Update docs: streaming, CLI container, use_cases tests

- /stream/{session_id} SSE endpoint replaces /reply/ for CLI
- Medium tier streams per-token via astream() with in_think filtering
- CLI now runs as Docker container (Dockerfile.cli, profile:tools)
- Correct medium model to qwen3:4b with real-time think block filtering
- Add use_cases/ test category to commands section
- Update files tree and services table

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

.gitignore

@@ -0,0 +1,2 @@
+__pycache__/
+*.pyc

ARCHITECTURE.md

@@ -18,7 +18,8 @@ Autonomous personal assistant with a multi-channel gateway. Three-tier model rou
 │          │                         │                │
 │          │  POST /message          │  ← all inbound │
 │          │  POST /chat  (legacy)   │                │
-│          │  GET  /reply/{id}  SSE  │  ← CLI polling │
+│          │  GET  /stream/{id} SSE  │  ← token stream│
+│          │  GET  /reply/{id}  SSE  │  ← legacy poll │
 │          │  GET  /health           │                │
 │          │                         │                │
 │          │  channels.py registry   │                │
@@ -42,7 +43,7 @@ Autonomous personal assistant with a multi-channel gateway. Three-tier model rou
 | Channel | session_id | Inbound | Outbound |
 |---------|-----------|---------|---------|
 | Telegram | `tg-<chat_id>` | Grammy long-poll → POST /message | channels.py → POST grammy:3001/send |
-| CLI | `cli-<user>` | POST /message directly | GET /reply/{id} SSE stream |
+| CLI | `cli-<user>` | POST /message directly | GET /stream/{id} SSE — Rich Live streaming |
 | Voice | `voice-<device>` | (future) | (future) |
 
 ## Unified Message Flow
@@ -52,23 +53,68 @@ Autonomous personal assistant with a multi-channel gateway. Three-tier model rou
 2. POST /message {text, session_id, channel, user_id}
 3. 202 Accepted immediately
 4. Background: run_agent_task(message, session_id, channel)
-5. Route → run agent tier → get reply text
-6. channels.deliver(session_id, channel, reply_text)
-   - always puts reply in pending_replies[session_id] queue (for SSE)
-   - calls channel-specific send callback
-7. GET /reply/{session_id} SSE clients receive the reply
+5. Parallel IO (asyncio.gather):
+   a. _fetch_urls_from_message() — Crawl4AI fetches any URLs in message
+   b. _retrieve_memories()       — openmemory semantic search for context
+6. router.route() with enriched history (url_context + memories as system msgs)
+   - if URL content fetched and tier=light → upgrade to medium
+7. Invoke agent for tier with url_context + memories in system prompt
+8. Token streaming:
+   - medium: astream() pushes per-token chunks to _stream_queues[session_id]; <think> blocks filtered in real time
+   - light/complex: full reply pushed as single chunk after completion
+   - _end_stream() sends [DONE] sentinel
+9. channels.deliver(session_id, channel, reply_text) — Telegram callback
+10. _store_memory() background task — stores turn in openmemory
+11. GET /stream/{session_id} SSE clients receive chunks; CLI renders with Rich Live + final Markdown
 ```
 
+## Tool Handling
+
+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.
+
+**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.
+
+**Memory tools (out-of-loop):** `add_memory` and `search_memory` are LangChain MCP tool objects (via `langchain_mcp_adapters`) but are excluded from both agents' tool lists. They are called directly — `await _memory_add_tool.ainvoke(...)` — outside the agent loop, before and after each turn.
+
 ## Three-Tier Model Routing
 
-| Tier | Model | VRAM | Trigger | Latency |
-|------|-------|------|---------|---------|
-| Light | qwen2.5:1.5b (router answers) | ~1.2 GB | Router classifies as light | ~2–4s |
-| Medium | qwen3:4b | ~2.5 GB | Default | ~20–40s |
-| Complex | qwen3:8b | ~6.0 GB | `/think` prefix | ~60–120s |
+| Tier | Model | Agent | Trigger | Latency |
+|------|-------|-------|---------|---------|
+| Light | `qwen2.5:1.5b` (router answers directly) | — | Regex pre-match or LLM 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 |
 
 **`/think` prefix**: forces complex tier, stripped before sending to agent.
 
+Complex tier is locked out unless the message starts with `/think` — any LLM classification of "complex" is downgraded to medium.
+
+## Crawl4AI Integration
+
+Crawl4AI runs as a Docker service (`crawl4ai:11235`) providing JS-rendered, bot-bypass page fetching.
+
+**Pre-routing fetch (all tiers):**
+- `_URL_RE` detects `https?://` URLs in any incoming message
+- `_crawl4ai_fetch_async()` uses `httpx.AsyncClient` to POST `{urls: [...]}` to `/crawl`
+- Up to 3 URLs fetched concurrently via `asyncio.gather`
+- Fetched content (up to 3000 chars/URL) injected as a system context block into enriched history before routing and into medium/complex system prompts
+- If fetch succeeds and router returns light → tier upgraded to medium
+
+**Complex agent tools:**
+- `web_search`: SearXNG query + Crawl4AI auto-fetch of top 2 result URLs → combined snippet + page text
+- `fetch_url`: Crawl4AI single-URL fetch for any specific URL
+
+## Memory Pipeline
+
+openmemory runs as a FastMCP server (`openmemory:8765`) backed by mem0 + Qdrant + nomic-embed-text.
+
+**Retrieval (before routing):** `_retrieve_memories()` calls `search_memory` MCP tool with the user message as query. Results (threshold ≥ 0.5) are prepended to enriched history so all tiers benefit.
+
+**Storage (after reply):** `_store_memory()` runs as an asyncio background task, calling `add_memory` with `"User: ...\nAssistant: ..."`. The extraction LLM (`qwen2.5:1.5b` on GPU Ollama) pulls facts; dedup is handled by mem0's update prompt.
+
+Memory tools (`add_memory`, `search_memory`, `get_all_memories`) are excluded from agent tool lists — memory management happens outside the agent loop.
+
 ## VRAM Management
 
 GTX 1070 — 8 GB. Ollama must be restarted if CUDA init fails (model loads on CPU).
@@ -76,7 +122,7 @@ GTX 1070 — 8 GB. Ollama must be restarted if CUDA init fails (model loads on C
 1. Flush explicitly before loading qwen3:8b (`keep_alive=0`)
 2. Verify eviction via `/api/ps` poll (15s timeout) before proceeding
 3. Fallback: timeout → run medium agent instead
-4. Post-complex: flush 8b, pre-warm 4b + router
+4. Post-complex: flush 8b, pre-warm medium + router
 
 ## Session ID Convention
 
@@ -89,18 +135,22 @@ Conversation history is keyed by session_id (5-turn buffer).
 
 ```
 adolf/
-├── docker-compose.yml      Services: deepagents, openmemory, grammy
+├── docker-compose.yml      Services: bifrost, deepagents, openmemory, grammy, crawl4ai, cli (profile:tools)
 ├── Dockerfile              deepagents container (Python 3.12)
-├── agent.py                FastAPI gateway + three-tier routing
+├── Dockerfile.cli          CLI container (python:3.12-slim + rich)
+├── agent.py                FastAPI gateway, run_agent_task, Crawl4AI pre-fetch, memory pipeline, /stream/ SSE
 ├── channels.py             Channel registry + deliver() + pending_replies
-├── router.py               Router class — qwen2.5:1.5b routing
+├── router.py               Router class — regex + LLM tier classification
 ├── vram_manager.py         VRAMManager — flush/prewarm/poll Ollama VRAM
-├── agent_factory.py        build_medium_agent / build_complex_agent
-├── cli.py                  Interactive CLI REPL client
+├── 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)
+├── tests/
+│   ├── integration/        Standalone integration test scripts (common.py + test_*.py)
+│   └── use_cases/          Claude Code skill markdown files — Claude acts as user + evaluator
 ├── .env                    TELEGRAM_BOT_TOKEN (not committed)
 ├── openmemory/
-│   ├── server.py           FastMCP + mem0 MCP tools
+│   ├── server.py           FastMCP + mem0: add_memory, search_memory, get_all_memories
 │   └── Dockerfile
 └── grammy/
     ├── bot.mjs             grammY Telegram bot + POST /send HTTP endpoint
@@ -108,11 +158,11 @@ adolf/
     └── Dockerfile
 ```
 
-## External Services (from openai/ stack)
+## External Services (host ports, from openai/ stack)
 
 | Service | Host Port | Role |
 |---------|-----------|------|
-| Ollama GPU | 11436 | All reply inference |
-| Ollama CPU | 11435 | Memory embedding (nomic-embed-text) |
+| Ollama GPU | 11436 | All LLM inference (via Bifrost) + VRAM management (direct) + memory extraction |
+| Ollama CPU | 11435 | nomic-embed-text embeddings for openmemory |
 | Qdrant | 6333 | Vector store for memories |
-| SearXNG | 11437 | Web search |
+| SearXNG | 11437 | Web search (used by `web_search` tool) |

CLAUDE.md

@@ -0,0 +1,154 @@
+# CLAUDE.md
+
+This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
+
+## Commands
+
+**Start all services:**
+```bash
+docker compose up --build
+```
+
+**Interactive CLI (Docker container, requires gateway running):**
+```bash
+docker compose --profile tools run --rm -it cli
+# or with options:
+docker compose --profile tools run --rm -it cli python3 cli.py --url http://deepagents:8000 --session cli-alvis
+```
+
+**Run integration tests** (from `tests/integration/`, require all Docker services running):
+```bash
+python3 test_health.py                          # service health: deepagents, bifrost, Ollama, Qdrant, SearXNG
+
+python3 test_memory.py                          # name store/recall + memory benchmark + dedup
+python3 test_memory.py --name-only              # only name store/recall pipeline
+python3 test_memory.py --bench-only             # only 5-fact store + 10-question recall
+python3 test_memory.py --dedup-only             # only deduplication test
+
+python3 test_routing.py                         # all routing benchmarks (easy + medium + hard)
+python3 test_routing.py --easy-only             # light-tier routing benchmark
+python3 test_routing.py --medium-only           # medium-tier routing benchmark
+python3 test_routing.py --hard-only             # complex-tier + VRAM flush benchmark
+```
+
+Shared config and helpers are in `tests/integration/common.py`.
+
+**Use case tests** (`tests/use_cases/`) — markdown skill files executed by Claude Code, which acts as mock user and quality evaluator. Run by reading the `.md` file and following its steps with tools (Bash, WebFetch, etc.).
+
+## Architecture
+
+Adolf is a multi-channel personal assistant. All LLM inference is routed through **Bifrost**, an open-source Go-based LLM gateway that adds retry logic, failover, and observability in front of Ollama.
+
+### Request flow
+
+```
+Channel adapter → POST /message {text, session_id, channel, user_id}
+                → 202 Accepted (immediate)
+                → background: run_agent_task()
+                    → asyncio.gather(
+                        _fetch_urls_from_message()  ← Crawl4AI, concurrent
+                        _retrieve_memories()         ← openmemory search, concurrent
+                      )
+                    → router.route() → tier decision (light/medium/complex)
+                        if URL content fetched → upgrade light→medium
+                    → invoke agent for tier via Bifrost (url_context + memories in system prompt)
+                        deepagents:8000 → bifrost:8080/v1 → ollama:11436
+                    → _push_stream_chunk() per token (medium streaming) / full reply (light, complex)
+                        → _stream_queues[session_id] asyncio.Queue
+                    → _end_stream() sends [DONE] sentinel
+                    → channels.deliver(session_id, channel, reply)
+                        → channel-specific callback (Telegram POST)
+                    → _store_memory() background task (openmemory)
+CLI streaming    → GET /stream/{session_id}  (SSE, per-token for medium, single-chunk for others)
+```
+
+### Bifrost integration
+
+Bifrost (`bifrost-config.json`) is configured with the `ollama` provider pointing to the GPU Ollama instance on host port 11436. It exposes an OpenAI-compatible API at `http://bifrost:8080/v1`.
+
+`agent.py` uses `langchain_openai.ChatOpenAI` with `base_url=BIFROST_URL`. Model names use the `provider/model` format that Bifrost expects: `ollama/qwen3:4b`, `ollama/qwen3:8b`, `ollama/qwen2.5:1.5b`. Bifrost strips the `ollama/` prefix before forwarding to Ollama.
+
+`VRAMManager` bypasses Bifrost and talks directly to Ollama via `OLLAMA_BASE_URL` (host:11436) for flush/poll/prewarm operations — Bifrost cannot manage GPU VRAM.
+
+### Three-tier routing (`router.py`, `agent.py`)
+
+| Tier | Model (env var) | Trigger |
+|------|-----------------|---------|
+| light | `qwen2.5:1.5b` (`DEEPAGENTS_ROUTER_MODEL`) | Regex pre-match or LLM classifies "light" — answered by router model directly, no agent invoked |
+| medium | `qwen3:4b` (`DEEPAGENTS_MODEL`) | Default for tool-requiring queries |
+| complex | `qwen3:8b` (`DEEPAGENTS_COMPLEX_MODEL`) | `/think ` prefix only |
+
+The router does regex pre-classification first, then LLM classification. Complex tier is blocked unless the message starts with `/think ` — any LLM classification of "complex" is downgraded to medium.
+
+A global `asyncio.Semaphore(1)` (`_reply_semaphore`) serializes all LLM inference — one request at a time.
+
+### Thinking mode and streaming
+
+qwen3 models produce chain-of-thought `<think>...</think>` tokens. Handling differs by tier:
+
+- **Medium** (`qwen3:4b`): streams via `astream()`. A state machine (`in_think` flag) filters `<think>` blocks in real time — only non-think tokens are pushed to `_stream_queues` and displayed to the user.
