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Compaction and Branch Summaries

Compaction and branch summaries are the two mechanisms that keep long sessions usable without losing prior work context.

  • Compaction rewrites old history into a summary on the current branch.
  • Branch summary captures abandoned branch context during /tree navigation.

Both are persisted as session entries and converted into agent-attributed developer context when rebuilding LLM input.

Key implementation files

  • packages/agent/src/compaction/compaction.ts (context-full summarization and handoff generation)
  • packages/agent/src/compaction/legacy-snapcompact-archive.ts (reads archives left by the removed image-archive engine so old sessions keep loading)
  • packages/agent/src/compaction/branch-summarization.ts
  • packages/agent/src/compaction/pruning.ts
  • packages/agent/src/compaction/utils.ts
  • packages/coding-agent/src/session/session-manager.ts
  • packages/coding-agent/src/session/agent-session.ts
  • packages/coding-agent/src/session/messages.ts
  • packages/coding-agent/src/extensibility/hooks/types.ts
  • packages/coding-agent/src/config/settings-schema.ts

Session entry model

Compaction and branch summaries are first-class session entries, not plain assistant/user messages.

  • CompactionEntry
    • type: "compaction"
    • summary, optional shortSummary (display only, and no longer produced by compact(): see “Short summary” below)
    • firstKeptEntryId (compaction boundary)
    • tokensBefore
    • optional details, preserveData, fromExtension
  • BranchSummaryEntry
    • type: "branch_summary"
    • fromId, summary
    • optional details, fromExtension

When context is rebuilt (buildSessionContext):

  1. Latest compaction on the active path is converted to one compactionSummary message.
  2. Kept entries from firstKeptEntryId to the compaction point are re-included.
  3. Later entries on the path are appended.
  4. branch_summary entries are converted to branchSummary messages.
  5. custom_message entries are converted to custom messages.

convertToLlm() transforms these custom roles into LLM-facing messages, through these static templates:

  • packages/agent/src/prompts/compaction/compaction-summary-context.md
  • packages/agent/src/prompts/compaction/branch-summary-context.md

branchSummary becomes an agent-attributed developer message.

compactionSummary becomes an agent-attributed user message. The role is the trust boundary: a compaction summary is model-generated history, so putting it in the user channel means it cannot outrank a live developer message that contradicts it. Any image attachments follow the summary text in the same message, which is also why the user slot is the safe one: every provider accepts images there.

The compaction template wraps the summary in its own <summary> delimiters, so the untrusted region has an explicit start and end. Exactly one wrapper is ever emitted: a legacy or model-authored <summary …> wrapper persisted inside the summary text is stripped first (withoutSummaryPresentationTags), and embedded or sibling <summary> elements that are not one enclosing wrapper are left alone as content. The branch template uses no delimiters.

Other custom messages pass through as developer messages with their raw content and no template.

Compaction pipeline

Triggers

Compaction/context maintenance can run in six ways:

  1. Manual context compaction: /compact [summary] [focus] calls AgentSession.compact(...).
  2. Automatic overflow recovery: after a same-model assistant error that matches context overflow.
  3. Automatic incomplete-output recovery: after a same-model assistant message ends with stopReason === "length" (OpenAI/Codex response.incomplete).
  4. Automatic threshold maintenance: after a successful turn when context exceeds the resolved threshold.
  5. Mid-turn threshold maintenance: before the next provider request when a tool-loop turn crosses the threshold and compaction.midTurnEnabled !== false.
  6. Idle maintenance: runIdleCompaction() can invoke the same auto-maintenance path with reason "idle".

Compaction shape (visual)

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

Overflow/incomplete recovery vs threshold/idle maintenance

The automatic paths are intentionally different:

  • Overflow recovery

    • Trigger: current-model assistant error is detected as context overflow and the error is not older than the latest compaction.
    • The failing assistant error message is removed from active agent state before retry.
    • Context promotion is tried first; if a configured larger model is available, the agent switches model and retries without compacting.
    • If promotion is unavailable and compaction is enabled, in-place compaction runs with reason: "overflow" and willRetry: true.
    • On success, agent.continue() is scheduled to retry the turn.

  • Incomplete-output recovery

    • Trigger: same-model assistant message ends with stopReason === "length" and the message is not older than the latest compaction.
    • The incomplete assistant message is removed from active agent state before recovery.
    • Context promotion is tried first.
    • If promotion is unavailable and compaction is enabled, auto maintenance runs with reason: "incomplete" and willRetry: true.
    • On context-full success, agent.continue() is scheduled to retry the turn.

