Fix tier logging: capture actual_tier, fix parse_run_block regex, remove reply_text truncation

- Add tier_capture param to _run_agent_pipeline; append tier after determination
- Capture actual_tier in run_agent_task from tier_capture list
- Log tier in replied-in line: [agent] replied in Xs tier=Y
- Remove reply_text[:200] truncation (was breaking benchmark keyword matching)
- Update parse_run_block regex to match new log format; llm/send fields now None

Fixes #1, #3, #4

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

.claude/rules/agent-pipeline.md

@@ -1,9 +1,11 @@
 # Agent Pipeline Rules
 
 ## Tiers
-- Complex tier requires `/think ` prefix. Any LLM classification of "complex" is downgraded to medium. Do not change this.
+- Routing is fully automatic: router classifies into light/medium/complex via 3-way embedding similarity.
-- Medium is the default tier. Light is only for trivial static-knowledge queries matched by regex or LLM.
+- Complex tier is reached automatically for deep research queries — no prefix required.
+- Medium is the default tier. Light is only for trivial static-knowledge queries matched by regex or embedding.
 - Light tier upgrade to medium is automatic when URL content is pre-fetched or a fast tool matches.
+- `tier_override` API parameter still allows callers to force a specific tier (e.g. `adolf-deep` model → complex).
 
 ## Medium agent
 - `_DirectModel` makes a single `ainvoke()` call with no tool schema. Do not add tools to the medium agent.

.claude/rules/llm-inference.md

@@ -1,7 +1,8 @@
 # LLM Inference Rules
 
-- All LLM calls must use `base_url=BIFROST_URL` with model name `ollama/<model>`. Never call Ollama directly for inference.
+- All LLM calls must use `base_url=LITELLM_URL` (points to LiteLLM at `host.docker.internal:4000/v1`). Never call Ollama directly for inference.
 - `_reply_semaphore` (asyncio.Semaphore(1)) serializes all GPU inference. Never bypass it or add a second semaphore.
-- Model names in code always use the `ollama/` prefix: `ollama/qwen3:4b`, `ollama/qwen3:8b`, `ollama/qwen2.5:1.5b`.
+- Local Ollama models use the `ollama/` prefix: `ollama/qwen3:4b`, `ollama/qwen2.5:1.5b`. Remote models (e.g. OpenRouter) use their full LiteLLM name: `openrouter/deepseek-r1`.
-- Timeout values: router=30s, medium=180s, complex=600s. Do not reduce them — GPU inference under load is slow.
+- Timeout values: router=30s, medium=180s, complex=600s. Do not reduce them.
-- `VRAMManager` is the only component that contacts Ollama directly (for flush/prewarm/poll). This is intentional — Bifrost cannot manage VRAM.
+- `VRAMManager` is the only component that contacts Ollama directly (for flush/prewarm/poll). This is intentional — LiteLLM cannot manage VRAM.
+- Complex tier uses a remote model (`DEEPAGENTS_COMPLEX_MODEL`) — no VRAM management is needed for it.

.gitignore

@@ -2,5 +2,7 @@ __pycache__/
 *.pyc
 logs/*.jsonl
 adolf_tuning_data/voice_audio/
-benchmark.json
+benchmarks/benchmark.json
-results_latest.json
+benchmarks/results_latest.json
+benchmarks/voice_results*.json
+benchmarks/voice_audio/

CLAUDE.md

@@ -18,8 +18,24 @@ python3 test_routing.py [--easy-only|--medium-only|--hard-only]
 
 # Use case tests — read the .md file and follow its steps as Claude Code
 # example: read tests/use_cases/weather_now.md and execute it
+
+# Routing benchmark — measures tier classification accuracy across 120 queries
+# Run from benchmarks/ — Adolf must be running. DO NOT run during active use (holds GPU).
+cd benchmarks
+python3 run_benchmark.py                       # full run (120 queries)
+python3 run_benchmark.py --tier light          # light tier only (30 queries)
+python3 run_benchmark.py --tier medium         # medium tier only (50 queries)
+python3 run_benchmark.py --tier complex --dry-run  # complex tier, medium model (no API cost)
+python3 run_benchmark.py --category smart_home_control
+python3 run_benchmark.py --ids 1,2,3
+python3 run_benchmark.py --list-categories
+
+# Voice benchmark
+python3 run_voice_benchmark.py
+
+# benchmark.json (dataset) and results_latest.json are gitignored — not committed
 ```
 
 ## Architecture
 
-@ARCHITECTURE.md
+@README.md

README.md

@@ -74,7 +74,7 @@ Autonomous personal assistant with a multi-channel gateway. Three-tier model rou
 
 Adolf uses LangChain's tool interface but only the complex agent actually invokes tools at runtime.
 
-**Complex agent (`/think` prefix):** `web_search` and `fetch_url` are defined as `langchain_core.tools.Tool` objects and passed to `create_deep_agent()`. The deepagents library runs an agentic loop (LangGraph `create_react_agent` under the hood) that sends the tool schema to the model via OpenAI function-calling format and handles tool dispatch.
+**Complex agent:** `web_search` and `fetch_url` are defined as `langchain_core.tools.Tool` objects and passed to `create_deep_agent()`. The deepagents library runs an agentic loop (LangGraph `create_react_agent` under the hood) that sends the tool schema to the model via OpenAI function-calling format and handles tool dispatch.
 
 **Medium agent (default):** `_DirectModel` makes a single `model.ainvoke(messages)` call with no tool schema. Context (memories, fetched URL content) is injected via the system prompt instead. This is intentional — `qwen3:4b` behaves unreliably when a tool array is present.
 
@@ -84,13 +84,13 @@ Adolf uses LangChain's tool interface but only the complex agent actually invoke
 
 | Tier | Model | Agent | Trigger | Latency |
 |------|-------|-------|---------|---------|
-| Light | `qwen2.5:1.5b` (router answers directly) | — | Regex pre-match or LLM classifies "light" | ~2–4s |
+| Light | `qwen2.5:1.5b` (router answers directly) | — | Regex pre-match or 3-way embedding classifies "light" | ~2–4s |
 | Medium | `qwen3:4b` (`DEEPAGENTS_MODEL`) | `_DirectModel` — single LLM call, no tools | Default; also forced when message contains URLs | ~10–20s |
-| Complex | `qwen3:8b` (`DEEPAGENTS_COMPLEX_MODEL`) | `create_deep_agent` — agentic loop with tools | `/think` prefix only | ~60–120s |
+| Complex | `deepseek/deepseek-r1:free` via LiteLLM (`DEEPAGENTS_COMPLEX_MODEL`) | `create_deep_agent` — agentic loop with tools | Auto-classified by embedding similarity | ~30–90s |
 
-**`/think` prefix**: forces complex tier, stripped before sending to agent.
+Routing is fully automatic via 3-way cosine similarity over pre-embedded utterance centroids (light / medium / complex). No prefix required. Use `adolf-deep` model name to force complex tier via API.
 
-Complex tier is locked out unless the message starts with `/think` — any LLM classification of "complex" is downgraded to medium.
+Complex tier is reached automatically for deep research queries — `исследуй`, `изучи все`, `напиши подробный`, etc. — via regex pre-classifier and embedding similarity. No prefix required. Use `adolf-deep` model name to force it via API.
 
 ## Fast Tools (`fast_tools.py`)
 
@@ -164,7 +164,7 @@ Conversation history is keyed by session_id (5-turn buffer).
 