+- **Complex** (`qwen3:8b`): `create_deep_agent` returns a complete reply; `_strip_think()` filters think blocks before the reply is pushed as a single chunk.
+- **Router/light** (`qwen2.5:1.5b`): no thinking support; `_strip_think()` used defensively.
+
+`_strip_think()` in `agent.py` and `router.py` strips any `<think>` blocks from non-streaming output.
+
+### VRAM management (`vram_manager.py`)
+
+Hardware: GTX 1070 (8 GB). Before running the 8b model, medium models are flushed via Ollama `keep_alive=0`, then `/api/ps` is polled (15s timeout) to confirm eviction. On timeout, falls back to medium tier. After complex reply, 8b is flushed and medium models are pre-warmed as a background task.
+
+### Channel adapters (`channels.py`)
+
+- **Telegram**: Grammy Node.js bot (`grammy/bot.mjs`) long-polls Telegram → `POST /message`; replies delivered via `POST grammy:3001/send`
+- **CLI**: `cli.py` (Docker container, `profiles: [tools]`) posts to `/message`, then streams from `GET /stream/{session_id}` SSE with Rich `Live` display and final Markdown render.
+
+Session IDs: `tg-<chat_id>` for Telegram, `cli-<username>` for CLI. Conversation history: 5-turn buffer per session.
+
+### Services (`docker-compose.yml`)
+
+| Service | Port | Role |
+|---------|------|------|
+| `bifrost` | 8080 | LLM gateway — retries, failover, observability; config from `bifrost-config.json` |
+| `deepagents` | 8000 | FastAPI gateway + agent core |
+| `openmemory` | 8765 | FastMCP server + mem0 memory tools (Qdrant-backed) |
+| `grammy` | 3001 | grammY Telegram bot + `/send` HTTP endpoint |
+| `crawl4ai` | 11235 | JS-rendered page fetching |
+| `cli` | — | Interactive CLI container (`profiles: [tools]`), Rich streaming display |
+
+External (from `openai/` stack, host ports):
+- Ollama GPU: `11436` — all reply inference (via Bifrost) + VRAM management (direct)
+- Ollama CPU: `11435` — nomic-embed-text embeddings for openmemory
+- Qdrant: `6333` — vector store for memories
+- SearXNG: `11437` — web search
+
+### Bifrost config (`bifrost-config.json`)
+
+The file is mounted into the bifrost container at `/app/data/config.json`. It declares one Ollama provider key pointing to `host.docker.internal:11436` with 2 retries and 300s timeout. To add fallback providers or adjust weights, edit this file and restart the bifrost container.
+
+### Crawl4AI integration
+
+Crawl4AI is embedded at all levels of the pipeline:
+
+- **Pre-routing (all tiers)**: `_fetch_urls_from_message()` detects URLs in any message via `_URL_RE`, fetches up to 3 URLs concurrently with `_crawl4ai_fetch_async()` (async httpx). URL content is injected as a system context block into enriched history before routing, and into the system prompt for medium/complex agents.
+- **Tier upgrade**: if URL content is successfully fetched, light tier is upgraded to medium (light model cannot process page content).
+- **Complex agent tools**: `web_search` (SearXNG + Crawl4AI auto-fetch of top 2 results) and `fetch_url` (single-URL Crawl4AI fetch) remain available for the complex agent's agentic loop. Complex tier also receives the pre-fetched content in system prompt to avoid redundant re-fetching.
+
+MCP tools from openmemory (`add_memory`, `search_memory`, `get_all_memories`) are **excluded** from agent tools — memory management is handled outside the agent loop.
+
+### Medium vs Complex agent
+
+| Agent | Builder | Speed | Use case |
+|-------|---------|-------|----------|
+| medium | `_DirectModel` (single LLM call, no tools) | ~3s | General questions, conversation |
+| complex | `create_deep_agent` (deepagents) | Slow — multi-step planner | Deep research via `/think` prefix |
+
+### Key files
+
+- `agent.py` — FastAPI app, lifespan wiring, `run_agent_task()`, Crawl4AI pre-fetch, memory pipeline, all endpoints
+- `bifrost-config.json` — Bifrost provider config (Ollama GPU, retries, timeouts)
+- `channels.py` — channel registry and `deliver()` dispatcher
+- `router.py` — `Router` class: regex + LLM classification, light-tier reply generation
+- `vram_manager.py` — `VRAMManager`: flush/poll/prewarm Ollama VRAM directly
+- `agent_factory.py` — `build_medium_agent` (`_DirectModel`, single call) / `build_complex_agent` (`create_deep_agent`)
+- `openmemory/server.py` — FastMCP + mem0 config with custom extraction/dedup prompts
+- `wiki_research.py` — batch research pipeline using `/message` + SSE polling
+- `grammy/bot.mjs` — Telegram long-poll + HTTP `/send` endpoint

Dockerfile

@@ -2,7 +2,7 @@ FROM python:3.12-slim
 
 WORKDIR /app
 
-RUN pip install --no-cache-dir deepagents langchain-ollama langgraph \
+RUN pip install --no-cache-dir deepagents langchain-openai langgraph \
     fastapi uvicorn langchain-mcp-adapters langchain-community httpx
 
 COPY agent.py channels.py vram_manager.py router.py agent_factory.py hello_world.py .

Dockerfile.cli

@@ -0,0 +1,9 @@
+FROM python:3.12-slim
+
+WORKDIR /app
+
+RUN pip install --no-cache-dir rich
+
+COPY cli.py .
+
+CMD ["python3", "cli.py"]

agent.py

@@ -10,7 +10,13 @@ from pydantic import BaseModel
 import re as _re
 import httpx as _httpx
 
-from langchain_ollama import ChatOllama
+_URL_RE = _re.compile(r'https?://[^\s<>"\']+')
+
+
+def _extract_urls(text: str) -> list[str]:
+    return _URL_RE.findall(text)
+
+from langchain_openai import ChatOpenAI
 from langchain_mcp_adapters.client import MultiServerMCPClient
 from langchain_community.utilities import SearxSearchWrapper
 from langchain_core.tools import Tool
@@ -20,8 +26,12 @@ from router import Router
 from agent_factory import build_medium_agent, build_complex_agent
 import channels
 
+# Bifrost gateway — all LLM inference goes through here
+BIFROST_URL = os.getenv("BIFROST_URL", "http://bifrost:8080/v1")
+# Direct Ollama URL — used only by VRAMManager for flush/prewarm/poll
 OLLAMA_BASE_URL = os.getenv("OLLAMA_BASE_URL", "http://localhost:11434")
-ROUTER_MODEL = os.getenv("DEEPAGENTS_ROUTER_MODEL", "qwen2.5:0.5b")
+
+ROUTER_MODEL = os.getenv("DEEPAGENTS_ROUTER_MODEL", "qwen2.5:1.5b")
 MEDIUM_MODEL = os.getenv("DEEPAGENTS_MODEL", "qwen3:4b")
 COMPLEX_MODEL = os.getenv("DEEPAGENTS_COMPLEX_MODEL", "qwen3:8b")
 SEARXNG_URL = os.getenv("SEARXNG_URL", "http://host.docker.internal:11437")
@@ -31,10 +41,59 @@ CRAWL4AI_URL = os.getenv("CRAWL4AI_URL", "http://crawl4ai:11235")
 MAX_HISTORY_TURNS = 5
 _conversation_buffers: dict[str, list] = {}
 
+# Per-session streaming queues — filled during inference, read by /stream/{session_id}
+_stream_queues: dict[str, asyncio.Queue] = {}
+
+
+async def _push_stream_chunk(session_id: str, chunk: str) -> None:
+    q = _stream_queues.setdefault(session_id, asyncio.Queue())
+    await q.put(chunk)
+
+
+async def _end_stream(session_id: str) -> None:
+    q = _stream_queues.setdefault(session_id, asyncio.Queue())
+    await q.put("[DONE]")
+
+
+async def _crawl4ai_fetch_async(url: str) -> str:
+    """Async fetch via Crawl4AI — JS-rendered, bot-bypass, returns clean markdown."""
+    try:
+        async with _httpx.AsyncClient(timeout=60) as client:
+            r = await client.post(f"{CRAWL4AI_URL}/crawl", json={"urls": [url]})
+            r.raise_for_status()
+            results = r.json().get("results", [])
+            if not results or not results[0].get("success"):
+                return ""
+            md_obj = results[0].get("markdown") or {}
+            md = md_obj.get("raw_markdown") if isinstance(md_obj, dict) else str(md_obj)
+            return (md or "")[:5000]
+    except Exception as e:
+        return f"[fetch error: {e}]"
+
+
+async def _fetch_urls_from_message(message: str) -> str:
+    """If message contains URLs, fetch their content concurrently via Crawl4AI.
+    Returns a formatted context block, or '' if no URLs or all fetches fail."""
+    urls = _extract_urls(message)
+    if not urls:
+        return ""
+    # Fetch up to 3 URLs concurrently
+    results = await asyncio.gather(*[_crawl4ai_fetch_async(u) for u in urls[:3]])
+    parts = []
+    for url, content in zip(urls[:3], results):
+        if content and not content.startswith("[fetch error"):
+            parts.append(f"### {url}\n{content[:3000]}")
+    if not parts:
+        return ""
+    return "User's message contains URLs. Fetched content:\n\n" + "\n\n".join(parts)
+
+
+# /no_think at the start of the system prompt disables qwen3 chain-of-thought.
+# create_deep_agent prepends our system_prompt before BASE_AGENT_PROMPT, so
+# /no_think lands at position 0 and is respected by qwen3 models via Ollama.
 MEDIUM_SYSTEM_PROMPT = (
-    "You are a helpful AI assistant. "
-    "Use web_search for questions about current events or facts you don't know. "
-    "Reply concisely."
+    "You are a helpful AI assistant. Reply concisely. "
+    "If asked to remember a fact or name, simply confirm: 'Got it, I'll remember that.'"
 )
 
 COMPLEX_SYSTEM_PROMPT = (
@@ -49,11 +108,14 @@ COMPLEX_SYSTEM_PROMPT = (
     "NEVER invent URLs. End with: **Sources checked: N**"
 )
 
+medium_model = None
 medium_agent = None
 complex_agent = None
 router: Router = None
 vram_manager: VRAMManager = None
 mcp_client = None
+_memory_add_tool = None
+_memory_search_tool = None
 
 # GPU mutex: one LLM inference at a time
 _reply_semaphore = asyncio.Semaphore(1)
@@ -61,21 +123,34 @@ _reply_semaphore = asyncio.Semaphore(1)
 
 @asynccontextmanager
 async def lifespan(app: FastAPI):
-    global medium_agent, complex_agent, router, vram_manager, mcp_client
+    global medium_model, medium_agent, complex_agent, router, vram_manager, mcp_client, \
+        _memory_add_tool, _memory_search_tool
 
     # Register channel adapters
     channels.register_defaults()
 
-    # Three model instances
-    router_model = ChatOllama(
-        model=ROUTER_MODEL, base_url=OLLAMA_BASE_URL, think=False, num_ctx=4096,
+    # 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,
+        api_key="dummy",
         temperature=0,
+        timeout=30,
     )
-    medium_model = ChatOllama(
-        model=MEDIUM_MODEL, base_url=OLLAMA_BASE_URL, think=False, num_ctx=8192
+    medium_model = ChatOpenAI(
+        model=f"ollama/{MEDIUM_MODEL}",
+        base_url=BIFROST_URL,
+        api_key="dummy",
+        timeout=180,
     )
-    complex_model = ChatOllama(
-        model=COMPLEX_MODEL, base_url=OLLAMA_BASE_URL, think=True, num_ctx=16384
+    complex_model = ChatOpenAI(
+        model=f"ollama/{COMPLEX_MODEL}",
+        base_url=BIFROST_URL,
+        api_key="dummy",
+        timeout=600,
     )
 
     vram_manager = VRAMManager(base_url=OLLAMA_BASE_URL)
@@ -97,6 +172,13 @@ async def lifespan(app: FastAPI):
 
     agent_tools = [t for t in mcp_tools if t.name not in ("add_memory", "search_memory", "get_all_memories")]
 
+    # Expose memory tools directly so run_agent_task can call them outside the agent loop
+    for t in mcp_tools:
+        if t.name == "add_memory":
+            _memory_add_tool = t
+        elif t.name == "search_memory":
+            _memory_search_tool = t
+
     searx = SearxSearchWrapper(searx_host=SEARXNG_URL)
 
     def _crawl4ai_fetch(url: str) -> str:
@@ -187,13 +269,15 @@ async def lifespan(app: FastAPI):
     )
 
     print(
-        f"[agent] three-tier: router={ROUTER_MODEL} | medium={MEDIUM_MODEL} | complex={COMPLEX_MODEL}",
+        f"[agent] bifrost={BIFROST_URL} | router=ollama/{ROUTER_MODEL} | "
+        f"medium=ollama/{MEDIUM_MODEL} | complex=ollama/{COMPLEX_MODEL}",
         flush=True,
     )
     print(f"[agent] agent tools: {[t.name for t in agent_tools]}", flush=True)
 
     yield
 
+    medium_model = None
     medium_agent = None
     complex_agent = None
     router = None
@@ -222,13 +306,19 @@ class ChatRequest(BaseModel):
 
 # ── helpers ────────────────────────────────────────────────────────────────────
 
+def _strip_think(text: str) -> str:
+    """Strip qwen3 chain-of-thought blocks that appear inline in content
+    when using Ollama's OpenAI-compatible endpoint (/v1/chat/completions)."""
