import type { FastifyBaseLogger } from 'fastify';
import type { Sql } from '../db.js';
import type { Config } from '../config.js';
import type {
  Agent,
  ErrorReason,
  Message,
  MessageMetadata,
  Project,
  Session,
  ToolCall,
  UserStreamFrame,
} from '../types/api.js';
import {
  ALL_TOOLS,
  READ_ONLY_TOOL_NAMES,
  TOOLS_BY_NAME,
  toolJsonSchemas,
  type ToolJsonSchema,
} from './tools.js';
import { PathScopeError, resolveProjectRoot } from './path_guard.js';
import { maybeAutoNameChat } from './auto_name.js';
import { getAgentById } from './agents.js';
import * as compaction from './compaction.js';
import * as modelContext from './model-context.js';
import type { Broker } from './broker.js';

const BASE_SYSTEM_PROMPT = (projectPath: string) =>
  `You are BooCode Chat, a code investigation assistant. The user is working on a project located at ${projectPath}. Use the file-read tools (view_file, list_dir, grep, find_files) to investigate code when needed. Be concise. Cite file paths and line numbers when discussing code. Do not hallucinate file contents — read the file first. Tool results may be truncated; if so, narrow your query rather than guessing.`;

const DB_FLUSH_INTERVAL_MS = 500;

// v1.8.2: tool-call budget defaults. Resolved per-turn by resolveToolBudget.
//   - Agent with explicit max_tool_calls: that value.
//   - Agent with read-only-only tools:    BUDGET_READ_ONLY (30).
//   - Agent with any non-read-only tool:  BUDGET_NON_READ_ONLY (10).
//   - No agent (raw chat):                BUDGET_NO_AGENT (15).
const BUDGET_READ_ONLY = 30;
const BUDGET_NON_READ_ONLY = 10;
const BUDGET_NO_AGENT = 15;

const READ_ONLY_SET: ReadonlySet<string> = new Set(READ_ONLY_TOOL_NAMES);

function resolveToolBudget(agent: Agent | null): number {
  if (agent?.max_tool_calls != null) return agent.max_tool_calls;
  if (!agent) return BUDGET_NO_AGENT;
  const allReadOnly = agent.tools.every((t) => READ_ONLY_SET.has(t));
  return allReadOnly ? BUDGET_READ_ONLY : BUDGET_NON_READ_ONLY;
}

// Synthetic system note appended to the cap-hit summary call. Verbatim from
// the v1.8.2 spec — do not paraphrase: the model is more reliable when the
// instruction is short, declarative, and identical across calls.
const CAP_HIT_SUMMARY_NOTE = (limit: number) =>
  `You've reached the tool budget (${limit} calls). Produce the best answer you can with what you have. Do not call more tools.`;

function isCapHitSentinel(m: Message): boolean {
  return (
    m.role === 'system' &&
    m.metadata !== null &&
    typeof m.metadata === 'object' &&
    (m.metadata as { kind?: unknown }).kind === 'cap_hit'
  );
}

export interface InferenceFrame {
  type:
    | 'message_started'
    | 'delta'
    | 'tool_call'
    | 'tool_result'
    | 'message_complete'
    | 'messages_deleted'
    | 'session_renamed'
    | 'chat_renamed'
    | 'error';
  message_id?: string;
  message_ids?: string[];
  chat_id?: string;
  tool_message_id?: string;
  tool_call_id?: string;
  // v1.8.2: 'system' added so cap-hit sentinel messages can announce themselves
  // through the normal message_started → delta → message_complete sequence.
  role?: 'assistant' | 'tool' | 'user' | 'system';
  content?: string;
  tool_call?: ToolCall;
  output?: unknown;
  truncated?: boolean;
  error?: string;
  // v1.8.2: structured error reason. Set on `type: 'error'` so the UI can
  // surface a specific message; `error` stays the human-readable text.
  reason?: ErrorReason;
  // v1.8.2: piggybacks on `message_complete` so static or terminally-resolved
  // messages can carry their persisted metadata to the live stream without a
  // refetch (sentinels carry { kind: 'cap_hit', ... }; failed messages carry
  // { kind: 'error', ... }).
  metadata?: MessageMetadata | null;
  tokens_used?: number | null;
  ctx_used?: number | null;
  ctx_max?: number | null;
  started_at?: string | null;
  finished_at?: string | null;
  model?: string;
  session_id?: string;
  name?: string;
}

export type FramePublisher = (sessionId: string, frame: InferenceFrame) => void;

interface OpenAiMessage {
  role: 'system' | 'user' | 'assistant' | 'tool';
  content: string | null;
  tool_calls?: Array<{
    id: string;
    type: 'function';
    function: { name: string; arguments: string };
  }>;
  tool_call_id?: string;
}

interface ChatCompletionDelta {
  role?: string;
  content?: string | null;
  tool_calls?: Array<{
    index: number;
    id?: string;
    type?: 'function';
    function?: { name?: string; arguments?: string };
  }>;
}

interface ChatCompletionChunk {
  choices?: Array<{
    delta: ChatCompletionDelta;
    finish_reason: string | null;
  }>;
  usage?: {
    prompt_tokens?: number;
    completion_tokens?: number;
    total_tokens?: number;
  };
}

export interface InferenceContext {
  sql: Sql;
  config: Config;
  log: FastifyBaseLogger;
  publish: FramePublisher;
  publishUser: (frame: UserStreamFrame) => void;
  // v1.11: passed through so compaction.process can publish 'compacted'
  // frames on the same session WS channel useSessionStream subscribes to.
  // Compaction is the only path that needs the raw broker handle (regular
  // inference goes through `publish`); keeping a separate field avoids
  // tempting other code paths into bypassing the session-id binding.
  broker: Broker;
}