 ```
 adolf/
-├── docker-compose.yml      Services: bifrost, deepagents, openmemory, grammy, crawl4ai, routecheck, cli
+├── docker-compose.yml      Services: deepagents, openmemory, grammy, crawl4ai, routecheck, cli
 ├── Dockerfile              deepagents container (Python 3.12)
 ├── Dockerfile.cli          CLI container (python:3.12-slim + rich)
 ├── agent.py                FastAPI gateway, run_agent_task, Crawl4AI pre-fetch, fast tools, memory pipeline
@@ -175,6 +175,11 @@ adolf/
 ├── agent_factory.py        _DirectModel (medium) / create_deep_agent (complex)
 ├── cli.py                  Interactive CLI REPL — Rich Live streaming + Markdown render
 ├── wiki_research.py        Batch wiki research pipeline (uses /message + SSE)
+├── benchmarks/
+│   ├── run_benchmark.py    Routing accuracy benchmark — 120 queries across 3 tiers
+│   ├── run_voice_benchmark.py  Voice path benchmark
+│   ├── benchmark.json      Query dataset (gitignored)
+│   └── results_latest.json Last run results (gitignored)
 ├── .env                    TELEGRAM_BOT_TOKEN, ROUTECHECK_TOKEN, YANDEX_ROUTING_KEY (not committed)
 ├── routecheck/
 │   ├── app.py              FastAPI: image captcha + /api/route Yandex proxy
@@ -195,7 +200,9 @@ adolf/
 
 | Service | Host Port | Role |
 |---------|-----------|------|
-| Ollama GPU | 11436 | All LLM inference (via Bifrost) + VRAM management (direct) + memory extraction |
+| LiteLLM | 4000 | LLM proxy — all inference goes through here (`LITELLM_URL` env var) |
+| Ollama GPU | 11436 | GPU inference backend + VRAM management (direct) + memory extraction |
 | Ollama CPU | 11435 | nomic-embed-text embeddings for openmemory |
+| Langfuse | 3200 | LLM observability — traces all requests via LiteLLM callbacks |
 | Qdrant | 6333 | Vector store for memories |
 | SearXNG | 11437 | Web search (used by `web_search` tool) |

agent.py

@@ -1,7 +1,9 @@
 import asyncio
+import json as _json_module
 import os
 import time
 from contextlib import asynccontextmanager
+from pathlib import Path
 
 from fastapi import FastAPI, BackgroundTasks, Request
 from fastapi.responses import JSONResponse, StreamingResponse
@@ -16,6 +18,7 @@ _URL_RE = _re.compile(r'https?://[^\s<>"\']+')
 def _extract_urls(text: str) -> list[str]:
     return _URL_RE.findall(text)
 
+from openai import AsyncOpenAI
 from langchain_openai import ChatOpenAI
 from langchain_mcp_adapters.client import MultiServerMCPClient
 from langchain_community.utilities import SearxSearchWrapper
@@ -27,8 +30,9 @@ from agent_factory import build_medium_agent, build_complex_agent
 from fast_tools import FastToolRunner, WeatherTool, CommuteTool
 import channels
 
-# Bifrost gateway — all LLM inference goes through here
+# LiteLLM proxy — all LLM inference goes through here
-BIFROST_URL = os.getenv("BIFROST_URL", "http://bifrost:8080/v1")
+LITELLM_URL = os.getenv("LITELLM_URL", "http://host.docker.internal:4000/v1")
+LITELLM_API_KEY = os.getenv("LITELLM_API_KEY", "dummy")
 # Direct Ollama URL — used only by VRAMManager for flush/prewarm/poll
 OLLAMA_BASE_URL = os.getenv("OLLAMA_BASE_URL", "http://localhost:11434")
 
@@ -44,6 +48,45 @@ ROUTECHECK_TOKEN = os.getenv("ROUTECHECK_TOKEN", "")
 MAX_HISTORY_TURNS = 5
 _conversation_buffers: dict[str, list] = {}
 
+# ── Interaction logging (RLHF data collection) ─────────────────────────────────
+_LOG_DIR = Path(os.getenv("ADOLF_LOG_DIR", "/app/logs"))
+_INTERACTIONS_LOG = _LOG_DIR / "interactions.jsonl"
+
+def _ensure_log_dir() -> None:
+    try:
+        _LOG_DIR.mkdir(parents=True, exist_ok=True)
+    except Exception as e:
+        print(f"[log] cannot create log dir {_LOG_DIR}: {e}", flush=True)
+
+
+async def _log_interaction(
+    session_id: str,
+    channel: str,
+    tier: str,
+    input_text: str,
+    response_text: str | None,
+    latency_ms: int,
+    metadata: dict | None = None,
+) -> None:
+    """Append one interaction record to the JSONL log for future RLHF/finetuning."""
+    record = {
+        "ts": time.time(),
+        "session_id": session_id,
+        "channel": channel,
+        "tier": tier,
+        "input": input_text,
+        "output": response_text or "",
+        "latency_ms": latency_ms,
+    }
+    if metadata:
+        record["metadata"] = metadata
+    try:
+        _ensure_log_dir()
+        with open(_INTERACTIONS_LOG, "a", encoding="utf-8") as f:
+            f.write(_json_module.dumps(record, ensure_ascii=False) + "\n")
+    except Exception as e:
+        print(f"[log] write error: {e}", flush=True)
+
 # Per-session streaming queues — filled during inference, read by /stream/{session_id}
 _stream_queues: dict[str, asyncio.Queue] = {}
 
@@ -140,31 +183,30 @@ async def lifespan(app: FastAPI):
     channels.register_defaults()
 
     # All three models route through Bifrost → Ollama GPU.
-    # Bifrost adds retry logic, observability, and failover.
-    # Model names use provider/model format: Bifrost strips the "ollama/" prefix
-    # before forwarding to Ollama's /v1/chat/completions endpoint.
     router_model = ChatOpenAI(
         model=f"ollama/{ROUTER_MODEL}",
-        base_url=BIFROST_URL,
+        base_url=LITELLM_URL,
-        api_key="dummy",
+        api_key=LITELLM_API_KEY,
         temperature=0,
         timeout=30,
     )
+    embedder = AsyncOpenAI(base_url=LITELLM_URL, api_key=LITELLM_API_KEY)
     medium_model = ChatOpenAI(
         model=f"ollama/{MEDIUM_MODEL}",
-        base_url=BIFROST_URL,
+        base_url=LITELLM_URL,
-        api_key="dummy",
+        api_key=LITELLM_API_KEY,
         timeout=180,
     )
     complex_model = ChatOpenAI(
-        model=f"ollama/{COMPLEX_MODEL}",
+        model=COMPLEX_MODEL,  # full model name — may be remote (OpenRouter) or local ollama/*
-        base_url=BIFROST_URL,
+        base_url=LITELLM_URL,
-        api_key="dummy",
+        api_key=LITELLM_API_KEY,
         timeout=600,
     )
 
     vram_manager = VRAMManager(base_url=OLLAMA_BASE_URL)
-    router = Router(model=router_model, fast_tool_runner=_fast_tool_runner)
+    router = Router(model=router_model, embedder=embedder, fast_tool_runner=_fast_tool_runner)
+    await router.initialize()
 
     mcp_connections = {
         "openmemory": {"transport": "sse", "url": f"{OPENMEMORY_URL}/sse"},
@@ -279,8 +321,8 @@ async def lifespan(app: FastAPI):
     )
 
     print(
-        f"[agent] bifrost={BIFROST_URL} | router=ollama/{ROUTER_MODEL} | "
+        f"[agent] litellm={LITELLM_URL} | router=semantic(ollama/{ROUTER_MODEL}+nomic-embed-text) | "
-        f"medium=ollama/{MEDIUM_MODEL} | complex=ollama/{COMPLEX_MODEL}",
+        f"medium=ollama/{MEDIUM_MODEL} | complex={COMPLEX_MODEL}",
         flush=True,
     )
     print(f"[agent] agent tools: {[t.name for t in agent_tools]}", flush=True)
@@ -346,6 +388,12 @@ def _log_messages(result):
 