+    return _re.sub(r"<think>.*?</think>", "", text, flags=_re.DOTALL).strip()
+
+
 def _extract_final_text(result) -> str | None:
     msgs = result.get("messages", [])
     for m in reversed(msgs):
         if type(m).__name__ == "AIMessage" and getattr(m, "content", ""):
-            return m.content
+            return _strip_think(m.content)
     if isinstance(result, dict) and result.get("output"):
-        return result["output"]
+        return _strip_think(result["output"])
     return None
 
 
@@ -244,6 +334,34 @@ def _log_messages(result):
             print(f"[agent]   {role} → {tc['name']}({tc['args']})", flush=True)
 
 
+# ── memory helpers ─────────────────────────────────────────────────────────────
+
+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:
+        return
+    t0 = time.monotonic()
+    try:
+        text = f"User: {user_msg}\nAssistant: {assistant_reply}"
+        await _memory_add_tool.ainvoke({"text": text, "user_id": session_id})
+        print(f"[memory] stored in {time.monotonic() - t0:.1f}s", flush=True)
+    except Exception as e:
+        print(f"[memory] error: {e}", flush=True)
+
+
+async def _retrieve_memories(message: str, session_id: str) -> str:
+    """Search openmemory for relevant context. Returns formatted string or ''."""
+    if _memory_search_tool is None:
+        return ""
+    try:
+        result = await _memory_search_tool.ainvoke({"query": message, "user_id": session_id})
+        if result and result.strip() and result.strip() != "[]":
+            return f"Relevant memories:\n{result}"
+    except Exception:
+        pass
+    return ""
+
+
 # ── core task ──────────────────────────────────────────────────────────────────
 
 async def run_agent_task(message: str, session_id: str, channel: str = "telegram"):
@@ -261,7 +379,28 @@ async def run_agent_task(message: str, session_id: str, channel: str = "telegram
         history = _conversation_buffers.get(session_id, [])
         print(f"[agent] running: {clean_message[:80]!r}", flush=True)
 
-        tier, light_reply = await router.route(clean_message, history, force_complex)
+        # Fetch URL content and memories concurrently — both are IO-bound, neither needs GPU
+        url_context, memories = await asyncio.gather(
+            _fetch_urls_from_message(clean_message),
+            _retrieve_memories(clean_message, session_id),
+        )
+        if url_context:
+            print(f"[agent] crawl4ai: {len(url_context)} chars fetched from message URLs", flush=True)
+
+        # Build enriched history: memories + url_context as system context for ALL tiers
+        enriched_history = list(history)
+        if url_context:
+            enriched_history = [{"role": "system", "content": url_context}] + enriched_history
+        if memories:
+            enriched_history = [{"role": "system", "content": memories}] + enriched_history
+
+        tier, light_reply = await router.route(clean_message, enriched_history, force_complex)
+
+        # Messages with URL content must be handled by at least medium tier
+        if url_context and tier == "light":
+            tier = "medium"
+            light_reply = None
+            print("[agent] URL in message → upgraded light→medium", flush=True)
         print(f"[agent] tier={tier} message={clean_message[:60]!r}", flush=True)
 
         final_text = None
@@ -270,9 +409,59 @@ async def run_agent_task(message: str, session_id: str, channel: str = "telegram
                 final_text = light_reply
                 llm_elapsed = time.monotonic() - t0
                 print(f"[agent] light path: answered by router", flush=True)
+                await _push_stream_chunk(session_id, final_text)
+                await _end_stream(session_id)
 
             elif tier == "medium":
                 system_prompt = MEDIUM_SYSTEM_PROMPT
+                if memories:
+                    system_prompt = system_prompt + "\n\n" + memories
+                if url_context:
+                    system_prompt = system_prompt + "\n\n" + url_context
+
+                # Stream tokens directly — filter out qwen3 <think> blocks
+                in_think = False
+                response_parts = []
+                async for chunk in medium_model.astream([
+                    {"role": "system", "content": system_prompt},
+                    *history,
+                    {"role": "user", "content": clean_message},
+                ]):
+                    token = chunk.content or ""
+                    if not token:
+                        continue
+                    if in_think:
+                        if "</think>" in token:
+                            in_think = False
+                            after = token.split("</think>", 1)[1]
+                            if after:
+                                await _push_stream_chunk(session_id, 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)
+                                response_parts.append(before)
+                        else:
+                            await _push_stream_chunk(session_id, 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
+                ok = await vram_manager.enter_complex_mode()
+                if not ok:
+                    print("[agent] complex→medium fallback (eviction timeout)", flush=True)
+                    tier = "medium"
+                    system_prompt = MEDIUM_SYSTEM_PROMPT
+                    if memories:
+                        system_prompt = system_prompt + "\n\n" + memories
+                    if url_context:
+                        system_prompt = system_prompt + "\n\n" + url_context
                     result = await medium_agent.ainvoke({
                         "messages": [
                             {"role": "system", "content": system_prompt},
@@ -280,24 +469,10 @@ async def run_agent_task(message: str, session_id: str, channel: str = "telegram
                             {"role": "user", "content": clean_message},
                         ]
                     })
-                llm_elapsed = time.monotonic() - t0
-                _log_messages(result)
-                final_text = _extract_final_text(result)
-
-            else:  # complex
-                ok = await vram_manager.enter_complex_mode()
-                if not ok:
-                    print("[agent] complex→medium fallback (eviction timeout)", flush=True)
-                    tier = "medium"
-                    result = await medium_agent.ainvoke({
-                        "messages": [
-                            {"role": "system", "content": MEDIUM_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},
@@ -310,17 +485,24 @@ async def run_agent_task(message: str, session_id: str, channel: str = "telegram
                 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)
+                await _end_stream(session_id)
 
         except Exception as e:
             import traceback
             llm_elapsed = time.monotonic() - t0
             print(f"[agent] error after {llm_elapsed:.1f}s for chat {session_id}: {e}", flush=True)
             traceback.print_exc()
+            await _end_stream(session_id)
 
         # Deliver reply through the originating channel
         if final_text:
             t1 = time.monotonic()
+            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 "
@@ -331,12 +513,13 @@ async def run_agent_task(message: str, session_id: str, channel: str = "telegram
         else:
             print("[agent] warning: no text reply from agent", flush=True)
 
-        # Update conversation buffer
+        # Update conversation buffer and schedule memory storage
         if final_text:
             buf = _conversation_buffers.get(session_id, [])
             buf.append({"role": "user", "content": clean_message})
             buf.append({"role": "assistant", "content": final_text})
             _conversation_buffers[session_id] = buf[-(MAX_HISTORY_TURNS * 2):]
+            asyncio.create_task(_store_memory(session_id, clean_message, final_text))
 
 
 # ── endpoints ──────────────────────────────────────────────────────────────────
@@ -374,13 +557,39 @@ async def reply_stream(session_id: str):
         try:
             text = await asyncio.wait_for(q.get(), timeout=900)
             # Escape newlines so entire reply fits in one SSE data line
-            yield f"data: {text.replace(chr(10), '\\n').replace(chr(13), '')}\n\n"
+            yield f"data: {text.replace(chr(10), chr(92) + 'n').replace(chr(13), '')}\n\n"
         except asyncio.TimeoutError:
             yield "data: [timeout]\n\n"
 
     return StreamingResponse(event_generator(), media_type="text/event-stream")
 
 
+@app.get("/stream/{session_id}")
+async def stream_reply(session_id: str):
+    """
+    SSE endpoint — streams reply tokens as they are generated.
+    Each chunk: data: <token>\\n\\n
+    Signals completion: data: [DONE]\\n\\n
+
+    Medium tier: real token-by-token streaming (think blocks filtered out).
+    Light and complex tiers: full reply delivered as one chunk then [DONE].
+    """
+    q = _stream_queues.setdefault(session_id, asyncio.Queue())
+
+    async def event_generator():
+        try:
+            while True:
+                chunk = await asyncio.wait_for(q.get(), timeout=900)
+                escaped = chunk.replace("\n", "\\n").replace("\r", "")
+                yield f"data: {escaped}\n\n"
+                if chunk == "[DONE]":
+                    break
+        except asyncio.TimeoutError:
+            yield "data: [DONE]\n\n"
+
+    return StreamingResponse(event_generator(), media_type="text/event-stream")
+
+
 @app.get("/health")
 async def health():
     return {"status": "ok", "agent_ready": medium_agent is not None}

agent_factory.py

@@ -1,13 +1,21 @@
 from deepagents import create_deep_agent
 
 
+class _DirectModel:
+    """Thin wrapper: single LLM call, no tools, same ainvoke interface as a graph."""
+
+    def __init__(self, model):
+        self._model = model
+
+    async def ainvoke(self, input_dict: dict) -> dict:
+        messages = input_dict["messages"]
+        response = await self._model.ainvoke(messages)
+        return {"messages": list(messages) + [response]}
+
+
 def build_medium_agent(model, agent_tools: list, system_prompt: str):
-    """Medium agent: create_deep_agent with TodoList planning, no subagents."""
-    return create_deep_agent(
-        model=model,
-        tools=agent_tools,
-        system_prompt=system_prompt,
-    )
+    """Medium agent: single LLM call, no tools — fast ~3s response."""
+    return _DirectModel(model)
 
 
 def build_complex_agent(model, agent_tools: list, system_prompt: str):

bifrost-config.json

@@ -0,0 +1,58 @@
+{
+  "client": {
+    "drop_excess_requests": false
+  },
+  "providers": {
+    "ollama": {
+      "keys": [
+        {
+          "name": "ollama-gpu",
+          "value": "dummy",
+          "models": [
+            "qwen2.5:0.5b",
+            "qwen2.5:1.5b",
+            "qwen3:4b",
+            "gemma3:4b",
+            "qwen3:8b"
+          ],
+          "weight": 1.0
+        }
+      ],
+      "network_config": {
+        "base_url": "http://host.docker.internal:11436",
+        "default_request_timeout_in_seconds": 300,
+        "max_retries": 2,
+        "retry_backoff_initial_ms": 500,
+        "retry_backoff_max_ms": 10000
+      }
+    },
+    "ollama-cpu": {
+      "keys": [
+        {
+          "name": "ollama-cpu-key",
+          "value": "dummy",
+          "models": [
+            "gemma3:1b",
+            "qwen2.5:1.5b",
+            "qwen2.5:3b"
+          ],
+          "weight": 1.0
+        }
+      ],
+      "network_config": {
+        "base_url": "http://host.docker.internal:11435",
+        "default_request_timeout_in_seconds": 120,
+        "max_retries": 2,
+        "retry_backoff_initial_ms": 500,
+        "retry_backoff_max_ms": 10000
+      },
+      "custom_provider_config": {
+        "base_provider_type": "openai",
+        "allowed_requests": {
+          "chat_completion": true,
+          "chat_completion_stream": true
+        }
+      }
+    }
+  }
+}

cli.py

@@ -1,9 +1,9 @@
 #!/usr/bin/env python3
 """
-Adolf CLI — interactive REPL for the multi-channel gateway.
+Adolf CLI — interactive REPL with Rich streaming display.
 
 Usage:
-    python3 cli.py [--url http://localhost:8000] [--session cli-alvis]
+    python3 cli.py [--url http://deepagents:8000] [--session cli-alvis]
 """
 
 import argparse
@@ -12,7 +12,13 @@ import os
 import sys
 import urllib.request
 
-GATEWAY = "http://localhost:8000"
+from rich.console import Console
+from rich.live import Live
+from rich.markdown import Markdown
+from rich.text import Text
+
+GATEWAY = "http://deepagents:8000"
+console = Console()
 
 
 def post_message(gateway: str, text: str, session_id: str) -> None:
@@ -20,7 +26,7 @@ def post_message(gateway: str, text: str, session_id: str) -> None:
         "text": text,
         "session_id": session_id,
         "channel": "cli",
-        "user_id": os.getlogin(),
+        "user_id": os.getenv("USER", "user"),
     }).encode()
     req = urllib.request.Request(
         f"{gateway}/message",
@@ -30,33 +36,49 @@ def post_message(gateway: str, text: str, session_id: str) -> None:
     )
     with urllib.request.urlopen(req, timeout=10) as r:
         if r.status != 202:
-            print(f"[error] gateway returned {r.status}", file=sys.stderr)
+            console.print(f"[red][error] gateway returned {r.status}[/red]")
             sys.exit(1)
 
 
-def wait_for_reply(gateway: str, session_id: str, timeout: int = 400) -> str:
-    """Open SSE stream and return first data event."""
+def stream_reply(gateway: str, session_id: str, timeout: int = 400) -> str:
+    """
+    Open the /stream/{session_id} SSE endpoint and display tokens live with
+    Rich. Returns the full assembled reply text.
