Initial commit: OpenClaw Skill Collection
6 custom skills (assign-task, dispatch-webhook, daily-briefing, task-capture, qmd-brain, tts-voice) with technical documentation. Compatible with Claude Code, OpenClaw, Codex CLI, and OpenCode.
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> ## Documentation Index
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> Fetch the complete documentation index at: https://docs.openclaw.ai/llms.txt
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> Use this file to discover all available pages before exploring further.
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# Session Management Deep Dive
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# Session Management & Compaction (Deep Dive)
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This document explains how OpenClaw manages sessions end-to-end:
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* **Session routing** (how inbound messages map to a `sessionKey`)
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* **Session store** (`sessions.json`) and what it tracks
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* **Transcript persistence** (`*.jsonl`) and its structure
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* **Transcript hygiene** (provider-specific fixups before runs)
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* **Context limits** (context window vs tracked tokens)
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* **Compaction** (manual + auto-compaction) and where to hook pre-compaction work
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* **Silent housekeeping** (e.g. memory writes that shouldn’t produce user-visible output)
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If you want a higher-level overview first, start with:
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* [/concepts/session](/concepts/session)
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* [/concepts/compaction](/concepts/compaction)
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* [/concepts/session-pruning](/concepts/session-pruning)
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* [/reference/transcript-hygiene](/reference/transcript-hygiene)
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***
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## Source of truth: the Gateway
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OpenClaw is designed around a single **Gateway process** that owns session state.
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* UIs (macOS app, web Control UI, TUI) should query the Gateway for session lists and token counts.
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* In remote mode, session files are on the remote host; “checking your local Mac files” won’t reflect what the Gateway is using.
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***
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## Two persistence layers
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OpenClaw persists sessions in two layers:
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1. **Session store (`sessions.json`)**
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* Key/value map: `sessionKey -> SessionEntry`
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* Small, mutable, safe to edit (or delete entries)
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* Tracks session metadata (current session id, last activity, toggles, token counters, etc.)
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2. **Transcript (`<sessionId>.jsonl`)**
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* Append-only transcript with tree structure (entries have `id` + `parentId`)
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* Stores the actual conversation + tool calls + compaction summaries
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* Used to rebuild the model context for future turns
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***
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## On-disk locations
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Per agent, on the Gateway host:
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* Store: `~/.openclaw/agents/<agentId>/sessions/sessions.json`
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* Transcripts: `~/.openclaw/agents/<agentId>/sessions/<sessionId>.jsonl`
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* Telegram topic sessions: `.../<sessionId>-topic-<threadId>.jsonl`
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OpenClaw resolves these via `src/config/sessions.ts`.
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***
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## Session keys (`sessionKey`)
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A `sessionKey` identifies *which conversation bucket* you’re in (routing + isolation).
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Common patterns:
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* Main/direct chat (per agent): `agent:<agentId>:<mainKey>` (default `main`)
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* Group: `agent:<agentId>:<channel>:group:<id>`
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* Room/channel (Discord/Slack): `agent:<agentId>:<channel>:channel:<id>` or `...:room:<id>`
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* Cron: `cron:<job.id>`
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* Webhook: `hook:<uuid>` (unless overridden)
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The canonical rules are documented at [/concepts/session](/concepts/session).
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***
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## Session ids (`sessionId`)
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Each `sessionKey` points at a current `sessionId` (the transcript file that continues the conversation).
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Rules of thumb:
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* **Reset** (`/new`, `/reset`) creates a new `sessionId` for that `sessionKey`.
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* **Daily reset** (default 4:00 AM local time on the gateway host) creates a new `sessionId` on the next message after the reset boundary.
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* **Idle expiry** (`session.reset.idleMinutes` or legacy `session.idleMinutes`) creates a new `sessionId` when a message arrives after the idle window. When daily + idle are both configured, whichever expires first wins.
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Implementation detail: the decision happens in `initSessionState()` in `src/auto-reply/reply/session.ts`.
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***
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## Session store schema (`sessions.json`)
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The store’s value type is `SessionEntry` in `src/config/sessions.ts`.
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Key fields (not exhaustive):
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* `sessionId`: current transcript id (filename is derived from this unless `sessionFile` is set)
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* `updatedAt`: last activity timestamp
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* `sessionFile`: optional explicit transcript path override
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* `chatType`: `direct | group | room` (helps UIs and send policy)
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* `provider`, `subject`, `room`, `space`, `displayName`: metadata for group/channel labeling
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* Toggles:
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* `thinkingLevel`, `verboseLevel`, `reasoningLevel`, `elevatedLevel`
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* `sendPolicy` (per-session override)
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* Model selection:
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* `providerOverride`, `modelOverride`, `authProfileOverride`
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* Token counters (best-effort / provider-dependent):
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* `inputTokens`, `outputTokens`, `totalTokens`, `contextTokens`
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* `compactionCount`: how often auto-compaction completed for this session key
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* `memoryFlushAt`: timestamp for the last pre-compaction memory flush
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* `memoryFlushCompactionCount`: compaction count when the last flush ran
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The store is safe to edit, but the Gateway is the authority: it may rewrite or rehydrate entries as sessions run.
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***
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## Transcript structure (`*.jsonl`)
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Transcripts are managed by `@mariozechner/pi-coding-agent`’s `SessionManager`.