 # ── memory helpers ─────────────────────────────────────────────────────────────
 
+def _resolve_user_id(session_id: str) -> str:
+    """Map any session_id to a canonical user identity for openmemory.
+    All channels share the same memory pool for the single user."""
+    return "alvis"
+
+
 async def _store_memory(session_id: str, user_msg: str, assistant_reply: str) -> None:
     """Store a conversation turn in openmemory (runs as a background task)."""
     if _memory_add_tool is None:
@@ -353,7 +401,8 @@ async def _store_memory(session_id: str, user_msg: str, assistant_reply: str) ->
     t0 = time.monotonic()
     try:
         text = f"User: {user_msg}\nAssistant: {assistant_reply}"
-        await _memory_add_tool.ainvoke({"text": text, "user_id": session_id})
+        user_id = _resolve_user_id(session_id)
+        await _memory_add_tool.ainvoke({"text": text, "user_id": user_id})
         print(f"[memory] stored in {time.monotonic() - t0:.1f}s", flush=True)
     except Exception as e:
         print(f"[memory] error: {e}", flush=True)
@@ -364,7 +413,8 @@ async def _retrieve_memories(message: str, session_id: str) -> str:
     if _memory_search_tool is None:
         return ""
     try:
-        result = await _memory_search_tool.ainvoke({"query": message, "user_id": session_id})
+        user_id = _resolve_user_id(session_id)
+        result = await _memory_search_tool.ainvoke({"query": message, "user_id": user_id})
         if result and result.strip() and result.strip() != "[]":
             return f"Relevant memories:\n{result}"
     except Exception:
@@ -372,36 +422,42 @@ async def _retrieve_memories(message: str, session_id: str) -> str:
     return ""
 
 
-# ── core task ──────────────────────────────────────────────────────────────────
+# ── core pipeline ──────────────────────────────────────────────────────────────
 
-async def run_agent_task(message: str, session_id: str, channel: str = "telegram"):
+from typing import AsyncGenerator
-    print(f"[agent] queued: {message[:80]!r} chat={session_id}", flush=True)
 
-    force_complex = False
+async def _run_agent_pipeline(
-    clean_message = message
+    message: str,
-    if message.startswith("/think "):
+    history: list[dict],
-        force_complex = True
+    session_id: str,
-        clean_message = message[len("/think "):]
+    tier_override: str | None = None,
-        print("[agent] /think prefix → force_complex=True", flush=True)
+    dry_run: bool = False,
+    tier_capture: list | None = None,
+) -> AsyncGenerator[str, None]:
+    """Core pipeline: pre-flight → routing → inference. Yields text chunks.
 
+    tier_override: "light" | "medium" | "complex" | None (auto-route)
+    dry_run: if True and tier=complex, log tier=complex but use medium model (avoids API cost)
+    Caller is responsible for scheduling _store_memory after consuming all chunks.
+    """
     async with _reply_semaphore:
         t0 = time.monotonic()
-        history = _conversation_buffers.get(session_id, [])
+        clean_message = message
         print(f"[agent] running: {clean_message[:80]!r}", flush=True)
 
-        # Fetch URL content, memories, and fast-tool context concurrently — all IO-bound
+        # Fetch URL content, memories, and fast-tool context concurrently
         url_context, memories, fast_context = await asyncio.gather(
             _fetch_urls_from_message(clean_message),
             _retrieve_memories(clean_message, session_id),
             _fast_tool_runner.run_matching(clean_message),
         )
         if url_context:
-            print(f"[agent] crawl4ai: {len(url_context)} chars fetched from message URLs", flush=True)
+            print(f"[agent] crawl4ai: {len(url_context)} chars fetched", flush=True)
         if fast_context:
             names = _fast_tool_runner.matching_names(clean_message)
             print(f"[agent] fast_tools={names}: {len(fast_context)} chars injected", flush=True)
 
-        # Build enriched history: memories + url_context + fast_context for ALL tiers
+        # Build enriched history
         enriched_history = list(history)
         if url_context:
             enriched_history = [{"role": "system", "content": url_context}] + enriched_history
@@ -410,45 +466,60 @@ async def run_agent_task(message: str, session_id: str, channel: str = "telegram
         if memories:
             enriched_history = [{"role": "system", "content": memories}] + enriched_history
 
-        # Short-circuit: fast tool result is already a complete reply — skip router+LLM
+        final_text = None
-        if fast_context and not force_complex and not url_context:
+        llm_elapsed = 0.0
-            tier = "fast"
-            final_text = fast_context
-            llm_elapsed = time.monotonic() - t0
-            names = _fast_tool_runner.matching_names(clean_message)
-            print(f"[agent] tier=fast tools={names} — delivering directly", flush=True)
-            await _push_stream_chunk(session_id, final_text)
-            await _end_stream(session_id)
-        else:
-            tier, light_reply = await router.route(clean_message, enriched_history, force_complex)
 
-            # Messages with URL content must be handled by at least medium tier
+        try:
-            if url_context and tier == "light":
+            # Short-circuit: fast tool already has the answer
-                tier = "medium"
+            if fast_context and tier_override is None and not url_context:
-                light_reply = None
+                tier = "fast"
-                print("[agent] URL in message → upgraded light→medium", flush=True)
+                final_text = fast_context
-            print(f"[agent] tier={tier} message={clean_message[:60]!r}", flush=True)
+                llm_elapsed = time.monotonic() - t0
+                names = _fast_tool_runner.matching_names(clean_message)
+                print(f"[agent] tier=fast tools={names} — delivering directly", flush=True)
+                yield final_text
+
+            else:
+                # Determine tier
+                if tier_override in ("light", "medium", "complex"):
+                    tier = tier_override
+                    light_reply = None
+                    if tier_override == "light":
+                        tier, light_reply = await router.route(clean_message, enriched_history)
+                        tier = "light"
+                else:
+                    tier, light_reply = await router.route(clean_message, enriched_history)
+                    if url_context and tier == "light":
+                        tier = "medium"
+                        light_reply = None
+                        print("[agent] URL in message → upgraded light→medium", flush=True)
+
+                # Dry-run: log as complex but infer with medium (no remote API call)
+                effective_tier = tier
+                if dry_run and tier == "complex":
+                    effective_tier = "medium"
+                    print(f"[agent] tier=complex (dry-run) → using medium model, message={clean_message[:60]!r}", flush=True)
+                else:
+                    print(f"[agent] tier={tier} message={clean_message[:60]!r}", flush=True)
+                tier = effective_tier
+                if tier_capture is not None:
+                    tier_capture.append(tier)
 
-        if tier != "fast":
-            final_text = None
-            try:
                 if tier == "light":
                     final_text = light_reply
                     llm_elapsed = time.monotonic() - t0
-                    print(f"[agent] light path: answered by router", flush=True)
+                    print("[agent] light path: answered by router", flush=True)
-                    await _push_stream_chunk(session_id, final_text)
+                    yield final_text
-                    await _end_stream(session_id)
 
                 elif tier == "medium":
                     system_prompt = MEDIUM_SYSTEM_PROMPT
                     if memories:
-                        system_prompt = system_prompt + "\n\n" + memories
+                        system_prompt += "\n\n" + memories
                     if url_context:
-                        system_prompt = system_prompt + "\n\n" + url_context
+                        system_prompt += "\n\n" + url_context
                     if fast_context:
-                        system_prompt = system_prompt + "\n\nLive web search results (use these to answer):\n\n" + fast_context
+                        system_prompt += "\n\nLive web search results (use these to answer):\n\n" + fast_context
 