+    """
     req = urllib.request.Request(
-        f"{gateway}/reply/{session_id}",
+        f"{gateway}/stream/{session_id}",
         headers={"Accept": "text/event-stream"},
     )
+    buffer = ""
     with urllib.request.urlopen(req, timeout=timeout + 5) as r:
+        with Live(Text(""), console=console, refresh_per_second=20, transient=True) as live:
             for raw_line in r:
                 line = raw_line.decode("utf-8").rstrip("\n")
-            if line.startswith("data:"):
-                return line[5:].strip().replace("\\n", "\n")
-    return ""
+                if not line.startswith("data:"):
+                    continue
+                chunk = line[5:].strip()
+                if chunk == "[DONE]":
+                    break
+                chunk = chunk.replace("\\n", "\n")
+                buffer += chunk
+                live.update(Text(buffer))
+
+    # Render the complete reply as Markdown once streaming is done
+    console.print(Markdown(buffer))
+    return buffer
 
 
 def main():
     parser = argparse.ArgumentParser(description="Adolf CLI")
     parser.add_argument("--url", default=GATEWAY, help="Gateway URL")
-    parser.add_argument("--session", default=f"cli-{os.getlogin()}", help="Session ID")
+    parser.add_argument("--session", default=f"cli-{os.getenv('USER', 'user')}",
+                        help="Session ID")
     parser.add_argument("--timeout", type=int, default=400, help="Reply timeout (seconds)")
     args = parser.parse_args()
 
-    print(f"Adolf CLI  (session={args.session}, gateway={args.url})")
-    print("Type your message and press Enter. Ctrl+C or Ctrl+D to exit.\n")
+    console.print(f"[bold]Adolf CLI[/bold]  (session=[cyan]{args.session}[/cyan], "
+                  f"gateway=[cyan]{args.url}[/cyan])")
+    console.print("Type your message and press Enter. Ctrl+C or Ctrl+D to exit.\n")
 
     try:
         while True:
@@ -68,12 +90,11 @@ def main():
                 continue
 
             post_message(args.url, text, args.session)
-            print("...", end="", flush=True)
-            reply = wait_for_reply(args.url, args.session, timeout=args.timeout)
-            print(f"\r{reply}\n")
+            stream_reply(args.url, args.session, timeout=args.timeout)
+            console.print()
 
     except KeyboardInterrupt:
-        print("\nbye")
+        console.print("\n[dim]bye[/dim]")
 
 
 if __name__ == "__main__":

docker-compose.yml

@@ -1,4 +1,19 @@
 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
@@ -6,6 +21,9 @@ services:
       - "8000:8000"
     environment:
       - PYTHONUNBUFFERED=1
+      # Bifrost gateway — all LLM inference goes through here
+      - BIFROST_URL=http://bifrost:8080/v1
+      # 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
@@ -19,6 +37,7 @@ services:
       - openmemory
       - grammy
       - crawl4ai
+      - bifrost
     restart: unless-stopped
 
   openmemory:
@@ -27,8 +46,9 @@ services:
     ports:
       - "8765:8765"
     environment:
-      # Extraction LLM (qwen2.5:1.5b) runs on GPU after reply — fast 2-5s extraction
+      # Extraction LLM runs on GPU — qwen2.5:1.5b for speed (~3s)
       - OLLAMA_GPU_URL=http://host.docker.internal:11436
+      - OLLAMA_EXTRACTION_MODEL=qwen2.5:1.5b
       # Embedding (nomic-embed-text) runs on CPU — fast enough for search (50-150ms)
       - OLLAMA_CPU_URL=http://host.docker.internal:11435
     extra_hosts:
@@ -45,6 +65,20 @@ services:
       - DEEPAGENTS_URL=http://deepagents:8000
     restart: unless-stopped
 
+  cli:
+    build:
+      context: .
+      dockerfile: Dockerfile.cli
+    container_name: cli
+    environment:
+      - DEEPAGENTS_URL=http://deepagents:8000
+    depends_on:
+      - deepagents
+    stdin_open: true
+    tty: true
+    profiles:
+      - tools
+
   crawl4ai:
     image: unclecode/crawl4ai:latest
     container_name: crawl4ai

openmemory/server.py

@@ -6,6 +6,7 @@ from mem0 import Memory
 # Extraction LLM — GPU Ollama (qwen3:4b, same model as medium agent)
 # Runs after reply when GPU is idle; spin-wait in agent.py prevents contention
 OLLAMA_GPU_URL = os.getenv("OLLAMA_GPU_URL", "http://host.docker.internal:11436")
+EXTRACTION_MODEL = os.getenv("OLLAMA_EXTRACTION_MODEL", "qwen2.5:1.5b")
 
 # Embedding — CPU Ollama (nomic-embed-text, 137 MB RAM)
 # Used for both search (50-150ms, acceptable) and store-time embedding
@@ -94,7 +95,7 @@ config = {
     "llm": {
         "provider": "ollama",
         "config": {
-            "model": "qwen3:4b",
+            "model": EXTRACTION_MODEL,
             "ollama_base_url": OLLAMA_GPU_URL,
             "temperature": 0.1,  # consistent JSON output
         },

pytest.ini

@@ -0,0 +1,4 @@
+[pytest]
+testpaths = tests/unit
+pythonpath = .
+asyncio_mode = auto

tests/integration/common.py

@@ -0,0 +1,273 @@
+"""
+Shared config, helpers, and utilities for Adolf integration tests.
+"""
+
+import http.client
+import json
+import re
+import subprocess
+import time
+import urllib.request
+
+# ── config ────────────────────────────────────────────────────────────────────
+DEEPAGENTS   = "http://localhost:8000"
+BIFROST      = "http://localhost:8080"
+OPENMEMORY   = "http://localhost:8765"
+GRAMMY_HOST  = "localhost"
+GRAMMY_PORT  = 3001
+OLLAMA_GPU   = "http://localhost:11436"
+OLLAMA_CPU   = "http://localhost:11435"
+QDRANT       = "http://localhost:6333"
+SEARXNG      = "http://localhost:11437"
+COMPOSE_FILE = "/home/alvis/adolf/docker-compose.yml"
+DEFAULT_CHAT_ID = "346967270"
+
+NAMES = [
+    "Maximilian", "Cornelius", "Zephyr", "Archibald", "Balthazar",
+    "Ignatius", "Lysander", "Octavian", "Reginald", "Sylvester",
+]
+
+BENCHMARK = {
+    "easy": [
+        "hi",
+        "what is 2+2?",
+        "what is the capital of France?",
+        "tell me a short joke",
+        "how are you doing today?",
+        "thanks!",
+        "what day comes after Wednesday?",
+        "name the three primary colors",
+        "is the sky blue?",
+        "what does CPU stand for?",
+    ],
+    "medium": [
+        "what is the current weather in Berlin?",
+        "find the latest news about artificial intelligence",
+        "what is the current price of Bitcoin?",
+        "search for a good pasta carbonara recipe",
+        "what movies are in theaters this week?",
+        "find Python tutorials for beginners",
+        "who won the last FIFA World Cup?",
+        "do you remember what we talked about before?",
+        "search for the best coffee shops in Tokyo",
+        "what is happening in the tech industry this week?",
+        "what's the weather like today?",
+    ],
+    "hard": [
+        "/think compare the top 3 Python web frameworks (Django, FastAPI, Flask) and recommend one for a production REST API",
+        "/think research the history of artificial intelligence and create a timeline of key milestones",
+        "/think plan a 7-day trip to Japan with daily itinerary, accommodation suggestions, and budget breakdown",
+        "/think analyze microservices vs monolithic architecture: pros, cons, and when to choose each",
+        "/think write a Python script that reads a CSV file, cleans the data, and generates summary statistics",
+        "/think research quantum computing: explain the key concepts and how it differs from classical computing",
+        "/think compare PostgreSQL, MongoDB, and Redis — when to use each and what are the trade-offs?",
+        "/think create a comprehensive Docker deployment guide covering best practices for production",
+        "/think research climate change: summarize the latest IPCC findings and key data points",
+        "/think design a REST API with authentication, rate limiting, and proper error handling — provide architecture and code outline",
+    ],
+}
+
+# ── terminal colours ──────────────────────────────────────────────────────────
+PASS = "\033[32mPASS\033[0m"
+FAIL = "\033[31mFAIL\033[0m"
+INFO = "\033[36mINFO\033[0m"
+WARN = "\033[33mWARN\033[0m"
+
+
+# ── result helpers ────────────────────────────────────────────────────────────
+
+def report(results: list, name: str, ok: bool, detail: str = ""):
+    tag = PASS if ok else FAIL
+    print(f"  [{tag}] {name}" + (f" — {detail}" if detail else ""))
+    results.append((name, ok))
+
+
+def print_summary(results: list):
+    print(f"\n{'─'*55}")
+    total  = len(results)
+    passed = sum(1 for _, ok in results if ok)
+    failed = total - passed
+    print(f"Results: {passed}/{total} passed", end="")
+    if failed:
+        print(f"  ({failed} failed)\n")
+        print("Failed checks:")
+        for name, ok in results:
+            if not ok:
+                print(f"  - {name}")
+    else:
+        print(" — all good")
+    print()
+
+
+def tf(v):
+    """Format timing value."""
+    return f"{v:6.2f}s" if v is not None else "   n/a"
+
+
+# ── HTTP helpers ──────────────────────────────────────────────────────────────
+
+def get(url, timeout=5):
+    with urllib.request.urlopen(urllib.request.Request(url), timeout=timeout) as r:
+        return r.status, r.read().decode()
+
+
+def post_json(url, payload, timeout=10):
+    data = json.dumps(payload).encode()
+    req = urllib.request.Request(
+        url, data=data,
+        headers={"Content-Type": "application/json"},
+        method="POST",
+    )
+    with urllib.request.urlopen(req, timeout=timeout) as r:
+        return r.status, json.loads(r.read().decode())
+
+
+def check_sse(host, port, path):
+    try:
+        conn = http.client.HTTPConnection(host, port, timeout=5)
+        conn.request("GET", path, headers={"Accept": "text/event-stream"})
+        r = conn.getresponse()
+        conn.close()
+        return r.status == 200, f"HTTP {r.status}"
+    except Exception as e:
+        return False, str(e)
+
+
+def qdrant_count():
+    try:
+        _, body = get(f"{QDRANT}/collections/adolf_memories")
+        return json.loads(body).get("result", {}).get("points_count", 0)
+    except Exception:
+        return 0
+
+
+# ── log helpers ───────────────────────────────────────────────────────────────
+
+def fetch_logs(since_s=600):
+    """Return deepagents log lines from the last since_s seconds."""
+    try:
+        r = subprocess.run(
+            ["docker", "compose", "-f", COMPOSE_FILE, "logs", "deepagents",
+             f"--since={int(since_s)}s", "--no-log-prefix"],
+            capture_output=True, text=True, timeout=15,
+        )
+        return r.stdout.splitlines()
+    except Exception:
+        return []
+
+
+def fetch_bifrost_logs(since_s=120):
+    """Return bifrost container log lines from the last since_s seconds."""
+    try:
+        r = subprocess.run(
+            ["docker", "compose", "-f", COMPOSE_FILE, "logs", "bifrost",
+             f"--since={int(since_s)}s", "--no-log-prefix"],
+            capture_output=True, text=True, timeout=10,
+        )
+        return r.stdout.splitlines()
+    except Exception:
+        return []
+
+
+def parse_run_block(lines, msg_prefix):
+    """
+    Scan log lines for the LAST '[agent] running: <msg_prefix>' block.
+    Extracts reply timing, tier, and memory timing from that block.
+
+    Returns dict or None if the reply has not appeared in logs yet.
+    Dict keys:
+      reply_total, llm, send, tier, reply_text  — from "[agent] replied in ..."
+      memory_s                                  — from "[memory] stored in ..."
+      memory_error                              — True if "[memory] error" found
+    """
+    search = msg_prefix[:50]
+    start_idx = None
+    for i, line in enumerate(lines):
+        if "[agent] running:" in line and search in line:
+            start_idx = i  # keep updating — we want the LAST occurrence
+
+    if start_idx is None:
+        return None
+
+    block = lines[start_idx:]
+    last_ai_text = None
+    reply_data = None
+
+    for j, line in enumerate(block):
+        if "AIMessage:" in line and "→" not in line:
+            txt = line.split("AIMessage:", 1)[-1].strip()
+            if txt:
+                last_ai_text = txt
+
+        m = re.search(r"replied in ([\d.]+)s \(llm=([\d.]+)s, send=([\d.]+)s\)", line)
+        if m:
+            tier_m = re.search(r"\btier=(\w+)", line)
+            tier = tier_m.group(1) if tier_m else "unknown"
+            reply_data = {
+                "reply_total": float(m.group(1)),
+                "llm":         float(m.group(2)),
+                "send":        float(m.group(3)),
+                "tier":        tier,
+                "reply_text":  last_ai_text,
+                "memory_s":    None,
+                "memory_error": False,
+                "_j": j,
+            }
+            break
+
+    if reply_data is not None:
+        next_lines = block[reply_data["_j"] + 1: reply_data["_j"] + 3]
+        for line in next_lines:
+            if line.startswith("[agent] reply_text:"):
+                reply_data["reply_text"] = line[len("[agent] reply_text:"):].strip()
+                break
+
+    if reply_data is None:
+        return None
+
+    for line in block[reply_data["_j"] + 1:]:
+        mm = re.search(r"\[memory\] stored in ([\d.]+)s", line)
+        if mm:
+            reply_data["memory_s"] = float(mm.group(1))
+            break
+        if "[memory] error" in line:
+            reply_data["memory_error"] = True
+            break
+
+    return reply_data
+
+
+def wait_for(label, msg_prefix, timeout_s=200, need_memory=True):
+    """
+    Poll deepagents logs until the message is fully processed.
+    Shows a live progress line. Returns timing dict or None on timeout.
+    """
+    t_start = time.monotonic()
+    deadline = t_start + timeout_s
+    tick = 0
+    last_result = None
+
+    while time.monotonic() < deadline:
+        since = int(time.monotonic() - t_start) + 90
+        lines = fetch_logs(since_s=since)
+        result = parse_run_block(lines, msg_prefix)
+
+        if result:
+            last_result = result
+            has_mem = result["memory_s"] is not None or result["memory_error"]
+            if (not need_memory) or has_mem:
+                elapsed = time.monotonic() - t_start
+                print(f"\r  [{label}] done after {elapsed:.0f}s{' ' * 30}")
+                return result
+
+        time.sleep(4)
+        tick += 1
+        rem = int(deadline - time.monotonic())
+        if last_result:
+            phase = "waiting for memory..." if need_memory else "done"
+        else:
+            phase = "waiting for LLM reply..."
+        print(f"\r  [{label}] {tick*4}s elapsed, {rem}s left — {phase}  ", end="", flush=True)
+
+    print(f"\r  [{label}] TIMEOUT after {timeout_s}s{' ' * 30}")
+    return None

tests/integration/test_health.py

@@ -0,0 +1,214 @@
+#!/usr/bin/env python3
+"""
+Adolf service health integration tests.