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The file is JSONL:
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* First line: session header (`type: "session"`, includes `id`, `cwd`, `timestamp`, optional `parentSession`)
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* Then: session entries with `id` + `parentId` (tree)
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Notable entry types:
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* `message`: user/assistant/toolResult messages
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* `custom_message`: extension-injected messages that *do* enter model context (can be hidden from UI)
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* `custom`: extension state that does *not* enter model context
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* `compaction`: persisted compaction summary with `firstKeptEntryId` and `tokensBefore`
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* `branch_summary`: persisted summary when navigating a tree branch
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OpenClaw intentionally does **not** “fix up” transcripts; the Gateway uses `SessionManager` to read/write them.
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***
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## Context windows vs tracked tokens
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Two different concepts matter:
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1. **Model context window**: hard cap per model (tokens visible to the model)
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2. **Session store counters**: rolling stats written into `sessions.json` (used for /status and dashboards)
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If you’re tuning limits:
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* The context window comes from the model catalog (and can be overridden via config).
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* `contextTokens` in the store is a runtime estimate/reporting value; don’t treat it as a strict guarantee.
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For more, see [/token-use](/reference/token-use).
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***
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## Compaction: what it is
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Compaction summarizes older conversation into a persisted `compaction` entry in the transcript and keeps recent messages intact.
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After compaction, future turns see:
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* The compaction summary
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* Messages after `firstKeptEntryId`
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Compaction is **persistent** (unlike session pruning). See [/concepts/session-pruning](/concepts/session-pruning).
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***
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## When auto-compaction happens (Pi runtime)
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In the embedded Pi agent, auto-compaction triggers in two cases:
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1. **Overflow recovery**: the model returns a context overflow error → compact → retry.
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2. **Threshold maintenance**: after a successful turn, when:
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`contextTokens > contextWindow - reserveTokens`
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Where:
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* `contextWindow` is the model’s context window
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* `reserveTokens` is headroom reserved for prompts + the next model output
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These are Pi runtime semantics (OpenClaw consumes the events, but Pi decides when to compact).
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***
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## Compaction settings (`reserveTokens`, `keepRecentTokens`)
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Pi’s compaction settings live in Pi settings:
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```json5 theme={null}
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{
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compaction: {
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enabled: true,
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reserveTokens: 16384,
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keepRecentTokens: 20000,
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},
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}
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```
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OpenClaw also enforces a safety floor for embedded runs:
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* If `compaction.reserveTokens < reserveTokensFloor`, OpenClaw bumps it.
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* Default floor is `20000` tokens.
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* Set `agents.defaults.compaction.reserveTokensFloor: 0` to disable the floor.
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* If it’s already higher, OpenClaw leaves it alone.
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Why: leave enough headroom for multi-turn “housekeeping” (like memory writes) before compaction becomes unavoidable.
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Implementation: `ensurePiCompactionReserveTokens()` in `src/agents/pi-settings.ts`
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(called from `src/agents/pi-embedded-runner.ts`).
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***
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## User-visible surfaces
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You can observe compaction and session state via:
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* `/status` (in any chat session)
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* `openclaw status` (CLI)
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* `openclaw sessions` / `sessions --json`
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* Verbose mode: `🧹 Auto-compaction complete` + compaction count
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***
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## Silent housekeeping (`NO_REPLY`)
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OpenClaw supports “silent” turns for background tasks where the user should not see intermediate output.
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Convention:
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* The assistant starts its output with `NO_REPLY` to indicate “do not deliver a reply to the user”.
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* OpenClaw strips/suppresses this in the delivery layer.
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As of `2026.1.10`, OpenClaw also suppresses **draft/typing streaming** when a partial chunk begins with `NO_REPLY`, so silent operations don’t leak partial output mid-turn.
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***
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## Pre-compaction “memory flush” (implemented)
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Goal: before auto-compaction happens, run a silent agentic turn that writes durable
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state to disk (e.g. `memory/YYYY-MM-DD.md` in the agent workspace) so compaction can’t
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erase critical context.
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OpenClaw uses the **pre-threshold flush** approach:
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1. Monitor session context usage.
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2. When it crosses a “soft threshold” (below Pi’s compaction threshold), run a silent
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“write memory now” directive to the agent.
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3. Use `NO_REPLY` so the user sees nothing.
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Config (`agents.defaults.compaction.memoryFlush`):
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* `enabled` (default: `true`)
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* `softThresholdTokens` (default: `4000`)
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* `prompt` (user message for the flush turn)
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* `systemPrompt` (extra system prompt appended for the flush turn)
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Notes:
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* The default prompt/system prompt include a `NO_REPLY` hint to suppress delivery.
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* The flush runs once per compaction cycle (tracked in `sessions.json`).
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* The flush runs only for embedded Pi sessions (CLI backends skip it).
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* The flush is skipped when the session workspace is read-only (`workspaceAccess: "ro"` or `"none"`).
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* See [Memory](/concepts/memory) for the workspace file layout and write patterns.
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Pi also exposes a `session_before_compact` hook in the extension API, but OpenClaw’s
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flush logic lives on the Gateway side today.
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***
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## Troubleshooting checklist
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* Session key wrong? Start with [/concepts/session](/concepts/session) and confirm the `sessionKey` in `/status`.
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* Store vs transcript mismatch? Confirm the Gateway host and the store path from `openclaw status`.
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* Compaction spam? Check:
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* model context window (too small)
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* compaction settings (`reserveTokens` too high for the model window can cause earlier compaction)
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* tool-result bloat: enable/tune session pruning
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* Silent turns leaking? Confirm the reply starts with `NO_REPLY` (exact token) and you’re on a build that includes the streaming suppression fix.
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