// Resolution order: base prompt < agent.system_prompt < user prompt, where
// user prompt = session.system_prompt if non-empty, else project's
// default_system_prompt if non-empty, else nothing. Empty/whitespace-only
// counts as "no override" for both layers (v1.9 inherit semantics — keeps
// the column non-nullable so the existing key/value store stays put).
export function buildSystemPrompt(
  project: Project,
  session: Session,
  agent: Agent | null
): string {
  let out = BASE_SYSTEM_PROMPT(project.path);
  if (agent && agent.system_prompt.trim().length > 0) {
    out += '\n\n' + agent.system_prompt.trim();
  }
  const sessionPrompt = session.system_prompt?.trim() ?? '';
  const projectPrompt = project.default_system_prompt?.trim() ?? '';
  const userPrompt = sessionPrompt || projectPrompt;
  if (userPrompt.length > 0) {
    out += '\n\n' + userPrompt;
  }
  return out;
}

export function buildMessagesPayload(
  session: Session,
  project: Project,
  history: Message[],
  agent: Agent | null = null
): OpenAiMessage[] {
  const out: OpenAiMessage[] = [];
  const systemPrompt = buildSystemPrompt(project, session, agent);
  out.push({ role: 'system', content: systemPrompt });

  // Find the latest compact marker — only send messages from that point onwards
  let startIdx = 0;
  for (let i = history.length - 1; i >= 0; i--) {
    if (history[i]!.kind === 'compact') {
      startIdx = i;
      break;
    }
  }

  for (let i = startIdx; i < history.length; i++) {
    const m = history[i]!;
    if (m.kind === 'compact') {
      out.push({ role: 'system', content: m.content });
      continue;
    }
    // v1.8.2: cap-hit sentinels are UI-only — never send them to the LLM. The
    // synthetic "you've reached the tool budget" note lives only inside the
    // summary call's messages array and is never persisted, so on Continue
    // the model resumes with a clean context.
    if (isCapHitSentinel(m)) continue;
    if (m.role === 'assistant' && m.status === 'streaming') continue;
    if (m.role === 'assistant' && m.status === 'cancelled') continue;
    if (m.role === 'tool') {
      const tr = m.tool_results;
      if (!tr) continue;
      const outputText = tr.error
        ? `error: ${tr.error}`
        : typeof tr.output === 'string'
          ? tr.output
          : JSON.stringify(tr.output);
      out.push({
        role: 'tool',
        content: outputText,
        tool_call_id: tr.tool_call_id,
      });
      continue;
    }
    if (m.role === 'assistant') {
      const msg: OpenAiMessage = {
        role: 'assistant',
        content: m.content && m.content.length > 0 ? m.content : null,
      };
      if (m.tool_calls && m.tool_calls.length > 0) {
        msg.tool_calls = m.tool_calls.map((tc) => ({
          id: tc.id,
          type: 'function' as const,
          function: { name: tc.name, arguments: JSON.stringify(tc.args) },
        }));
      }
      out.push(msg);
      continue;
    }
    out.push({ role: 'user', content: m.content });
  }
  return out;
}

async function loadContext(
  sql: Sql,
  sessionId: string,
  chatId: string
): Promise<{ session: Session; project: Project; history: Message[] } | null> {
  const sessionRows = await sql<Session[]>`
    SELECT id, project_id, name, model, system_prompt, status, created_at, updated_at,
           agent_id, web_search_enabled
    FROM sessions WHERE id = ${sessionId}
  `;
  if (sessionRows.length === 0) return null;
  const session = sessionRows[0]!;

  const projectRows = await sql<Project[]>`
    SELECT id, name, path, added_at, last_session_id, status, gitea_remote,
           default_system_prompt, default_web_search_enabled
    FROM projects WHERE id = ${session.project_id}
  `;
  if (projectRows.length === 0) return null;
  const project = projectRows[0]!;

  // v1.11: filter compacted messages out of the inference assembly. The GET
  // /api/sessions/:id/messages endpoint still returns everything (so the UI
  // can show history with the summary card inline); only LLM payloads skip
  // compacted rows. compacted_at IS NULL keeps the active summary + tail.
  const history = await sql<Message[]>`
    SELECT id, session_id, chat_id, role, content, kind, tool_calls, tool_results, status, last_seq,
           tokens_used, ctx_used, ctx_max, started_at, finished_at, created_at, metadata
    FROM messages
    WHERE chat_id = ${chatId} AND compacted_at IS NULL
    ORDER BY created_at ASC, id ASC
  `;

  return { session, project, history };
}

// v1.11: shared helper used after both finalizeCompletion and executeToolPhase
// persist their token counts. Reads tokens off the just-UPDATEd row (which
// the caller returns from RETURNING), runs compaction.isOverflow, and flips
// chats.needs_compaction. The next runAssistantTurn invocation acts on it.
// Silent on missing tokens — llama-swap occasionally omits usage on truncated
// streams, and we'd rather miss one overflow than crash the inference path.
async function maybeFlagForCompaction(
  ctx: InferenceContext,
  chatId: string,
  updated: { tokens_used: number | null; ctx_used: number | null; ctx_max: number | null } | undefined,
): Promise<void> {
  if (!updated) return;
  const promptTokens = updated.ctx_used;
  const completionTokens = updated.tokens_used;
  const contextLimit = updated.ctx_max;
  if (typeof promptTokens !== 'number') return;
  if (typeof completionTokens !== 'number') return;
  if (typeof contextLimit !== 'number') return;
  const overflow = compaction.isOverflow(
    { prompt_tokens: promptTokens, completion_tokens: completionTokens },
    contextLimit,
  );
  if (!overflow) return;
  await ctx.sql`UPDATE chats SET needs_compaction = true WHERE id = ${chatId}`;
  ctx.log.info({ chatId, promptTokens, completionTokens, contextLimit }, 'inference: flagged for compaction');
}

async function* sseLines(stream: ReadableStream<Uint8Array>): AsyncGenerator<string> {
  const reader = stream.getReader();
  const decoder = new TextDecoder('utf-8');
  let buffer = '';
  try {
    while (true) {
      const { value, done } = await reader.read();
      if (done) break;
      buffer += decoder.decode(value, { stream: true });
      let idx;
      while ((idx = buffer.indexOf('\n')) >= 0) {
        const line = buffer.slice(0, idx).replace(/\r$/, '');
        buffer = buffer.slice(idx + 1);
        if (line.length === 0) continue;
        yield line;
      }
    }
    if (buffer.length > 0) yield buffer;
  } finally {
    reader.releaseLock();
  }
}

interface StreamResult {
  finishReason: string | null;
  content: string;
  toolCalls: ToolCall[];
  promptTokens: number | null;
  completionTokens: number | null;
}

interface StreamOptions {
  // null = omit tools entirely (compact phase); [] = caller stripped all tools
  // (rare; we still omit from the request body to avoid OpenAI 400).
  tools: ToolJsonSchema[] | null;
  temperature?: number;
}

