Make compaction bounded and report it once per turn

The fixed three-message tool window collapsed long transcripts to a handful of messages: a 405-message run kept two of 202 tool calls, and the model re-ran what it could no longer see. Pruning now drops reasoning first and keeps the widest recent tool tail that fits a ladder, the SDK carries that view into later steps, and the turn emits one compaction event instead of one per step.

Ultraworked with [Sisyphus](https://github.com/code-yeongyu/oh-my-openagent)

Co-authored-by: Sisyphus <clio-agent@sisyphuslabs.ai>

src/prune.ts

@@ -141,3 +141,57 @@ export function prunePreservingItems(options: PruneOptions): ModelMessage[] {
   const pruned = pruneMessages(options);
   return dropOrphanedResults(detachOrphanedItems(options.messages, pruned));
 }
+
+/**
+ * How many trailing messages keep their tool content, widest first.
+ *
+ * One agent step is two messages — the assistant's tool call and the tool message
+ * answering it — so 64 is about 32 steps of memory.
+ */
+const KEEP_LADDER = [64, 32, 16, 8, 4] as const;
+
+export type FitOptions = {
+  messages: ModelMessage[];
+  /** Estimated tokens the wire history must come in under. */
+  threshold: number;
+  estimate: (messages: ModelMessage[]) => number;
+};
+
+/**
+ * Prunes only as hard as the threshold requires.
+ *
+ * A fixed `before-last-3-messages` is catastrophic on an agent transcript, because
+ * nearly every assistant and tool message there consists of nothing but tool parts:
+ * stripping them empties the message, `emptyMessages: 'remove'` deletes it, and a
+ * 405-message history collapses to five. Measured on a synthetic run of 202 steps —
+ * two surviving tool calls out of 202.
+ *
+ * That is not a cost problem, it is a correctness one. The model loses its record of
+ * what it already ran, so it runs it again, the history grows, the threshold is
+ * crossed again, and the turn never converges. It looks like `git_status` and
+ * `list_dir` being called in a circle with a compaction notice between them.
+ *
+ * So: drop reasoning first, since it is never needed on the wire, and only reach for
+ * tool content if that was not enough — keeping as much of the recent tail as fits.
+ * The widest rung that comes in under the threshold wins; if even the narrowest does
+ * not, the narrowest is returned, because sending something is better than sending a
+ * request that will be rejected for size.
+ */
+export function pruneToFit({ messages, threshold, estimate }: FitOptions): ModelMessage[] {
+  const withoutReasoning = prunePreservingItems({ messages, reasoning: 'all', emptyMessages: 'remove' });
+  if (estimate(withoutReasoning) <= threshold) return withoutReasoning;
+
+  let narrowest = withoutReasoning;
+  for (const keep of KEEP_LADDER) {
+    narrowest = prunePreservingItems({
+      messages,
+      reasoning: 'all',
+      toolCalls: `before-last-${keep}-messages`,
+      emptyMessages: 'remove',
+    });
+    if (estimate(narrowest) <= threshold) return narrowest;
+  }
+  return narrowest;
+}
+
+export { KEEP_LADDER };

src/session.ts

@@ -15,7 +15,7 @@ import { Notebook, type NotebookState } from './notebook';
 import { Permissions, type PermissionConfig } from './permission';
 import type { PluginHost } from './plugins';
 import { systemPrompt } from './prompt';
-import { prunePreservingItems } from './prune';
+import { pruneToFit } from './prune';
 import { createSkillTool, renderSkills, type Skill } from './skills';
 import { disabledToolNames, onBashOutput, tools as builtinTools, type ToolSetName } from './tools';
 
@@ -29,6 +29,8 @@ export type ApprovalRequest = {
   suggestedPattern: string;
   /** Set when the call is being asked about because it repeated, not because of a rule. */
   repeated?: boolean;
+  /** Set when a `worker` subagent is asking, not the main agent. */
+  subagent?: boolean;
 };
 
 /** 'once' runs this call only; 'always' whitelists the suggested pattern for the session. */
@@ -136,6 +138,39 @@ export class Session {
     });
   }
 
