/**
 * Context compaction tests.
 *
 * - Trigger: context usage (the request.total of the most recent token_usage) or the session's
 *   cumulative turn count **reaching** the threshold (>=); the check runs after every LLM
 *   request emits token_usage, both mid-task and at the wrap-up round (reaching the threshold at
 *   task end triggers compaction immediately, without waiting for the next task).
 * - summarize: appends a compaction prompt to the old LLM (merging in all of this round's tool
 *   results first if mid-task); the summary is wrapped as a `[context_summary]` user text and fed
 *   as the first input to the new LLM instance; on failure the original context is kept, never downgraded to discard.
 *   The compaction request carries the session's toolset unchanged (the prompt-cache prefix must
 *   stay byte-identical, #84), and a completed response only counts as success with a valid
 *   summary — non-empty extracted text and no tool calls (issue #83). A rejected response has its
 *   tool calls answered by synthesized failed outputs (pairing repair) and is retried under a
 *   dedicated cap of 5 rejections; then the compaction fails. Transport timeout/malformed
 *   attempts keep the shared reconnect cap.
 * - discard: deferred until task end if mid-task; sends no compaction request, just swaps in a new LLM instance directly.
 * - Process visibility: the compaction request's streamed output is never surfaced to the human,
 *   only the paired compaction events are emitted; the dialogue is written to the old trace, and
 *   on success the trace rotates into a new file (index+1, the new file starts with session_meta;
 *   rotation is deferred until the new context has its first message to write).
 */
import { mkdtemp, rm, access, readdir } from "node:fs/promises";
import { tmpdir } from "node:os";
import { dirname, join } from "node:path";
import { afterEach, beforeEach, describe, expect, it } from "vitest";
import {
  assistantText,
  sessionMeta,
  thinkingMessage,
  tokenUsage,
  toolCall,
  toolCallOutput,
  userText,
} from "../src/omnimessage/index.js";
import type {
  CompactionBeginPayload,
  CompactionEndPayload,
  OmniMessage,
  TextPayload,
  TokenCounts,
  TokenUsagePayload,
} from "../src/omnimessage/index.js";
import type {
  ApproveFn,
  EnvironmentInterface,
  GenerativeModelParameters,
  LLMInterface,
  LLMOutcome,
} from "../src/interfaces.js";
import { ContextEngine } from "../src/engine/context-engine.js";
import type { CompactionSettings } from "../src/engine/context-engine.js";
import { GenerativeModel } from "../src/llm/index.js";
import type { UniConfig, UniEvent, UniMessage } from "@prismshadow/agenthub";
import { Writer, readTrace } from "../src/trace/index.js";

// ---------------------------------------------------------------------------
// Test fixtures
// ---------------------------------------------------------------------------

interface ScriptedResponse {
  messages: OmniMessage[];
  outcome?: LLMOutcome;
}

/** Fake LLM that responds according to a script, recording each input it receives. */
class ScriptedLLM implements LLMInterface {
  calls: OmniMessage[][] = [];
  constructor(
    private readonly responses: ScriptedResponse[],
    readonly label = "llm",
  ) {}

  async *streamGenerate(
    params: GenerativeModelParameters,
  ): AsyncGenerator<OmniMessage, LLMOutcome> {
    this.calls.push(params.newMessages);
    const next = this.responses.shift();
    if (!next) {
      return { status: "failed", message: `${this.label}: no scripted response` };
    }
    for (const msg of next.messages) yield msg;
    return next.outcome ?? { status: "completed" };
  }
}

/** Fake Environment that never runs real commands: any tool call returns a fixed output. */
const fakeEnvironment: EnvironmentInterface = {
  async listTools() {
    return [];
  },
  async *executeTool({ toolCall: tc }) {
    yield toolCallOutput({
      output: "tool ran",
      toolCallId: tc.payload.tool_call_id,
    });
  },
  toolPermission() {
    return "rw";
  },
};

const allowAll: ApproveFn = async () => "allow";

/** Builds a token_usage: request.total is the context-usage figure, session.total is the cumulative one. */
const usage = (requestTotal: number, sessionTotal: number): OmniMessage =>
  tokenUsage(
    { cache_read: 0, cache_write: 0, output: 0, total: sessionTotal },
    { cache_read: 0, cache_write: 0, output: 0, total: requestTotal },
  );

const settings = (over: Partial<CompactionSettings> = {}): CompactionSettings => ({
  maxContextLength: 100,
  maxSessionTurns: -1,
  mode: "summarize",
  prompt: "COMPACT NOW",
  ...over,
});

const metaMessage = sessionMeta({
  session_id: "sess_compact",
  provider: "custom",
  model_id: "test-model",
  model_context_window: 200000,
  system_prompt: "sp",
  tools: [],
  agent_state: "/tmp/state",
  workspace: "/tmp/ws",
});

async function collect(gen: AsyncGenerator<OmniMessage>): Promise<OmniMessage[]> {
  const all: OmniMessage[] = [];
  for await (const msg of gen) all.push(msg);
  return all;
}

type CompactionEventPayload = CompactionBeginPayload | CompactionEndPayload;

const compactionEvents = (msgs: OmniMessage[]): CompactionEventPayload[] =>
  msgs
    .filter((m) => {
      const t = (m.payload as { type?: string }).type ?? "";
      return t === "compaction_begin" || t === "compaction_end";
    })
    .map((m) => m.payload as CompactionEventPayload);

const payloadTypes = (msgs: OmniMessage[]): (string | undefined)[] =>
  msgs.map((m) => (m.payload as { type?: string }).type);

const textOf = (m: OmniMessage): string => (m.payload as TextPayload).text;