// v1.10.5 Qwen-coder XML fallback. Some local models (notably qwen3-coder via
// llama-swap) emit tool calls as inline XML inside delta.content rather than
// the structured delta.tool_calls field. The XML shape is:
//   <tool_call>
//   <function=NAME>
//   <parameter=KEY>
//   VALUE
//   </parameter>
//   ...more parameters...
//   </function>
//   </tool_call>
// Multiple <tool_call> blocks may appear back-to-back; they never nest.
// streamCompletion buffers delta.content, extracts complete blocks, parses
// them via parseXmlToolCall, and pushes synthetic entries into the existing
// toolCallsBuffer alongside any native JSON-format tool calls.
const XML_TOOL_OPEN = '<tool_call>';
const XML_TOOL_CLOSE = '</tool_call>';

function parseXmlToolCall(
  block: string,
): { name: string; args: Record<string, unknown> } | null {
  const nameMatch = block.match(/<function=([^>]+)>/);
  if (!nameMatch || !nameMatch[1]) return null;
  const name = nameMatch[1].trim();
  if (!name) return null;
  const args: Record<string, unknown> = {};
  // Non-greedy body so each <parameter=…>…</parameter> pair is matched
  // independently even when multiple appear in the same block.
  const paramRe = /<parameter=([^>]+)>([\s\S]*?)<\/parameter>/g;
  for (const m of block.matchAll(paramRe)) {
    const key = (m[1] ?? '').trim();
    if (!key) continue;
    const raw = (m[2] ?? '').trim();
    try {
      args[key] = JSON.parse(raw);
    } catch {
      args[key] = raw;
    }
  }
  return { name, args };
}

// Locate the first character that begins (or completely contains) an
// unfinished <tool_call> opener in `s`. Returns -1 when `s` can be flushed
// to the client in full without risking a partial tag leak.
//   Case 1: a full `<tool_call>` opener with no matching closer — caller
//           must keep everything from that index forward until the next
//           chunk arrives with the closer.
//   Case 2: `s` ends with a strict prefix of `<tool_call>` (e.g. `<tool_c`).
//           Caller must keep just that suffix in the buffer.
// Note: case 1 assumes the calling loop already extracted every complete
// <tool_call>…</tool_call> pair before reaching this check.
function partialXmlOpenerStart(s: string): number {
  const fullOpener = s.indexOf(XML_TOOL_OPEN);
  if (fullOpener !== -1) return fullOpener;
  const lastLt = s.lastIndexOf('<');
  if (lastLt === -1) return -1;
  const suffix = s.slice(lastLt);
  if (XML_TOOL_OPEN.startsWith(suffix) && suffix.length < XML_TOOL_OPEN.length) {
    return lastLt;
  }
  return -1;
}

async function streamCompletion(
  ctx: InferenceContext,
  model: string,
  messages: OpenAiMessage[],
  opts: StreamOptions,
  onDelta: (content: string) => void,
  signal?: AbortSignal
): Promise<StreamResult> {
  const body: Record<string, unknown> = {
    model,
    messages,
    stream: true,
    stream_options: { include_usage: true },
  };
  if (opts.tools && opts.tools.length > 0) {
    body['tools'] = opts.tools;
    body['tool_choice'] = 'auto';
  }
  if (typeof opts.temperature === 'number') {
    body['temperature'] = opts.temperature;
  }

  const res = await fetch(`${ctx.config.LLAMA_SWAP_URL}/v1/chat/completions`, {
    method: 'POST',
    headers: { 'Content-Type': 'application/json' },
    body: JSON.stringify(body),
    signal,
  });
  if (!res.ok || !res.body) {
    const text = await res.text().catch(() => '');
    throw new Error(`llama-swap returned ${res.status}: ${text.slice(0, 200)}`);
  }

  let content = '';
  // v1.10.5: holds delta.content bytes that may contain a partial XML tool
  // call. Anything not part of a (possibly forming) <tool_call>…</tool_call>
  // pair is flushed to content + onDelta as soon as we know it's safe.
  let pendingBuffer = '';
  let finishReason: string | null = null;
  let promptTokens: number | null = null;
  let completionTokens: number | null = null;
  const toolCallsBuffer = new Map<number, { id: string; name: string; argsText: string }>();

  for await (const line of sseLines(res.body)) {
    if (!line.startsWith('data:')) continue;
    const payload = line.slice(5).trim();
    if (payload === '[DONE]') break;
    let parsed: ChatCompletionChunk;
    try {
      parsed = JSON.parse(payload);
    } catch {
      continue;
    }

    if (parsed.usage) {
      if (typeof parsed.usage.prompt_tokens === 'number') {
        promptTokens = parsed.usage.prompt_tokens;
      }
      if (typeof parsed.usage.completion_tokens === 'number') {
        completionTokens = parsed.usage.completion_tokens;
      }
    }
    // v1.11.3: removed dead `parsed.timings.n_ctx` read. llama-server's
    // streaming completion does NOT emit n_ctx in timings (verified
    // empirically); the authoritative source is llama-swap's
    // /upstream/<model>/props endpoint, fetched per-turn via
    // model-context.getModelContext() at the finalization sites below.