  • Threshold maintenance

    • Trigger: successful, non-error assistant message whose adjusted context tokens exceed resolveThresholdTokens(...).
    • Mid-turn maintenance also checks safe tool-loop boundaries before the next provider request when compaction.midTurnEnabled !== false.
    • Tool-output pruning can reduce the measured token count before threshold comparison.
    • Context promotion is tried before post-turn compaction.
    • If promotion is unavailable, auto maintenance runs with reason: "threshold" and willRetry: false.
    • On success, if compaction.autoContinue !== false, post-turn maintenance schedules an agent-authored developer auto-continue prompt from prompts/turn-control/auto-continue.md; mid-turn maintenance never schedules a separate continuation because the core loop already defines the next provider request.

  • Idle maintenance

    • Trigger: runIdleCompaction() when not streaming or already compacting.
    • Uses reason: "idle" and does not auto-continue afterward.

Compaction and manual handoff

summary is the sole compaction strategy, and it continues the SAME session. The generated summary is prefixed onto a retained raw tail in one message array: buildSessionContext pushes the summary, then re-emits every entry from firstKeptEntryId onward. findCutPoint walks backwards accumulating until keepRecentTokens (default 10000), and because it can only cut at a turn boundary, prepareCompaction then hard-bounds the tail: a kept turn whose bulk exceeds the budget has its heavy non-error tool results replaced with an elision marker (largest first, originals offloaded to a recovery artifact:// blob), so the tail stays within budget even when one turn alone is bigger. User messages, assistant text, tool calls, and error results are never elided.

Note that the summary prompt does not state any of that. Its opening line requests “a structured handoff summary for another LLM to resume the task”, which describes a cold restart that compaction does not perform. This is inherited from upstream, whose engine keeps the same recent tail, so the mismatch is upstream’s rather than a fork difference. It is recorded here because a summarizer told it is writing for a fresh reader will restate turns that are still in context. Changing the prompt is an operator decision, not a fix to apply locally.

/handoff is a separate, explicit operation that starts a new session. Nothing carries over except its generated transfer document. Automatic compaction never selects or schedules a handoff.

Legacy compaction strategies

Earlier versions offered snap, handoff, and other strategy values. They now migrate to summary. Legacy off also sets compaction.enabled: false.

Sessions compacted by the old engine still open without loss. The removed engine always stored the full plaintext source alongside its image frames, so a legacy archive degrades gracefully:

  • On each context rebuild, legacyArchiveSourceText (in packages/agent/src/compaction/legacy-snapcompact-archive.ts) reads the archived source from CompactionEntry.preserveData.snapcompact and re-attaches it as a single recovered text block on the compaction summary. The old image frames are never rehydrated, which also removes the oversized-payload hazard they carried.
  • The next compaction over such a session drains that recovered source into the fresh LLM summary and drops the legacy archive from preserveData, so the session converges to a plain summarized history.

Display transcript

By default the live TUI collapses pre-compaction history: display.collapseCompacted defaults to true, so only the latest compacted tail renders live above the summary divider and the scrollback is cleared at the compaction point. Set display.collapseCompacted to false to keep the full display transcript inline instead (buildSessionContext({ transcript: true }) / AgentSession.buildTranscriptSessionContext()): every path entry in chronological order, with each compaction shown as a slim divider, ── 📷 compacted · ctrl+o ──, at the point it fired. Expanding (ctrl+o) reveals the summary. In the collapsed default the LLM context and the visible transcript reset together; in the inline mode only the LLM context resets, and the scrollback above the divider stays intact, including across session resume.

Per-turn and pre-compaction pruning

Two passes run from AgentSession.#checkCompaction(), after every completed turn, and both persist through rewriteEntries() so the session file matches the live context (/fork, /tan and resume read the file, and a divergent prefix cold-misses the provider prompt cache):

  1. Stale-result pass (#pruneStaleToolResults → pruneSupersededToolResults) runs first, before any threshold gating, so it fires even with compaction.enabled off. It is skipped entirely when both compaction.supersedeReads and compaction.dropUseless are false.
  2. Threshold prune (#pruneToolOutputs → pruneToolOutputs) runs only on the threshold path, after the compaction.enabled / strategy check and after error turns are skipped, and only once the turn has usable usage data. Its savings feed postMaintenanceContextTokens, which is the trigger figure reported to the compaction it may schedule.