+
+Checks:
+  1.  deepagents /health — agent_ready
+  1b. openmemory /sse reachable
+  1c. grammy /sse reachable
+  2.  Bifrost /health, /v1/models, direct inference, deepagents startup log
+  3.  GPU Ollama — reachable, qwen3:8b present
+  4.  CPU Ollama — reachable, nomic-embed-text present
+  5.  Qdrant — reachable, adolf_memories collection, vector dims=768
+  6.  SearXNG — reachable, JSON results, latency < 5s
+
+Usage:
+    python3 test_health.py
+"""
+
+import json
+import sys
+import time
+import urllib.request
+
+from common import (
+    DEEPAGENTS, BIFROST, GRAMMY_HOST, GRAMMY_PORT,
+    OLLAMA_GPU, OLLAMA_CPU, QDRANT, SEARXNG, COMPOSE_FILE,
+    INFO, FAIL,
+    report, print_summary, tf,
+    get, post_json, check_sse, fetch_logs,
+)
+
+results = []
+timings = {}
+
+
+# ── 1. Service health ─────────────────────────────────────────────────────────
+print(f"\n[{INFO}] 1. Service health")
+t0 = time.monotonic()
+
+try:
+    status, body = get(f"{DEEPAGENTS}/health")
+    data = json.loads(body)
+    ok = status == 200 and data.get("agent_ready") is True
+    report(results, "deepagents /health — agent_ready", ok,
+           f"agent_ready={data.get('agent_ready')}")
+except Exception as e:
+    report(results, "deepagents /health", False, str(e))
+
+ok, detail = check_sse("localhost", 8765, "/sse")
+report(results, "openmemory /sse reachable", ok, detail)
+
+ok, detail = check_sse(GRAMMY_HOST, GRAMMY_PORT, "/sse")
+report(results, "grammy /sse reachable", ok, detail)
+
+timings["health_check"] = time.monotonic() - t0
+
+
+# ── 2. Bifrost gateway ────────────────────────────────────────────────────────
+print(f"\n[{INFO}] 2. Bifrost gateway (port 8080)")
+t0 = time.monotonic()
+
+try:
+    status, body = get(f"{BIFROST}/health", timeout=5)
+    report(results, "Bifrost /health reachable", status == 200, f"HTTP {status}")
+except Exception as e:
+    report(results, "Bifrost /health reachable", False, str(e))
+
+try:
+    status, body = get(f"{BIFROST}/v1/models", timeout=5)
+    data = json.loads(body)
+    model_ids = [m.get("id", "") for m in data.get("data", [])]
+    gpu_models = [m for m in model_ids if m.startswith("ollama/")]
+    report(results, "Bifrost lists ollama GPU models", len(gpu_models) > 0,
+           f"found: {gpu_models}")
+    for expected in ["ollama/qwen3:4b", "ollama/qwen3:8b", "ollama/qwen2.5:1.5b"]:
+        report(results, f"  model {expected} listed", expected in model_ids)
+except Exception as e:
+    report(results, "Bifrost /v1/models", False, str(e))
+
+print(f"  [bifrost-infer] POST /v1/chat/completions → ollama/qwen2.5:0.5b ...")
+t_infer = time.monotonic()
+try:
+    infer_payload = {
+        "model": "ollama/qwen2.5:0.5b",
+        "messages": [{"role": "user", "content": "Reply with exactly one word: pong"}],
+        "max_tokens": 16,
+    }
+    data = json.dumps(infer_payload).encode()
+    req = urllib.request.Request(
+        f"{BIFROST}/v1/chat/completions",
+        data=data,
+        headers={"Content-Type": "application/json"},
+        method="POST",
+    )
+    with urllib.request.urlopen(req, timeout=60) as r:
+        infer_status = r.status
+        infer_body = json.loads(r.read().decode())
+    infer_elapsed = time.monotonic() - t_infer
+    reply_content = infer_body.get("choices", [{}])[0].get("message", {}).get("content", "")
+    used_model = infer_body.get("model", "")
+    report(results, "Bifrost → Ollama GPU inference succeeds",
+           infer_status == 200 and bool(reply_content),
+           f"{infer_elapsed:.1f}s  model={used_model!r}  reply={reply_content[:60]!r}")
+    timings["bifrost_direct_infer"] = infer_elapsed
+except Exception as e:
+    report(results, "Bifrost → Ollama GPU inference succeeds", False, str(e))
+    timings["bifrost_direct_infer"] = None
+
+try:
+    import subprocess
+    r = subprocess.run(
+        ["docker", "compose", "-f", COMPOSE_FILE, "logs", "deepagents",
+         "--since=3600s", "--no-log-prefix"],
+        capture_output=True, text=True, timeout=10,
+    )
+    log_lines = r.stdout.splitlines()
+    bifrost_line = next(
+        (l for l in log_lines if "[agent] bifrost=" in l and "bifrost:8080" in l),
+        None,
+    )
+    report(results, "deepagents startup log confirms bifrost URL",
+           bifrost_line is not None,
+           bifrost_line.strip() if bifrost_line else "line not found in logs")
+    if bifrost_line:
+        has_prefix = "router=ollama/" in bifrost_line and "medium=ollama/" in bifrost_line
+        report(results, "deepagents model names use ollama/ prefix", has_prefix,
+               bifrost_line.strip())
+except Exception as e:
+    report(results, "deepagents startup log check", False, str(e))
+
+timings["bifrost_check"] = time.monotonic() - t0
+
+
+# ── 3. GPU Ollama ─────────────────────────────────────────────────────────────
+print(f"\n[{INFO}] 3. GPU Ollama (port 11436)")
+t0 = time.monotonic()
+
+try:
+    status, body = get(f"{OLLAMA_GPU}/api/tags")
+    models = [m["name"] for m in json.loads(body).get("models", [])]
+    has_qwen = any("qwen3" in m for m in models)
+    report(results, "GPU Ollama reachable", True, f"models: {models}")
+    report(results, "qwen3:8b present", has_qwen)
+except Exception as e:
+    report(results, "GPU Ollama reachable", False, str(e))
+    report(results, "qwen3:8b present", False, "skipped")
+
+timings["gpu_ollama_ping"] = time.monotonic() - t0
+
+
+# ── 4. CPU Ollama ─────────────────────────────────────────────────────────────
+print(f"\n[{INFO}] 4. CPU Ollama (port 11435)")
+t0 = time.monotonic()
+
+try:
+    status, body = get(f"{OLLAMA_CPU}/api/tags")
+    models = [m["name"] for m in json.loads(body).get("models", [])]
+    has_embed = any("nomic-embed-text" in m for m in models)
+    report(results, "CPU Ollama reachable", True, f"models: {models}")
+    report(results, "nomic-embed-text present", has_embed)
+except Exception as e:
+    report(results, "CPU Ollama reachable", False, str(e))
+    report(results, "nomic-embed-text present", False, "skipped")
+
+timings["cpu_ollama_ping"] = time.monotonic() - t0
+
+
+# ── 5. Qdrant ─────────────────────────────────────────────────────────────────
+print(f"\n[{INFO}] 5. Qdrant (port 6333)")
+t0 = time.monotonic()
+
+try:
+    status, body = get(f"{QDRANT}/collections")
+    cols = [c["name"] for c in json.loads(body).get("result", {}).get("collections", [])]
+    report(results, "Qdrant reachable", True, f"collections: {cols}")
+    report(results, "adolf_memories collection exists", "adolf_memories" in cols)
+except Exception as e:
+    report(results, "Qdrant reachable", False, str(e))
+    report(results, "adolf_memories collection exists", False, "skipped")
+
+try:
+    status, body = get(f"{QDRANT}/collections/adolf_memories")
+    info = json.loads(body).get("result", {})
+    dims = info.get("config", {}).get("params", {}).get("vectors", {}).get("size")
+    report(results, "vector dims = 768", dims == 768, f"got {dims}")
+except Exception as e:
+    report(results, "adolf_memories collection info", False, str(e))
+
+timings["qdrant_ping"] = time.monotonic() - t0
+
+
+# ── 6. SearXNG ────────────────────────────────────────────────────────────────
+print(f"\n[{INFO}] 6. SearXNG (port 11437)")
+t0 = time.monotonic()
+
+try:
+    status, body = get(f"{SEARXNG}/search?q=test&format=json", timeout=15)
+    elapsed = time.monotonic() - t0
+    n = len(json.loads(body).get("results", []))
+    report(results, "SearXNG reachable + JSON results", status == 200 and n > 0,
+           f"{n} results in {elapsed:.1f}s")
+    report(results, "SearXNG response < 5s", elapsed < 5, f"{elapsed:.2f}s")
+    timings["searxng_latency"] = elapsed
+except Exception as e:
+    report(results, "SearXNG reachable", False, str(e))
+    report(results, "SearXNG response < 5s", False, "skipped")
+    timings["searxng_latency"] = None
+
+timings["searxng_check"] = time.monotonic() - t0
+
+
+# ── summary ───────────────────────────────────────────────────────────────────
+print_summary(results)
+sys.exit(0 if all(ok for _, ok in results) else 1)

tests/integration/test_memory.py

@@ -0,0 +1,438 @@
+#!/usr/bin/env python3
+"""
+Adolf memory integration tests.
+
+Tests:
+  1. Name store   — POST "remember that your name is <RandomName>"
+  2. Qdrant point — verifies a new vector was written after store
+  3. Name recall  — POST "what is your name?" → reply must contain <RandomName>
+  4. Bifrost      — verifies store/recall requests passed through Bifrost
+  5. Timing profile — breakdown of store and recall latencies
+  6. Memory benchmark — store 5 personal facts, recall with 10 questions
+  7. Dedup test   — same fact stored twice must not grow Qdrant by 2 points
+
+Usage:
+    python3 test_memory.py [--chat-id CHAT_ID] [--name-only] [--bench-only] [--dedup-only]
+"""
+
+import argparse
+import json
+import random
+import subprocess
+import sys
+import time
+import urllib.request
+
+from common import (
+    DEEPAGENTS, QDRANT, COMPOSE_FILE, DEFAULT_CHAT_ID,
+    NAMES,
+    INFO, PASS, FAIL, WARN,
+    report, print_summary, tf,
+    get, post_json, qdrant_count, fetch_logs, fetch_bifrost_logs,
+    parse_run_block, wait_for,
+)
+
+# ── args ──────────────────────────────────────────────────────────────────────
+parser = argparse.ArgumentParser(description="Adolf memory integration tests")
+parser.add_argument("--chat-id", default=DEFAULT_CHAT_ID)
+parser.add_argument("--name-only",  action="store_true", help="Run only the name store/recall test")
+parser.add_argument("--bench-only", action="store_true", help="Run only the memory benchmark")
+parser.add_argument("--dedup-only", action="store_true", help="Run only the deduplication test")
+args = parser.parse_args()
+
+CHAT_ID = args.chat_id
+_only = args.name_only or args.bench_only or args.dedup_only
+_run_name  = not _only or args.name_only
+_run_bench = not _only or args.bench_only
+_run_dedup = not _only or args.dedup_only
+
+results = []
+timings = {}
+
+random_name = random.choice(NAMES)
+TEST_CHAT_ID = f"{CHAT_ID}-{random_name.lower()}"
+
+if _run_name:
+    print(f"\n  Test name : \033[1m{random_name}\033[0m")
+    print(f"  Chat ID   : {TEST_CHAT_ID}")
+
+
+# ── 1–4. Name store / recall pipeline ────────────────────────────────────────
+if _run_name:
+    print(f"\n[{INFO}] 1. Name store / recall pipeline")
+
+    store_msg  = f"remember that your name is {random_name}"
+    recall_msg = "what is your name?"