-                    # Stream tokens directly — filter out qwen3 <think> blocks
                     in_think = False
                     response_parts = []
                     async for chunk in medium_model.astream([
@@ -464,91 +535,117 @@ async def run_agent_task(message: str, session_id: str, channel: str = "telegram
                                 in_think = False
                                 after = token.split("</think>", 1)[1]
                                 if after:
-                                    await _push_stream_chunk(session_id, after)
+                                    yield after
                                     response_parts.append(after)
                         else:
                             if "<think>" in token:
                                 in_think = True
                                 before = token.split("<think>", 1)[0]
                                 if before:
-                                    await _push_stream_chunk(session_id, before)
+                                    yield before
                                     response_parts.append(before)
                             else:
-                                await _push_stream_chunk(session_id, token)
+                                yield token
                                 response_parts.append(token)
 
-                    await _end_stream(session_id)
                     llm_elapsed = time.monotonic() - t0
                     final_text = "".join(response_parts).strip() or None
 
-                else:  # complex
+                else:  # complex — remote model, no VRAM management needed
-                    ok = await vram_manager.enter_complex_mode()
+                    system_prompt = COMPLEX_SYSTEM_PROMPT.format(user_id=session_id)
-                    if not ok:
+                    if url_context:
-                        print("[agent] complex→medium fallback (eviction timeout)", flush=True)
+                        system_prompt += "\n\n[Pre-fetched URL content from user's message:]\n" + url_context
-                        tier = "medium"
+                    result = await complex_agent.ainvoke({
-                        system_prompt = MEDIUM_SYSTEM_PROMPT
+                        "messages": [
-                        if memories:
+                            {"role": "system", "content": system_prompt},
-                            system_prompt = system_prompt + "\n\n" + memories
+                            *history,
-                        if url_context:
+                            {"role": "user", "content": clean_message},
-                            system_prompt = system_prompt + "\n\n" + url_context
+                        ]
-                        result = await medium_agent.ainvoke({
+                    })
-                            "messages": [
-                                {"role": "system", "content": system_prompt},
-                                *history,
-                                {"role": "user", "content": clean_message},
-                            ]
-                        })
-                    else:
-                        system_prompt = COMPLEX_SYSTEM_PROMPT.format(user_id=session_id)
-                        if url_context:
-                            system_prompt = system_prompt + "\n\n[Pre-fetched URL content from user's message:]\n" + url_context
-                        result = await complex_agent.ainvoke({
-                            "messages": [
-                                {"role": "system", "content": system_prompt},
-                                *history,
-                                {"role": "user", "content": clean_message},
-                            ]
-                        })
-                        asyncio.create_task(vram_manager.exit_complex_mode())
 
                     llm_elapsed = time.monotonic() - t0
                     _log_messages(result)
                     final_text = _extract_final_text(result)
                     if final_text:
-                        await _push_stream_chunk(session_id, final_text)
+                        yield final_text
-                    await _end_stream(session_id)
 
-            except Exception as e:
+        except Exception as e:
-                import traceback
+            import traceback
-                llm_elapsed = time.monotonic() - t0
+            llm_elapsed = time.monotonic() - t0
-                print(f"[agent] error after {llm_elapsed:.1f}s for chat {session_id}: {e}", flush=True)
+            print(f"[agent] error after {llm_elapsed:.1f}s for {session_id}: {e}", flush=True)
-                traceback.print_exc()
+            traceback.print_exc()
-                await _end_stream(session_id)
 
-        # Deliver reply through the originating channel
+        print(f"[agent] pipeline done in {time.monotonic() - t0:.1f}s tier={tier if 'tier' in dir() else '?'}", flush=True)
+
+        # Store memory as side-effect (non-blocking, best-effort)
         if final_text:
-            t1 = time.monotonic()
+            asyncio.create_task(_store_memory(session_id, clean_message, final_text))
+
+
+# ── core task (Telegram / Matrix / CLI wrapper) ─────────────────────────────────
+
+async def run_agent_task(
+    message: str,
+    session_id: str,
+    channel: str = "telegram",
+    metadata: dict | None = None,
+):
+    print(f"[agent] queued: {message[:80]!r} chat={session_id}", flush=True)
+    t0 = time.monotonic()
+
+    meta = metadata or {}
+    dry_run = bool(meta.get("dry_run", False))
+    is_benchmark = bool(meta.get("benchmark", False))
+
+    history = _conversation_buffers.get(session_id, [])
+    final_text = None
+    actual_tier = "unknown"
+    tier_capture: list = []
+
+    async for chunk in _run_agent_pipeline(message, history, session_id, dry_run=dry_run, tier_capture=tier_capture):
+        await _push_stream_chunk(session_id, chunk)
+        if final_text is None:
+            final_text = chunk
+        else:
+            final_text += chunk
+
+    await _end_stream(session_id)
+    actual_tier = tier_capture[0] if tier_capture else "unknown"
+
+    elapsed_ms = int((time.monotonic() - t0) * 1000)
+
+    if final_text:
+        final_text = final_text.strip()
+
+        # Skip channel delivery for benchmark sessions (no Telegram spam)
+        if not is_benchmark:
             try:
                 await channels.deliver(session_id, channel, final_text)
             except Exception as e:
                 print(f"[agent] delivery error (non-fatal): {e}", flush=True)
-            send_elapsed = time.monotonic() - t1
-            print(
-                f"[agent] replied in {time.monotonic() - t0:.1f}s "
-                f"(llm={llm_elapsed:.1f}s, send={send_elapsed:.1f}s) tier={tier}",
-                flush=True,
-            )
-            print(f"[agent] reply_text: {final_text}", flush=True)
-        else:
-            print("[agent] warning: no text reply from agent", flush=True)
 
-        # Update conversation buffer and schedule memory storage
+        print(f"[agent] replied in {elapsed_ms / 1000:.1f}s tier={actual_tier}", flush=True)
-        if final_text:
+        print(f"[agent] reply_text: {final_text}", flush=True)
-            buf = _conversation_buffers.get(session_id, [])
+
-            buf.append({"role": "user", "content": clean_message})
+        # Update conversation buffer
-            buf.append({"role": "assistant", "content": final_text})
+        buf = _conversation_buffers.get(session_id, [])
-            _conversation_buffers[session_id] = buf[-(MAX_HISTORY_TURNS * 2):]
+        buf.append({"role": "user", "content": message})
-            asyncio.create_task(_store_memory(session_id, clean_message, final_text))
+        buf.append({"role": "assistant", "content": final_text})
+        _conversation_buffers[session_id] = buf[-(MAX_HISTORY_TURNS * 2):]
+
+        # Log interaction for RLHF data collection (skip benchmark sessions to avoid noise)
+        if not is_benchmark:
+            asyncio.create_task(_log_interaction(
+                session_id=session_id,
+                channel=channel,
+                tier=actual_tier,
+                input_text=message,
+                response_text=final_text,
+                latency_ms=elapsed_ms,
+                metadata=meta if meta else None,
+            ))
+    else:
+        print("[agent] warning: no text reply from agent", flush=True)
 
 
 # ── endpoints ──────────────────────────────────────────────────────────────────
@@ -560,7 +657,7 @@ async def message(request: InboundMessage, background_tasks: BackgroundTasks):
         return JSONResponse(status_code=503, content={"error": "Agent not ready"})
     session_id = request.session_id
     channel = request.channel
-    background_tasks.add_task(run_agent_task, request.text, session_id, channel)
+    background_tasks.add_task(run_agent_task, request.text, session_id, channel, request.metadata)
     return JSONResponse(status_code=202, content={"status": "accepted"})
 