Default prune policy:

  • Protect newest 40_000 tool-output tokens.
  • Require at least 20_000 total estimated savings.
  • Never blank a result below 50 tokens (MIN_PRUNE_TOKENS): the [Output truncated - N tokens] placeholder costs ~8 tokens, so pruning a sub-floor result would grow the context and churn the prompt cache for nothing. (Superseded and useless results keep their own rules: the useless collector already drops no-savings candidates; superseded reads prune for correctness regardless of size.)
  • Never prune skill tool results, read results of skill:// paths, or reads of the active plan reference file (added via AgentSession’s plan protection).

Pruned tool results are replaced with:

  • [Output truncated - N tokens]

Superseded-read elision

Gated by compaction.supersedeReads (default on). When it is on, the stale-result pass keys every read result by readToolSupersedeKey (path plus selector grammar; a selector-free read supersedes range reads of the same base path, URL-scheme paths are exempt), and every result but the newest in a key group is blanked to the exact placeholder [Superseded by a newer read of this file] (SUPERSEDED_NOTICE). Turning the setting off passes no key function, so no read is ever grouped and every read result survives at full length.

Blanking happens only where it is cheap: when the messages after the candidate total at most ~8k estimated tokens (PRUNE_CACHE_WARM_SUFFIX_TOKENS, the read→edit→read tail), or when the last message is at least 90 minutes old (PRUNE_IDLE_FLUSH_MS, past the 1h Anthropic “long” prompt-cache retention), in which case every still-sent candidate flushes at once. Entries before the latest compaction’s firstKeptEntryId are summarized away and are never rewritten.

Useless-result elision

Tools can flag a finished result as contextually useless, a search with zero matches, a job poll that timed out with everything still running, an empty irc inbox drain. The flag originates on the tool result (AgentToolResult.useless, set via ToolResultBuilder.useless() or directly on the returned object), is copied by the agent loop onto the persisted ToolResultMessage (never together with isError, errors always win), and is consumed in three places:

  • Per-turn stale-result pass (pruneSupersededToolResults, gated by compaction.dropUseless, default on): flagged results are blanked to the exact placeholder [Uneventful result elided] (USELESS_NOTICE) with the same cache-aware timing as superseded reads: only when the suffix after the candidate is small (≤ ~8k tokens) or the session has idled past the provider prompt-cache lifetime. Results smaller than the notice itself are never blanked (no savings), and protected tools are exempt.
  • Threshold prune (pruneToolOutputs): flagged results bypass the protect-recent window, same as superseded reads, and receive USELESS_NOTICE instead of the token-count placeholder.
  • Summary serialization: serializeConversation drops the whole tool call/result pair from summarizer input: the source region is discarded after summarization anyway, so the exclusion costs no cache.

The flag never reaches provider wire formats, and flagged pairs are never removed from history (only blanked in place), so tool-call/result pairing stays intact.

What the summary prompts request

compaction-summary.md, compaction-update-summary.md, and compaction-summary-context.md are oh-my-pi’s text verbatim, by operator order, on the measurement that upstream scores higher on long-run evals. packages/agent/test/compaction-strategy-contracts.test.ts pins each one by SHA-256 and preflight runs it, so an unapproved edit fails the build instead of quietly changing summary quality. Approving a change means updating the digest in the same commit.

Both prompts request the same ten sections, in the same order: ## Goal, ## Constraints & Preferences, ## Progress (### Done, ### In Progress, ### Blocked), ## Key Decisions, ## Next Steps, ## Critical Context, ## Additional Notes. Sections may be omitted when they do not apply. The lists have to match, because iterative compaction feeds its own output back in: a section the update prompt failed to name would be dropped on every cycle.

Both require exact file paths, function names, and error messages preserved rather than paraphrased, require repository state changes (branch, uncommitted changes) when mentioned, forbid any text outside the structured summary, and require an unanswered question to the user to survive. The initial prompt preserves that question verbatim; the update prompt files it into ## Critical Context, replacing a previous pending question once it has been answered.

## Goal is a single undifferentiated field: the prompts do not separate a durable overarching goal from the current task, and only handoff-document.md still draws that line. The update prompt also instructs the model to preserve all information from the previous summary and permits removing only what is no longer relevant, so iterative compaction accumulates rather than replacing drift.

