AI: personal assistants without the dead API, and keep Ollama warm

Two things the field report asked for.

1) PERSONAL ASSISTANTS (replacing the sunset OpenAI Assistants API)

The old implementation gave three capabilities. Two are reimplemented here,
the third was confirmed unused and is deliberately not replaced:

 * per-user persona - it already lived in system_gpt_settings.json; it was
   only ever being shipped to OpenAI. It is now the system prompt.
 * per-user conversation thread - OpenAI held this server-side. It now lives
   in assistant_memory.json, keyed by discord user id, trimmed to the most
   recent turns (CONJURER_ASSISTANT_MEMORY_TURNS) and written atomically so a
   torn write cannot lose someone's history. Deliberately a plain trim, not
   the AI summarisation used for the bar's shared memory: these are private
   DMs and must not end up in a public "legend".
 * file_search - not replaced. Confirmed not in use.

The conversation goes through handle_response with request_type="NONE" and an
explicit message list, which keeps it out of the bar's shared memory. The big
win: create_chat_assistant hardcoded model="gpt-4o", so assistants were locked
to OpenAI. They now run on whatever $gadaj_teraz selects - Claude and Ollama
included.

create_chat_assistant / chat_with_assistant are gone, and with them the last
call to beta.threads in the startup path - so the cog cannot be killed by that
API again. (add_files_to_vector_store / delete_files_from_vector_store still
reference beta.assistants but are dead code - nothing calls them - so they
cannot crash anything; left alone rather than widening this change.)

2) KEEPING A SELF-HOSTED MODEL WARM

Loading is the slow part - the GPU is shared with other users - so we preload
via Ollama's documented mechanism: /api/generate with a model, a keep_alive
and NO prompt. It loads the model and generates nothing.

 * on switching to ollama, $gadaj_teraz fires a preload in the BACKGROUND
   (not awaited: loading can take minutes and the command must answer at
   once), so the wait lands on the operator rather than the first user;
 * a warm loop re-asserts keep_alive every CONJURER_OLLAMA_WARM_MINUTES.

Both are hard-guarded on the ACTIVE provider being ollama. Warming a metered
API would burn tokens and money for nothing, so that guard is pinned by a test
asserting the preload is never called for gpt/claude, and another asserting the
preload body carries no prompt (a prompt would make every warm-up generate).

Tests: 82 unit + 71 integration green.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

ai_commands.py

@@ -1,4 +1,5 @@
 # ai command cogs
+import asyncio
 import logging
 import re
 import sys
@@ -17,7 +18,7 @@ from communication_subroutine import AI_QUERY_Q
 
 import ai_functions
 from constants import (
-    ASSISTANTS,
+    OLLAMA_WARM_MINUTES,
     DATA,
     GRAPHICS_PATH,
     INITIAL_TIME_WAIT,
@@ -101,74 +102,45 @@ class Events(commands.Cog):
             text = text[1900:]
 