+
+    # Clear memories so each run starts clean
+    try:
+        post_json(f"{QDRANT}/collections/adolf_memories/points/delete",
+                  {"filter": {}}, timeout=5)
+    except Exception:
+        pass
+
+    pts_before = qdrant_count()
+    print(f"  Qdrant points before: {pts_before}")
+
+    # ── 1. Store ──────────────────────────────────────────────────────────────
+    print(f"\n  [store] '{store_msg}'")
+    t_store = time.monotonic()
+
+    try:
+        status, _ = post_json(f"{DEEPAGENTS}/chat",
+                              {"message": store_msg, "chat_id": TEST_CHAT_ID}, timeout=5)
+        t_accept = time.monotonic() - t_store
+        report(results, "POST /chat (store) returns 202 immediately",
+               status == 202 and t_accept < 1, f"status={status}, t={t_accept:.3f}s")
+        timings["store_http_accept"] = t_accept
+    except Exception as e:
+        report(results, "POST /chat (store)", False, str(e))
+        print_summary(results)
+        sys.exit(1)
+
+    store = wait_for("store", store_msg, timeout_s=220, need_memory=True)
+
+    if store:
+        timings.update({
+            "store_llm":    store["llm"],
+            "store_send":   store["send"],
+            "store_reply":  store["reply_total"],
+            "store_memory": store["memory_s"],
+        })
+        report(results, "Agent replied to store message", True,
+               f"{store['reply_total']:.1f}s total  llm={store['llm']:.1f}s  "
+               f"send={store['send']:.1f}s  tier={store['tier']}")
+        if store["memory_s"] is not None:
+            report(results, "Memory stored without error", True, f"{store['memory_s']:.1f}s")
+        elif store["memory_error"]:
+            report(results, "Memory stored without error", False, "error in [memory] log")
+        else:
+            report(results, "Memory stored without error", False, "not found in logs")
+        print(f"    Store reply: {store['reply_text']!r}")
+    else:
+        report(results, "Agent replied to store message", False, "timeout")
+        report(results, "Memory stored without error", False, "timeout")
+        print_summary(results)
+        sys.exit(1)
+
+    # ── 2. Qdrant point check ─────────────────────────────────────────────────
+    pts_after = qdrant_count()
+    new_pts = pts_after - pts_before
+    report(results, "New memory point(s) added to Qdrant", new_pts > 0,
+           f"{pts_before} → {pts_after} (+{new_pts})")
+    timings["qdrant_new_points"] = new_pts
+
+    # ── 3. Recall ─────────────────────────────────────────────────────────────
+    print(f"\n  [recall] '{recall_msg}'")
+    t_recall = time.monotonic()
+
+    try:
+        status, _ = post_json(f"{DEEPAGENTS}/chat",
+                              {"message": recall_msg, "chat_id": TEST_CHAT_ID}, timeout=5)
+        t_accept2 = time.monotonic() - t_recall
+        report(results, "POST /chat (recall) returns 202 immediately",
+               status == 202 and t_accept2 < 1, f"status={status}, t={t_accept2:.3f}s")
+        timings["recall_http_accept"] = t_accept2
+    except Exception as e:
+        report(results, "POST /chat (recall)", False, str(e))
+
+    recall = wait_for("recall", recall_msg, timeout_s=160, need_memory=False)
+
+    if recall:
+        timings.update({
+            "recall_llm":   recall["llm"],
+            "recall_send":  recall["send"],
+            "recall_reply": recall["reply_total"],
+        })
+        report(results, "Agent replied to recall message", True,
+               f"{recall['reply_total']:.1f}s total  llm={recall['llm']:.1f}s  "
+               f"send={recall['send']:.1f}s  tier={recall['tier']}")
+        reply_text = recall["reply_text"] or ""
+        name_in_reply = random_name.lower() in reply_text.lower()
+        report(results, f"Reply contains '{random_name}'", name_in_reply,
+               f"reply: {reply_text[:120]!r}")
+    else:
+        report(results, "Agent replied to recall message", False, "timeout")
+        report(results, f"Reply contains '{random_name}'", False, "no reply")
+
+    # ── 4. Bifrost pass-through check ─────────────────────────────────────────
+    bifrost_lines = fetch_bifrost_logs(since_s=300)
+    report(results, "Bifrost container has log output (requests forwarded)",
+           len(bifrost_lines) > 0, f"{len(bifrost_lines)} lines in bifrost logs")
+    bifrost_raw = "\n".join(bifrost_lines)
+    report(results, "  Bifrost log shows AsyncOpenAI agent requests",
+           "AsyncOpenAI" in bifrost_raw,
+           f"{'found' if 'AsyncOpenAI' in bifrost_raw else 'NOT found'} in bifrost logs")
+
+    # ── 5. Timing profile ─────────────────────────────────────────────────────
+    print(f"\n[{INFO}] 5. Timing profile")
+    W = 36
+    print(f"\n  {'Stage':<{W}}  {'Time':>8}")
+    print(f"  {'─'*W}  {'─'*8}")
+
+    for label, key in [
+        ("[GPU] HTTP accept — store turn",        "store_http_accept"),
+        ("[GPU] qwen3:Xb inference — store turn", "store_llm"),
+        ("[GPU] Telegram send — store turn",      "store_send"),
+        ("[GPU] Total reply latency — store",     "store_reply"),
+        ("[GPU] qwen2.5:1.5b+embed — async mem",  "store_memory"),
+    ]:
+        print(f"  {label:<{W}}  {tf(timings.get(key)):>8}")
+
+    print(f"  {'─'*W}  {'─'*8}")
+
+    for label, key in [
+        ("[GPU] HTTP accept — recall turn",    "recall_http_accept"),
+        ("[GPU] qwen3:Xb inference — recall",  "recall_llm"),
+        ("[GPU] Telegram send — recall turn",  "recall_send"),
+        ("[GPU] Total reply latency — recall", "recall_reply"),
+    ]:
+        print(f"  {label:<{W}}  {tf(timings.get(key)):>8}")
+
+    print(f"\n  Bottleneck analysis (each █ ≈ 5s):")
+    print(f"  {'─'*(W+12)}")
+    candidates = [
+        ("[GPU] qwen3:Xb — store reply ", timings.get("store_llm")   or 0),
+        ("[GPU] qwen3:Xb — recall reply", timings.get("recall_llm")  or 0),
+        ("[GPU] qwen2.5:1.5b+embed (async)", timings.get("store_memory") or 0),
+    ]
+    candidates.sort(key=lambda x: x[1], reverse=True)
+    for label, t in candidates:
+        bar = "█" * min(int(t / 5), 24)
+        total_pipeline = (timings.get("store_reply") or 0) + (timings.get("store_memory") or 0)
+        pct = f"  {t/total_pipeline*100:4.0f}%" if total_pipeline > 0 else ""
+        print(f"  {label}  {t:6.1f}s  {bar}{pct}")
+    print()
+
+
+# ── 6. Memory benchmark ───────────────────────────────────────────────────────
+if _run_bench:
+    _mem_name     = random.choice(["Alice", "Bruno", "Camille", "Diego", "Elena",
+                                   "Farid", "Greta", "Hiroshi", "Irina", "Jonas"])
+    _mem_city     = random.choice(["Tokyo", "Berlin", "Cairo", "Sydney", "Oslo",
+                                   "Nairobi", "Lisbon", "Seoul", "Montreal", "Bangkok"])
+    _mem_allergy  = random.choice(["nuts", "gluten", "dairy", "shellfish", "eggs"])
+    _mem_job      = random.choice([
+        ("software engineer", "startup"),
+        ("data scientist", "research lab"),
+        ("product manager", "tech company"),
+        ("DevOps engineer", "cloud provider"),
+    ])
+    _mem_lang     = random.choice(["Python", "Rust", "Go", "TypeScript", "Kotlin"])
+    _mem_pet_name = random.choice(["Whiskers", "Biscuit", "Mango", "Pebble", "Shadow",
+                                   "Noodle", "Cheddar", "Cosmo", "Pippin", "Ziggy"])
+
+    print(f"\n[{INFO}] 6. Memory benchmark")
+    print(f"  name={_mem_name}  city={_mem_city}  allergy={_mem_allergy}  "
+          f"job={_mem_job[0]}@{_mem_job[1]}  lang={_mem_lang}  pet={_mem_pet_name}")
+    print(f"  Storing 5 facts, then querying with 10 recall questions")
+    print(f"  Chat ID: {CHAT_ID}")
+    print()
+
+    # Wipe collection and restart openmemory for a clean slate
+    try:
+        req = urllib.request.Request(f"{QDRANT}/collections/adolf_memories", method="DELETE")
+        with urllib.request.urlopen(req, timeout=5):
+            pass
+        print(f"  [{INFO}] Wiped adolf_memories collection")
+    except Exception as e:
+        print(f"  [{WARN}] Could not wipe collection: {e}")
+
+    try:
+        subprocess.run(
+            ["docker", "compose", "-f", COMPOSE_FILE, "restart", "openmemory"],
+            capture_output=True, timeout=30,
+        )
+        time.sleep(6)
+        print(f"  [{INFO}] Restarted openmemory — fresh collection ready")
+    except Exception as e:
+        print(f"  [{WARN}] Could not restart openmemory: {e}")
+
+    MEMORY_FACTS = [
+        f"My name is {_mem_name} and I live in {_mem_city}",
+        f"I prefer vegetarian food and I'm allergic to {_mem_allergy}",
+        f"I work as a {_mem_job[0]} at a {_mem_job[1]}",
+        f"My favorite programming language is {_mem_lang}",
+        f"I have a cat named {_mem_pet_name}",
+    ]
+
+    MEMORY_RECALLS = [
+        ("What is my name?",                       [_mem_name.lower()]),
+        ("Where do I live?",                       [_mem_city.lower()]),
+        ("Do I have any food allergies?",          [_mem_allergy.lower()]),
+        ("What is my job?",                        [_mem_job[0].split()[0].lower()]),
+        ("What programming language do I prefer?", [_mem_lang.lower()]),
+        ("Do I have any pets?",                    [_mem_pet_name.lower()]),
+        ("Am I vegetarian or do I eat meat?",      ["vegetarian"]),
+        ("What city am I in?",                     [_mem_city.lower()]),
+        ("Tell me what you know about me",         [_mem_name.lower(), _mem_city.lower()]),
+        ("What's the name of my pet?",             [_mem_pet_name.lower()]),
+    ]
+
+    STORE_TIMEOUT  = 180
+    RECALL_TIMEOUT = 180
+
+    print(f"  Storing {len(MEMORY_FACTS)} facts...")
+    store_ok = 0
+    for i, fact in enumerate(MEMORY_FACTS, 1):
+        print(f"  [mem-store-{i:02d}] {fact!r}")
+        try:
+            status, _ = post_json(f"{DEEPAGENTS}/chat",
+                                  {"message": fact, "chat_id": CHAT_ID}, timeout=5)
+            if status != 202:
+                print(f"              → [{FAIL}] POST returned {status}")
+                continue
+        except Exception as e:
+            print(f"              → [{FAIL}] POST error: {e}")
+            continue
+
+        found = wait_for(f"mem-store-{i:02d}", fact, timeout_s=STORE_TIMEOUT, need_memory=True)
+        if found:
+            store_ok += 1
+            print(f"              → [{PASS}] stored  tier={found['tier']}  mem={found['memory_s']}s")
+        else:
+            print(f"              → [{FAIL}] timeout")
+
+    report(results, f"All memory facts stored ({store_ok}/{len(MEMORY_FACTS)})",
+           store_ok == len(MEMORY_FACTS))
+
+    # Wait for async extraction to settle
+    print(f"\n  Waiting for memory extraction to settle (up to 60s)...")
+    _prev_count = -1
+    _stable_ticks = 0
+    _cur_count = 0
+    for _ in range(30):
+        time.sleep(2)
+        try:
+            _, body = get(f"{QDRANT}/collections/adolf_memories")
+            _cur_count = json.loads(body).get("result", {}).get("points_count", 0)
+        except Exception:
+            _cur_count = _prev_count
+        if _cur_count == _prev_count:
+            _stable_ticks += 1
+            if _stable_ticks >= 3:
+                break
+        else:
+            _stable_ticks = 0
+        _prev_count = _cur_count
+    print(f"  Memory settled: {_cur_count} points in Qdrant")
+
+    print(f"\n  Querying with {len(MEMORY_RECALLS)} recall questions...")
+    recall_results = []
+
+    for i, (question, keywords) in enumerate(MEMORY_RECALLS, 1):
+        print(f"  [mem-recall-{i:02d}] {question!r}")
+        try:
+            status, _ = post_json(f"{DEEPAGENTS}/chat",
+                                  {"message": question, "chat_id": CHAT_ID}, timeout=5)
+            if status != 202:
+                print(f"               → [{FAIL}] POST returned {status}")
+                recall_results.append((question, keywords, None, False))
+                continue
+        except Exception as e:
+            print(f"               → [{FAIL}] POST error: {e}")
+            recall_results.append((question, keywords, None, False))
+            continue
+
+        t_start = time.monotonic()
+        found = None
+        while time.monotonic() - t_start < RECALL_TIMEOUT:
+            since = int(time.monotonic() - t_start) + 30
+            lines = fetch_logs(since_s=since)
+            found = parse_run_block(lines, question)
+            if found:
+                break
+            time.sleep(2)
+
+        if not found:
+            print(f"               → [{FAIL}] timeout")
+            recall_results.append((question, keywords, None, False))
+            continue
+
+        reply_text = (found.get("reply_text") or "").lower()
+        hit_keywords = [kw for kw in keywords if kw.lower() in reply_text]
+        passed = len(hit_keywords) == len(keywords)
+        tag_str = PASS if passed else WARN
+        missing = [kw for kw in keywords if kw.lower() not in reply_text]
+        detail = f"tier={found['tier']}  lat={found['reply_total']:.1f}s"
+        if missing:
+            detail += f"  missing keywords: {missing}"
+        print(f"               → [{tag_str}] {detail}")
+        recall_results.append((question, keywords, found.get("reply_text"), passed))
+        time.sleep(1)
+
+    print(f"\n  {'#':<4}  {'Pass':<5}  {'Question':<45}  {'Keywords'}")
+    print(f"  {'─'*4}  {'─'*5}  {'─'*45}  {'─'*30}")
+    for idx, (q, kws, reply, ok) in enumerate(recall_results, 1):
+        ok_str = "✓" if ok else "✗"
+        print(f"  {ok_str} {idx:<3}  {'yes' if ok else 'no':<5}  {q[:45]:<45}  {kws}")
+
+    recall_pass = sum(1 for _, _, _, ok in recall_results if ok)
+    total_recall = len(recall_results)
+    print(f"\n  Memory recall score: {recall_pass}/{total_recall}")
+    report(results, f"Memory recall ({recall_pass}/{total_recall} keywords found)",
+           recall_pass == total_recall,
+           f"{recall_pass}/{total_recall} questions had all expected keywords in reply")
+
+
+# ── 7. Deduplication test ─────────────────────────────────────────────────────
+if _run_dedup:
+    print(f"\n[{INFO}] 7. Memory deduplication test")
+    print(f"  Sends the same fact twice — Qdrant point count must not increase by 2")
+    print(f"  Chat ID: {CHAT_ID}")
+    print()
+
+    DEDUP_TIMEOUT = 120
+    _dedup_fact = f"My lucky number is {random.randint(1000, 9999)}"
+    print(f"  Fact: {_dedup_fact!r}")
+
+    pts_before = qdrant_count()
+    print(f"  Qdrant points before: {pts_before}")
+
+    print(f"  [dedup-1] sending fact (first time)")
+    found1 = None
+    try:
+        status, _ = post_json(f"{DEEPAGENTS}/chat",
+                              {"message": _dedup_fact, "chat_id": CHAT_ID}, timeout=5)
+        if status != 202:
+            report(results, "Dedup: first POST accepted", False, f"status={status}")
+        else:
+            found1 = wait_for("dedup-1", _dedup_fact, timeout_s=DEDUP_TIMEOUT, need_memory=True)
+            if found1:
+                print(f"  [dedup-1] stored  tier={found1['tier']}  mem={found1['memory_s']}s")
+            else:
+                print(f"  [dedup-1] timeout")
+    except Exception as e:
+        report(results, "Dedup: first POST accepted", False, str(e))
+
+    pts_after_first = qdrant_count()
+    new_first = pts_after_first - pts_before
+    print(f"  Qdrant after first send: {pts_before} → {pts_after_first} (+{new_first})")
+
+    print(f"  [dedup-2] sending same fact (second time)")
+    try:
+        status, _ = post_json(f"{DEEPAGENTS}/chat",
+                              {"message": _dedup_fact, "chat_id": CHAT_ID}, timeout=5)
+        if status != 202:
+            report(results, "Dedup: second POST accepted", False, f"status={status}")
+        else:
+            found2 = wait_for("dedup-2", _dedup_fact, timeout_s=DEDUP_TIMEOUT, need_memory=True)
+            if found2:
+                print(f"  [dedup-2] stored  tier={found2['tier']}  mem={found2['memory_s']}s")
+            else:
+                print(f"  [dedup-2] timeout")
+    except Exception as e:
+        report(results, "Dedup: second POST accepted", False, str(e))
+
+    pts_after_second = qdrant_count()
+    new_second = pts_after_second - pts_after_first
+    print(f"  Qdrant after second send: {pts_after_first} → {pts_after_second} (+{new_second})")
+
+    dedup_ok = new_second <= new_first
+    report(results, "Deduplication: second identical fact not added to Qdrant", dedup_ok,
+           f"first send: +{new_first} pts, second send: +{new_second} pts (want second ≤ first)")
+
+
+# ── summary ───────────────────────────────────────────────────────────────────
+print_summary(results)
+sys.exit(0 if all(ok for _, ok in results) else 1)

tests/integration/test_routing.py

@@ -0,0 +1,317 @@
+#!/usr/bin/env python3
+"""
+Adolf tier routing benchmark.