    const choice = parsed.choices?.[0];
    if (!choice) continue;
    const delta = choice.delta ?? {};
    if (typeof delta.content === 'string' && delta.content.length > 0) {
      // v1.10.5 XML fallback. Append, then extract any complete tool_call
      // blocks before deciding what's safe to flush as visible content.
      pendingBuffer += delta.content;
      while (true) {
        const startIdx = pendingBuffer.indexOf(XML_TOOL_OPEN);
        if (startIdx === -1) break;
        const closeIdx = pendingBuffer.indexOf(XML_TOOL_CLOSE, startIdx);
        if (closeIdx === -1) break;
        const blockEnd = closeIdx + XML_TOOL_CLOSE.length;
        const block = pendingBuffer.slice(startIdx, blockEnd);
        // Any text before the opener is plain content — flush it now.
        if (startIdx > 0) {
          const before = pendingBuffer.slice(0, startIdx);
          content += before;
          onDelta(before);
        }
        const parsedCall = parseXmlToolCall(block);
        if (parsedCall) {
          const synthIdx = toolCallsBuffer.size;
          toolCallsBuffer.set(synthIdx, {
            id: `xml_call_${synthIdx}`,
            name: parsedCall.name,
            argsText: JSON.stringify(parsedCall.args),
          });
        }
        // If parsing failed we still drop the block — emitting unparseable
        // XML to the chat would look worse than silently swallowing it.
        pendingBuffer = pendingBuffer.slice(blockEnd);
      }
      // After all complete blocks are out, hold back any (partial or full)
      // unclosed opener; flush the rest.
      const partialIdx = partialXmlOpenerStart(pendingBuffer);
      if (partialIdx >= 0) {
        if (partialIdx > 0) {
          const flush = pendingBuffer.slice(0, partialIdx);
          content += flush;
          onDelta(flush);
        }
        pendingBuffer = pendingBuffer.slice(partialIdx);
      } else if (pendingBuffer.length > 0) {
        content += pendingBuffer;
        onDelta(pendingBuffer);
        pendingBuffer = '';
      }
    }
    if (Array.isArray(delta.tool_calls)) {
      for (const tc of delta.tool_calls) {
        const idx = tc.index;
        const existing = toolCallsBuffer.get(idx) ?? { id: '', name: '', argsText: '' };
        if (tc.id) existing.id = tc.id;
        if (tc.function?.name) existing.name = tc.function.name;
        if (typeof tc.function?.arguments === 'string') existing.argsText += tc.function.arguments;
        toolCallsBuffer.set(idx, existing);
      }
    }
    if (choice.finish_reason) finishReason = choice.finish_reason;
  }

  // v1.10.5: if the stream ended mid-XML (e.g. model truncated, no closer
  // ever arrived), flush whatever was buffered as plain content so it isn't
  // silently dropped. Better to show a stray `<tool_call>` than vanish text.
  if (pendingBuffer.length > 0) {
    content += pendingBuffer;
    onDelta(pendingBuffer);
    pendingBuffer = '';
  }

  const toolCalls: ToolCall[] = [];
  for (const [, t] of [...toolCallsBuffer.entries()].sort(([a], [b]) => a - b)) {
    let args: Record<string, unknown> = {};
    if (t.argsText.length > 0) {
      try {
        args = JSON.parse(t.argsText);
      } catch {
        args = { _raw: t.argsText };
      }
    }
    toolCalls.push({ id: t.id || `call_${toolCalls.length}`, name: t.name, args });
  }

  return { finishReason, content, toolCalls, promptTokens, completionTokens };
}

async function executeToolCall(
  projectRoot: string,
  toolCall: ToolCall
): Promise<{ output: unknown; truncated: boolean; error?: string }> {
  const tool = TOOLS_BY_NAME[toolCall.name];
  if (!tool) {
    return { output: null, truncated: false, error: `unknown tool: ${toolCall.name}` };
  }
  const parsed = tool.inputSchema.safeParse(toolCall.args);
  if (!parsed.success) {
    return {
      output: null,
      truncated: false,
      error: `invalid input: ${JSON.stringify(parsed.error.flatten())}`,
    };
  }
  try {
    const output = await tool.execute(parsed.data, projectRoot);
    const truncated =
      typeof output === 'object' && output !== null && 'truncated' in output
        ? Boolean((output as { truncated: unknown }).truncated)
        : false;
    return { output, truncated };
  } catch (err) {
    if (err instanceof PathScopeError) {
      return { output: null, truncated: false, error: err.message };
    }
    return {
      output: null,
      truncated: false,
      error: err instanceof Error ? err.message : String(err),
    };
  }
}

interface TurnArgs {
  sessionId: string;
  chatId: string;
  assistantMessageId: string;
  // v1.8.2: cumulative tool calls executed this run. Compared against the
  // resolved budget at the top of each turn. Replaces the older `depth`
  // counter (which counted iterations, not invocations).
  toolsUsed: number;
  signal: AbortSignal | undefined;
}

interface StreamPhaseState {
  accumulated: string;
  startedAt: string | null;
}

async function executeStreamPhase(
  ctx: InferenceContext,
  args: TurnArgs,
  session: Session,
  messages: OpenAiMessage[],
  state: StreamPhaseState,
  agent: Agent | null
): Promise<StreamResult> {
  const { sessionId, chatId, assistantMessageId, signal } = args;

  const startedRow = await ctx.sql<{ started_at: string }[]>`
    UPDATE messages
    SET started_at = clock_timestamp()
    WHERE id = ${assistantMessageId}
    RETURNING started_at
  `;
  state.startedAt = startedRow[0]?.started_at ?? null;

  ctx.publish(sessionId, {
    type: 'message_started',
    message_id: assistantMessageId,
    chat_id: chatId,
    role: 'assistant',
  });

  let pendingFlushTimer: NodeJS.Timeout | null = null;
  let flushPromise: Promise<unknown> = Promise.resolve();

  const flushNow = () => {
    if (pendingFlushTimer) {
      clearTimeout(pendingFlushTimer);
      pendingFlushTimer = null;
    }
    const snapshot = state.accumulated;
    flushPromise = flushPromise.then(() =>
      ctx.sql`UPDATE messages SET content = ${snapshot} WHERE id = ${assistantMessageId}`
    );
  };

  const scheduleFlush = () => {
    if (pendingFlushTimer) return;
    pendingFlushTimer = setTimeout(() => {
      pendingFlushTimer = null;
      flushNow();
    }, DB_FLUSH_INTERVAL_MS);
  };