     async def cog_load(self):
-        # The AI query worker must run regardless of the OpenAI guard below - it
+        # The AI query worker answers via handle_response, so it works on every
-        # answers via handle_response, which works on Claude too. Start it first.
+        # backend. Start it first.
         if not self.ai_query_worker.is_running():
             self.ai_query_worker.start()
-        self.logger.info("Starting personal assistants")
+        # Keeps a self-hosted model resident; it no-ops on any other provider.
-        # Personal assistants use the OpenAI Assistants API (threads/runs), which
+        if not self.ollama_warm_loop.is_running():
-        # has no Anthropic equivalent - skip cleanly when OpenAI isn't wired up
+            self.ollama_warm_loop.start()
-        # (e.g. a Claude-only deployment) instead of crashing the cog load.
+        # NOTE: there is no OpenAI-Assistants bootstrap any more. It called a
-        if OPENAICLIENT is None:
+        # sunset API (beta threads), 404'd, and failed the WHOLE extension -
-            self.logger.warning(
+        # taking every AI command with it. Personal assistants now ride
-                "OPENAICLIENT niedostępny - osobiści asystenci (OpenAI Assistants API) wyłączeni"
+        # handle_response with per-user memory (ai_functions), so they work on
-            )
+        # Claude and Ollama too and nothing has to be created at startup.
-            return
+        self.logger.info("Osobiści asystenci: pamięć per-user, aktywny backend AI")
-        # The bootstrap below must NEVER take the cog down with it. It calls the
+
-        # OpenAI Assistants API (beta threads/runs), which is a legacy surface -
+    @tasks.loop(minutes=OLLAMA_WARM_MINUTES)
-        # it now answers 404, and that exception propagated out of cog_load,
+    async def ollama_warm_loop(self):
-        # failed the whole extension, and took EVERY AI command with it
+        """Keep a self-hosted model resident so users don't pay the load wait.
-        # ($gadaj_teraz, $modele_ai, the conversation handler). Personal
+
-        # assistants are one optional feature; losing them must not disable the
+        Loading is the slow part on a GPU shared with other users, so we
-        # AI cog, which otherwise works fine on Claude and Ollama.
+        re-assert Ollama's keep_alive well inside its window. This preloads
+        WITHOUT generating - no tokens, no cost.
+
+        Hard guard: it does nothing unless the ACTIVE backend is Ollama. Firing
+        warm-ups at a metered API would burn tokens and money for nothing.
+        """
         try:
-            await self._start_personal_assistants()
+            if ai_functions.active_provider() != "ollama":
+                return
+            await ai_functions.warm_active_model()
         except Exception as exc:  # pylint: disable=broad-exception-caught
-            self.logger.warning(
+            self.logger.info("Rozgrzewanie Ollamy nieudane (nieszkodliwe): %s", exc)
-                "Osobiści asystenci (OpenAI Assistants API) wyłączeni - %s: %s. "
-                "Reszta AI (rozmowy, $gadaj_teraz, $modele_ai) działa normalnie.",
-                type(exc).__name__, exc,
-            )
 
-    async def _start_personal_assistants(self):
+    @ollama_warm_loop.before_loop
-        """Bootstrap the per-user OpenAI Assistants threads. Optional feature:
+    async def before_ollama_warm_loop(self):
-        callers must treat a failure here as non-fatal (see cog_load)."""
+        await self.bot.wait_until_ready()
-        for superfryta_id, superfryta in SPECJALNE_ZIEMNIACZKI.items():
-
-            if superfryta[4] != "":
-                self.logger.info(
-                    "Personal assistant for user: %s, exists id: %s,name: %s, owner: %s, special instructions: %s assistant id: %s ",
-                    superfryta_id,
-                    superfryta[0],
-                    superfryta[1],
-                    superfryta[2],
-                    superfryta[3],
-                    superfryta[4],
-                )
-                thread = await OPENAICLIENT.beta.threads.create()
-                self.logger.info("Thread id: %s", thread.id)
-                ASSISTANTS[superfryta[1]] = (
-                    superfryta[2],
-                    superfryta[4],
-                    superfryta[0],
-                    thread,
-                )
-            else:
-                self.logger.info(
-                    "Creating personal assistant for user: %s, id: %s,name: %s, owner: %s, special instructions: %s",
-                    superfryta_id,
-                    superfryta[0],
-                    superfryta[1],
-                    superfryta[2],
-                    superfryta[3],
-                )
-                await ai_functions.create_chat_assistant(
-                    superfryta_id, superfryta[0], superfryta[1], superfryta[2], superfryta[3]
-                )
-        self.logger.info("Started personal assistants")
 
     async def cog_unload(self):
         self.ai_query_worker.cancel()
+        self.ollama_warm_loop.cancel()
 
