-agent/src/core/compaction/branch-summarization.ts - Branch summarization
• packages/coding-agent/src/core/compaction/utils.ts - Shared utilities (file tracking, serialization)
• packages/coding-agent/src/core/session-manager.ts - Entry types (CompactionEntry, BranchSummaryEntry)
• packages/coding-agent/src/core/extensions/types.ts - Extension event types

For TypeScript definitions in your project, inspect node_modules/@earendil-works/pi-coding-agent/dist/.

Pi has two summarization mechanisms:

Mechanism	Trigger	Purpose
Compaction	Context exceeds threshold, or /compact	Summarize old messages to free up context
Branch summarization	/tree navigation	Preserve context when switching branches

Both use the same structured summary format and track file operations cumulatively. Compaction and branch-summary requests use fresh routing session IDs and, where supported by the provider, disable prompt-cache writes because these one-off prompts are unlikely to be reused.

Auto-compaction triggers when:

contextTokens > contextWindow - reserveTokens

By default, reserveTokens is 16384 tokens (configurable in ~/.pi/agent/settings.json or <project-dir>/.pi/settings.json). This leaves room for the LLM's response.

During a multi-turn agent run, Pi checks this threshold after tools finish and their results are appended, before starting the next assistant response. If the threshold is crossed, Pi compacts inside the same agent run and resumes with the summary and retained messages. It skips this between-turn check when the completed tool batch terminates the run and no queued message requires another response. Pi also checks the threshold before a new user prompt and after a low-level agent run ends.

You can also trigger manually with /compact [instructions], where optional instructions focus the summary.

1. Find cut point: Walk backwards from newest message, accumulating token estimates until keepRecentTokens (default 20k, configurable in ~/.pi/agent/settings.json or <project-dir>/.pi/settings.json) is reached
2. Extract messages: Collect messages from the previous kept boundary (or session start) up to the cut point
3. Generate summary: Call LLM to summarize with structured format, passing the previous summary as iterative context when present
4. Append entry: Save CompactionEntry with summary and firstKeptEntryId
5. Rebuilds context: Session rebuilds the context for the next request, using summary + messages from firstKeptEntryId onwards

Before compaction:

  entry:  0     1     2     3      4     5     6      7      8     9
        ┌─────┬─────┬─────┬──────┬─────┬─────┬──────┬──────┬─────┬─────┐
        │ hdr │ usr │ ass │ tool │ usr │ ass │ tool │ tool │ ass │ tool│
        └─────┴─────┴─────┴──────┴─────┴─────┴──────┴──────┴─────┴─────┘
                └────────┬───────┘ └──────────────┬──────────────┘
               messagesToSummarize            kept messages
                                   ↑
                          firstKeptEntryId (entry 4)

After compaction (new entry appended):

  entry:  0     1     2     3      4     5     6      7      8     9     10
        ┌─────┬─────┬─────┬──────┬─────┬─────┬──────┬──────┬─────┬─────┬─────┐
        │ hdr │ usr │ ass │ tool │ usr │ ass │ tool │ tool │ ass │ tool│ cmp │
        └─────┴─────┴─────┴──────┴─────┴─────┴──────┴──────┴─────┴─────┴─────┘
               └──────────┬──────┘ └──────────────────────┬───────────────────┘
                 not sent to LLM                    sent to LLM
                                                         ↑
                                              starts from firstKeptEntryId

What the LLM sees:

  ┌────────┬─────────┬─────┬─────┬──────┬──────┬─────┬──────┐
  │ system │ summary │ usr │ ass │ tool │ tool │ ass │ tool │
  └────────┴─────────┴─────┴─────┴──────┴──────┴─────┴──────┘
       ↑         ↑      └─────────────────┬────────────────┘
    prompt   from cmp          messages from firstKeptEntryId

On repeated compactions, the summarized span starts at the previous compaction's kept boundary (firstKeptEntryId), not at the compaction entry itself, falling back to the entry after the previous compaction if that kept entry cannot be found in the path. This preserves messages that survived the earlier compaction by including them in the next summarization pass as well. Pi also recalculates tokensBefore from the rebuilt session context before writing the new CompactionEntry, so the token count reflects the actual pre-compaction context being replaced.

A "turn" starts with a user message and includes all assistant responses and tool calls until the next user message. Normally, compaction cuts at turn boundaries.

When a single turn exceeds keepRecentTokens, the cut point lands mid-turn at an assistant message. This is a "split turn":

Split turn (one huge turn exceeds budget):

  entry:  0     1     2      3     4      5      6     7      8
        ┌─────┬─────┬─────┬──────┬─────┬──────┬──────┬─────┬──────┐
        │ hdr │ usr │ ass │ tool │ ass │ tool │ tool │ ass │ tool │
        └─────┴─────┴─────┴──────┴─────┴──────┴──────┴─────┴──────┘
                ↑                                     ↑
         turnStartIndex = 1                  firstKeptEntryId = 7
                │                                     │
                └──── turnPrefixMessages (1-6) ───────┘
                                                      └── kept (7-8)

  isSplitTurn = true
  messagesToSummarize = []  (no complete turns before)
  turnPrefixMessages = [usr, ass, tool, ass, tool, tool]

For split turns, Pi generates two summaries and merges them:

1. History summary: Previous context (if any)
2. Turn prefix summary: The early part of the split turn

Valid cut points are:

• User messages
• Assistant messages
• BashExecution messages
• Custom messages (custom_message, branch_summary)

Never cut at tool results (they must stay with their tool call).