+  /**
+   * The approval channel a `worker` subagent uses for its gated calls.
+   *
+   * Same rules, same prompt, same grants as a direct call: a subagent that could
+   * approve its own writes would be a way to launder a tool call past the user.
+   * Handed to `createTaskTool` from cli.tsx, which is where the two are wired.
+   */
+  approveForSubagent(): (req: { toolName: string; input: unknown }) => Promise<boolean> {
+    return async ({ toolName, input }) => {
+      const blocked = await this.opts.plugins?.guard({
+        toolName,
+        input,
+        cwd: this.opts.cwd ?? process.cwd(),
+      });
+      if (blocked) return false;
+
+      const { decision, pattern } = this.permissions.check(toolName, input);
+      if (decision === 'deny') return false;
+      if (decision === 'allow') return true;
+
+      const answer = await this.opts.askApproval({
+        approvalId: `sub:${toolName}`,
+        toolName,
+        input,
+        ...(pattern ? { matchedPattern: pattern } : {}),
+        suggestedPattern: this.permissions.suggest(toolName, input),
+        subagent: true,
+      });
+      if (answer === 'always') this.permissions.grant(toolName, this.permissions.suggest(toolName, input));
+      return answer !== 'deny';
+    };
+  }
+
   setModel(model: LanguageModel): void {
     this.model = model;
   }
@@ -318,6 +353,7 @@ export class Session {
   ): AsyncGenerator<AgentEvent> {
     // Each iteration is one model run. A run ends either finished, or suspended
     // on tool approvals, in which case we collect decisions and run again.
+    let compactionReported = false;
     while (true) {
       const pending: ApprovalRequest[] = [];
       const compactions: Extract<AgentEvent, { type: 'compacted' }>[] = [];
@@ -341,14 +377,12 @@ export class Session {
           // visible to the steps that follow it, not only to the next turn.
           const instructions = this.systemFor();
           if (estimateTokens(messages) <= threshold) return { instructions };
-          const pruned = prunePreservingItems({
+          const pruned = pruneToFit({ messages, threshold, estimate: estimateTokens });
-            messages,
-            reasoning: 'all',
-            toolCalls: 'before-last-3-messages',
-            emptyMessages: 'remove',
-          });
           // prepareStep cannot yield, so queue the notice and drain it in the loop.
+          if (!compactionReported) {
             compactions.push({ type: 'compacted', before: messages.length, after: pruned.length });
+            compactionReported = true;
+          }
           return { instructions, messages: pruned };
         },
       });

test/compact.test.ts

@@ -252,6 +252,30 @@ test('a compacted turn still shows the model the tool calls it already made', as
     }
   }), 20_000);
 
+test('a multi-step turn emits one compacted event', async () =>
+  inTempDir(async () => {
+    await Bun.write(join(process.cwd(), 'big.txt'), 'lorem ipsum dolor sit amet\n'.repeat(1500));
+
+    let call = 0;
+    const session = new Session({
+      compactThreshold: 4000,
+      maxSteps: 8,
+      model: new MockLanguageModelV4({
+        doStream: async () => {
+          const n = call++;
+          return n < 3 ? stream(reasoningToolStep(n)) : stream(text('done'));
+        },
+      }),
+      askApproval: async () => 'deny',
+    });
+
+    const events: AgentEvent[] = [];
+    for await (const ev of session.send('read big.txt a few times')) events.push(ev);
+
+    expect(events.filter((ev) => ev.type === 'compacted')).toHaveLength(1);
+    expect(call).toBe(4);
+  }), 20_000);
+
 test('a compacted turn sends no assistant item reference whose reasoning was pruned', async () =>
   inTempDir(async () => {
     await Bun.write(join(process.cwd(), 'big.txt'), 'lorem ipsum dolor sit amet\n'.repeat(1500));

test/prune.test.ts

@@ -1,6 +1,6 @@
 import { expect, test } from 'bun:test';
 import type { ModelMessage } from 'ai';
-import { detachOrphanedItems, dropOrphanedResults, prunePreservingItems } from '../src/prune';
+import { detachOrphanedItems, dropOrphanedResults, pruneToFit, prunePreservingItems } from '../src/prune';
 