Empty responses

Neither a compaction summary nor an explicit handoff document may be empty. A provider can finish with stopReason: "stop" after spending its output budget on reasoning and emit no text. Both call sites raise instead of persisting an empty artifact. Lower the compaction thinking level if this repeats so the model spends its budget on the document.

Boundary and cut-point logic

prepareCompaction() only considers entries since the last compaction entry (if any).

  1. Find previous compaction index.
  2. Compute boundaryStart = prevCompactionIndex + 1.
  3. Adapt keepRecentTokens using measured usage ratio when available.
  4. Run findCutPoint() over the boundary window.

Valid cut points include:

  • message entries with roles: user, assistant, bashExecution, hookMessage, branchSummary, compactionSummary
  • custom_message entries
  • branch_summary entries

Hard rule: never cut at toolResult.

If there are non-message metadata entries immediately before the cut point (model_change, thinking_level_change, labels, etc.), they are pulled into the kept region by moving cut index backward until a message or compaction boundary is hit.

Split-turn handling

If cut point is not at a user-turn start, compaction treats it as a split turn.

Turn start detection treats these as user-turn boundaries:

  • message.role === "user"
  • message.role === "bashExecution"
  • custom_message entry
  • branch_summary entry

Split-turn compaction generates two summaries:

  1. History summary (messagesToSummarize)
  2. Turn-prefix summary (turnPrefixMessages)

Final stored summary is merged as:

<history summary>

---

**Turn Context (split turn):**

<turn prefix summary>

Summary generation

compact(...) builds summaries from serialized conversation text:

  1. Convert messages via convertToLlm().
  2. Serialize with serializeConversation().
  3. Wrap in <conversation>...</conversation>.
  4. Optionally include <previous-summary>...</previous-summary>.
  5. Optionally inject extension hook context and active memory-backend compaction context as <additional-context> entries.
  6. Execute summarization prompt with SUMMARIZATION_SYSTEM_PROMPT.

Prompt selection:

  • first compaction: compaction-summary.md
  • iterative compaction with prior summary: compaction-update-summary.md
  • split-turn second pass: compaction-turn-prefix.md
  • handoff document: handoff-document.md (used only by explicit generateHandoff(...), not serialized compaction)

Short summary

CompactionEntry.shortSummary is a display-only, pull-request-style line. compact() no longer generates one: a second model request per compaction, spent on text the model never reads, is not worth the input cost. Every reader stays, because compaction hooks still set the field and sessions written before the change still carry it.

Its one display consumer is the session-listing title fallback (title: header.title ?? shortSummary in packages/coding-agent/src/session/session-listing.ts), which veyyon reaches only when its own tiny-model titler declined: VEYYON_NO_TITLE set, or a first message too low-signal to title from. In that case the session picker falls back again to the first user message, so nothing renders blank.

Remote summarizer endpoint:

  • When compaction.remoteEndpoint is set, summary generation POSTs one of two wire formats:
    • custom veyyon summarizer endpoints receive { systemPrompt, prompt } and must return JSON containing at least { summary }.
    • OpenAI-compatible endpoints whose path ends in /chat/completions receive { model, messages, stream: false }, where messages contains one system prompt and one user prompt. The summary is read from choices[0].message.content, which lets self-hosted servers such as llama.cpp and vLLM act as summarizers without a separate shim.
  • When it is unset, the active model generates the summary locally. That is the default for every provider.

Server-side compaction (compaction.remote, on by default):

OpenAI and Azure OpenAI serve POST /responses/compact, which compacts a session’s context inside the provider and returns the compacted window. Veyyon uses it when the model’s compat.supportsServerCompaction flag is set, which is resolved per host at model build time: the official OpenAI API and Azure’s v1 API today, and any gateway that opts in with an override. The Codex provider stays out, because its transport owns history state server-side and a client-minted window has no replay contract there. A re-pointed openai model also stays out, since another vendor’s host does not serve that path. Turning compaction.remote off is the only thing that disables it; leaving it unset leaves it on.

A server-side compaction stores no summary text, and that is deliberate. The window it returns is an encrypted_content blob minted under the provider’s key. There is nothing in it to read, and nothing to decrypt: it is the compacted context itself, meant to be handed straight back to the same provider. The path used to run a full local summarization of the same span alongside the remote call and store both, which cost the remote call plus the exact summary the remote call was supposed to replace, and only one of the two was ever read. Writing readable text here is not a missing feature that could be added later. The only way to produce it is to pay a second model to describe a span, which is the local strategy with an extra network round trip in front of it, and any text derived from the blob rather than the span would be invented. An empty summary is the honest record of what happened.