+
+Tests:
+  easy   — 10 questions that must route to 'light' tier
+  medium — 11 questions that must route to 'medium' (light acceptable for some; complex = fail)
+  hard   — 10 /think questions that must route to 'complex' (medium fallback acceptable)
+
+Usage:
+    python3 test_routing.py [--chat-id CHAT_ID]
+                            [--easy-only]    # only easy benchmark
+                            [--medium-only]  # only medium benchmark
+                            [--hard-only]    # only hard benchmark
+"""
+
+import argparse
+import sys
+import time
+
+from common import (
+    DEEPAGENTS, COMPOSE_FILE, DEFAULT_CHAT_ID,
+    BENCHMARK,
+    INFO, PASS, FAIL, WARN,
+    report, print_summary,
+    post_json, fetch_logs,
+    parse_run_block,
+)
+
+# ── args ──────────────────────────────────────────────────────────────────────
+parser = argparse.ArgumentParser(description="Adolf routing benchmark")
+parser.add_argument("--chat-id",     default=DEFAULT_CHAT_ID)
+parser.add_argument("--easy-only",   action="store_true")
+parser.add_argument("--medium-only", action="store_true")
+parser.add_argument("--hard-only",   action="store_true")
+args = parser.parse_args()
+
+CHAT_ID = args.chat_id
+_only = args.easy_only or args.medium_only or args.hard_only
+_run_easy   = not _only or args.easy_only
+_run_medium = not _only or args.medium_only
+_run_hard   = not _only or args.hard_only
+
+results = []
+
+
+# ── easy benchmark ────────────────────────────────────────────────────────────
+if _run_easy:
+    print(f"\n[{INFO}] Easy routing benchmark")
+    print(f"  {len(BENCHMARK['easy'])} questions — all must route to 'light'")
+    print(f"  Chat ID: {CHAT_ID}")
+    print()
+
+    bench_results = []
+    LIGHT_TIMEOUT = 60
+
+    for i, question in enumerate(BENCHMARK["easy"], 1):
+        tag = f"easy-{i:02d}"
+        print(f"  [{tag}] {question[:55]!r}")
+
+        t_send = time.monotonic()
+        try:
+            status, _ = post_json(f"{DEEPAGENTS}/chat",
+                                  {"message": question, "chat_id": CHAT_ID}, timeout=5)
+            if status != 202:
+                print(f"          → [{FAIL}] POST returned {status}")
+                bench_results.append((question, "?", None, False))
+                continue
+        except Exception as e:
+            print(f"          → [{FAIL}] POST error: {e}")
+            bench_results.append((question, "?", None, False))
+            continue
+
+        t_start = time.monotonic()
+        found = None
+        while time.monotonic() - t_start < LIGHT_TIMEOUT:
+            since = int(time.monotonic() - t_start) + 30
+            lines = fetch_logs(since_s=since)
+            found = parse_run_block(lines, question)
+            if found:
+                break
+            time.sleep(1)
+
+        if not found:
+            print(f"          → [{FAIL}] no reply within {LIGHT_TIMEOUT}s")
+            bench_results.append((question, "timeout", None, False))
+            continue
+
+        tier = found.get("tier", "unknown")
+        is_light = (tier == "light")
+        tag_str = PASS if is_light else FAIL
+        print(f"          → [{tag_str}] tier={tier}  latency={found['reply_total']:.1f}s  llm={found['llm']:.1f}s")
+        bench_results.append((question, tier, found["reply_total"], is_light))
+        time.sleep(1)
+
+    print(f"\n  {'#':<4}  {'Tier':<8}  {'Latency':>8}  {'Question'}")
+    print(f"  {'─'*4}  {'─'*8}  {'─'*8}  {'─'*50}")
+    for idx, (q, tier, lat, ok) in enumerate(bench_results, 1):
+        lat_str = f"{lat:.1f}s" if lat is not None else "timeout"
+        ok_str = "✓" if ok else "✗"
+        print(f"  {ok_str} {idx:<3}  {tier:<8}  {lat_str:>8}  {q[:50]!r}")
+
+    light_count = sum(1 for _, _, _, ok in bench_results if ok)
+    total_bench = len(bench_results)
+    lats = [lat for _, _, lat, ok in bench_results if ok and lat is not None]
+    avg_lat = sum(lats) / len(lats) if lats else 0
+
+    print(f"\n  Light-path score: {light_count}/{total_bench}")
+    if lats:
+        print(f"  Avg latency (light): {avg_lat:.1f}s  min={min(lats):.1f}s  max={max(lats):.1f}s")
+
+    report(results, f"All easy questions routed to light ({light_count}/{total_bench})",
+           light_count == total_bench,
+           f"{light_count}/{total_bench} via light path, avg {avg_lat:.1f}s")
+
+
+# ── medium benchmark ──────────────────────────────────────────────────────────
+if _run_medium:
+    print(f"\n[{INFO}] Medium routing benchmark")
+    print(f"  {len(BENCHMARK['medium'])} questions — must route to medium (light ok for some; complex = fail)")
+    print(f"  Chat ID: {CHAT_ID}")
+    print()
+
+    LIGHT_ACCEPTABLE = {
+        "who won the last FIFA World Cup?",
+        "search for a good pasta carbonara recipe",
+        "find Python tutorials for beginners",
+        "search for the best coffee shops in Tokyo",
+    }
+
+    med_results = []
+    MEDIUM_TIMEOUT = 120
+
+    for i, question in enumerate(BENCHMARK["medium"], 1):
+        tag = f"med-{i:02d}"
+        print(f"  [{tag}] {question[:60]!r}")
+
+        t_send = time.monotonic()
+        try:
+            status, _ = post_json(f"{DEEPAGENTS}/chat",
+                                  {"message": question, "chat_id": CHAT_ID}, timeout=5)
+            if status != 202:
+                print(f"          → [{FAIL}] POST returned {status}")
+                med_results.append((question, "?", None, False))
+                continue
+        except Exception as e:
+            print(f"          → [{FAIL}] POST error: {e}")
+            med_results.append((question, "?", None, False))
+            continue
+
+        t_start = time.monotonic()
+        found = None
+        while time.monotonic() - t_start < MEDIUM_TIMEOUT:
+            since = int(time.monotonic() - t_start) + 60
+            lines = fetch_logs(since_s=since)
+            found = parse_run_block(lines, question)
+            if found:
+                break
+            time.sleep(3)
+
+        if not found:
+            print(f"          → [{FAIL}] no reply within {MEDIUM_TIMEOUT}s")
+            med_results.append((question, "timeout", None, False))
+            continue
+
+        tier = found.get("tier", "unknown")
+        light_ok = question in LIGHT_ACCEPTABLE
+
+        if tier == "medium":
+            correct, label, note = True, PASS, "medium ✓"
+        elif tier == "light":
+            correct = light_ok
+            label = PASS if light_ok else WARN
+            note = "light (acceptable)" if light_ok else "light (should be medium)"
+        elif tier == "complex":
+            correct, label, note = False, FAIL, "complex — wrong escalation"
+        else:
+            correct, label, note = False, FAIL, f"unknown tier {tier!r}"
+
+        print(f"          → [{label}] {note}  latency={found['reply_total']:.1f}s  llm={found['llm']:.1f}s")
+        med_results.append((question, tier, found["reply_total"], correct))
+        time.sleep(1)
+
+    print(f"\n  {'#':<4}  {'Tier':<8}  {'Latency':>8}  {'Question'}")
+    print(f"  {'─'*4}  {'─'*8}  {'─'*8}  {'─'*55}")
+    for idx, (q, tier, lat, ok) in enumerate(med_results, 1):
+        lat_str = f"{lat:.1f}s" if lat is not None else "timeout"
+        ok_str = "✓" if ok else ("~" if tier == "light" else "✗")
+        print(f"  {ok_str} {idx:<3}  {tier:<8}  {lat_str:>8}  {q[:55]!r}")
+
+    total_med     = len(med_results)
+    medium_count  = sum(1 for _, tier, _, _ in med_results if tier == "medium")
+    light_count   = sum(1 for _, tier, _, _ in med_results if tier == "light")
+    complex_count = sum(1 for _, tier, _, _ in med_results if tier == "complex")
+    timeout_count = sum(1 for _, tier, _, _ in med_results if tier == "timeout")
+    light_misroute = sum(1 for q, tier, _, _ in med_results
+                         if tier == "light" and q not in LIGHT_ACCEPTABLE)
+    lats = [lat for _, _, lat, _ in med_results if lat is not None]
+
+    print(f"\n  Breakdown: medium={medium_count}  light={light_count}  "
+          f"complex={complex_count}  timeout={timeout_count}")
+    if light_misroute:
+        print(f"  [{WARN}] {light_misroute} question(s) answered via light when medium expected")
+    if lats:
+        print(f"  Avg latency: {sum(lats)/len(lats):.1f}s  min={min(lats):.1f}s  max={max(lats):.1f}s")
+
+    report(results,
+           f"Medium questions: no complex escalation ({medium_count + light_count}/{total_med} routed)",
+           complex_count == 0,
+           f"medium={medium_count} light={light_count} complex={complex_count} timeout={timeout_count}")
+    if timeout_count:
+        report(results, f"Medium questions: all completed within {MEDIUM_TIMEOUT}s", False,
+               f"{timeout_count} question(s) timed out")
+
+
+# ── hard benchmark ────────────────────────────────────────────────────────────
+if _run_hard:
+    print(f"\n[{INFO}] Hard routing benchmark")
+    print(f"  {len(BENCHMARK['hard'])} /think questions — must route to 'complex'")
+    print(f"  Acceptable fallback: 'medium' if VRAM eviction timed out")
+    print(f"  Fail condition: tier=light or timeout")
+    print(f"  Chat ID: {CHAT_ID}")
+    print()
+
+    hard_results  = []
+    COMPLEX_TIMEOUT = 300
+    _VRAM_ENTER = "[vram] enter_complex_mode"
+    _VRAM_EXIT  = "[vram] exit_complex_mode"
+
+    for i, question in enumerate(BENCHMARK["hard"], 1):
+        tag = f"hard-{i:02d}"
+        short_q = question[len("/think "):].strip()[:60]
+        print(f"  [{tag}] /think {short_q!r}")
+
+        t_send = time.monotonic()
+        try:
+            status, _ = post_json(f"{DEEPAGENTS}/chat",
+                                  {"message": question, "chat_id": CHAT_ID}, timeout=5)
+            if status != 202:
+                print(f"          → [{FAIL}] POST returned {status}")
+                hard_results.append((question, "?", None, False))
+                continue
+        except Exception as e:
+            print(f"          → [{FAIL}] POST error: {e}")
+            hard_results.append((question, "?", None, False))
+            continue
+
+        t_start = time.monotonic()
+        found = None
+        while time.monotonic() - t_start < COMPLEX_TIMEOUT:
+            since = int(time.monotonic() - t_start) + 90
+            lines = fetch_logs(since_s=since)
+            found = parse_run_block(lines, question[len("/think "):].strip())
+            if found:
+                break
+            time.sleep(5)
+
+        elapsed = time.monotonic() - t_send
+
+        if not found:
+            print(f"          → [{FAIL}] no reply within {COMPLEX_TIMEOUT}s")
+            hard_results.append((question, "timeout", None, False))
+            continue
+
+        tier = found.get("tier", "unknown")
+
+        if tier == "complex":
+            ok, label, note = True, PASS, "complex ✓"
+        elif tier == "medium":
+            ok, label, note = True, WARN, "medium (VRAM fallback — check [vram] logs)"
+        else:
+            ok, label, note = False, FAIL, f"tier={tier} — unexpected"
+
+        lines_block = fetch_logs(since_s=int(elapsed) + 120)
+        recent = "\n".join(lines_block[-200:])
+        vram_enter_seen = _VRAM_ENTER in recent
+        vram_note = ""
+        if tier == "complex":
+            vram_note = " [vram:flush✓]" if vram_enter_seen else f" [{WARN}:no vram flush log]"
+
+        print(f"          → [{label}] {note}  latency={found['reply_total']:.1f}s  llm={found['llm']:.1f}s{vram_note}")
+        hard_results.append((question, tier, found["reply_total"], ok))
+        time.sleep(5)
+
+    print(f"\n  {'#':<4}  {'Tier':<8}  {'Latency':>8}  {'Question (/think ...)'}")
+    print(f"  {'─'*4}  {'─'*8}  {'─'*8}  {'─'*55}")
+    for idx, (q, tier, lat, ok) in enumerate(hard_results, 1):
+        lat_str = f"{lat:.1f}s" if lat is not None else "timeout"
+        ok_str = "✓" if tier == "complex" else ("~" if tier == "medium" else "✗")
+        short = q[len("/think "):].strip()[:55]
+        print(f"  {ok_str} {idx:<3}  {tier:<8}  {lat_str:>8}  {short!r}")
+
+    total_hard    = len(hard_results)
+    complex_count = sum(1 for _, t, _, _ in hard_results if t == "complex")
+    medium_fb     = sum(1 for _, t, _, _ in hard_results if t == "medium")
+    light_count   = sum(1 for _, t, _, _ in hard_results if t == "light")
+    timeout_count = sum(1 for _, t, _, _ in hard_results if t == "timeout")
+    lats = [lat for _, _, lat, _ in hard_results if lat is not None]
+
+    print(f"\n  Breakdown: complex={complex_count}  medium(fallback)={medium_fb}  "
+          f"light={light_count}  timeout={timeout_count}")
+    if medium_fb:
+        print(f"  [{WARN}] {medium_fb} question(s) fell back to medium (VRAM eviction timeout)")
+    if light_count:
+        print(f"  [{FAIL}] {light_count} question(s) routed to light — /think prefix not detected")
+    if lats:
+        print(f"  Avg latency: {sum(lats)/len(lats):.1f}s  min={min(lats):.1f}s  max={max(lats):.1f}s")
+
+    report(results,
+           f"Hard questions routed to complex (not light) ({complex_count + medium_fb}/{total_hard})",
+           light_count == 0 and timeout_count == 0,
+           f"complex={complex_count} medium_fallback={medium_fb} light={light_count} timeout={timeout_count}")
+
+
+# ── summary ───────────────────────────────────────────────────────────────────
+print_summary(results)
+sys.exit(0 if all(ok for _, ok in results) else 1)

tests/requirements.txt

@@ -0,0 +1,2 @@
+pytest>=8.0
+pytest-asyncio>=0.23

tests/unit/conftest.py

@@ -0,0 +1,80 @@
+"""
+Stub out all third-party packages that Adolf's source modules import.