Defined in session-manager.ts:

interface CompactionEntry<T = unknown> {
  type: "compaction";
  id: string;
  parentId: string;
  timestamp: number;
  summary: string;
  firstKeptEntryId: string;
  tokensBefore: number;
  usage?: Usage;       // LLM usage that generated the summary
  fromHook?: boolean;  // true if provided by extension (legacy field name)
  details?: T;         // implementation-specific data
}

// Default compaction uses this for details (from compaction.ts):
interface CompactionDetails {
  readFiles: string[];
  modifiedFiles: string[];
}

Extensions can store any JSON-serializable data in details. The default compaction tracks file operations, but custom extension implementations can use their own structure. Generated and extension-provided summaries store their LLM usage when available so session totals include summarization work.

See prepareCompaction() and compact() for the implementation. For direct programmatic summarization, generateSummary() returns the summary text and generateSummaryWithUsage() returns { text, usage }.

When you use /tree to navigate to a different branch, Pi offers to summarize the work you're leaving. This injects context from the left branch into the new branch.

1. Find common ancestor: Deepest node shared by old and new positions
2. Collect entries: Walk from old leaf back to common ancestor
3. Prepare with budget: Include messages up to token budget (newest first)
4. Generate summary: Call LLM with structured format
5. Append entry: Save BranchSummaryEntry at navigation point

Tree before navigation:

         ┌─ B ─ C ─ D (old leaf, being abandoned)
    A ───┤
         └─ E ─ F (target)

Common ancestor: A
Entries to summarize: B, C, D

After navigation with summary:

         ┌─ B ─ C ─ D
    A ───┤
         └─ E ─ F ─ [summary of B,C,D] (new leaf)

Both compaction and branch summarization track files cumulatively. When generating a summary, pi extracts file operations from:

• Tool calls in the messages being summarized
• Previous compaction or branch summary details (if any)

This means file tracking accumulates across multiple compactions or nested branch summaries, preserving the full history of read and modified files.

Defined in session-manager.ts:

interface BranchSummaryEntry<T = unknown> {
  type: "branch_summary";
  id: string;
  parentId: string;
  timestamp: number;
  summary: string;
  fromId: string;      // Entry we navigated from
  usage?: Usage;       // LLM usage that generated the summary
  fromHook?: boolean;  // true if provided by extension (legacy field name)
  details?: T;         // implementation-specific data
}

// Default branch summarization uses this for details (from branch-summarization.ts):
interface BranchSummaryDetails {
  readFiles: string[];
  modifiedFiles: string[];
}

Same as compaction, extensions can store custom data in details.

See collectEntriesForBranchSummary(), prepareBranchEntries(), and generateBranchSummary() for the implementation.

Both compaction and branch summarization use the same structured format:

## Goal
[What the user is trying to accomplish]

## Constraints & Preferences
- [Requirements mentioned by user]

## Progress
### Done
- [x] [Completed tasks]

### In Progress
- [ ] [Current work]

### Blocked
- [Issues, if any]

## Key Decisions
- **[Decision]**: [Rationale]

## Next Steps
1. [What should happen next]

## Critical Context
- [Data needed to continue]

<read-files>
path/to/file1.ts
path/to/file2.ts
</read-files>

<modified-files>
path/to/changed.ts
</modified-files>

Before summarization, messages are serialized to text via serializeConversation():

[User]: What they said
[Assistant thinking]: Internal reasoning
[Assistant]: Response text
[Assistant tool calls]: read(path="foo.ts"); edit(path="bar.ts", ...)
[Tool result]: Output from tool

This prevents the model from treating it as a conversation to continue.

Tool results are truncated to 2000 characters during serialization. Content beyond that limit is replaced with a marker indicating how many characters were truncated. This keeps summarization requests within reasonable token budgets, since tool results (especially from read and bash) are typically the largest contributors to context size.

Extensions can intercept and customize both compaction and branch summarization. See extensions/types.ts for event type definitions.