// ---------------------------------------------------------------------------
// Tests
// ---------------------------------------------------------------------------

describe("context compaction", () => {
  let traces: string;

  beforeEach(async () => {
    traces = await mkdtemp(join(tmpdir(), "penguin-compaction-"));
  });

  afterEach(async () => {
    await rm(traces, { recursive: true, force: true });
  });

  it("summarize at task boundary: paired events, hidden dialogue, trace rotation, summary joins next prompt", async () => {
    const llm1 = new ScriptedLLM(
      [
        // Task 1's final reply: context usage 150 > threshold 100 -> triggers at the boundary.
        { messages: [assistantText("answer one"), usage(150, 150)] },
        // Compaction request: summary + usage (counted into the session cumulative total).
        {
          messages: [assistantText("[summary]the distilled summary[/summary]"), usage(160, 310)],
        },
      ],
      "llm1",
    );
    const llm2 = new ScriptedLLM(
      [{ messages: [assistantText("answer two"), usage(20, 330)] }],
      "llm2",
    );
    let factoryTokens: TokenCounts | null = null;
    const trace = new Writer({ tracesDir: traces, sessionId: "sess_compact" });
    const engine = new ContextEngine({
      llm: llm1,
      environment: fakeEnvironment,
      trace,
      sessionMeta: metaMessage,
      compaction: settings(),
      createLLM: (tokens) => {
        factoryTokens = tokens;
        return llm2;
      },
    });
    const oldPath = trace.currentPath();

    const out1 = await collect(engine.run([userText("task one")], { approve: allowAll }));

    // Paired compaction events: start carries reason/mode/context/turns, stop carries status.
    const events = compactionEvents(out1);
    expect(events).toHaveLength(2);
    expect(events[0]).toMatchObject({
      type: "compaction_begin",
      reason: "context",
      mode: "summarize",
      context: 150,
      turns: 1,
    });
    expect(events[1]).toMatchObject({ type: "compaction_end", status: "completed" });
    // The compaction process is invisible to the human: the compaction prompt and summary text are never pushed to the output stream.
    const texts = out1.filter((m) => (m.payload as { type?: string }).type === "text").map(textOf);
    expect(texts.some((t) => t.includes("COMPACT NOW"))).toBe(false);
    expect(texts.some((t) => t.includes("distilled"))).toBe(false);
    // Exception: the compaction request's token_usage IS pushed to the output stream, sitting between the paired compaction events (the frontend counts it in stats).
    const types1 = payloadTypes(out1);
    const between = out1.slice(
      types1.indexOf("compaction_begin") + 1,
      types1.lastIndexOf("compaction_end"),
    );
    const usageBetween = between.filter(
      (m) => (m.payload as { type?: string }).type === "token_usage",
    );
    expect(usageBetween).toHaveLength(1);
    expect((usageBetween[0]!.payload as TokenUsagePayload).request.total).toBe(160);

    // The new LLM instance carries over the session's cumulative tokens (including compaction request usage).
    expect(factoryTokens).toMatchObject({ total: 310 });

    // The summary is merged with the next user prompt as the new LLM instance's first input.
    await collect(engine.run([userText("task two")], { approve: allowAll }));
    expect(llm1.calls).toHaveLength(2);
    expect(llm2.calls).toHaveLength(1);
    const firstInput = llm2.calls[0]!.map(textOf);
    expect(firstInput[0]).toBe("[context_summary]\nthe distilled summary\n[/context_summary]");
    expect(firstInput[1]).toBe("task two");

    // Trace splits into files: the old file contains the compaction dialogue and paired events; the new file starts with session_meta.
    const oldTrace = await readTrace(oldPath);
    const oldTypes = payloadTypes(oldTrace);
    expect(oldTypes.filter((t) => t?.startsWith("compaction_"))).toHaveLength(2);
    expect(oldTrace.some((m) => (m.payload as { text?: string }).text === "COMPACT NOW")).toBe(
      true,
    );
    const newTrace = await readTrace(trace.currentPath());
    expect(trace.currentPath()).not.toBe(oldPath);
    expect(newTrace[0]!.type).toBe("session_meta");
    expect(
      newTrace.some((m) =>
        ((m.payload as { text?: string }).text ?? "").startsWith("[context_summary]"),
      ),
    ).toBe(true);
  });

  it("summarize mid-task: tool outputs pair into the compaction request, summary alone feeds the new LLM", async () => {
    const llm1 = new ScriptedLLM(
      [
        // Round 1: tool call + over-threshold usage -> triggers mid-task.
        {
          messages: [toolCall({ name: "t", arguments: "{}", toolCallId: "c1" }), usage(150, 150)],
        },
        // Compaction request (should include c1's tool_call_output plus the compaction prompt).
        { messages: [assistantText("[summary]continue: finish step 2[/summary]")] },
      ],
      "llm1",
    );
    const llm2 = new ScriptedLLM(
      [{ messages: [assistantText("task done"), usage(30, 200)] }],
      "llm2",
    );
    const engine = new ContextEngine({
      llm: llm1,
      environment: fakeEnvironment,
      sessionMeta: metaMessage,
      compaction: settings(),
      createLLM: () => llm2,
    });

    const out = await collect(engine.run([userText("do task")], { approve: allowAll }));

    // Compaction request: all of this round's tool results, paired with their tool_calls, are sent to the old instance along with the compaction prompt.
    expect(llm1.calls).toHaveLength(2);
    const compactionInput = llm1.calls[1]!;
    const inputTypes = payloadTypes(compactionInput);
    expect(inputTypes).toEqual(["tool_call_output", "text"]);
    expect((compactionInput[1]!.payload as TextPayload).text).toBe("COMPACT NOW");