  // Tool whitelist: if an agent is set, filter the global tool list to only the
  // tool names it allows. Unknown names in agent.tools are dropped silently
  // (handled here by intersection). When no agent: send all tools.
  const effectiveTools: ToolJsonSchema[] = agent
    ? toolJsonSchemas().filter((t) => agent.tools.includes(t.function.name))
    : toolJsonSchemas();
  const effectiveTemperature = agent?.temperature;

  try {
    return await streamCompletion(
      ctx,
      session.model,
      messages,
      { tools: effectiveTools, temperature: effectiveTemperature },
      (delta) => {
        state.accumulated += delta;
        ctx.publish(sessionId, {
          type: 'delta',
          message_id: assistantMessageId,
          chat_id: chatId,
          content: delta,
        });
        ctx.log.debug({ sessionId, delta }, 'inference delta');
        scheduleFlush();
      },
      signal
    );
  } finally {
    if (pendingFlushTimer) {
      clearTimeout(pendingFlushTimer);
      pendingFlushTimer = null;
    }
    await flushPromise;
  }
}

async function handleAbortOrError(
  ctx: InferenceContext,
  args: TurnArgs,
  accumulated: string,
  err: unknown
): Promise<void> {
  const { sessionId, chatId, assistantMessageId } = args;
  const isAbort = err instanceof Error && err.name === 'AbortError';
  const finalStatus = isAbort ? 'cancelled' : 'failed';
  const errMsg = err instanceof Error ? err.message : String(err);
  // v1.8.2: persist a structured error metadata blob on genuine failures so
  // the bubble can render the reason on reload without re-deriving from the
  // (one-shot) WS error frame. User-initiated abort skips this — there's no
  // "reason" to surface for a stop the user already explicitly chose.
  const errorMetadata: MessageMetadata | null = isAbort
    ? null
    : { kind: 'error', error_reason: 'llm_provider_error', error_text: errMsg };
  if (errorMetadata) {
    await ctx.sql`
      UPDATE messages
      SET status = ${finalStatus},
          content = ${accumulated},
          finished_at = clock_timestamp(),
          metadata = ${ctx.sql.json(errorMetadata as never)}
      WHERE id = ${assistantMessageId}
    `;
  } else {
    await ctx.sql`
      UPDATE messages
      SET status = ${finalStatus},
          content = ${accumulated},
          finished_at = clock_timestamp()
      WHERE id = ${assistantMessageId}
    `;
  }
  const [failSessRow] = await ctx.sql<{ project_id: string; name: string; updated_at: string }[]>`
    UPDATE sessions SET updated_at = clock_timestamp()
    WHERE id = ${sessionId}
    RETURNING project_id, name, updated_at
  `;
  ctx.publishUser({ type: 'session_updated', session_id: sessionId, project_id: failSessRow!.project_id, name: failSessRow!.name, updated_at: failSessRow!.updated_at });
  // v1.8 mobile-tabs: cancellation is a user-initiated stop, treat as idle;
  // genuine errors flip the dot red. v1.8.2: error path also carries a
  // machine-readable `reason` so the UI can render specifics inline.
  if (isAbort) {
    ctx.publishUser({ type: 'chat_status', chat_id: chatId, status: 'idle', at: new Date().toISOString() });
    ctx.publish(sessionId, {
      type: 'message_complete',
      message_id: assistantMessageId,
      chat_id: chatId,
    });
    ctx.log.info({ sessionId, chatId, assistantMessageId }, 'inference cancelled');
  } else {
    ctx.publishUser({
      type: 'chat_status',
      chat_id: chatId,
      status: 'error',
      at: new Date().toISOString(),
      reason: 'llm_provider_error',
    });
    ctx.publish(sessionId, {
      type: 'error',
      message_id: assistantMessageId,
      chat_id: chatId,
      error: errMsg,
      reason: 'llm_provider_error',
    });
    ctx.log.error({ err, sessionId, assistantMessageId }, 'inference failed');
  }
}

async function executeToolPhase(
  ctx: InferenceContext,
  args: TurnArgs,
  result: StreamResult,
  startedAt: string | null,
  session: Session,
  projectRoot: string
): Promise<void> {
  const { sessionId, chatId, assistantMessageId, toolsUsed, signal } = args;
  const { content, toolCalls, promptTokens, completionTokens } = result;

  // v1.11.3: ctx_max comes from llama-swap /upstream/<model>/props, not the
  // streaming completion (which doesn't emit n_ctx). getModelContext caches
  // the positive lookup for the process lifetime, so this is a single Map
  // hit after the first invocation per model.
  const mctx = await modelContext.getModelContext(session.model);
  const nCtx = mctx?.n_ctx ?? null;

  const [updated] = await ctx.sql<
    { tokens_used: number | null; ctx_used: number | null; ctx_max: number | null; finished_at: string | null }[]
  >`
    UPDATE messages
    SET content = ${content},
        status = 'complete',
        tool_calls = ${ctx.sql.json(toolCalls as never)},
        tokens_used = ${completionTokens},
        ctx_used = ${promptTokens},
        ctx_max = ${nCtx},
        finished_at = clock_timestamp()
    WHERE id = ${assistantMessageId}
    RETURNING tokens_used, ctx_used, ctx_max, finished_at
  `;
  // v1.11: flag for compaction if this turn pushed us over the usable budget.
  // We never compact mid-loop (the recursive runAssistantTurn keeps tools
  // flowing); the flag fires on the NEXT turn's pre-fetch hook above.
  await maybeFlagForCompaction(ctx, chatId, updated);
  const [toolSessRow] = await ctx.sql<{ project_id: string; name: string; updated_at: string }[]>`
    UPDATE sessions SET updated_at = clock_timestamp()
    WHERE id = ${sessionId}
    RETURNING project_id, name, updated_at
  `;
  ctx.publishUser({ type: 'session_updated', session_id: sessionId, project_id: toolSessRow!.project_id, name: toolSessRow!.name, updated_at: toolSessRow!.updated_at });
  for (const tc of toolCalls) {
    ctx.publish(sessionId, {
      type: 'tool_call',
      message_id: assistantMessageId,
      chat_id: chatId,
      tool_call: tc,
    });
  }
  ctx.publish(sessionId, {
    type: 'message_complete',
    message_id: assistantMessageId,
    chat_id: chatId,
    tokens_used: updated?.tokens_used ?? null,
    ctx_used: updated?.ctx_used ?? null,
    ctx_max: updated?.ctx_max ?? null,
    started_at: startedAt,
    finished_at: updated?.finished_at ?? null,
    model: session.model,
  });