     @commands.hybrid_command(
         name="switch_dm_mode",
@@ -297,6 +269,15 @@ class Events(commands.Cog):
                 f"Teraz gadam przez **{nazwa_konfigu}** — "
                 f"{cfg.get('provider')} / {cfg.get('latest_model')}."
             )
+            if cfg.get("provider") == "ollama":
+                # Pay the (slow, shared-GPU) load cost NOW, in the background,
+                # so it lands on the operator switching backends rather than on
+                # whoever sends the first message. Not awaited: loading can take
+                # minutes and the command must answer immediately.
+                asyncio.create_task(ai_functions.warm_active_model())
+                message += (
+                    "\nRozgrzewam model w tle — pierwsza odpowiedź może chwilę potrwać."
+                )
             # Switched without pinning a model: show what else is on offer.
             if not model:
                 try:
@@ -430,8 +411,10 @@ class Events(commands.Cog):
                 if message.author.id == superfryta[0]:
                     self.logger.info("Specjalny ziemniak")
                     if self.armia[message.author.id] == Dm_Mode.SPECJALNY_ZIEMNIACZEK:
-                        #await self.bot.process_commands(message)
+                        # superfryta = [discord_id, assistant_name, owner, instructions, legacy_assistant_id]
-                        await ai_functions.chat_with_assistant(message, superfryta[1])
+                        await ai_functions.chat_with_personal_assistant(
+                            message, superfryta[2], superfryta[3]
+                        )
                         return
                     elif self.armia[message.author.id] == Dm_Mode.ECHO_ECHO:
                         await ai_functions.echo(message)

ai_functions.py

@@ -1,16 +1,21 @@
 import asyncio
 import json
 import logging
+import os
 import random
+import tempfile
 
 import openai
 import tiktoken
 import time
 from other_functions import discord_friendly_send
+import requests
+
 from constants import (
     AI_CONFIGS,
     AI_TIMEOUT_SECONDS,
-    ASSISTANTS,
+    ASSISTANT_MEMORY_FILE,
+    ASSISTANT_MEMORY_TURNS,
     CLAUDECLIENT,
     CYCLIC_WORDS,
     DEFAULT_AI_CONFIG,
@@ -21,6 +26,9 @@ from constants import (
     MESSAGE_TABLE,
     MESSAGE_TABLE_MUZYKA,
     OLLAMACLIENT,
+    OLLAMA_KEEP_ALIVE,
+    OLLAMA_PRELOAD_TIMEOUT,
+    OLLAMA_URL,
     OPENAICLIENT,
     SYSTEM_GPT_SETTINGS,
     WORD_REACTIONS,
@@ -290,6 +298,52 @@ async def _ollama_call(messages, model, cfg):
     return (resp.choices[0].message.content or "").strip()
 
 
+def _ollama_preload(model, keep_alive=None) -> bool:
+    """Load ``model`` into Ollama and keep it resident, generating NOTHING.
+
+    Ollama's /api/generate with a model and no prompt is the documented preload:
+    it pays the (slow, GPU-shared) load cost once and returns, producing no
+    tokens. Used to warm up on switch and to re-assert keep_alive periodically.
+
+    Blocking on purpose - callers wrap it in asyncio.to_thread.
+    """
+    if not OLLAMA_URL:
+        return False
+    logger = logging.getLogger("discord")
+    try:
+        resp = requests.post(
+            f"{OLLAMA_URL}/api/generate",
+            json={"model": model, "keep_alive": keep_alive or OLLAMA_KEEP_ALIVE},
+            timeout=OLLAMA_PRELOAD_TIMEOUT,
+        )
+        ok = resp.status_code == 200
+        logger.info("Ollama preload %s -> HTTP %s", model, resp.status_code)
+        return ok
+    except requests.exceptions.RequestException as exc:
+        logger.info("Ollama preload %s failed: %s", model, exc)
+        return False
+
+
+def active_provider() -> str:
+    """Provider of the active config - the guard every warm-up must check.
+
+    Preloading only makes sense for a self-hosted model; firing it at a metered
+    API would burn tokens (and money) for nothing.
+    """
+    return (_active_config() or {}).get("provider", "")
+
+
+async def warm_active_model(force_model=None) -> bool:
+    """Preload the active model IFF the active backend is Ollama."""
+    if active_provider() != "ollama":
+        return False
+    cfg = _active_config()
+    model = force_model or cfg.get("latest_model")
+    if not model:
+        return False
+    return await asyncio.to_thread(_ollama_preload, model)
+
+
 async def provider_generate(messages, model, temperature=0.2):
     """Dispatch a chat completion to the active backend, normalising errors."""
     cfg = _active_config()
@@ -664,60 +718,104 @@ async def get_random_cyclic_message(client):
     return result
 