    // The summary itself is the new instance's first input (no hardcoded continuation instruction appended); the task is finished by the new context.
    expect(llm2.calls).toHaveLength(1);
    expect(llm2.calls[0]!.map(textOf)).toEqual([
      "[context_summary]\ncontinue: finish step 2\n[/context_summary]",
    ]);
    const finalTexts = out
      .filter((m) => (m.payload as { type?: string }).type === "text")
      .map(textOf);
    expect(finalTexts).toContain("task done");
    expect(compactionEvents(out).map((e) => e.type)).toEqual([
      "compaction_begin",
      "compaction_end",
    ]);
  });

  it("summarize failure keeps the old context and does NOT downgrade to discard", async () => {
    const llm1 = new ScriptedLLM(
      [
        {
          messages: [toolCall({ name: "t", arguments: "{}", toolCallId: "c1" }), usage(150, 150)],
        },
        // Compaction request fails (not retryable).
        { messages: [], outcome: { status: "failed", message: "auth error" } },
        // Original context is kept: the task continues, tool outputs feed back into the old instance as usual (context usage keeps growing).
        { messages: [assistantText("finished on old context"), usage(190, 340)] },
        // Second trigger (context still over the limit) -> retries compaction at the boundary, this time succeeding.
        { messages: [assistantText("[summary]second try[/summary]")] },
      ],
      "llm1",
    );
    const llm2 = new ScriptedLLM([], "llm2");
    let created = 0;
    const trace = new Writer({ tracesDir: traces, sessionId: "sess_keep" });
    const engine = new ContextEngine({
      llm: llm1,
      environment: fakeEnvironment,
      trace,
      sessionMeta: metaMessage,
      compaction: settings(),
      createLLM: () => {
        created += 1;
        return llm2;
      },
    });
    const oldPath = trace.currentPath();

    const out = await collect(engine.run([userText("go")], { approve: allowAll }));

    // Two event pairs: the first has stop=failed (abandoned, original context kept), the second succeeds.
    const events = compactionEvents(out);
    expect(
      events.map((e) => `${e.type}:${(e as Partial<CompactionEndPayload>).status ?? ""}`),
    ).toEqual([
      "compaction_begin:",
      "compaction_end:failed",
      "compaction_begin:",
      "compaction_end:completed",
    ]);
    // No LLM swap and no trace file split at the moment of failure; rotation happens only after success.
    expect(created).toBe(1);
    expect(llm1.calls).toHaveLength(4);
    // After the failure, the input fed back into the old instance is this round's tool output.
    expect(payloadTypes(llm1.calls[2]!)).toEqual(["tool_call_output"]);
    const oldTrace = await readTrace(oldPath);
    // Still written to the same file after a failed stop (the failed compaction attempt stays auditable); the old file is closed off only after success.
    expect(payloadTypes(oldTrace).filter((t) => t?.startsWith("compaction_"))).toHaveLength(4);
    // Trace rotation is deferred until the next message to write: the current path is unchanged right after a successful compaction.
    expect(trace.currentPath()).toBe(oldPath);
  });

  it("defers trace rotation after boundary compaction until the next run writes", async () => {
    const llm1 = new ScriptedLLM(
      [
        { messages: [assistantText("answer"), usage(150, 150)] },
        { messages: [assistantText("[summary]s[/summary]")] },
      ],
      "llm1",
    );
    const llm2 = new ScriptedLLM(
      [{ messages: [assistantText("next done"), usage(10, 160)] }],
      "llm2",
    );
    const trace = new Writer({ tracesDir: traces, sessionId: "sess_lazy" });
    const engine = new ContextEngine({
      llm: llm1,
      environment: fakeEnvironment,
      trace,
      sessionMeta: metaMessage,
      compaction: settings(),
      createLLM: () => llm2,
    });
    const oldPath = trace.currentPath();

    await collect(engine.run([userText("task 1")], { approve: allowAll }));
    // A new file is not created right after boundary compaction completes: the current path is unchanged, and only the old file exists on disk.
    expect(trace.currentPath()).toBe(oldPath);
    expect(await readdir(dirname(oldPath))).toEqual(["sess_lazy_001.jsonl"]);

    await collect(engine.run([userText("task 2")], { approve: allowAll }));
    // Rotation happens only once the next round has a message to write: the new file opens with session_meta, followed by the summary and the new prompt.
    expect(trace.currentPath()).not.toBe(oldPath);
    const newTrace = await readTrace(trace.currentPath());
    expect(newTrace[0]!.type).toBe("session_meta");
    expect(
      ((newTrace[1]!.payload as { text?: string }).text ?? "").startsWith("[context_summary]"),
    ).toBe(true);
    expect((newTrace[2]!.payload as { text?: string }).text).toBe("task 2");
  });

  it("reconnect exhaustion on the compaction request converges to failed", async () => {
    const llm1 = new ScriptedLLM(
      [
        { messages: [assistantText("answer"), usage(150, 150)] },
        { messages: [], outcome: { status: "timeout" } },
        { messages: [], outcome: { status: "timeout" } },
      ],
      "llm1",
    );
    const engine = new ContextEngine({
      llm: llm1,
      environment: fakeEnvironment,
      compaction: settings(),
      createLLM: () => llm1,
      compactionMaxReconnects: 1,
      reconnectBackoffMs: 1,
    });

    const out = await collect(engine.run([userText("go")], { approve: allowAll }));
    const events = compactionEvents(out);
    expect(events[1]).toMatchObject({ type: "compaction_end", status: "failed" });
    // The retry resends the original input (tool results + prompt; here there are no tool results, just the prompt).
    expect(llm1.calls).toHaveLength(3);
    expect(payloadTypes(llm1.calls[2]!)).toEqual(["text"]);
  });

  it("the compaction loop uses its own cap, not the shared maxReconnects ladder", async () => {
    // Compaction failure has graceful semantics (the original context is kept, compaction
    // retries on the next trigger), so it fails fast: with compactionMaxReconnects=2, the
    // compaction request runs 1+2 attempts and gives up — even though maxReconnects is far
    // larger and would have kept the session stalled through the full exponential ladder.
    const llm1 = new ScriptedLLM(
      [
        { messages: [assistantText("answer"), usage(150, 150)] },
        { messages: [], outcome: { status: "timeout" } },
        { messages: [], outcome: { status: "timeout" } },
        { messages: [], outcome: { status: "timeout" } },
        // Never reached: the compaction cap stops at 3 compaction attempts in total.
        { messages: [assistantText("[summary]late[/summary]")] },
      ],
      "llm1",
    );
    const recorded: OmniMessage[] = [];
    const engine = new ContextEngine({
      llm: llm1,
      environment: fakeEnvironment,
      compaction: settings(),
      createLLM: () => llm1,
      maxReconnects: 8,
      compactionMaxReconnects: 2,
      reconnectBackoffMs: 1,
      trace: {
        write: async (m) => {
          recorded.push(m);
        },
      },
    });