 
@@ -622,3 +719,96 @@ async def stream_reply(session_id: str):
 @app.get("/health")
 async def health():
     return {"status": "ok", "agent_ready": medium_agent is not None}
+
+
+# ── OpenAI-compatible API (for OpenWebUI and other clients) ────────────────────
+
+_TIER_MAP = {
+    "adolf": None,
+    "adolf-light": "light",
+    "adolf-medium": "medium",
+    "adolf-deep": "complex",
+}
+
+
+@app.get("/v1/models")
+async def list_models():
+    return {
+        "object": "list",
+        "data": [
+            {"id": "adolf", "object": "model", "owned_by": "adolf"},
+            {"id": "adolf-light", "object": "model", "owned_by": "adolf"},
+            {"id": "adolf-medium", "object": "model", "owned_by": "adolf"},
+            {"id": "adolf-deep", "object": "model", "owned_by": "adolf"},
+        ],
+    }
+
+
+@app.post("/v1/chat/completions")
+async def chat_completions(request: Request):
+    if medium_agent is None:
+        return JSONResponse(status_code=503, content={"error": {"message": "Agent not ready", "type": "server_error"}})
+
+    body = await request.json()
+    model = body.get("model", "adolf")
+    messages = body.get("messages", [])
+    stream = body.get("stream", True)
+
+    # Extract current user message and history
+    user_messages = [m for m in messages if m.get("role") == "user"]
+    if not user_messages:
+        return JSONResponse(status_code=400, content={"error": {"message": "No user message", "type": "invalid_request_error"}})
+
+    current_message = user_messages[-1]["content"]
+    # History = everything before the last user message (excluding system messages from OpenWebUI)
+    last_user_idx = len(messages) - 1 - next(
+        i for i, m in enumerate(reversed(messages)) if m.get("role") == "user"
+    )
+    history = [m for m in messages[:last_user_idx] if m.get("role") in ("user", "assistant")]
+
+    session_id = request.headers.get("X-Session-Id", "owui-default")
+    tier_override = _TIER_MAP.get(model)
+
+    import json as _json
+    import uuid as _uuid
+
+    response_id = f"chatcmpl-{_uuid.uuid4().hex[:12]}"
+
+    if stream:
+        async def event_stream():
+            # Opening chunk with role
+            opening = {
+                "id": response_id, "object": "chat.completion.chunk",
+                "choices": [{"index": 0, "delta": {"role": "assistant"}, "finish_reason": None}]
+            }
+            yield f"data: {_json.dumps(opening)}\n\n"
+
+            async for chunk in _run_agent_pipeline(current_message, history, session_id, tier_override):
+                data = {
+                    "id": response_id, "object": "chat.completion.chunk",
+                    "choices": [{"index": 0, "delta": {"content": chunk}, "finish_reason": None}]
+                }
+                yield f"data: {_json.dumps(data)}\n\n"
+
+            # Final chunk
+            final = {
+                "id": response_id, "object": "chat.completion.chunk",
+                "choices": [{"index": 0, "delta": {}, "finish_reason": "stop"}]
+            }
+            yield f"data: {_json.dumps(final)}\n\n"
+            yield "data: [DONE]\n\n"
+
+        return StreamingResponse(event_stream(), media_type="text/event-stream")
+
+    else:
+        # Non-streaming: collect all chunks
+        parts = []
+        async for chunk in _run_agent_pipeline(current_message, history, session_id, tier_override):
+            if chunk:
+                parts.append(chunk)
+        full_text = "".join(parts).strip()
+        return {
+            "id": response_id, "object": "chat.completion",
+            "choices": [{"index": 0, "message": {"role": "assistant", "content": full_text}, "finish_reason": "stop"}],
+            "model": model,
+        }

bifrost-config.json

@@ -1,4 +1,21 @@
 {
+  "auth_config": {
+    "is_enabled": true,
+    "admin_username": "admin",
+    "admin_password": "env.BIFROST_ADMIN_PASSWORD"
+  },
+  "config_store": {
+    "enabled": true,
+    "type": "postgres",
+    "config": {
+      "host": "bifrost-db",
+      "port": "5432",
+      "user": "bifrost",
+      "password": "bifrost",
+      "db_name": "bifrost",
+      "ssl_mode": "disable"
+    }
+  },
   "client": {
     "drop_excess_requests": false
   },

channels.py

@@ -49,6 +49,7 @@ async def deliver(session_id: str, channel: str, text: str) -> None:
 # ── built-in channel adapters ─────────────────────────────────────────────────
 
 GRAMMY_URL = os.getenv("GRAMMY_URL", "http://grammy:3001")
+MATRIX_URL = os.getenv("MATRIX_URL", "http://matrix:3002")
 
 
 async def _telegram_send(session_id: str, text: str) -> None:
@@ -64,12 +65,26 @@ async def _telegram_send(session_id: str, text: str) -> None:
             )
 
 
+async def _matrix_send(session_id: str, text: str) -> None:
+    """Send reply to Matrix via the matrix adapter POST /send endpoint."""
+    room_id = session_id.removeprefix("mx-")
+    MAX_MTX = 4000
+    chunks = [text[i:i + MAX_MTX] for i in range(0, len(text), MAX_MTX)]
+    async with httpx.AsyncClient(timeout=15) as client:
+        for chunk in chunks:
+            await client.post(
+                f"{MATRIX_URL}/send",
+                json={"room_id": room_id, "text": chunk},
+            )
+
+
 async def _cli_send(session_id: str, text: str) -> None:
     """CLI replies are handled entirely through the pending_replies queue — no-op here."""
     pass
 
 
 def register_defaults() -> None:
-    """Register the built-in Telegram and CLI channel adapters."""
+    """Register the built-in Telegram, Matrix, and CLI channel adapters."""
     register("telegram", _telegram_send)
+    register("matrix", _matrix_send)
     register("cli", _cli_send)

docker-compose.yml

@@ -1,19 +1,4 @@
 services:
-  bifrost:
-    image: maximhq/bifrost
-    container_name: bifrost
-    ports:
-      - "8080:8080"
-    volumes:
-      - ./bifrost-config.json:/app/data/config.json:ro
-    environment:
-      - APP_DIR=/app/data
-      - APP_PORT=8080
-      - LOG_LEVEL=info
-    extra_hosts:
-      - "host.docker.internal:host-gateway"
-    restart: unless-stopped
-
   deepagents:
     build: .
     container_name: deepagents
@@ -21,25 +6,28 @@ services:
       - "8000:8000"
     environment:
       - PYTHONUNBUFFERED=1
-      # Bifrost gateway — all LLM inference goes through here
+      # LiteLLM proxy — all LLM inference goes through here
-      - BIFROST_URL=http://bifrost:8080/v1
+      - LITELLM_URL=http://host.docker.internal:4000/v1
+      - LITELLM_API_KEY=sk-fjQC1BxAiGFSMs
       # Direct Ollama GPU URL — used only by VRAMManager for flush/prewarm
       - OLLAMA_BASE_URL=http://host.docker.internal:11436
       - DEEPAGENTS_MODEL=qwen3:4b
-      - DEEPAGENTS_COMPLEX_MODEL=qwen3:8b
+      - DEEPAGENTS_COMPLEX_MODEL=deepseek/deepseek-r1:free
       - DEEPAGENTS_ROUTER_MODEL=qwen2.5:1.5b
       - SEARXNG_URL=http://host.docker.internal:11437
       - GRAMMY_URL=http://grammy:3001
+      - MATRIX_URL=http://host.docker.internal:3002
       - CRAWL4AI_URL=http://crawl4ai:11235
       - ROUTECHECK_URL=http://routecheck:8090
       - ROUTECHECK_TOKEN=${ROUTECHECK_TOKEN}
+    volumes:
+      - ./logs:/app/logs
     extra_hosts:
       - "host.docker.internal:host-gateway"
     depends_on:
       - openmemory
       - grammy
       - crawl4ai
-      - bifrost
       - routecheck
     restart: unless-stopped
 

router.py

@@ -1,11 +1,38 @@
+import asyncio
 import re
+import math
 from typing import Optional
+from openai import AsyncOpenAI
 from langchain_core.messages import SystemMessage, HumanMessage
 from fast_tools import FastToolRunner
 