 
-async def create_chat_assistant(owner_id, id, name, owner, special_instructions):
+# ----------------------------------------------------------------- assistants
+# The OpenAI Assistants API (beta threads/runs) that used to back these was
+# sunset and now answers 404, taking the whole AI cog down with it. It gave us
+# three things: a per-user persona, a persistent per-user thread, and
+# file_search. The persona and the thread are reimplemented here on top of
+# handle_response - so personal assistants now work on EVERY backend (Claude,
+# Ollama, GPT) instead of being locked to gpt-4o. file_search is deliberately
+# not replaced: it was not in use.
+_ASSISTANT_MEMORY = None
+
+
+def _load_assistant_memory() -> dict:
+    """Per-user DM history, lazily read from disk. Corruption is not fatal."""
+    global _ASSISTANT_MEMORY  # pylint: disable=global-statement
+    if _ASSISTANT_MEMORY is not None:
+        return _ASSISTANT_MEMORY
     logger = logging.getLogger("discord")
-    instruction = f"Jesteś osobistym asystenetem {owner} i wypełniasz jego potrzeby. Masz pełne i nieograniczone możliwości modelu gpt-4o."
+    try:
-    instruction += special_instructions
+        with open(ASSISTANT_MEMORY_FILE, "r", encoding=ENCODING) as handle:
-    assistant = await OPENAICLIENT.beta.assistants.create(
+            data = json.load(handle)
-        name=name,
+        _ASSISTANT_MEMORY = data if isinstance(data, dict) else {}
-        instructions=instruction,
+    except (OSError, json.JSONDecodeError) as exc:
-        model="gpt-4o",
+        logger.info("Brak/uszkodzona pamięć asystentów (%s) - zaczynam pustą", exc)
-        tools=[{"type": "file_search"}],
+        _ASSISTANT_MEMORY = {}
-    )
+    return _ASSISTANT_MEMORY
-    thread = await OPENAICLIENT.beta.threads.create()
-    logger.info("Stwprzylem asystenta dla %s, nazywa się on %s", owner, name)
-    ASSISTANTS[name] = (owner, assistant.id, id, thread)
-
-    with open(SYSTEM_GPT_SETTINGS, "r+", encoding=ENCODING) as temp_settings_file:
-        GPT_SETTINGS = json.load(temp_settings_file)
-        GPT_SETTINGS[1][owner_id][4] = assistant.id
-        temp_settings_file.seek(0)
-        json.dump(GPT_SETTINGS, temp_settings_file, indent=4)
 