    const out = await collect(engine.run([userText("go")], { approve: allowAll }));
    const events = compactionEvents(out);
    expect(events[1]).toMatchObject({ type: "compaction_end", status: "failed" });
    // 1 turn request + 3 compaction attempts (initial + compactionMaxReconnects retries).
    expect(llm1.calls).toHaveLength(4);
    // The Trace-written compaction request_ends announce the planned backoff under the
    // COMPACTION cap (base 1ms: 1, then 2), with none on the final failure — these events
    // are never streamed, so the value lands in the Trace record only.
    const retryPlans = recorded
      .filter((m) => (m.payload as { type?: string }).type === "request_end")
      .map((m) => (m.payload as { retry_in_ms?: number }).retry_in_ms);
    expect(retryPlans).toEqual([undefined, 1, 2, undefined]);
  });

  it("an empty compaction response (thinking only, no text) is rejected: 5 attempts, then failed with the context kept", async () => {
    // Issue #83: the compaction request completes but yields no text. Committing the empty
    // summary would discard the whole context and lose the task state — the response is
    // rejected and retried under the dedicated rejection cap (5 attempts, #84), then the
    // compaction fails while the original context and Trace file stay current.
    const empty = (n: number): ScriptedResponse => ({
      messages: [thinkingMessage(`pondering, attempt ${n}, no text`)],
    });
    const llm1 = new ScriptedLLM(
      [
        { messages: [assistantText("answer one"), usage(150, 150)] },
        // Five completed-but-empty compaction attempts: the dedicated cap allows exactly 5
        // rejections. compactionMaxReconnects is 1 here on purpose — rejections must NOT
        // consume the transport reconnect budget, or the loop would stop after 2 attempts.
        empty(1),
        empty(2),
        empty(3),
        empty(4),
        empty(5),
        // Original context kept: the next run stays on this instance (usage under the
        // threshold here so the failed compaction isn't immediately retriggered).
        { messages: [assistantText("continuing on the old context"), usage(60, 370)] },
      ],
      "llm1",
    );
    let created = 0;
    const trace = new Writer({ tracesDir: traces, sessionId: "sess_empty" });
    const engine = new ContextEngine({
      llm: llm1,
      environment: fakeEnvironment,
      trace,
      sessionMeta: metaMessage,
      compaction: settings(),
      createLLM: () => {
        created += 1;
        return new ScriptedLLM([], "llm2");
      },
      compactionMaxReconnects: 1,
      reconnectBackoffMs: 1,
    });
    const oldPath = trace.currentPath();

    const out1 = await collect(engine.run([userText("task one")], { approve: allowAll }));

    // Exactly one event pair, ending failed — an empty summary is never a completed compaction.
    const events = compactionEvents(out1);
    expect(
      events.map((e) => `${e.type}:${(e as Partial<CompactionEndPayload>).status ?? ""}`),
    ).toEqual(["compaction_begin:", "compaction_end:failed"]);
    // No rejected attempt's token_usage is surfaced (only a successful compaction yields
    // its usage between the paired events).
    const types1 = payloadTypes(out1);
    const between = out1.slice(
      types1.indexOf("compaction_begin") + 1,
      types1.lastIndexOf("compaction_end"),
    );
    expect(between.filter((m) => (m.payload as { type?: string }).type === "token_usage")).toEqual(
      [],
    );
    // Turn + exactly five compaction attempts (the 5th rejection exhausts the cap, no 6th
    // request), each resending the prompt unchanged — an empty rejection needs no repair.
    expect(llm1.calls).toHaveLength(6);
    for (let i = 1; i <= 5; i += 1) {
      expect(llm1.calls[i]!.map(textOf)).toEqual(["COMPACT NOW"]);
    }
    // Rejection resends are immediate, never announced: no compaction request_end carries a
    // retry_in_ms (they all end `completed`, unlike the transport ladder's timeout ends).
    const rejectionPlans = (await readTrace(oldPath))
      .filter((m) => (m.payload as { type?: string }).type === "request_end")
      .map((m) => (m.payload as { retry_in_ms?: number }).retry_in_ms);
    expect(rejectionPlans.every((p) => p === undefined)).toBe(true);
    // No LLM swap and no Trace rotation: the old file is still current.
    expect(created).toBe(0);
    expect(trace.currentPath()).toBe(oldPath);

    // Subsequent turns still run on the original context: the same instance serves the next
    // run and its input is the plain new prompt — no [context_summary] injected.
    await collect(engine.run([userText("task two")], { approve: allowAll }));
    expect(llm1.calls).toHaveLength(7);
    expect(llm1.calls[6]!.map(textOf)).toEqual(["task two"]);
    expect(trace.currentPath()).toBe(oldPath);
    expect(await readdir(dirname(oldPath))).toEqual(["sess_empty_001.jsonl"]);
  });

  it("tool-calling rejections exhaust the 5-attempt cap: every call is paired, and the next ordinary turn stays clean", async () => {
    // A tool-calling response is not a summary — even when it also carries plausible summary
    // text — but its assistant turn IS committed on the live LLM object. Each rejection's
    // calls are answered with synthesized failed outputs (written to Trace, prepended to the
    // retried input), so no tool_use ever dangles: after the compaction fails, the same
    // object must still serve ordinary turns with a well-formed history (#84 review).
    const callWith = (id: string): ScriptedResponse => ({
      messages: [
        assistantText("[summary]looks plausible[/summary]"),
        toolCall({ name: "t", arguments: "{}", toolCallId: id }),
      ],
    });
    const llm1 = new ScriptedLLM(
      [
        { messages: [assistantText("answer"), usage(150, 150)] },
        callWith("c1"),
        callWith("c2"),
        callWith("c3"),
        callWith("c4"),
        callWith("c5"),
        { messages: [assistantText("still on the old context"), usage(60, 300)] },
      ],
      "llm1",
    );
    let created = 0;
    const trace = new Writer({ tracesDir: traces, sessionId: "sess_paired" });
    const engine = new ContextEngine({
      llm: llm1,
      environment: fakeEnvironment,
      trace,
      sessionMeta: metaMessage,
      compaction: settings(),
      createLLM: () => {
        created += 1;
        return new ScriptedLLM([], "llm2");
      },
      compactionMaxReconnects: 1,
      reconnectBackoffMs: 1,
    });
    const oldPath = trace.currentPath();

    const out = await collect(engine.run([userText("go")], { approve: allowAll }));
    const events = compactionEvents(out);
    expect(events[1]).toMatchObject({ type: "compaction_end", status: "failed" });
    // The rejected tool calls are never approved or executed, and nothing of the compaction
    // dialogue (repairs included) reaches the output stream.
    expect(payloadTypes(out)).not.toContain("approval_decision");
    expect(payloadTypes(out)).not.toContain("tool_call_output");
    expect(created).toBe(0);