  // Batch 9.7: ask_user_input pauses the loop. The tool row is still inserted
  // (the answer endpoint needs a target row to UPDATE), but tool_results is
  // pre-stamped with output=null as a "pending" sentinel and no tool_result
  // frame goes out — the card renders from the tool_call frame alone. Mixed
  // batches still execute the other tools normally.
  let pausingForUserInput = false;
  await Promise.all(
    toolCalls.map(async (tc) => {
      const [toolRow] = await ctx.sql<{ id: string }[]>`
        INSERT INTO messages (session_id, chat_id, role, content, status, created_at)
        VALUES (${sessionId}, ${chatId}, 'tool', '', 'complete', clock_timestamp())
        RETURNING id
      `;
      const toolMessageId = toolRow!.id;
      if (tc.name === 'ask_user_input') {
        pausingForUserInput = true;
        const sentinel = { tool_call_id: tc.id, output: null, truncated: false };
        await ctx.sql`
          UPDATE messages
          SET tool_results = ${ctx.sql.json(sentinel as never)}
          WHERE id = ${toolMessageId}
        `;
        return;
      }
      const tres = await executeToolCall(projectRoot, tc);
      const stored = {
        tool_call_id: tc.id,
        output: tres.output,
        truncated: tres.truncated,
        ...(tres.error ? { error: tres.error } : {}),
      };
      await ctx.sql`
        UPDATE messages
        SET tool_results = ${ctx.sql.json(stored as never)}
        WHERE id = ${toolMessageId}
      `;
      ctx.publish(sessionId, {
        type: 'tool_result',
        tool_message_id: toolMessageId,
        chat_id: chatId,
        tool_call_id: tc.id,
        output: tres.output,
        truncated: tres.truncated,
        ...(tres.error ? { error: tres.error } : {}),
      });
    })
  );

  if (pausingForUserInput) {
    // Drop the dot back to idle — the card is the actionable surface now.
    // The next inference turn fires from POST /api/chats/:id/answer_user_input
    // once the user submits their answers.
    ctx.publishUser({
      type: 'chat_status',
      chat_id: chatId,
      status: 'idle',
      at: new Date().toISOString(),
    });
    ctx.log.info(
      { sessionId, chatId, assistantMessageId },
      'inference paused awaiting user input',
    );
    return;
  }

  const [nextAssistant] = await ctx.sql<{ id: string }[]>`
    INSERT INTO messages (session_id, chat_id, role, content, status, created_at)
    VALUES (${sessionId}, ${chatId}, 'assistant', '', 'streaming', clock_timestamp())
    RETURNING id
  `;
  await runAssistantTurn(ctx, {
    sessionId,
    chatId,
    assistantMessageId: nextAssistant!.id,
    // v1.8.2: charge this turn's actual tool invocations against the budget.
    // One assistant message can emit multiple tool_calls, so we add the run
    // count, not 1. The next turn's budget check sees the cumulative total.
    toolsUsed: toolsUsed + result.toolCalls.length,
    signal,
  });
}

async function finalizeCompletion(
  ctx: InferenceContext,
  args: TurnArgs,
  result: StreamResult,
  startedAt: string | null,
  session: Session
): Promise<void> {
  const { sessionId, chatId, assistantMessageId } = args;
  const { content, finishReason, promptTokens, completionTokens } = result;

  // v1.11.3: see executeToolPhase for the rationale.
  const mctx = await modelContext.getModelContext(session.model);
  const nCtx = mctx?.n_ctx ?? null;

  const [updated] = await ctx.sql<
    { tokens_used: number | null; ctx_used: number | null; ctx_max: number | null; finished_at: string | null }[]
  >`
    UPDATE messages
    SET content = ${content},
        status = 'complete',
        tokens_used = ${completionTokens},
        ctx_used = ${promptTokens},
        ctx_max = ${nCtx},
        finished_at = clock_timestamp()
    WHERE id = ${assistantMessageId}
    RETURNING tokens_used, ctx_used, ctx_max, finished_at
  `;
  // v1.11: flag for compaction on the terminal turn too. Catches the common
  // case of a turn that hit the limit without invoking tools.
  await maybeFlagForCompaction(ctx, chatId, updated);
  const [completeSessRow] = await ctx.sql<{ project_id: string; name: string; updated_at: string }[]>`
    UPDATE sessions SET updated_at = clock_timestamp()
    WHERE id = ${sessionId}
    RETURNING project_id, name, updated_at
  `;
  ctx.publishUser({ type: 'session_updated', session_id: sessionId, project_id: completeSessRow!.project_id, name: completeSessRow!.name, updated_at: completeSessRow!.updated_at });
  ctx.publishUser({ type: 'chat_status', chat_id: chatId, status: 'idle', at: new Date().toISOString() });
  ctx.publish(sessionId, {
    type: 'message_complete',
    message_id: assistantMessageId,
    chat_id: chatId,
    tokens_used: updated?.tokens_used ?? null,
    ctx_used: updated?.ctx_used ?? null,
    ctx_max: updated?.ctx_max ?? null,
    started_at: startedAt,
    finished_at: updated?.finished_at ?? null,
    model: session.model,
  });
  ctx.log.info(
    {
      sessionId,
      chatId,
      assistantMessageId,
      finishReason,
      chars: content.length,
      tokens_used: updated?.tokens_used,
      ctx_used: updated?.ctx_used,
    },
    'inference complete'
  );
}

async function runAssistantTurn(
  ctx: InferenceContext,
  args: TurnArgs,
): Promise<void> {
  const { sessionId, chatId } = args;

  // v1.11: if the prior turn flagged this chat for compaction, run it first
  // so loadContext below reads the post-compaction history. We swallow
  // compaction failures (clearing the flag so we don't loop) and proceed
  // with the un-compacted history — a slow turn that hits the model's
  // hard limit is recoverable; a dead session is not.
  const chatFlag = await ctx.sql<{ needs_compaction: boolean }[]>`
    SELECT needs_compaction FROM chats WHERE id = ${chatId}
  `;
  if (chatFlag[0]?.needs_compaction) {
    try {
      await compaction.process({
        sql: ctx.sql,
        config: ctx.config,
        log: ctx.log,
        broker: ctx.broker,
        chatId,
      });
    } catch (err) {
      ctx.log.warn({ err, chatId }, 'auto-compaction failed; clearing flag and proceeding');
      await ctx.sql`UPDATE chats SET needs_compaction = false WHERE id = ${chatId}`;
    }
  }

  const loaded = await loadContext(ctx.sql, sessionId, chatId);
  if (!loaded) {
    ctx.log.warn({ sessionId }, 'inference: session or project missing');
    return;
  }
  const { session, project, history } = loaded;
  const projectRoot = await resolveProjectRoot(project.path);
  // Agent resolution is per-turn so PATCH agent_id mid-conversation takes
  // effect on the next message. Unknown agent_id returns null silently —
  // session falls back to base prompt + all tools + default temperature.
  const agent = session.agent_id
    ? await getAgentById(project.path, session.agent_id)
    : null;