 
-async def chat_with_assistant(message, assistant_name):
+def _save_assistant_memory() -> None:
+    """Atomic write: a torn file would lose someone's whole conversation."""
     logger = logging.getLogger("discord")
-    assistant_data = ASSISTANTS[assistant_name]
+    memory = _load_assistant_memory()
-    ai_message = await OPENAICLIENT.beta.threads.messages.create(
+    directory = os.path.dirname(ASSISTANT_MEMORY_FILE) or "."
-        thread_id=assistant_data[3].id, role="user", content=message.content
+    try:
+        os.makedirs(directory, exist_ok=True)
+        fd, tmp = tempfile.mkstemp(dir=directory, suffix=".tmp")
+        with os.fdopen(fd, "w", encoding=ENCODING) as handle:
+            json.dump(memory, handle, ensure_ascii=False)
+        os.replace(tmp, ASSISTANT_MEMORY_FILE)
+    except OSError as exc:
+        logger.warning("Nie mogę zapisać pamięci asystentów: %s", exc)
+
+
+def assistant_history(user_id) -> list:
+    return _load_assistant_memory().setdefault(str(user_id), [])
+
+
+def remember_assistant_turn(user_id, user_text, reply_text) -> list:
+    """Append one exchange and trim to the most recent turns.
+
+    A plain trim, not the AI summarisation used for the bar's shared memory:
+    these are private DMs and must not end up in a public 'legend'.
+    """
+    history = assistant_history(user_id)
+    history.append({"role": "user", "content": user_text})
+    history.append({"role": "assistant", "content": reply_text})
+    if len(history) > ASSISTANT_MEMORY_TURNS:
+        del history[: len(history) - ASSISTANT_MEMORY_TURNS]
+    _save_assistant_memory()
+    return history
+
+
+def build_assistant_messages(user_id, owner, special_instructions, prompt) -> list:
+    """System persona + this user's own history + the new turn."""
+    system = (
+        f"Jesteś osobistym asystentem {owner} i wypełniasz jego potrzeby. "
+        f"{special_instructions or ''}"
+    ).strip()
+    return (
+        [{"role": "system", "content": system}]
+        + list(assistant_history(user_id))
+        + [{"role": "user", "content": prompt}]
     )
-    logger.info(ai_message)
+
-    run = await OPENAICLIENT.beta.threads.runs.create_and_poll(
+
-        thread_id=assistant_data[3].id,
+async def chat_with_personal_assistant(message, owner, special_instructions):
-        assistant_id=assistant_data[1],
+    """Answer a DM as this user's personal assistant, on the active backend.
-        instructions=f"Pisze do Ciebie {assistant_data[0]} udziel mu wszelkiej pomocy",
+
+    request_type="NONE" with an explicit message list keeps this OUT of the
+    bar's shared memory - the conversation is carried by the per-user history
+    built above and stored separately.
+    """
+    logger = logging.getLogger("discord")
+    user_id = message.author.id
+    prompt = message.content
+    messages = build_assistant_messages(user_id, owner, special_instructions, prompt)
+    result, _table = await handle_response(
+        prompt,
+        False,
+        False,
+        [],
+        str(owner),
+        "NONE",
+        none_request=messages,
     )
-    done = False
+    remember_assistant_turn(user_id, prompt, result)
-    while not done:
+    logger.info("Asystent odpowiedział %s (%d znaków)", owner, len(result or ""))
-        if run.status == "completed":
+    await discord_friendly_send(message.channel, result)
-            messsages = await OPENAICLIENT.beta.threads.messages.list(
+    return result
-                thread_id=assistant_data[3].id
-            )
-            logger.info(messsages)
-            reply_content = messsages.data[0].content
-            logger.info(reply_content)
-            chat_response = ""
-            for block in reply_content:
-                logger.info(block.text.value)
-                chat_response += block.text.value
-                await discord_friendly_send(message.channel, chat_response)
-                # await message.channel.send(chat_response)
-            done = True
-        elif run.status == "cancelled":
-            await discord_friendly_send(message.channel, "Cos sie wywaliło")
-        else:
-            logger.info(run.status)
-        asyncio.sleep(5)
 
 
 async def echo(message):

constants.py

@@ -245,6 +245,16 @@ DELIVERED_DIR = os.getenv(
 )
 DELIVERED_MAX = int(os.getenv("CONJURER_DELIVERED_MAX", "10000"))
 
+# Personal DM assistants. Replaces the OpenAI Assistants API (threads/runs),
+# which was sunset and answers 404: the persona now rides handle_response, so it
+# works on EVERY backend, and the conversation lives here instead of on OpenAI's
+# server. Kept per user so private DMs never bleed into the bar's shared memory,
+# and trimmed to the most recent turns so it cannot grow without bound.
+ASSISTANT_MEMORY_FILE = os.getenv(
+    "CONJURER_ASSISTANT_MEMORY", os.path.join(_STATE_ROOT, "assistant_memory.json")
+)
+ASSISTANT_MEMORY_TURNS = int(os.getenv("CONJURER_ASSISTANT_MEMORY_TURNS", "40"))
+
 FILE_SERVICE_ADDRESS = os.getenv("CONJURER_FILE_SERVICE", "http://192.168.1.15:5000")
 RADIO_HARBOR_ADDRESS = os.getenv("CONJURER_RADIO_HARBOR", "http://192.168.1.15:54321")
 # Betoniarka (radio-operator service colocated with Liquidsoap). Falls back to
@@ -411,6 +421,19 @@ else:
 AI_TIMEOUT_SECONDS = int(os.getenv("CONJURER_AI_TIMEOUT_SECONDS", "120"))
 