-# ── Regex pre-classifier ──────────────────────────────────────────────────────
+# ── Regex pre-classifiers ─────────────────────────────────────────────────────
-# Catches obvious light-tier patterns before calling the LLM.
+
-# Keyed by regex → compiled pattern.
+# Complex: keyword triggers that reliably signal deep multi-source research
+_COMPLEX_PATTERNS = re.compile(
+    r"(?:^|\s)("
+    r"research|investigate|deep.dive|think carefully"
+    r"|write a (?:detailed|comprehensive|full|thorough|complete)"
+    r"|compare all|find and (?:compare|summarize|analyze)"
+    r"|in[- ]depth analysis|comprehensive guide"
+    r"|detailed (?:report|analysis|comparison|breakdown|overview)"
+    r"|everything about|all (?:major|available|self-hosted|open.source)"
+    r"|pros and cons|with (?:sources|citations|references)"
+    # Russian complex research keywords (no trailing \b — stems like подробн match подробное/подробный)
+    r"|исследуй|изучи все|сравни все|найди и сравни|найди и опиши"
+    r"|напиши подробн|напиши детальн|напиши полн"
+    r"|подробный отчет|детальн\w+ (?:анализ|сравнение|отчет)"
+    r"|подробное (?:руководство|сравнение)|полное руководство"
+    r"|все варианты|все способы|все доступные|все самохостируемые|все платформы"
+    r"|лучшие практики|все инструменты|все решения|все протоколы"
+    r"|найди детальн|найди и кратко опиши"
+    r"|изучи свежие|изучи лучши|изучи все"
+    r"|сравни все\b"
+    r")",
+    re.IGNORECASE,
+)
+
+# Light: trivial queries that need no tools or memory
 _LIGHT_PATTERNS = re.compile(
     r"^("
     # Greetings / farewells
@@ -15,35 +42,301 @@ _LIGHT_PATTERNS = re.compile(
     r"|thanks?|thank you|thx|ty|ok|okay|k|cool|great|awesome|perfect|sounds good|got it|nice|sure"
     r"|how are you|how are you\?|how are you doing(\s+today)?[?!.]*"
     r"|what.?s up"
-    # Calendar facts: "what day comes after X?" / "what comes after X?"
+    # Calendar facts
     r"|what\s+day\s+(comes\s+after|follows|is\s+after)\s+\w+[?!.]*"
     r"|what\s+comes\s+after\s+\w+[?!.]*"
-    # Acronym expansions: "what does X stand for?"
+    # Acronym expansions
     r"|what\s+does\s+\w+\s+stand\s+for[?!.]*"
+    # Russian greetings / farewells / acknowledgements
+    r"|привет|пока|спасибо|здравствуй|здравствуйте|добрый день|добрый вечер|доброе утро"
+    r"|окей|хорошо|отлично|понятно|ок|ладно|договорились|спс|благодарю"
+    r"|пожалуйста|не за что|всё понятно|ясно"
+    r"|как дела|как ты|как жизнь|всё хорошо|всё ок"
     r")[\s!.?]*$",
     re.IGNORECASE,
 )
 
-# ── LLM classification prompt ─────────────────────────────────────────────────
+# ── Semantic router utterances ────────────────────────────────────────────────
-CLASSIFY_PROMPT = """Classify the message. Output ONLY one word: light, medium, or complex.
+# These are embedded at startup. New messages are classified by cosine
+# similarity — whichever tier's centroid is closest wins.
+_LIGHT_UTTERANCES = [
+    # General facts (English)
+    "what is 2+2",
+    "what is the capital of France",
+    "name the three primary colors",
+    "tell me a short joke",
+    "is the sky blue",
+    "is water wet",
+    "how many days in a week",
+    "what is the speed of light",
+    "what is the boiling point of water",
+    "spell the word beautiful",
+    "what color is the ocean",
+    "how many inches in a foot",
+    "who wrote hamlet",
+    "what is pi",
+    "what year did world war two end",
+    "what is the largest planet",
+    "how many continents are there",
+    "what does DNA stand for",
+    "what language do they speak in Brazil",
+    "what is the square root of 144",
+    # Tech definitions — static knowledge (English)
+    "what is Docker",
+    "what is a VPN",
+    "what is SSH",
+    "what is a reverse proxy",
+    "what is an API",
+    "what is a firewall",
+    "what is a container",
+    "what is DNS",
+    "what is HTTPS",
+    "what is a load balancer",
+    "what is Kubernetes",
+    "what is Git",
+    "what is a network port",
+    "what is an IP address",
+    "what is a subnet mask",
+    "what is the OSI model",
+    "how many bits in a byte",
+    "how many bytes in a gigabyte",
+    "what is TCP",
+    "what is a REST API",
+    # Russian — static facts and definitions
+    "что такое IP-адрес",
+    "что такое VPN",
+    "что такое Docker",
+    "что такое DNS",
+    "что такое SSH",
+    "что означает API",
+    "сколько байт в гигабайте",
+    "сколько бит в байте",
+    "что такое Zigbee",
+    "что такое Z-Wave",
+    "что такое брандмауэр",
+    "что такое виртуальная машина",
+    "что такое обратный прокси",
+    "привет",
+    "пока",
+    "спасибо",
+    "как дела",
+    "что такое Matter протокол",
+    "сколько планет в солнечной системе",
+    "чему равно число Пи",
+    # Russian — more static definitions
+    "что такое TCP/IP",
+    "что такое подсеть",
+    "скорость света",
+    "сколько дней в году",
+    "что такое Kubernetes",
+    "что такое Git",
+    "что такое REST API",
+    "что такое TCP",
+    "что такое UDP",
+    "что такое VLAN",
+    "сколько мегабайт в гигабайте",
+    "что такое процессор",
+    "что такое оперативная память",
+    "что такое виртуализация",
+    "что такое Linux",
+    "что такое умный дом",
+    "что такое Home Assistant",
+    "что такое Matter",
+]
 
-LIGHT = answerable from general knowledge, no internet needed:
+_MEDIUM_UTTERANCES = [
-  what is 2+2 / what is the capital of France / name the three primary colors
+    # English — current data, memory, actions
-  tell me a short joke / is the sky blue / is water wet
+    "what is the weather today",
+    "what is the bitcoin price right now",
+    "what are the latest news",
+    "what did we talk about last time",
+    "what is my name",
+    "where do I live",
+    "what do you know about me",
+    "what did I tell you before",
+    "what is the current temperature outside",
+    "remind me what I said about my project",
+    "search for the latest iPhone release",
+    "find me a restaurant nearby",
+    "turn on the lights in the living room",
+    "turn off all lights",
+    "set temperature to 22 degrees",
+    "what is the current traffic to Moscow",
+    "check if anyone is home",
+    "what devices are currently on",
+    "look up my public IP address",
+    "show me recent news about Proxmox",
+    # Russian — weather and commute
+    "какая сегодня погода в Балашихе",
+    "пойдет ли сегодня дождь",
+    "какая температура на улице сейчас",
+    "погода на завтра",
+    "будет ли снег сегодня",
+    "сколько ехать до Москвы сейчас",
+    "какие пробки на дороге до Москвы",
+    "время в пути на работу",
+    "есть ли пробки сейчас",
+    "стоит ли брать зонтик",
+    # Russian — smart home control
+    "включи свет в гостиной",
+    "выключи свет на кухне",
+    "какая температура дома",
+    "установи температуру 22 градуса",
+    "выключи все лампочки",
+    "какие устройства сейчас включены",
+    "включи ночной режим",
+    "открой шторы в гостиной",
+    "включи свет в спальне на 50 процентов",
+    "выключи свет во всём доме",
+    "включи вентилятор в детской",
+    "закрыты ли все окна",
+    "выключи телевизор",
+    "какое потребление электричества сегодня",
+    "включи кофемашину",
+    "сколько у нас датчиков движения",
+    "состояние всех дверных замков",
+    "есть ли кто-нибудь дома",
+    "установи будильник на 7 утра",
+    # Russian — personal memory
+    "как меня зовут",
+    "где я живу",
+    "что мы обсуждали в прошлый раз",
+    "что ты знаешь о моем домашнем сервере",
+    "напомни, какие сервисы я запускаю",
+    "что я просил тебя запомнить",
+    "что я говорил о своей сети",
+    # Russian — current info lookups requiring network/tools
+    "какой сейчас курс биткоина",
+    "курс доллара к рублю сейчас",
+    "какая последняя версия Docker",
+    "как перезапустить Docker контейнер",
+    "как посмотреть логи Docker контейнера",
+    "какие новые функции в Home Assistant 2024",
+    "есть ли проблемы у Cloudflare сегодня",
+    "какие новые Zigbee устройства вышли в 2024 году",
+    "найди хороший опенсорс менеджер фотографий",
+    "последние новости Proxmox",
+    "напиши bash команду для поиска больших файлов",
+    "как вывести список всех запущенных контейнеров",
+    "как проверить использование диска в Linux",
+]
 
