Context engine

A context engine controls how OpenClaw builds model context for each run: which messages to include, how to summarize older history, and how to manage context across subagent boundaries.

OpenClaw ships with a built-in legacy engine and uses it by default - most users never need to change this. Install and select a plugin engine only when you want different assembly, compaction, or cross-session recall behavior.

Quick start

1. Check which engine is active

   openclaw doctor
   # or inspect config directly:
   cat ~/.openclaw/openclaw.json | jq '.plugins.slots.contextEngine'

2. Install a plugin engine

   Context engine plugins are installed like any other OpenClaw plugin.

   From npmFrom a local path

   openclaw plugins install @martian-engineering/lossless-claw

   openclaw plugins install -l ./my-context-engine

3. Enable and select the engine

   openclaw.json

   {
     plugins: {
       slots: {
         contextEngine: "lossless-claw", // must match the plugin's registered engine id
       },
       entries: {
         "lossless-claw": {
           enabled: true,
           // Plugin-specific config goes here (see the plugin's docs)
         },
       },
     },
   }

   Restart the gateway after installing and configuring.

4. Switch back to legacy (optional)

   Set contextEngine to "legacy" (or remove the key entirely - "legacy" is the default).

How it works

Every time OpenClaw runs a model prompt, the context engine participates at four lifecycle points:

1. Ingest

Called when a new message is added to the session. The engine can store or index the message in its own data store.

2. Assemble

Called before each model run. The engine returns an ordered set of messages (and an optional systemPromptAddition) that fit within the token budget.

3. Compact

Called when the context window is full, or when the user runs /compact. The engine summarizes older history to free space.

4. After turn

Called after a run completes. The engine can persist state, trigger background compaction, or update indexes.

For the bundled non-ACP Codex harness, OpenClaw applies the same lifecycle by projecting assembled context into Codex developer instructions and the current turn prompt. Codex still owns its native thread history and native compactor.

Subagent lifecycle (optional)

OpenClaw calls two optional subagent lifecycle hooks:

Prepare shared context state before a child run starts. The hook receives parent/child session keys, `contextMode` (`isolated` or `fork`), available transcript ids/files, and optional TTL. If it returns a rollback handle, OpenClaw calls it when spawn fails after preparation succeeds. Clean up when a subagent session completes or is swept.

System prompt addition

The assemble method can return a systemPromptAddition string. OpenClaw prepends this to the system prompt for the run. This lets engines inject dynamic recall guidance, retrieval instructions, or context-aware hints without requiring static workspace files.

The legacy engine

The built-in legacy engine preserves OpenClaw’s original behavior:

• Ingest: no-op (the session manager handles message persistence directly).
• Assemble: pass-through (the existing sanitize → validate → limit pipeline in the runtime handles context assembly).
• Compact: delegates to the built-in summarization compaction, which creates a single summary of older messages and keeps recent messages intact.
• After turn: no-op.

The legacy engine does not register tools or provide a systemPromptAddition.

When no plugins.slots.contextEngine is set (or it’s set to "legacy"), this engine is used automatically.

Plugin engines

A plugin can register a context engine using the plugin API:

import { buildMemorySystemPromptAddition } from "openclaw/plugin-sdk/core";

export default function register(api) {
  api.registerContextEngine("my-engine", (ctx) => ({
    info: {
      id: "my-engine",
      name: "My Context Engine",
      ownsCompaction: true,
    },

    async ingest({ sessionId, message, isHeartbeat }) {
      // Store the message in your data store
      return { ingested: true };
    },

    async assemble({ sessionId, messages, tokenBudget, availableTools, citationsMode }) {
      // Return messages that fit the budget
      return {
        messages: buildContext(messages, tokenBudget),
        estimatedTokens: countTokens(messages),
        systemPromptAddition: buildMemorySystemPromptAddition({
          availableTools: availableTools ?? new Set(),
          citationsMode,
        }),
      };
    },

    async compact({ sessionId, force }) {
      // Summarize older context
      return { ok: true, compacted: true };
    },
  }));
}

The factory ctx includes optional config, agentDir, and workspaceDir values so plugins can initialize per-agent or per-workspace state before the first lifecycle hook runs.

Then enable it in config:

{
  plugins: {
    slots: {
      contextEngine: "my-engine",
    },
    entries: {
      "my-engine": {
        enabled: true,
      },
    },
  },
}

The ContextEngine interface

Required members:

Member	Kind	Purpose
info	Property	Engine id, name, version, and whether it owns compaction
ingest(params)	Method	Store a single message
assemble(params)	Method	Build context for a model run (returns AssembleResult)
compact(params)	Method	Summarize/reduce context

assemble returns an AssembleResult with:

The ordered messages to send to the model. The engine's estimate of total tokens in the assembled context. OpenClaw uses this for compaction threshold decisions and diagnostic reporting. Prepended to the system prompt. Controls which token estimate the runner uses for preemptive overflow prechecks. Defaults to `"assembled"`, which means only the assembled prompt's estimate is checked - appropriate for engines that return a windowed, self-contained context. Set to `"preassembly_may_overflow"` only when your assembled view can hide overflow risk in the underlying transcript; the runner then takes the maximum of the assembled estimate and the pre-assembly (unwindowed) session-history estimate when deciding whether to preemptively compact. Either way, the messages you return are still what the model sees - `promptAuthority` only affects the precheck.

compact returns a CompactResult. When compaction rotates the active transcript, result.sessionId and result.sessionFile identify the successor session that the next retry or turn must use.