Because the entry cannot explain itself, the rebuild will not trust it outside the provider that minted it. buildSessionContext treats a compaction as usable only when the stored window replays on the active provider, or when there is real summary text. When neither holds, which is a fork or resume onto a different provider, it re-expands every message the compaction hid. Nothing was lost to recover: compaction only advances firstKeptEntryId, so the discarded span is still in the session file.

Sessions compacted by the earlier, removed path (preserveData.openaiRemoteCompaction, whose summary field held a fixed placeholder) load through the same rule and re-expand.

Handoff generation

packages/agent/src/compaction/compaction.ts also exports generateHandoff(...). Handoff generation uses the same completeSimple(...) oneshot style as summarization, but it preserves the live agent cache prefix by sending the active system prompt, tool array, and real LLM message history, then appending one agent-attributed user message containing the handoff prompt. It forces toolChoice: "none" and returns joined text blocks directly.

Handoff does not write a CompactionEntry. AgentSession.handoff() performs the session transition: it starts a new session, injects the generated document as a visible custom_message with customType: "handoff", and rebuilds agent messages from that new session.

File-operation context in summaries

Compaction tracks cumulative file activity using assistant tool calls:

  • read(path) → read set
  • write(path) → modified set
  • edit(path) → modified set

Cumulative behavior:

  • Includes prior compaction details only when prior entry is pi-generated (fromExtension !== true).
  • In split turns, includes turn-prefix file ops too.
  • details.readFiles excludes files also modified; details.modifiedFiles contains the rest (persisted shape is unchanged).

The file list is a grouped, prefix-folded directory tree (find-tool shape) with a per-file access marker, (Read) for read-only files, (Write) for modified files never read, (RW) for modified files also present in the cumulative read set. Capped at 20 files with an […N files elided…] line. Compaction and explicit handoff append it as a <files> tag (via upsertFileOperations).

<files>
# packages/agent/src/compaction/
compaction.ts (Read)
utils.ts (RW)
## prompts/
file-operations.md (Write)
</files>

Legacy <read-files>/<modified-files> tags from summaries written by earlier versions are stripped (alongside <files>) before re-appending, so old summaries self-heal on the next compaction.

Persist and reload

After summary generation (or a hook-provided summary), agent session:

  1. Appends a CompactionEntry with appendCompaction(...).
  2. Rebuilds display context from the active leaf via buildDisplaySessionContext().
  3. Replaces live agent messages with rebuilt context.
  4. Synchronizes active todo phases from the rebuilt branch and closes provider sessions whose history was rewritten.
  5. Emits session_compact hook event.

Branch summarization pipeline

Branch summarization is tied to tree navigation, not token overflow.

Trigger

During navigateTree(...):

  1. Compute abandoned entries from old leaf to common ancestor using collectEntriesForBranchSummary(...).
  2. If caller requested summary (options.summarize), generate summary before switching leaf.
  3. If summary exists, attach it at the navigation target using branchWithSummary(...).

Operationally this is commonly driven by /tree flow when branchSummary.enabled is enabled.

Branch switch shape (visual)

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

Preparation and token budget

generateBranchSummary(...) computes budget as:

  • tokenBudget = model.contextWindow - branchSummary.reserveTokens

prepareBranchEntries(...) then:

  1. First pass: collect cumulative file ops from all summarized entries, including prior pi-generated branch_summary details.
  2. Second pass: walk newest → oldest, adding messages until token budget is reached.
  3. Prefer preserving recent context.
  4. May still include large summary entries near budget edge for continuity.

Compaction entries are included as messages (compactionSummary) during branch summarization input.

Summary generation and persistence

Branch summarization:

  1. Converts and serializes selected messages.
  2. Wraps in <conversation>.
  3. Uses custom instructions if supplied, otherwise branch-summary.md.
  4. Calls summarization model with SUMMARIZATION_SYSTEM_PROMPT.
  5. Prepends branch-summary-preamble.md.
  6. Appends file-operation tags.

Result is stored as BranchSummaryEntry with optional details (readFiles, modifiedFiles).

Extension and hook touchpoints

session_before_compact

Pre-compaction hook.