-MEDIUM = requires web search or the user's stored memories:
+_COMPLEX_UTTERANCES = [
-  current weather / today's news / Bitcoin price / what did we talk about
+    # English
-  what is my name / where do I live / what is my job / do I have any pets
+    "research everything about Elon Musk's recent projects and investments",
-  what do you know about me / what are my preferences / what did I tell you
+    "write a detailed report on climate change solutions with sources",
+    "investigate the history and current state of quantum computing",
+    "find and summarize the latest academic papers on transformer architectures",
+    "analyze in depth the pros and cons of nuclear energy with citations",
+    "research the background and controversies around this person",
+    "compare all major cloud providers with detailed pricing and features",
+    "write a comprehensive biography of this historical figure",
+    "investigate what caused the 2008 financial crisis with multiple sources",
+    "research the best programming languages in 2024 with detailed comparison",
+    "find everything published about this medical condition and treatments",
+    "do a deep dive into the latest developments in artificial general intelligence",
+    "research and compare all options for starting a business in Europe",
+    "investigate recent news and controversies around this company",
+    "write a thorough analysis of geopolitical tensions in the Middle East",
+    "find detailed information on the side effects and studies for this medication",
+    "research the top 10 JavaScript frameworks with benchmarks and community data",
+    "investigate who is funding AI research and what their goals are",
+    "write a detailed market analysis for the electric vehicle industry",
+    "research everything you can find about this startup or technology",
+    # Russian — deep research
+    "исследуй и сравни все варианты умного домашнего освещения",
+    "напиши подробный отчет о протоколах умного дома",
+    "изучи все самохостируемые медиасерверы и сравни их",
+    "исследуй лучшие практики безопасности домашнего сервера",
+    "сравни все системы резервного копирования для Linux",
+    "напиши детальное сравнение WireGuard и OpenVPN",
+    "исследуй все варианты голосового управления на русском языке",
+    "изучи все опенсорс альтернативы Google сервисам",
+    "напиши подробный анализ локальных языковых моделей",
+    "исследуй лучшие инструменты мониторинга для домашнего сервера",
+    # Russian — more deep research queries matching benchmark
+    "исследуй и сравни Proxmox, Unraid и TrueNAS для домашней лаборатории",
+    "напиши подробное руководство по безопасности домашнего сервера",
+    "исследуй все доступные дашборды для самохостинга и сравни их",
+    "найди детальные бенчмарки ARM одноплатных компьютеров для домашней лаборатории",
+    "исследуй лучший стек мониторинга для самохостинга в 2024 году",
+    "исследуй и сравни WireGuard, OpenVPN и Tailscale для домашней сети",
+    "исследуй лучшие практики сегментации домашней сети с VLAN",
+    "изучи все самохостируемые DNS решения и их возможности",
+    "исследуй и сравни все платформы умного дома: Home Assistant и другие",
+    "изучи лучшие Zigbee координаторы и их совместимость с Home Assistant",
+    "напиши детальный отчет о поддержке протокола Matter и совместимости устройств",
+    "исследуй все способы интеграции умных ламп с Home Assistant",
+    "найди и сравни все варианты датчиков движения для умного дома",
+    "исследуй и сравни все самохостируемые решения для хранения фотографий",
+    "изучи лучшие самохостируемые медиасерверы: Jellyfin, Plex и Emby",
+    "исследуй последние достижения в локальном LLM инференсе и обзор моделей",
+    "изучи лучшие опенсорс альтернативы Google сервисов для приватности",
+    "найди и кратко опиши все крупные самохостируемые менеджеры паролей",
+    "напиши детальный анализ текущего состояния AI ассистентов для самохостинга",
+    "исследуй и сравни все инструменты оркестрации контейнеров для домашней лаборатории",
+    "изучи лучшие подходы к автоматическому резервному копированию в Linux",
+    "исследуй и сравни все самохостируемые инструменты личных финансов",
+    "изучи свежие CVE и уязвимости в популярном самохостируемом ПО",
+    "напиши подробное руководство по настройке автоматизаций в Home Assistant",
+    "исследуй все варианты голосового управления умным домом на русском языке",
+    "сравни все системы резервного копирования для Linux: Restic, BorgBackup и другие",
+    "исследуй лучшие самохостируемые системы мониторинга сети: Zabbix, Grafana",
+    "изучи все варианты локального запуска языковых моделей на видеокарте",
+    "напиши подробный отчет о технологиях синтеза речи с открытым исходным кодом",
+    "исследуй все способы интеграции умных розеток с мониторингом потребления",
+    "напиши полное руководство по настройке обратного прокси Caddy",
+    "исследуй лучшие практики написания Docker Compose файлов для продакшена",
+    "сравни все самохостируемые облачные хранилища: Nextcloud, Seafile и другие",
+    "изучи все доступные локальные ассистенты с голосовым управлением",
+    "исследуй все самохостируемые решения для блокировки рекламы: Pi-hole, AdGuard",
+    "напиши детальное сравнение систем управления конфигурацией: Ansible, Puppet",
+    "исследуй все протоколы умного дома и их плюсы и минусы: Zigbee, Z-Wave, Matter",
+    "найди и сравни все фреймворки для создания локальных AI ассистентов",
+    "исследуй лучшие решения для автоматического управления медиатекой",
+    "изучи все варианты самохостируемых систем учёта расходов с возможностью импорта",
+    "напиши сравнение всех вариантов самохостинга для хранения и синхронизации файлов",
+    "исследуй все открытые протоколы для умного дома и их экосистемы",
+    "изучи лучшие инструменты для автоматизации домашней инфраструктуры",
+]
 
-COMPLEX = /think prefix only:
+# Medium: queries that require tools, actions, or real-time data (not static knowledge)
-  /think compare frameworks / /think plan a trip
+_MEDIUM_PATTERNS = re.compile(
-
+    r"(?:"
-Message: {message}
+    # Russian smart home commands — always need HA integration
-Output (one word only — light, medium, or complex):"""
+    r"(?:включи|выключи|открой|закрой|установи|поставь|убавь|прибавь|переключи)\s"
+    r"|(?:какая|какой|какое|каково)\s+(?:температура|влажность|потребление|состояние|статус)\s"
+    r"|(?:сколько|есть ли)\s.*(?:датчик|устройств|замк)"
+    # Russian memory queries
+    r"|как меня зовут|где я живу|что мы обсуждали|что я говорил|что я просил"
+    r"|напомни\b|что ты знаешь обо мне"
+    # Russian current info
+    r"|курс (?:доллара|биткоина|евро|рубл)"
+    r"|(?:последние |свежие )?новости\b"
+    r"|(?:погода|температура)\s+(?:на завтра|на неделю)"
+    r")",
+    re.IGNORECASE,
+)
 
 LIGHT_REPLY_PROMPT = """You are a helpful Telegram assistant. Answer briefly and naturally (1-3 sentences). Be friendly."""
 