    // Five attempts; from the second on, the input leads with the repair answering the
    // previous rejection's call, then re-issues the prompt.
    expect(llm1.calls).toHaveLength(6);
    for (let attempt = 2; attempt <= 5; attempt += 1) {
      const retry = llm1.calls[attempt]!;
      expect(payloadTypes(retry)).toEqual(["tool_call_output", "text"]);
      const repair = retry[0]!.payload as {
        tool_call_id: string;
        output: string;
        stop_reason?: string;
      };
      expect(repair.tool_call_id).toBe(`c${attempt - 1}`);
      expect(repair.output).toBe(
        "[tool error] the compaction request expects a summary, not tool calls",
      );
      expect(repair.stop_reason).toBe("failed");
      expect(textOf(retry[1]!)).toBe("COMPACT NOW");
    }

    // All five synthesized repairs are written to the (old) Trace for replay to mirror.
    const repairIds = (await readTrace(oldPath))
      .filter((m) => {
        const p = m.payload as { type?: string; output?: string };
        return (
          p.type === "tool_call_output" &&
          p.output === "[tool error] the compaction request expects a summary, not tool calls"
        );
      })
      .map((m) => (m.payload as { tool_call_id: string }).tool_call_id);
    expect(repairIds).toEqual(["c1", "c2", "c3", "c4", "c5"]);

    // The next ordinary turn on the SAME engine runs cleanly: the final rejection's repair is
    // held as carry-over and leads the next request, so the committed history the LLM sees
    // never leaves c5's tool_use unanswered — no dangling pairing, no [context_summary].
    await collect(engine.run([userText("next")], { approve: allowAll }));
    expect(llm1.calls).toHaveLength(7);
    const nextTurn = llm1.calls[6]!;
    expect(payloadTypes(nextTurn)).toEqual(["tool_call_output", "text"]);
    expect((nextTurn[0]!.payload as { tool_call_id: string }).tool_call_id).toBe("c5");
    expect(textOf(nextTurn[1]!)).toBe("next");
    // Carry-over is spent: it does not leak into later runs.
    await collect(engine.run([userText("later")], { approve: allowAll }));
    expect(llm1.calls[7]!.map(textOf)).toEqual(["later"]);
  });

  it("a valid summary on the 5th and final allowed attempt completes the compaction", async () => {
    // Counting pin for the rejection cap: four rejected attempts spend the budget but the 5th
    // attempt still gets its chance — a valid summary there succeeds (5 rejections would fail).
    const llm1 = new ScriptedLLM(
      [
        { messages: [assistantText("answer"), usage(150, 150)] },
        // Attempts 1-4: empty (thinking only) -> rejected, retried.
        { messages: [thinkingMessage("blank stare 1"), usage(160, 310)] },
        { messages: [thinkingMessage("blank stare 2")] },
        { messages: [thinkingMessage("blank stare 3")] },
        { messages: [thinkingMessage("blank stare 4")] },
        // Attempt 5: a real summary -> the compaction completes with THIS attempt's output.
        { messages: [assistantText("[summary]fifth attempt wins[/summary]"), usage(170, 480)] },
      ],
      "llm1",
    );
    const llm2 = new ScriptedLLM([{ messages: [assistantText("fresh"), usage(20, 500)] }], "llm2");
    let factoryTokens: TokenCounts | null = null;
    const engine = new ContextEngine({
      llm: llm1,
      environment: fakeEnvironment,
      compaction: settings(),
      createLLM: (tokens) => {
        factoryTokens = tokens;
        return llm2;
      },
      maxReconnects: 1,
      reconnectBackoffMs: 1,
    });

    const out = await collect(engine.run([userText("task one")], { approve: allowAll }));
    const events = compactionEvents(out);
    expect(events).toHaveLength(2);
    expect(events[1]).toMatchObject({ type: "compaction_end", status: "completed" });
    // Only the adopted attempt's token_usage is surfaced between the paired events; rejected
    // attempts' usage still feeds the Session cumulative totals (see below) but is not shown.
    const types = payloadTypes(out);
    const between = out.slice(
      types.indexOf("compaction_begin") + 1,
      types.lastIndexOf("compaction_end"),
    );
    const usageBetween = between.filter(
      (m) => (m.payload as { type?: string }).type === "token_usage",
    );
    expect(usageBetween).toHaveLength(1);
    expect((usageBetween[0]!.payload as TokenUsagePayload).request.total).toBe(170);
    // Session cumulative tokens carried into the new instance include the rejected attempts' usage.
    expect(factoryTokens).toMatchObject({ total: 480 });