Optional members:

Member	Kind	Purpose
bootstrap(params)	Method	Initialize engine state for a session. Called once when the engine first sees a session (e.g., import history).
ingestBatch(params)	Method	Ingest a completed turn as a batch. Called after a run completes, with all messages from that turn at once.
afterTurn(params)	Method	Post-run lifecycle work (persist state, trigger background compaction).
prepareSubagentSpawn(params)	Method	Set up shared state for a child session before it starts.
onSubagentEnded(params)	Method	Clean up after a subagent ends.
dispose()	Method	Release resources. Called during gateway shutdown or plugin reload - not per-session.

Host requirements

Context engines can declare host capability requirements on info.hostRequirements. OpenClaw checks these requirements before starting the operation and fails closed with a descriptive error when the selected runtime cannot satisfy them.

For agent runs, declare assemble-before-prompt when the engine must control the actual model prompt through assemble():

info: {
  id: "my-context-engine",
  name: "My Context Engine",
  hostRequirements: {
    "agent-run": {
      requiredCapabilities: ["assemble-before-prompt"],
      unsupportedMessage:
        "Use the native Codex or Pi embedded runtime, or select the legacy context engine.",
    },
  },
}

Native Codex and Pi embedded agent runs satisfy assemble-before-prompt. Generic CLI backends do not, so engines that require it are rejected before the CLI process starts.

ownsCompaction

ownsCompaction controls whether Pi’s built-in in-attempt auto-compaction stays enabled for the run:

ownsCompaction: true

The engine owns compaction behavior. OpenClaw disables Pi’s built-in auto-compaction for that run, and the engine’s compact() implementation is responsible for /compact, overflow recovery compaction, and any proactive compaction it wants to do in afterTurn(). OpenClaw may still run the pre-prompt overflow safeguard; when it predicts the full transcript will overflow, the recovery path calls the active engine’s compact() before submitting another prompt.

ownsCompaction: false or unset

Pi’s built-in auto-compaction may still run during prompt execution, but the active engine’s compact() method is still called for /compact and overflow recovery.

Warning

ownsCompaction: false does not mean OpenClaw automatically falls back to the legacy engine’s compaction path.

That means there are two valid plugin patterns:

Owning modeDelegating mode

Implement your own compaction algorithm and set ownsCompaction: true.

Set ownsCompaction: false and have compact() call delegateCompactionToRuntime(...) from openclaw/plugin-sdk/core to use OpenClaw’s built-in compaction behavior.

A no-op compact() is unsafe for an active non-owning engine because it disables the normal /compact and overflow-recovery compaction path for that engine slot.

Configuration reference

{
  plugins: {
    slots: {
      // Select the active context engine. Default: "legacy".
      // Set to a plugin id to use a plugin engine.
      contextEngine: "legacy",
    },
  },
}

Note

The slot is exclusive at run time - only one registered context engine is resolved for a given run or compaction operation. Other enabled kind: "context-engine" plugins can still load and run their registration code; plugins.slots.contextEngine only selects which registered engine id OpenClaw resolves when it needs a context engine.

Note

Plugin uninstall: when you uninstall the plugin currently selected as plugins.slots.contextEngine, OpenClaw resets the slot back to the default (legacy). The same reset behavior applies to plugins.slots.memory. No manual config edit is required.

Relationship to compaction and memory

Compaction

Compaction is one responsibility of the context engine. The legacy engine delegates to OpenClaw’s built-in summarization. Plugin engines can implement any compaction strategy (DAG summaries, vector retrieval, etc.).

Memory plugins

Memory plugins (plugins.slots.memory) are separate from context engines. Memory plugins provide search/retrieval; context engines control what the model sees. They can work together - a context engine might use memory plugin data during assembly. Plugin engines that want the active memory prompt path should prefer buildMemorySystemPromptAddition(...) from openclaw/plugin-sdk/core, which converts the active memory prompt sections into a ready-to-prepend systemPromptAddition. If an engine needs lower-level control, it can still pull raw lines from openclaw/plugin-sdk/memory-host-core via buildActiveMemoryPromptSection(...).

Session pruning

Trimming old tool results in-memory still runs regardless of which context engine is active.

Tips

• Use openclaw doctor to verify your engine is loading correctly.
• If switching engines, existing sessions continue with their current history. The new engine takes over for future runs.
• Engine errors are logged and surfaced in diagnostics. If a plugin engine fails to register or the selected engine id cannot be resolved, OpenClaw does not fall back automatically; runs fail until you fix the plugin or switch plugins.slots.contextEngine back to "legacy".
• For development, use openclaw plugins install -l ./my-engine to link a local plugin directory without copying.

Related

• Compaction - summarizing long conversations
• Context - how context is built for agent turns
• Plugin Architecture - registering context engine plugins
• Plugin manifest - plugin manifest fields
• Plugins - plugin overview