+_EMBED_MODEL = "ollama/nomic-embed-text"
+
+
+def _cosine(a: list[float], b: list[float]) -> float:
+    dot = sum(x * y for x, y in zip(a, b))
+    norm_a = math.sqrt(sum(x * x for x in a))
+    norm_b = math.sqrt(sum(x * x for x in b))
+    if norm_a == 0 or norm_b == 0:
+        return 0.0
+    return dot / (norm_a * norm_b)
+
+
+def _centroid(embeddings: list[list[float]]) -> list[float]:
+    n = len(embeddings)
+    dim = len(embeddings[0])
+    return [sum(embeddings[i][d] for i in range(n)) / n for d in range(dim)]
+
 
 def _format_history(history: list[dict]) -> str:
     if not history:
@@ -56,71 +349,93 @@ def _format_history(history: list[dict]) -> str:
     return "\n".join(lines)
 
 
-def _parse_tier(text: str) -> str:
-    """Extract tier from raw model output. Default to medium."""
-    t = text.strip().lower()
-    snippet = t[:60]
-    if "complex" in snippet:
-        return "complex"
-    if "medium" in snippet:
-        return "medium"
-    if "light" in snippet:
-        return "light"
-    # Model invented a descriptive category (e.g. "simplefact", "trivial", "basic") →
-    # treat as light since it recognised the question doesn't need tools
-    if any(w in snippet for w in ("simple", "fact", "trivial", "basic", "easy", "general")):
-        return "light"
-    return "medium"  # safe default
-
-
 class Router:
-    def __init__(self, model, fast_tool_runner: FastToolRunner | None = None):
+    def __init__(
-        self.model = model
+        self,
+        model,
+        embedder: AsyncOpenAI,
+        fast_tool_runner: FastToolRunner | None = None,
+    ):
+        self.model = model  # qwen2.5:1.5b — used only for generating light replies
+        self._embedder = embedder
         self._fast_tool_runner = fast_tool_runner
+        self._light_centroid: list[float] | None = None
+        self._medium_centroid: list[float] | None = None
+        self._complex_centroid: list[float] | None = None
+
+    async def initialize(self) -> None:
+        """Pre-compute utterance embeddings. Call once at startup. Retries until LiteLLM is ready."""
+        print("[router] embedding utterances for semantic classifier...", flush=True)
+        texts = _LIGHT_UTTERANCES + _MEDIUM_UTTERANCES + _COMPLEX_UTTERANCES
+        for attempt in range(10):
+            try:
+                resp = await self._embedder.embeddings.create(model=_EMBED_MODEL, input=texts)
+                embeddings = [item.embedding for item in resp.data]
+                n_light = len(_LIGHT_UTTERANCES)
+                n_medium = len(_MEDIUM_UTTERANCES)
+                self._light_centroid = _centroid(embeddings[:n_light])
+                self._medium_centroid = _centroid(embeddings[n_light:n_light + n_medium])
+                self._complex_centroid = _centroid(embeddings[n_light + n_medium:])
+                print("[router] semantic classifier ready (3-tier)", flush=True)
+                return
+            except Exception as e:
+                print(f"[router] embedding attempt {attempt+1}/10 failed: {e}", flush=True)
+                await asyncio.sleep(3)
+        print("[router] WARNING: could not initialize semantic classifier — will default to medium", flush=True)
+
+    async def _classify_by_embedding(self, message: str) -> str:
+        """Embed message and return 'light', 'medium', or 'complex' based on centroid similarity."""
+        if self._light_centroid is None or self._medium_centroid is None or self._complex_centroid is None:
+            return "medium"
+        try:
+            resp = await self._embedder.embeddings.create(model=_EMBED_MODEL, input=[message])
+            emb = resp.data[0].embedding
+            score_light = _cosine(emb, self._light_centroid)
+            score_medium = _cosine(emb, self._medium_centroid)
+            score_complex = _cosine(emb, self._complex_centroid)
+            tier = max(
+                [("light", score_light), ("medium", score_medium), ("complex", score_complex)],
+                key=lambda x: x[1],
+            )[0]
+            print(
+                f"[router] semantic: light={score_light:.3f} medium={score_medium:.3f} "
+                f"complex={score_complex:.3f} → {tier}",
+                flush=True,
+            )
+            return tier
+        except Exception as e:
+            print(f"[router] embedding classify error, defaulting to medium: {e}", flush=True)
+            return "medium"
 
     async def route(
         self,
         message: str,
         history: list[dict],
-        force_complex: bool = False,
     ) -> tuple[str, Optional[str]]:
         """
         Returns (tier, reply_or_None).
-        For light tier: also generates the reply with a second call.
+        For light tier: also generates the reply inline.
         For medium/complex: reply is None.
         """
-        if force_complex:
-            return "complex", None
-
-        # Step 0a: fast tool match — agent.py short-circuits before reaching router
-        # This branch is only hit if force_complex=True with a fast-tool message (rare)
         if self._fast_tool_runner and self._fast_tool_runner.any_matches(message.strip()):
             names = self._fast_tool_runner.matching_names(message.strip())
             print(f"[router] fast_tool_match={names} → medium", flush=True)
             return "medium", None
 
-        # Step 0b: regex pre-classification for obvious light patterns
         if _LIGHT_PATTERNS.match(message.strip()):
-            print(f"[router] regex→light", flush=True)
+            print("[router] regex→light", flush=True)
             return await self._generate_light_reply(message, history)
 
-        # Step 1: LLM classification with raw text output
+        if _COMPLEX_PATTERNS.search(message.strip()):
-        try:
+            print("[router] regex→complex", flush=True)
-            classify_response = await self.model.ainvoke([
+            return "complex", None
-                HumanMessage(content=CLASSIFY_PROMPT.format(message=message)),
-            ])
-            raw = classify_response.content or ""
-            raw = re.sub(r"<think>.*?</think>", "", raw, flags=re.DOTALL).strip()
-            tier = _parse_tier(raw)
 
-            if tier == "complex" and not message.startswith("/think"):
+        if _MEDIUM_PATTERNS.search(message.strip()):
-                tier = "medium"
+            print("[router] regex→medium", flush=True)
-
-            print(f"[router] raw={raw[:30]!r} → tier={tier}", flush=True)
-        except Exception as e:
-            print(f"[router] classify error, defaulting to medium: {e}", flush=True)
             return "medium", None
 
+        tier = await self._classify_by_embedding(message)
+
         if tier != "light":
             return tier, None
 
@@ -129,7 +444,7 @@ class Router:
     async def _generate_light_reply(
         self, message: str, history: list[dict]
     ) -> tuple[str, Optional[str]]:
-        """Generate a short reply using the router model for light-tier messages."""
+        """Generate a short reply using qwen2.5:1.5b for light-tier messages."""
         history_text = _format_history(history)
         context = f"\nConversation history:\n{history_text}" if history else ""
         try:

tests/integration/common.py

@@ -199,14 +199,13 @@ def parse_run_block(lines, msg_prefix):
             if txt:
                 last_ai_text = txt
 
-        m = re.search(r"replied in ([\d.]+)s \(llm=([\d.]+)s, send=([\d.]+)s\)", line)
+        m = re.search(r"replied in ([\d.]+)s(?:\s+tier=(\w+))?", line)
         if m:
-            tier_m = re.search(r"\btier=(\w+)", line)
+            tier = m.group(2) if m.group(2) else "unknown"
-            tier = tier_m.group(1) if tier_m else "unknown"
             reply_data = {
                 "reply_total": float(m.group(1)),
-                "llm":         float(m.group(2)),
+                "llm":         None,
-                "send":        float(m.group(3)),
+                "send":        None,
                 "tier":        tier,
                 "reply_text":  last_ai_text,
                 "memory_s":    None,