Can:

  • cancel compaction ({ cancel: true })
  • provide full custom compaction payload ({ compaction: CompactionResult })

session_compacting

Prompt/context customization hook for default compaction.

Can return:

  • prompt (override base summary prompt)
  • context (extra context lines injected into <additional-context>)
  • preserveData (stored on compaction entry)

session_compact

Post-compaction notification with saved compactionEntry and fromExtension flag.

session_before_tree

Runs on tree navigation before default branch summary generation.

Can:

  • cancel navigation
  • provide custom { summary: { summary, details } } used when user requested summarization

session_tree

Post-navigation event exposing new/old leaf and optional summary entry.

Which model compacts

compaction.model (settings/config.yml, --compaction-model CLI, or the Compaction Model picker in /settings) selects the model used for LLM compaction and handoff generation. Default: unset, compaction inherits the main session model live, so switching the session model also switches the compactor. When set, resolveCompactionModelPatterns expands the value through the normal pattern/role resolution (role aliases like "@smol" and :thinking suffixes work), and auto compaction tries the resulting candidates in order. The value is a chain and can be written either way, as a comma-separated string (opus,sonnet) or as a YAML list; both normalize to the same ordered candidates. Legacy config keys compaction.compactionModel / top-level compactionModel are migrated to compaction.model on load.

Runtime behavior and failure semantics

  • Manual compaction aborts current agent operation first.
  • abortCompaction() cancels manual compaction, auto-compaction, and handoff generation controllers.
  • Auto compaction emits start/end session events for UI/state updates.
  • Auto compaction can try multiple model candidates and retry transient failures; long retry delays prefer the next candidate when one is available.
  • Overflow errors are excluded from generic retry path because they are handled by context promotion/compaction.
  • If auto-compaction fails:
    • overflow path emits Context overflow recovery failed: ...
    • incomplete-output path emits Incomplete response recovery failed: ...
    • threshold/idle paths emit Auto-compaction failed: ...
  • Branch summarization can be cancelled via abort signal (e.g., Escape), returning canceled/aborted navigation result.

Settings and defaults

From settings-schema.ts:

  • compaction.enabled = true
  • compaction.strategy = "summary", the sole strategy. Every stored legacy strategy token migrates to summary; legacy off also sets compaction.enabled: false. Use /handoff for an explicit transfer to a new session.
  • compaction.reserveTokens = unset (absent key). When unset the compaction layer falls back to DEFAULT_RESERVE_TOKENS = 16384, and small-window recovery may substitute a proportional 15%-of-window reserve when the default does not fit the window (resolveBudgetReserveTokens).
  • compaction.keepRecentTokens = 10000
  • compaction.supersedeReads = true (drop earlier file reads that a later read of the same file makes redundant)
  • compaction.dropUseless = true
  • compaction.handoffSaveToDisk = false (also write the handoff packet to disk)
  • compaction.modelContextWindow = unset (absent key); overrides the window size the compaction budget resolves against
  • compaction.autoContinue = true
  • compaction.midTurnEnabled = true
  • compaction.remoteEndpoint = undefined
  • compaction.threshold = auto; the one trigger setting, with its unit in the value. auto is contextWindow - max(15% of contextWindow, reserveTokens). 85% is a percent of the current model’s window. 170000 is an absolute token amount, model-independent: compaction runs once context exceeds that many tokens whatever the current model’s window is, and when the amount is larger than that window it is honored up to contextWindow - 1 with a one-time warning (never silently reinterpreted). Resolution and the migration off the two retired keys live in packages/agent/src/compaction/threshold.ts.
  • compaction.thresholdTokens = -1 and compaction.thresholdPercent = -1; retired. The global config is rewritten on load (#migrateRawSettings): a positive amount becomes threshold: <amount>, a positive percent becomes threshold: <percent>% (the amount wins when both are set), and both keys are dropped, so the ambiguity leaves the file without moving the trigger. Config sources that are never rewritten — project files, --config overlays — are folded in at read time by withLegacyCompactionThreshold with the same precedence, and the session reports which retired key supplied the value.
  • compaction.idleEnabled = false
  • compaction.idleThresholdTokens = 200000
  • compaction.idleTimeoutSeconds = 300
  • branchSummary.enabled = false
  • branchSummary.reserveTokens = 16384

These values are consumed at runtime by AgentSession and compaction/branch summarization modules.