    // The new context opens with the 5th attempt's summary.
    await collect(engine.run([userText("task two")], { approve: allowAll }));
    expect(llm1.calls).toHaveLength(6);
    expect(llm2.calls[0]!.map(textOf)).toEqual([
      "[context_summary]\nfifth attempt wins\n[/context_summary]",
      "task two",
    ]);
  });

  it("a tool-calling rejection is repaired and the retry's summary completes the compaction", async () => {
    // The pairing repair (#84 review): the rejected attempt's assistant turn — committed by
    // the stateful LLM — ends in tool_use blocks that were never dispatched. Before retrying,
    // the engine answers each with a synthesized failed tool_call_output (written to Trace),
    // prepended to the retried input so the provider sees tool_use/tool_result paired, and
    // the retry then has a real chance to summarize.
    const llm1 = new ScriptedLLM(
      [
        { messages: [assistantText("answer"), usage(150, 150)] },
        // Attempt 1: rejected — the model reached for a tool instead of summarizing.
        {
          messages: [
            assistantText("[summary]tempting[/summary]"),
            toolCall({ name: "t", arguments: "{}", toolCallId: "c1" }),
          ],
        },
        // Attempt 2 (carrying the repair): a real summary.
        { messages: [assistantText("[summary]real summary[/summary]"), usage(170, 400)] },
      ],
      "llm1",
    );
    const llm2 = new ScriptedLLM([{ messages: [assistantText("fresh"), usage(20, 420)] }], "llm2");
    const trace = new Writer({ tracesDir: traces, sessionId: "sess_repair" });
    const engine = new ContextEngine({
      llm: llm1,
      environment: fakeEnvironment,
      trace,
      sessionMeta: metaMessage,
      compaction: settings(),
      createLLM: () => llm2,
      reconnectBackoffMs: 1,
    });
    const oldPath = trace.currentPath();

    const out = await collect(engine.run([userText("task one")], { approve: allowAll }));
    expect(compactionEvents(out)[1]).toMatchObject({
      type: "compaction_end",
      status: "completed",
    });

    // The retried input answers the rejected attempt's call first, then re-issues the prompt.
    expect(llm1.calls).toHaveLength(3);
    const retry = llm1.calls[2]!;
    expect(payloadTypes(retry)).toEqual(["tool_call_output", "text"]);
    const repair = retry[0]!.payload as {
      tool_call_id: string;
      output: string;
      stop_reason?: string;
    };
    expect(repair.tool_call_id).toBe("c1");
    expect(repair.output).toBe(
      "[tool error] the compaction request expects a summary, not tool calls",
    );
    expect(repair.stop_reason).toBe("failed");
    expect(textOf(retry[1]!)).toBe("COMPACT NOW");

    // The repair belongs to the compaction dialogue: written to the old Trace (so replay
    // mirrors the pairing), never pushed to the output stream.
    const repairsInTrace = (await readTrace(oldPath)).filter((m) => {
      const p = m.payload as { type?: string; tool_call_id?: string };
      return p.type === "tool_call_output" && p.tool_call_id === "c1";
    });
    expect(repairsInTrace).toHaveLength(1);
    expect(payloadTypes(out)).not.toContain("tool_call_output");

    // The new context opens with the retry's summary.
    await collect(engine.run([userText("task two")], { approve: allowAll }));
    expect(llm2.calls[0]!.map(textOf)).toEqual([
      "[context_summary]\nreal summary\n[/context_summary]",
      "task two",
    ]);
  });

  it("a non-completed tool_call (interruption-closure shape) does not reject the summary", async () => {
    // Same filter as the ordinary turn loop: only stop_reason === "completed" tool_calls are
    // real requests. A tool_call synthesized to close out an interruption carries the
    // interruption reason — it is structural, gets no synthesized repair, and must not cost
    // a rejection attempt.
    const llm1 = new ScriptedLLM(
      [
        { messages: [assistantText("answer"), usage(150, 150)] },
        {
          messages: [
            toolCall({ name: "t", arguments: "", toolCallId: "cz", stopReason: "timeout" }),
            assistantText("[summary]still fine[/summary]"),
          ],
        },
      ],
      "llm1",
    );
    const llm2 = new ScriptedLLM([{ messages: [assistantText("ok"), usage(10, 200)] }], "llm2");
    const trace = new Writer({ tracesDir: traces, sessionId: "sess_closure" });
    const engine = new ContextEngine({
      llm: llm1,
      environment: fakeEnvironment,
      trace,
      sessionMeta: metaMessage,
      compaction: settings(),
      createLLM: () => llm2,
    });
    const oldPath = trace.currentPath();

    const out = await collect(engine.run([userText("go")], { approve: allowAll }));
    expect(compactionEvents(out)[1]).toMatchObject({
      type: "compaction_end",
      status: "completed",
    });
    // One compaction attempt, no retry, and no repair synthesized for the closure call.
    expect(llm1.calls).toHaveLength(2);
    const closureRepairs = (await readTrace(oldPath)).filter((m) => {
      const p = m.payload as { type?: string; tool_call_id?: string };
      return p.type === "tool_call_output" && p.tool_call_id === "cz";
    });
    expect(closureRepairs).toEqual([]);
  });

  it("the compaction request carries exactly the same tools as ordinary requests (prompt-cache pin)", async () => {
    // Owner constraint (#84): compaction runs exactly when the context is at its largest, and
    // the provider's prompt cache only holds if the request prefix — the tool list included —
    // stays byte-identical to the ordinary turns'. The engine passes no per-request tool
    // override of any kind, and GenerativeModel serves every request from the same frozen
    // config, so the compaction request's tools are the session's tools, verbatim.
    const configs: (UniConfig | undefined)[] = [];
    const scripted = [
      // Turn 1 answer: usage above the compaction threshold (total 151 >= 100).
      { text: "answer one", promptTokens: 150 },
      // Compaction request: a valid summary.
      { text: "[summary]s[/summary]", promptTokens: 160 },
    ];
    class CapturingModel extends GenerativeModel {
      protected override openStream(
        _uni: UniMessage,
        _signal: AbortSignal,
        config?: UniConfig,
      ): AsyncIterable<UniEvent> {
        configs.push(config);
        const next = scripted.shift()!;
        return (async function* () {
          const event: UniEvent = {
            role: "assistant",
            event_type: "delta",
            content_items: [{ type: "text", text: next.text }],
            finish_reason: "stop",
            usage_metadata: {
              cached_tokens: 0,
              prompt_tokens: next.promptTokens,
              thoughts_tokens: 0,
              response_tokens: 1,
            },
          };
          yield event;
        })();
      }
    }
    const model = new CapturingModel({
      modelId: "claude-sonnet-4-6",
      tools: [
        { name: "exec_command", description: "run a command" },
        { name: "read_file", description: "read a file" },
      ],
    });
    const engine = new ContextEngine({
      llm: model,
      environment: fakeEnvironment,
      compaction: settings(),
      createLLM: () => new ScriptedLLM([], "llm2"),
    });