  // v1.8.2: cap-hit replaces the older "tool loop depth exceeded" failure.
  // When we've already burned the budget *before* this turn even runs, we
  // skip straight to the summary flow — the in-flight assistant message slot
  // gets reused for the wrap-up reply instead of being marked failed.
  const budget = resolveToolBudget(agent);
  if (args.toolsUsed >= budget) {
    await runCapHitSummary(ctx, args, session, project, history, agent, budget);
    return;
  }

  const messages = buildMessagesPayload(session, project, history, agent);

  const state: StreamPhaseState = { accumulated: '', startedAt: null };
  let result: StreamResult;
  try {
    result = await executeStreamPhase(ctx, args, session, messages, state, agent);
  } catch (err) {
    await handleAbortOrError(ctx, args, state.accumulated, err);
    return;
  }

  if (result.toolCalls.length > 0) {
    await executeToolPhase(ctx, args, result, state.startedAt, session, projectRoot);
    return;
  }

  await finalizeCompletion(ctx, args, result, state.startedAt, session);
}

export async function runInference(
  ctx: InferenceContext,
  sessionId: string,
  chatId: string,
  assistantMessageId: string,
  signal?: AbortSignal
): Promise<void> {
  // v1.8.2: every fresh inference (initial send, regenerate, force_send,
  // continue) starts with a clean budget. Tool-call accumulation across
  // Continue invocations is what the hard ceiling guards against, not the
  // per-call budget.
  return runAssistantTurn(ctx, { sessionId, chatId, assistantMessageId, toolsUsed: 0, signal });
}

// v1.8.2: cap-hit summary flow. Called instead of erroring when the loop
// hits its budget. Reuses the in-flight assistant message slot to stream a
// short wrap-up reply with the synthetic note prepended and tools disabled,
// then always inserts a cap_hit sentinel afterward (regardless of summary
// outcome) so the UI can show a Continue affordance.
async function runCapHitSummary(
  ctx: InferenceContext,
  args: TurnArgs,
  session: Session,
  project: Project,
  history: Message[],
  agent: Agent | null,
  budget: number,
): Promise<void> {
  const { sessionId, chatId, assistantMessageId, signal } = args;

  const messages = buildMessagesPayload(session, project, history, agent);
  messages.push({ role: 'system', content: CAP_HIT_SUMMARY_NOTE(budget) });

  const startedRow = await ctx.sql<{ started_at: string }[]>`
    UPDATE messages
    SET started_at = clock_timestamp()
    WHERE id = ${assistantMessageId}
    RETURNING started_at
  `;
  const startedAt = startedRow[0]?.started_at ?? null;

  ctx.publish(sessionId, {
    type: 'message_started',
    message_id: assistantMessageId,
    chat_id: chatId,
    role: 'assistant',
  });

  let accumulated = '';
  let pendingFlushTimer: NodeJS.Timeout | null = null;
  let flushPromise: Promise<unknown> = Promise.resolve();
  const flushNow = () => {
    if (pendingFlushTimer) {
      clearTimeout(pendingFlushTimer);
      pendingFlushTimer = null;
    }
    const snapshot = accumulated;
    flushPromise = flushPromise.then(() =>
      ctx.sql`UPDATE messages SET content = ${snapshot} WHERE id = ${assistantMessageId}`
    );
  };
  const scheduleFlush = () => {
    if (pendingFlushTimer) return;
    pendingFlushTimer = setTimeout(() => {
      pendingFlushTimer = null;
      flushNow();
    }, DB_FLUSH_INTERVAL_MS);
  };

  let summaryOk = false;
  let summarySoftCancelled = false;
  let summaryError: string | null = null;
  let result: StreamResult | null = null;
  try {
    result = await streamCompletion(
      ctx,
      session.model,
      messages,
      { tools: null, temperature: agent?.temperature },
      (delta) => {
        accumulated += delta;
        ctx.publish(sessionId, {
          type: 'delta',
          message_id: assistantMessageId,
          chat_id: chatId,
          content: delta,
        });
        scheduleFlush();
      },
      signal,
    );
    summaryOk = true;
  } catch (err) {
    if (err instanceof Error && err.name === 'AbortError') {
      summarySoftCancelled = true;
    } else {
      summaryError = err instanceof Error ? err.message : String(err);
    }
  } finally {
    if (pendingFlushTimer) {
      clearTimeout(pendingFlushTimer);
      pendingFlushTimer = null;
    }
    await flushPromise;
  }

  // Finalize the summary message based on the three outcomes. The sentinel
  // is inserted regardless so the user always has the Continue affordance —
  // even on a partial / failed summary the chat history shows where the
  // budget was hit.
  if (summaryOk && result) {
    // v1.11.3: see executeToolPhase for the rationale.
    const mctx = await modelContext.getModelContext(session.model);
    const nCtx = mctx?.n_ctx ?? null;
    const [updated] = await ctx.sql<
      { tokens_used: number | null; ctx_used: number | null; ctx_max: number | null; finished_at: string | null }[]
    >`
      UPDATE messages
      SET content = ${result.content},
          status = 'complete',
          tokens_used = ${result.completionTokens},
          ctx_used = ${result.promptTokens},
          ctx_max = ${nCtx},
          finished_at = clock_timestamp()
      WHERE id = ${assistantMessageId}
      RETURNING tokens_used, ctx_used, ctx_max, finished_at
    `;
    ctx.publish(sessionId, {
      type: 'message_complete',
      message_id: assistantMessageId,
      chat_id: chatId,
      tokens_used: updated?.tokens_used ?? null,
      ctx_used: updated?.ctx_used ?? null,
      ctx_max: updated?.ctx_max ?? null,
      started_at: startedAt,
      finished_at: updated?.finished_at ?? null,
      model: session.model,
    });
  } else if (summarySoftCancelled) {
    await ctx.sql`
      UPDATE messages
      SET content = ${accumulated},
          status = 'cancelled',
          finished_at = clock_timestamp()
      WHERE id = ${assistantMessageId}
    `;
    ctx.publish(sessionId, {
      type: 'message_complete',
      message_id: assistantMessageId,
      chat_id: chatId,
    });
  } else {
    const errMeta: MessageMetadata = {
      kind: 'error',
      error_reason: 'summary_after_cap_failed',
      error_text: summaryError ?? 'summary failed',
    };
    await ctx.sql`
      UPDATE messages
      SET content = ${accumulated},
          status = 'failed',
          finished_at = clock_timestamp(),
          metadata = ${ctx.sql.json(errMeta as never)}
      WHERE id = ${assistantMessageId}
    `;
    ctx.publish(sessionId, {
      type: 'error',
      message_id: assistantMessageId,
      chat_id: chatId,
      error: summaryError ?? 'summary failed',
      reason: 'summary_after_cap_failed',
    });
  }