Fired before auto-compaction or /compact. Can cancel or provide custom summary. See SessionBeforeCompactEvent and CompactionPreparation in the types file.

pi.on("session_before_compact", async (event, ctx) => {
  const { preparation, branchEntries, customInstructions, reason, willRetry, signal } = event;

  // preparation.messagesToSummarize - messages to summarize
  // preparation.turnPrefixMessages - split turn prefix (if isSplitTurn)
  // preparation.previousSummary - previous compaction summary
  // preparation.fileOps - extracted file operations
  // preparation.tokensBefore - context tokens before compaction
  // preparation.firstKeptEntryId - where kept messages start
  // preparation.settings - effective settings after applying model overrides

  // branchEntries - all entries on current branch (for custom state)
  // reason - "manual" (/compact), "threshold", or "overflow"
  // willRetry - whether the aborted turn is retried after compaction (overflow recovery)
  // signal - AbortSignal (pass to LLM calls)

  // Cancel:
  return { cancel: true };

  // Custom summary:
  return {
    compaction: {
      summary: "Your summary...",
      firstKeptEntryId: preparation.firstKeptEntryId,
      tokensBefore: preparation.tokensBefore,
      // usage: summaryResponse.usage, // Optional; included in session totals
      details: { /* custom data */ },
    }
  };
});

To generate a summary with your own model, convert messages to text using serializeConversation:

import { convertToLlm, serializeConversation } from "@earendil-works/pi-coding-agent";

pi.on("session_before_compact", async (event, ctx) => {
  const { preparation } = event;
  
  // Convert AgentMessage[] to Message[], then serialize to text
  const conversationText = serializeConversation(
    convertToLlm(preparation.messagesToSummarize)
  );
  // Returns:
  // [User]: message text
  // [Assistant thinking]: thinking content
  // [Assistant]: response text
  // [Assistant tool calls]: read(path="..."); bash(command="...")
  // [Tool result]: output text

  // Now send to your model for summarization
  const { summary, usage } = await myModel.summarize(conversationText);
  
  return {
    compaction: {
      summary,
      firstKeptEntryId: preparation.firstKeptEntryId,
      tokensBefore: preparation.tokensBefore,
      usage,
    }
  };
});

See custom-compaction.ts for a complete example using a different model.

Fired when manual or automatic compaction fails or is aborted. This is useful for telemetry extensions that need to pair session_before_compact attempts with terminal outcomes.

pi.on("session_compact_failed", async (event, ctx) => {
  const { reason, errorMessage, aborted, willRetry, fromExtension } = event;
  // reason - "manual" (/compact), "threshold", or "overflow"
  // errorMessage - present for non-abort failures
  // aborted - true for cancelled/aborted compactions
  // willRetry - whether the aborted turn would have retried after compaction
  // fromExtension - whether extension-provided compaction content was being used
});

Fired before /tree navigation. Always fires regardless of whether user chose to summarize. Can cancel navigation or provide custom summary.

pi.on("session_before_tree", async (event, ctx) => {
  const { preparation, signal } = event;

  // preparation.targetId - where we're navigating to
  // preparation.oldLeafId - current position (being abandoned)
  // preparation.commonAncestorId - shared ancestor
  // preparation.entriesToSummarize - entries that would be summarized
  // preparation.userWantsSummary - whether user chose to summarize

  // Cancel navigation entirely:
  return { cancel: true };

  // Provide custom summary (only used if userWantsSummary is true):
  if (preparation.userWantsSummary) {
    return {
      summary: {
        summary: "Your summary...",
        // usage: summaryResponse.usage, // Optional; included in session totals
        details: { /* custom data */ },
      }
    };
  }
});

See SessionBeforeTreeEvent and TreePreparation in the types file.

Configure compaction in ~/.pi/agent/settings.json or <project-dir>/.pi/settings.json:

{
  "compaction": {
    "enabled": true,
    "reserveTokens": 16384,
    "keepRecentTokens": 20000
  }
}

Setting	Default	Description
enabled	true	Enable auto-compaction
reserveTokens	16384	Tokens to reserve for LLM response
keepRecentTokens	20000	Recent tokens to keep (not summarized)

Disable auto-compaction with "enabled": false. You can still compact manually with /compact.

Use compaction.modelOverrides to tune token budgets for different models:

{
  "compaction": {
    "reserveTokens": 16384,
    "keepRecentTokens": 20000,
    "modelOverrides": {
      "some-provider/big-model": {
        "reserveTokens": 400000
      }
    }
  }
}

For a model with a 1M context window, this override triggers compaction above 600K tokens and keeps the ordinary 20000 recent tokens. Other models retain the ordinary 16384-token reserve. reserveTokens also influences summarization output limits, capped by the model's maximum output tokens; it is not solely a trigger threshold.

Keys are exact, case-sensitive provider/modelId values, including any slashes within the model ID. Each reserveTokens and keepRecentTokens value falls back independently from the model override to the ordinary setting to the built-in default. Values must be non-negative safe integers. Invalid values in the matching model override produce an error when read; only omitted fields fall back to the ordinary setting. Model override entries must be objects. Invalid ordinary token settings produce an error when read, even if the active model has a valid override. Only omitted ordinary values use built-in defaults. enabled remains global, not model-specific.

These resolved values are used for manual compaction, all automatic threshold checks, overflow recovery, and extension-visible preparation.settings. Model switches affect subsequent checks and compactions without changing ordinary settings. Compaction already in progress uses the model and settings captured for that operation. Branch summarization settings are unaffected.

Overrides work in both global and project settings. The files merge recursively before lookup, so a global model-specific value beats a project-wide fallback; a project must override that model entry to change it. See settings.md for details.