    const out = await collect(engine.run([userText("go")], { approve: allowAll }));
    expect(compactionEvents(out)[1]).toMatchObject({
      type: "compaction_end",
      status: "completed",
    });
    expect(configs).toHaveLength(2);
    // Identical config object -> identical serialized prefix; the tool list is present and
    // unchanged (not omitted, not [], no tool_choice override).
    expect(configs[1]).toBe(configs[0]);
    expect(configs[1]?.tools?.map((t) => t.name)).toEqual(["exec_command", "read_file"]);
    expect(configs[1] !== undefined && "tool_choice" in configs[1]).toBe(false);
  });

  it("session turns reaching (==) the threshold compact at task end — no waiting for the next task", async () => {
    const llm1 = new ScriptedLLM(
      [
        // Task 1: two LLM requests (a tool round + the final reply).
        // After round 1, turns=1 < 2 doesn't trigger; round 2 (task wrap-up), turns=2 >= 2 -> compacts immediately.
        {
          messages: [toolCall({ name: "t", arguments: "{}", toolCallId: "c1" }), usage(10, 10)],
        },
        { messages: [assistantText("t1 done"), usage(10, 20)] },
        // Compaction request (sent out immediately when task 1 ends).
        { messages: [assistantText("[summary]s[/summary]")] },
      ],
      "llm1",
    );
    const llm2 = new ScriptedLLM([{ messages: [assistantText("t2 done"), usage(10, 30)] }], "llm2");
    const engine = new ContextEngine({
      llm: llm1,
      environment: fakeEnvironment,
      compaction: settings({ maxContextLength: -1, maxSessionTurns: 2 }),
      createLLM: () => llm2,
    });

    // Triggers right at task 1's wrap-up (compacts as soon as the threshold is reached, without waiting for the next task); the summary request goes to the old instance.
    const out1 = await collect(engine.run([userText("task 1")], { approve: allowAll }));
    const events = compactionEvents(out1);
    expect(events[0]).toMatchObject({ type: "compaction_begin", reason: "turns", turns: 2 });
    expect(events[1]).toMatchObject({ type: "compaction_end", status: "completed" });
    expect(llm1.calls).toHaveLength(3);

    // The counter resets after compaction completes: task 2 is picked up by the new instance (summary + new prompt), and no further trigger fires.
    const out2 = await collect(engine.run([userText("task 2")], { approve: allowAll }));
    expect(compactionEvents(out2)).toHaveLength(0);
    expect(llm2.calls).toHaveLength(1);
    expect(llm2.calls[0]!.map(textOf)).toEqual([
      "[context_summary]\ns\n[/context_summary]",
      "task 2",
    ]);
  });

  it("context usage exactly equal to the threshold triggers compaction (>=, not >)", async () => {
    const llm1 = new ScriptedLLM(
      [
        // Wrap-up round context usage 100 == threshold 100 -> triggers.
        { messages: [assistantText("answer"), usage(100, 100)] },
        { messages: [assistantText("[summary]eq[/summary]")] },
      ],
      "llm1",
    );
    const llm2 = new ScriptedLLM([], "llm2");
    const engine = new ContextEngine({
      llm: llm1,
      environment: fakeEnvironment,
      compaction: settings(),
      createLLM: () => llm2,
    });

    const out = await collect(engine.run([userText("go")], { approve: allowAll }));
    const events = compactionEvents(out);
    expect(events[0]).toMatchObject({
      type: "compaction_begin",
      reason: "context",
      context: 100,
    });
    expect(events[1]).toMatchObject({ type: "compaction_end", status: "completed" });
  });

  it("discard defers mid-task, then swaps the LLM at task end without a compaction request", async () => {
    const llm1 = new ScriptedLLM(
      [
        // Round 1: over the limit, but the task is still in progress -> deferred.
        {
          messages: [toolCall({ name: "t", arguments: "{}", toolCallId: "c1" }), usage(150, 150)],
        },
        // Round 2: task ends -> performs discard (no compaction request sent).
        { messages: [assistantText("done"), usage(160, 310)] },
      ],
      "llm1",
    );
    const llm2 = new ScriptedLLM([{ messages: [assistantText("fresh"), usage(10, 320)] }], "llm2");
    const trace = new Writer({ tracesDir: traces, sessionId: "sess_discard" });
    const engine = new ContextEngine({
      llm: llm1,
      environment: fakeEnvironment,
      trace,
      sessionMeta: metaMessage,
      compaction: settings({ mode: "discard" }),
      createLLM: () => llm2,
    });
    const oldPath = trace.currentPath();

    const out = await collect(engine.run([userText("go")], { approve: allowAll }));

    // Triggers exactly once, at task end; the old LLM is called exactly twice (no compaction request).
    const events = compactionEvents(out);
    expect(events.map((e) => `${e.type}:${e.mode}`)).toEqual([
      "compaction_begin:discard",
      "compaction_end:discard",
    ]);
    expect(llm1.calls).toHaveLength(2);

    // The next round's input is used as-is as the new instance's first input (no [context_summary]).
    await collect(engine.run([userText("next task")], { approve: allowAll }));
    expect(llm2.calls[0]!.map(textOf)).toEqual(["next task"]);