  // Bump session/chat updated_at exactly once for this turn.
  const [sessRow] = await ctx.sql<{ project_id: string; name: string; updated_at: string }[]>`
    UPDATE sessions SET updated_at = clock_timestamp()
    WHERE id = ${sessionId}
    RETURNING project_id, name, updated_at
  `;
  ctx.publishUser({
    type: 'session_updated',
    session_id: sessionId,
    project_id: sessRow!.project_id,
    name: sessRow!.name,
    updated_at: sessRow!.updated_at,
  });

  await insertCapHitSentinel(ctx, sessionId, chatId, agent, budget);

  // Status frame fires last so the dot color reflects the terminal state.
  // Success → idle, abort → idle (user-driven stop), error → error+reason.
  if (summaryOk) {
    ctx.publishUser({ type: 'chat_status', chat_id: chatId, status: 'idle', at: new Date().toISOString() });
  } else if (summarySoftCancelled) {
    ctx.publishUser({ type: 'chat_status', chat_id: chatId, status: 'idle', at: new Date().toISOString() });
  } else {
    ctx.publishUser({
      type: 'chat_status',
      chat_id: chatId,
      status: 'error',
      at: new Date().toISOString(),
      reason: 'summary_after_cap_failed',
    });
  }

  ctx.log.info(
    { sessionId, chatId, assistantMessageId, budget, summaryOk, summaryCancelled: summarySoftCancelled },
    'inference cap-hit summary finished',
  );
}

async function insertCapHitSentinel(
  ctx: InferenceContext,
  sessionId: string,
  chatId: string,
  agent: Agent | null,
  budget: number,
): Promise<void> {
  // Hard ceiling: count prior cap_hit sentinels in this chat. After two
  // continues (sentinel count of 2), the next sentinel reports can_continue
  // false and the UI disables the Continue button.
  const priorRows = await ctx.sql<{ count: number }[]>`
    SELECT COUNT(*)::int AS count
    FROM messages
    WHERE chat_id = ${chatId}
      AND role = 'system'
      AND metadata->>'kind' = 'cap_hit'
  `;
  const priorCount = priorRows[0]?.count ?? 0;
  const canContinue = priorCount < 2;
  const metadata: MessageMetadata = {
    kind: 'cap_hit',
    used: budget,
    limit: budget,
    agent_name: agent?.name ?? null,
    can_continue: canContinue,
  };
  const content = `Reached tool budget (${budget}/${budget}). Continue to extend.`;

  const [row] = await ctx.sql<{ id: string }[]>`
    INSERT INTO messages (session_id, chat_id, role, content, status, created_at, metadata)
    VALUES (${sessionId}, ${chatId}, 'system', ${content}, 'complete', clock_timestamp(), ${ctx.sql.json(metadata as never)})
    RETURNING id
  `;

  // The sentinel content is static, but we still walk the standard frame
  // sequence (started → delta → complete) so useSessionStream's reducer
  // appends it via the same path it uses for streaming assistant messages.
  // The delta carries the full text in one chunk.
  ctx.publish(sessionId, {
    type: 'message_started',
    message_id: row!.id,
    chat_id: chatId,
    role: 'system',
  });
  ctx.publish(sessionId, {
    type: 'delta',
    message_id: row!.id,
    chat_id: chatId,
    content,
  });
  ctx.publish(sessionId, {
    type: 'message_complete',
    message_id: row!.id,
    chat_id: chatId,
    metadata,
  });
}

interface InferenceRegistration {
  controller: AbortController;
  completed: Promise<void>;
}

export function createInferenceRunner(
  ctx: Omit<InferenceContext, 'publishUser'>,
  publishUserFn: (user: string, frame: UserStreamFrame) => void
) {
  const registry = new Map<string, InferenceRegistration>();

  return {
    enqueue(sessionId: string, chatId: string, assistantMessageId: string, user: string) {
      const callCtx: InferenceContext = {
        ...ctx,
        publishUser: (frame) => publishUserFn(user, frame),
        // v1.11: broker comes in via ctx (set at registration time). Repeated
        // here so the destructure carries it onto the per-call ctx without
        // having to add it to every enqueue/cancel signature individually.
        broker: ctx.broker,
      };
      // v1.8 mobile-tabs: announce working before the async loop starts so
      // every device subscribed to the user channel sees the amber dot.
      callCtx.publishUser({ type: 'chat_status', chat_id: chatId, status: 'working', at: new Date().toISOString() });
      const controller = new AbortController();
      let resolveCompleted!: () => void;
      const completed = new Promise<void>((res) => { resolveCompleted = res; });
      const registration: InferenceRegistration = { controller, completed };
      registry.set(chatId, registration);
      void (async () => {
        try {
          await runInference(callCtx, sessionId, chatId, assistantMessageId, controller.signal);
          setImmediate(() => {
            void maybeAutoNameChat(callCtx, chatId, sessionId).catch((err: Error) => {
              callCtx.log.warn({ err, chatId }, 'auto-name failed');
            });
          });
        } catch (err) {
          callCtx.log.error({ err }, 'unhandled inference error');
        } finally {
          resolveCompleted();
          // Only clear our own registration; a force-send may have replaced it.
          if (registry.get(chatId) === registration) {
            registry.delete(chatId);
          }
        }
      })();
    },

    async cancel(_sessionId: string, chatId: string): Promise<boolean> {
      const reg = registry.get(chatId);
      if (!reg) return false;
      reg.controller.abort();
      // Swallow — we just need to wait for the catch/finally to persist state.
      await reg.completed.catch(() => {});
      return true;
    },

    hasActive(chatId: string): boolean {
      return registry.has(chatId);
    },
  };
}

export const _toolNames = ALL_TOOLS.map((t) => t.name);