 OLLAMA_URL = os.getenv("CONJURER_OLLAMA_URL", "").rstrip("/")
+# Keeping a self-hosted model resident. Loading it is the slow part (it is
+# offloaded to a GPU shared with other users), so we preload it - Ollama's
+# /api/generate with a model and NO prompt loads it and generates nothing, which
+# costs no tokens and no money. KEEP_ALIVE is how long Ollama should then hold
+# it; the warm loop re-asserts that well inside the window.
+# STRICTLY Ollama-only: doing this against a paid API would burn tokens for
+# nothing, so every caller checks the active provider first.
+OLLAMA_KEEP_ALIVE = os.getenv("CONJURER_OLLAMA_KEEP_ALIVE", "30m")
+OLLAMA_WARM_MINUTES = float(os.getenv("CONJURER_OLLAMA_WARM_MINUTES", "10"))
+# A preload waits for the model to finish loading, which on a shared GPU is the
+# slow path we are trying to move off the user's first message.
+OLLAMA_PRELOAD_TIMEOUT = int(os.getenv("CONJURER_OLLAMA_PRELOAD_TIMEOUT", "600"))
+
 OLLAMA_LATEST_MODEL = os.getenv("CONJURER_OLLAMA_MODEL", "llama3.1:8b")
 OLLAMA_CHEAP_MODEL = os.getenv("CONJURER_OLLAMA_CHEAP_MODEL", OLLAMA_LATEST_MODEL)
 if openai and OLLAMA_URL:

tests/unit/test_ai_provider_switch.py

@@ -394,3 +394,143 @@ def test_persist_survives_an_unreadable_settings_file(tmp_path, monkeypatch):
     broken.write_text("{ not json", encoding="utf-8")
     monkeypatch.setattr(ai_functions, "SYSTEM_GPT_SETTINGS", str(broken))
     ai_functions._persist_active_ai_config("gpt", model_for="gpt")  # must not raise
+
+
+# ----------------------------------------------- keep-warm (Ollama ONLY) ----
+# The money guard: preloading a self-hosted model is free, but firing the same
+# thing at a metered API would burn tokens for nothing. These pin that it can
+# only ever happen for Ollama.
+
+
+def test_warm_active_model_is_a_noop_for_paid_providers(monkeypatch):
+    called = []
+    monkeypatch.setattr(
+        ai_functions, "_ollama_preload", lambda *a, **k: called.append(a) or True
+    )
+    for paid in ("gpt", "claude"):
+        _reset_active(paid)
+        try:
+            assert asyncio.run(ai_functions.warm_active_model()) is False
+        finally:
+            _reset_active("gpt")
+    assert called == [], "a paid backend must never be preloaded"
+
+
+def test_warm_active_model_preloads_when_ollama_is_active(monkeypatch):
+    monkeypatch.setitem(
+        ai_functions.AI_CONFIGS,
+        "ollama",
+        {"provider": "ollama", "latest_model": "qwen2.5:7b", "cheap_model": "c"},
+    )
+    seen = {}
+    monkeypatch.setattr(
+        ai_functions, "_ollama_preload", lambda model, *a, **k: seen.update(model=model) or True
+    )
+    _reset_active("ollama")
+    try:
+        assert asyncio.run(ai_functions.warm_active_model()) is True
+    finally:
+        _reset_active("gpt")
+    assert seen["model"] == "qwen2.5:7b"
+
+
+def test_active_provider_reports_the_switch():
+    _reset_active("gpt")
+    assert ai_functions.active_provider() == "openai"
+    _reset_active("claude")
+    try:
+        assert ai_functions.active_provider() == "anthropic"
+    finally:
+        _reset_active("gpt")
+
+
+def test_preload_sends_no_prompt_so_it_generates_nothing(monkeypatch):
+    # Ollama's documented preload: a model and keep_alive, and NO prompt. If a
+    # prompt ever crept in, every warm-up would silently generate tokens.
+    sent = {}
+
+    class _Resp:
+        status_code = 200
+
+    monkeypatch.setattr(ai_functions, "OLLAMA_URL", "http://ollama:11434")
+    monkeypatch.setattr(
+        ai_functions.requests, "post",
+        lambda url, json=None, timeout=None: sent.update(url=url, body=json) or _Resp(),
+    )
+    assert ai_functions._ollama_preload("qwen2.5:7b") is True
+    assert sent["url"].endswith("/api/generate")
+    assert sent["body"]["model"] == "qwen2.5:7b"
+    assert "keep_alive" in sent["body"]
+    assert "prompt" not in sent["body"], "a preload must not generate"
+
+
+def test_preload_without_endpoint_is_a_noop(monkeypatch):
+    monkeypatch.setattr(ai_functions, "OLLAMA_URL", "")
+    assert ai_functions._ollama_preload("x") is False
+
+
+# ------------------------------------------- personal assistants (per user) --
+# Replaces the sunset OpenAI Assistants API. The two properties that matter:
+# each user's DM history is ISOLATED (private DMs must not leak into another
+# user's context or the bar's shared memory), and it stays BOUNDED.
+
+
+def _fresh_assistant_memory(tmp_path, monkeypatch, turns=40):
+    monkeypatch.setattr(
+        ai_functions, "ASSISTANT_MEMORY_FILE", str(tmp_path / "assistant_memory.json")
+    )
+    monkeypatch.setattr(ai_functions, "ASSISTANT_MEMORY_TURNS", turns)
+    monkeypatch.setattr(ai_functions, "_ASSISTANT_MEMORY", None)
+
+
+def test_assistant_history_is_isolated_per_user(tmp_path, monkeypatch):
+    _fresh_assistant_memory(tmp_path, monkeypatch)
+    ai_functions.remember_assistant_turn(111, "sekret Anny", "ok Anna")
+    ai_functions.remember_assistant_turn(222, "sekret Bartka", "ok Bartek")
+
+    anna = ai_functions.assistant_history(111)
+    bartek = ai_functions.assistant_history(222)
+    assert [m["content"] for m in anna] == ["sekret Anny", "ok Anna"]
+    assert [m["content"] for m in bartek] == ["sekret Bartka", "ok Bartek"]
+    assert "sekret Anny" not in str(bartek)   # no cross-user bleed
+
+
+def test_assistant_history_is_trimmed_to_the_bound(tmp_path, monkeypatch):
+    _fresh_assistant_memory(tmp_path, monkeypatch, turns=4)
+    for i in range(10):
+        ai_functions.remember_assistant_turn(1, f"u{i}", f"a{i}")
+    history = ai_functions.assistant_history(1)
+    assert len(history) == 4                          # bounded
+    assert history[-1]["content"] == "a9"             # newest kept
+    assert all("u0" != m["content"] for m in history)  # oldest dropped
+
+
+def test_assistant_history_survives_a_restart(tmp_path, monkeypatch):
+    _fresh_assistant_memory(tmp_path, monkeypatch)
+    ai_functions.remember_assistant_turn(7, "pamietaj", "pamietam")
+    # Simulate a restart: drop the in-memory cache, re-read from disk.
+    monkeypatch.setattr(ai_functions, "_ASSISTANT_MEMORY", None)
+    assert [m["content"] for m in ai_functions.assistant_history(7)] == [
+        "pamietaj",
+        "pamietam",
+    ]
+
+
+def test_assistant_messages_carry_persona_history_and_new_turn(tmp_path, monkeypatch):
+    _fresh_assistant_memory(tmp_path, monkeypatch)
+    ai_functions.remember_assistant_turn(5, "wczoraj", "odpowiedz")
+    msgs = ai_functions.build_assistant_messages(
+        5, "Towarzysz Młotek", "Mówisz po polsku.", "dzisiaj"
+    )
+    assert msgs[0]["role"] == "system"
+    assert "Towarzysz Młotek" in msgs[0]["content"]
+    assert "Mówisz po polsku." in msgs[0]["content"]
+    assert [m["content"] for m in msgs[1:]] == ["wczoraj", "odpowiedz", "dzisiaj"]
+
+
+def test_corrupt_assistant_memory_starts_empty_instead_of_crashing(tmp_path, monkeypatch):
+    path = tmp_path / "assistant_memory.json"
+    path.write_text("{ not json", encoding="utf-8")
+    monkeypatch.setattr(ai_functions, "ASSISTANT_MEMORY_FILE", str(path))
+    monkeypatch.setattr(ai_functions, "_ASSISTANT_MEMORY", None)
+    assert ai_functions.assistant_history(1) == []