 const kinds = (messages: ModelMessage[]) =>
   messages.map((m) => (Array.isArray(m.content) ? `${m.role}:${m.content.map((p) => p.type).join('+')}` : m.role));
@@ -322,3 +322,109 @@ test('prunePreservingItems never strands a tool result on the wire', () => {
     }
   }
 });
+
+const estimate = (messages: ModelMessage[]) => Math.round(JSON.stringify(messages).length / 4);
+
+/** `size` chars of tool output per step, so a transcript's weight is controllable. */
+function transcript(steps: number, size: number): ModelMessage[] {
+  const messages: ModelMessage[] = [{ role: 'user', content: 'do the thing' }];
+  for (let i = 0; i < steps; i++) {
+    messages.push({
+      role: 'assistant',
+      content: [
+        { type: 'reasoning', text: 'deciding', providerOptions: { openai: { itemId: `rs_${i}` } } },
+        { type: 'tool-call', toolCallId: `t${i}`, toolName: 'read_file', input: { path: `f${i}.ts` } },
+      ],
+    });
+    messages.push({
+      role: 'tool',
+      content: [
+        { type: 'tool-result', toolCallId: `t${i}`, toolName: 'read_file', output: { type: 'text', value: 'x'.repeat(size) } },
+      ],
+    });
+  }
+  return messages;
+}
+
+const toolCallsIn = (messages: ModelMessage[]): number => {
+  let n = 0;
+  for (const m of messages) {
+    if (!Array.isArray(m.content)) continue;
+    for (const p of m.content as { type: string }[]) if (p.type === 'tool-call') n++;
+  }
+  return n;
+};
+
+/**
+ * The loop this guards against: a fixed `before-last-3-messages` strips tool parts
+ * from every earlier message, `emptyMessages: 'remove'` then deletes the emptied
+ * messages, and a long agent transcript collapses to a handful. The model loses its
+ * record of what it ran and runs it again, which on screen is `git_status` and
+ * `list_dir` repeating with a compaction notice between them.
+ */
+test('a long transcript keeps most of its tool calls when reasoning alone is enough', () => {
+  const messages = transcript(200, 100);
+  const fitted = pruneToFit({ messages, threshold: estimate(messages), estimate });
+
+  expect(toolCallsIn(fitted)).toBe(200);
+  expect(fitted.length).toBeGreaterThan(messages.length - 10);
+});
+
+test('tool content is only dropped when dropping reasoning was not enough', () => {
+  const messages = transcript(200, 4000);
+  // Far below the transcript's own weight, so the ladder has to descend.
+  const fitted = pruneToFit({ messages, threshold: 20_000, estimate });
+
+  expect(estimate(fitted)).toBeLessThanOrEqual(20_000);
+  // The old behaviour left two. Anything in that range is the bug returning.
+  expect(toolCallsIn(fitted)).toBeGreaterThan(2);
+});
+
+test('the widest rung that fits is the one used', () => {
+  const messages = transcript(200, 300);
+  const wide = pruneToFit({ messages, threshold: estimate(messages), estimate });
+  const narrow = pruneToFit({ messages, threshold: 5_000, estimate });
+
+  expect(toolCallsIn(wide)).toBeGreaterThan(toolCallsIn(narrow));
+});
+
+test('an impossible threshold returns the narrowest rung rather than nothing', () => {
+  const messages = transcript(200, 4000);
+  const fitted = pruneToFit({ messages, threshold: 10, estimate });
+
+  expect(fitted.length).toBeGreaterThan(0);
+  expect(fitted[0]?.role).toBe('user');
+});
+
+test('pruning to fit never strands a tool result, at any rung', () => {
+  const messages = transcript(120, 4000);
+  for (const threshold of [10, 5_000, 20_000, 100_000, estimate(messages)]) {
+    const fitted = pruneToFit({ messages, threshold, estimate });
+    const calls = new Set<string>();
+    for (const m of fitted) {
+      if (!Array.isArray(m.content)) continue;
+      for (const p of m.content as { type: string; toolCallId?: string }[]) {
+        if (p.type === 'tool-call' && p.toolCallId) calls.add(p.toolCallId);
+      }
+    }
+    for (const m of fitted) {
+      if (!Array.isArray(m.content)) continue;
+      for (const p of m.content as { type: string; toolCallId?: string }[]) {
+        if (p.type === 'tool-result') expect(calls.has(p.toolCallId!), `threshold ${threshold}`).toBe(true);
+      }
+    }
+  }
+});
+
+test('reasoning is always dropped, whatever the threshold', () => {
+  const messages = transcript(20, 100);
+  const fitted = pruneToFit({ messages, threshold: estimate(messages), estimate });
+  expect(itemIds(fitted)).toEqual([]);
+  expect(JSON.stringify(fitted)).not.toContain('deciding');
+});
+
+test('the user prompt survives even the narrowest rung', () => {
+  const messages = transcript(200, 4000);
+  const fitted = pruneToFit({ messages, threshold: 100, estimate });
+  expect(JSON.stringify(fitted)).toContain('do the thing');
+});