    // Trace splits into files: the new file starts with session_meta.
    const newTrace = await readTrace(trace.currentPath());
    expect(trace.currentPath()).not.toBe(oldPath);
    expect(newTrace[0]!.type).toBe("session_meta");
  });

  it("lenient extraction: output without [summary] tags is used verbatim", async () => {
    const llm1 = new ScriptedLLM(
      [
        { messages: [assistantText("answer"), usage(150, 150)] },
        { messages: [assistantText("plain summary text without tags")] },
      ],
      "llm1",
    );
    const llm2 = new ScriptedLLM([{ messages: [assistantText("ok"), usage(10, 160)] }], "llm2");
    const engine = new ContextEngine({
      llm: llm1,
      environment: fakeEnvironment,
      compaction: settings(),
      createLLM: () => llm2,
    });

    await collect(engine.run([userText("go")], { approve: allowAll }));
    await collect(engine.run([userText("next")], { approve: allowAll }));
    expect(textOf(llm2.calls[0]![0]!)).toBe(
      "[context_summary]\nplain summary text without tags\n[/context_summary]",
    );
  });

  it("manual compaction skips threshold checks and reuses the same flow", async () => {
    const llm1 = new ScriptedLLM(
      [
        // One ordinary task (well under the limit).
        { messages: [assistantText("small"), usage(10, 10)] },
        // Manual compaction request.
        { messages: [assistantText("[summary]manual s[/summary]")] },
      ],
      "llm1",
    );
    const llm2 = new ScriptedLLM([{ messages: [assistantText("after"), usage(5, 20)] }], "llm2");
    const engine = new ContextEngine({
      llm: llm1,
      environment: fakeEnvironment,
      compaction: settings(),
      createLLM: () => llm2,
    });

    await collect(engine.run([userText("hi")], { approve: allowAll }));
    const out = await collect(engine.compact());
    const events = compactionEvents(out);
    expect(events[0]).toMatchObject({ type: "compaction_begin", reason: "manual" });
    expect(events[1]).toMatchObject({ type: "compaction_end", status: "completed" });

    await collect(engine.run([userText("next")], { approve: allowAll }));
    expect(llm2.calls[0]!.map(textOf)).toEqual([
      "[context_summary]\nmanual s\n[/context_summary]",
      "next",
    ]);
  });

  it("no compaction capability (createLLM missing) means thresholds never fire and compact() is a no-op", async () => {
    const llm1 = new ScriptedLLM(
      [{ messages: [assistantText("big"), usage(999999, 999999)] }],
      "llm1",
    );
    const engine = new ContextEngine({
      llm: llm1,
      environment: fakeEnvironment,
      compaction: settings(),
    });
    const out = await collect(engine.run([userText("go")], { approve: allowAll }));
    expect(compactionEvents(out)).toHaveLength(0);
    expect(await collect(engine.compact())).toHaveLength(0);
    expect(llm1.calls).toHaveLength(1);
  });

  it('compactability(): reports each specific "cannot compact" reason instead of a blanket false', async () => {
    // compact() emits **zero messages** when there is nothing compactable. The caller (Web / CLI)
    // must be able to ask ahead of time, otherwise it can only wait forever for a compaction
    // banner that never comes -- exactly how "no response from /compact after interrupting on the
    // web" happens: interrupt the first request -> token_usage is never received -> sessionTurns
    // stays at 0 -> compact() returns immediately.
    // The reason also needs to distinguish "just compacted" from "haven't chatted yet" -- both
    // have sessionTurns == 0, but they're two completely different messages to the user: telling
    // someone who just finished compacting that there's "no completed conversation turn yet" is
    // effectively saying nothing useful.
    // The compaction request goes to the **current** LLM (the script's second entry); createLLM
    // supplies the LLM used for the new context after compaction.
    const llm1 = new ScriptedLLM(
      [
        // Usage is kept under maxContextLength (100 per settings()) so automatic compaction doesn't jump in first and reset sessionTurns.
        { messages: [assistantText("hi"), usage(10, 10)] },
        { messages: [assistantText("[summary]s[/summary]")] }, // manual compaction request
      ],
      "llm1",
    );
    const llm2 = new ScriptedLLM([{ messages: [assistantText("after"), usage(5, 20)] }], "llm2");

    // (1) No compaction capability configured (no compaction / createLLM).
    const noCap = new ContextEngine({ llm: llm1, environment: fakeEnvironment });
    expect(noCap.compactability()).toBe("unsupported");

    // (2) Capability configured, but the current context hasn't finished a single round yet: not compactable, and compact() indeed emits no messages.
    const engine = new ContextEngine({
      llm: llm1,
      environment: fakeEnvironment,
      compaction: settings(),
      createLLM: () => llm2,
    });
    expect(engine.compactability()).toBe("empty");
    expect(await collect(engine.compact())).toHaveLength(0);

    // (3) Compactable only after a round finishes (token_usage received).
    await collect(engine.run([userText("go")], { approve: allowAll }));
    expect(engine.compactability()).toBe("ok");
    expect(compactionEvents(await collect(engine.compact()))).not.toHaveLength(0);

    // (4) Compacting again right after a compaction: also not compactable (the new context is
    // empty), but the reason is "just compacted" -- it must not say "no completed conversation
    // turn yet" again, since the user clearly just finished a whole round.
    expect(engine.compactability()).toBe("just_compacted");
    expect(await collect(engine.compact())).toHaveLength(0);

    // (5) Compactable again once a round finishes in the new context.
    await collect(engine.run([userText("next")], { approve: allowAll }));
    expect(engine.compactability()).toBe("ok");
  });

  it("user abort during the compaction request keeps the context and carries tool outputs over", async () => {
    const controller = new AbortController();
    const llm1 = new ScriptedLLM(
      [
        {
          messages: [toolCall({ name: "t", arguments: "{}", toolCallId: "c1" }), usage(150, 150)],
        },
        { messages: [], outcome: { status: "aborted" } },
      ],
      "llm1",
    );
    const engine = new ContextEngine({
      llm: llm1,
      environment: fakeEnvironment,
      compaction: settings(),
      createLLM: () => llm1,
    });
    // The abort signal is already pending before the compaction request: the fake LLM finishes straight to aborted.
    const approveThenAbort: ApproveFn = async () => "allow";
    const runGen = engine.run([userText("go")], {
      approve: approveThenAbort,
      signal: controller.signal,
    });
    const out: OmniMessage[] = [];
    for await (const msg of runGen) {
      out.push(msg);
      // Simulates a user abort right after the compaction start event (the compaction request then returns aborted).
      const p = msg.payload as { type?: string };
      if (p.type === "compaction_begin") controller.abort();
    }

    const events = compactionEvents(out);
    expect(events[1]).toMatchObject({ type: "compaction_end", status: "aborted" });
    // Interrupt cleanup: the tool output is held as carry-over per case A, and the run wraps up with an abort event.
    expect(payloadTypes(out)).toContain("abort");
  });
});