+This lets the unit tests run without a virtualenv or Docker environment.
+Stubs are installed into sys.modules before any test file is collected.
+"""
+
+import sys
+from unittest.mock import MagicMock
+
+
+# ── helpers ────────────────────────────────────────────────────────────────────
+
+def _mock(name: str) -> MagicMock:
+    m = MagicMock(name=name)
+    sys.modules[name] = m
+    return m
+
+
+# ── pydantic: BaseModel must be a real class so `class Foo(BaseModel)` works ──
+
+class _FakeBaseModel:
+    model_fields: dict = {}
+
+    def __init_subclass__(cls, **kwargs):
+        pass
+
+    def __init__(self, **data):
+        for k, v in data.items():
+            setattr(self, k, v)
+
+
+_pydantic = _mock("pydantic")
+_pydantic.BaseModel = _FakeBaseModel
+
+# ── httpx: used by channels.py, vram_manager.py, agent.py ────────────────────
+
+_mock("httpx")
+
+# ── fastapi ───────────────────────────────────────────────────────────────────
+
+_fastapi = _mock("fastapi")
+_mock("fastapi.responses")
+
+# ── langchain stack ───────────────────────────────────────────────────────────
+
+_mock("langchain_openai")
+
+_lc_core = _mock("langchain_core")
+_lc_msgs = _mock("langchain_core.messages")
+_mock("langchain_core.tools")
+
+# Provide real-ish message classes so router.py can instantiate them
+class _FakeMsg:
+    def __init__(self, content=""):
+        self.content = content
+
+class SystemMessage(_FakeMsg):
+    pass
+
+class HumanMessage(_FakeMsg):
+    pass
+
+class AIMessage(_FakeMsg):
+    def __init__(self, content="", tool_calls=None):
+        super().__init__(content)
+        self.tool_calls = tool_calls or []
+
+_lc_msgs.SystemMessage = SystemMessage
+_lc_msgs.HumanMessage = HumanMessage
+_lc_msgs.AIMessage = AIMessage
+
+_mock("langchain_mcp_adapters")
+_mock("langchain_mcp_adapters.client")
+_mock("langchain_community")
+_mock("langchain_community.utilities")
+
+# ── deepagents (agent_factory.py) ─────────────────────────────────────────────
+
+_mock("deepagents")
+

tests/unit/test_agent_helpers.py

@@ -0,0 +1,198 @@
+"""
+Unit tests for agent.py helper functions:
+  - _strip_think(text)
+  - _extract_final_text(result)
+
+agent.py has heavy FastAPI/LangChain imports; conftest.py stubs them out so
+these pure functions can be imported and tested in isolation.
+"""
+
+import pytest
+
+# conftest.py has already installed all stubs into sys.modules.
+# The FastAPI app is instantiated at module level in agent.py —
+# with the mocked fastapi, that just creates a MagicMock() object
+# and the route decorators are no-ops.
+from agent import _strip_think, _extract_final_text, _extract_urls
+
+
+# ── _strip_think ───────────────────────────────────────────────────────────────
+
+class TestStripThink:
+    def test_removes_single_think_block(self):
+        text = "<think>internal reasoning</think>Final answer."
+        assert _strip_think(text) == "Final answer."
+
+    def test_removes_multiline_think_block(self):
+        text = "<think>\nLine one.\nLine two.\n</think>\nResult here."
+        assert _strip_think(text) == "Result here."
+
+    def test_no_think_block_unchanged(self):
+        text = "This is a plain answer with no think block."
+        assert _strip_think(text) == text
+
+    def test_removes_multiple_think_blocks(self):
+        text = "<think>step 1</think>middle<think>step 2</think>end"
+        assert _strip_think(text) == "middleend"
+
+    def test_strips_surrounding_whitespace(self):
+        text = "  <think>stuff</think>  answer  "
+        assert _strip_think(text) == "answer"
+
+    def test_empty_think_block(self):
+        text = "<think></think>Hello."
+        assert _strip_think(text) == "Hello."
+
+    def test_empty_string(self):
+        assert _strip_think("") == ""
+
+    def test_only_think_block_returns_empty(self):
+        text = "<think>nothing useful</think>"
+        assert _strip_think(text) == ""
+
+    def test_think_block_with_nested_tags(self):
+        text = "<think>I should use <b>bold</b> here</think>Done."
+        assert _strip_think(text) == "Done."
+
+    def test_preserves_markdown(self):
+        text = "<think>plan</think>## Report\n\n- Point one\n- Point two"
+        result = _strip_think(text)
+        assert result == "## Report\n\n- Point one\n- Point two"
+
+
+# ── _extract_final_text ────────────────────────────────────────────────────────
+
+class TestExtractFinalText:
+    def _ai_msg(self, content: str, tool_calls=None):
+        """Create a minimal AIMessage-like object."""
+        class AIMessage:
+            pass
+        m = AIMessage()
+        m.content = content
+        m.tool_calls = tool_calls or []
+        return m
+
+    def _human_msg(self, content: str):
+        class HumanMessage:
+            pass
+        m = HumanMessage()
+        m.content = content
+        return m
+
+    def test_returns_last_ai_message_content(self):
+        result = {
+            "messages": [
+                self._human_msg("what is 2+2"),
+                self._ai_msg("The answer is 4."),
+            ]
+        }
+        assert _extract_final_text(result) == "The answer is 4."
+
+    def test_returns_last_of_multiple_ai_messages(self):
+        result = {
+            "messages": [
+                self._ai_msg("First response."),
+                self._human_msg("follow-up"),
+                self._ai_msg("Final response."),
+            ]
+        }
+        assert _extract_final_text(result) == "Final response."
+
+    def test_skips_empty_ai_messages(self):
+        result = {
+            "messages": [
+                self._ai_msg("Real answer."),
+                self._ai_msg(""),  # empty — should be skipped
+            ]
+        }
+        assert _extract_final_text(result) == "Real answer."
+
+    def test_strips_think_tags_from_ai_message(self):
+        result = {
+            "messages": [
+                self._ai_msg("<think>reasoning here</think>Clean reply."),
+            ]
+        }
+        assert _extract_final_text(result) == "Clean reply."
+
+    def test_falls_back_to_output_field(self):
+        result = {
+            "messages": [],
+            "output": "Fallback output.",
+        }
+        assert _extract_final_text(result) == "Fallback output."
+
+    def test_strips_think_from_output_field(self):
+        result = {
+            "messages": [],
+            "output": "<think>thoughts</think>Actual output.",
+        }
+        assert _extract_final_text(result) == "Actual output."
+
+    def test_returns_none_when_no_content(self):
+        result = {"messages": []}
+        assert _extract_final_text(result) is None
+
+    def test_returns_none_when_no_messages_and_no_output(self):
+        result = {"messages": [], "output": ""}
+        # output is falsy → returns None
+        assert _extract_final_text(result) is None
+
+    def test_skips_non_ai_messages(self):
+        result = {
+            "messages": [
+                self._human_msg("user question"),
+            ]
+        }
+        assert _extract_final_text(result) is None
+
+    def test_handles_ai_message_with_tool_calls_but_no_content(self):
+        """AIMessage that only has tool_calls (no content) should be skipped."""
+        msg = self._ai_msg("", tool_calls=[{"name": "web_search", "args": {}}])
+        result = {"messages": [msg]}
+        assert _extract_final_text(result) is None
+
+    def test_multiline_think_stripped_correctly(self):
+        result = {
+            "messages": [
+                self._ai_msg("<think>\nLong\nreasoning\nblock\n</think>\n## Report\n\nSome content."),
+            ]
+        }
+        assert _extract_final_text(result) == "## Report\n\nSome content."
+
+
+# ── _extract_urls ──────────────────────────────────────────────────────────────
+
+class TestExtractUrls:
+    def test_single_url(self):
+        assert _extract_urls("check this out https://example.com please") == ["https://example.com"]
+
+    def test_multiple_urls(self):
+        urls = _extract_urls("see https://foo.com and https://bar.org/path?q=1")
+        assert urls == ["https://foo.com", "https://bar.org/path?q=1"]
+
+    def test_no_urls(self):
+        assert _extract_urls("no links here at all") == []
+
+    def test_http_and_https(self):
+        urls = _extract_urls("http://old.site and https://new.site")
+        assert "http://old.site" in urls
+        assert "https://new.site" in urls
+
+    def test_url_at_start_of_message(self):
+        assert _extract_urls("https://example.com is interesting") == ["https://example.com"]
+
+    def test_url_only(self):
+        assert _extract_urls("https://example.com/page") == ["https://example.com/page"]
+
+    def test_url_with_path_and_query(self):
+        url = "https://example.com/articles/123?ref=home&page=2"
+        assert _extract_urls(url) == [url]
+
+    def test_empty_string(self):
+        assert _extract_urls("") == []
+
+    def test_does_not_include_surrounding_quotes(self):
+        # URLs inside quotes should not include the quote character
+        urls = _extract_urls('visit "https://example.com" today')
+        assert urls == ["https://example.com"]

tests/unit/test_channels.py

@@ -0,0 +1,125 @@
+"""Unit tests for channels.py — register, deliver, pending_replies queue."""
+
+import asyncio
+import pytest
+from unittest.mock import AsyncMock, patch
+
+import channels
+
+
+@pytest.fixture(autouse=True)
+def reset_channels_state():
+    """Clear module-level state before and after every test."""
+    channels._callbacks.clear()
+    channels.pending_replies.clear()
+    yield
+    channels._callbacks.clear()
+    channels.pending_replies.clear()
+
+
+# ── register ───────────────────────────────────────────────────────────────────
+
+class TestRegister:
+    def test_register_stores_callback(self):
+        cb = AsyncMock()
+        channels.register("test_channel", cb)
+        assert channels._callbacks["test_channel"] is cb
+
+    def test_register_overwrites_existing(self):
+        cb1 = AsyncMock()
+        cb2 = AsyncMock()
+        channels.register("ch", cb1)
+        channels.register("ch", cb2)
+        assert channels._callbacks["ch"] is cb2
+
+    def test_register_multiple_channels(self):
+        cb_a = AsyncMock()
+        cb_b = AsyncMock()
+        channels.register("a", cb_a)
+        channels.register("b", cb_b)
+        assert channels._callbacks["a"] is cb_a
+        assert channels._callbacks["b"] is cb_b
+
+
+# ── deliver ────────────────────────────────────────────────────────────────────
+
+class TestDeliver:
+    async def test_deliver_enqueues_reply(self):
+        channels.register("cli", AsyncMock())
+        await channels.deliver("cli-alvis", "cli", "hello world")
+        q = channels.pending_replies["cli-alvis"]
+        assert not q.empty()
+        assert await q.get() == "hello world"
+
+    async def test_deliver_calls_channel_callback(self):
+        cb = AsyncMock()
+        channels.register("telegram", cb)
+        await channels.deliver("tg-123", "telegram", "reply text")
+        cb.assert_awaited_once_with("tg-123", "reply text")
+
+    async def test_deliver_unknown_channel_still_enqueues(self):
+        """No registered callback for channel → reply still goes to the queue."""
+        await channels.deliver("cli-bob", "nonexistent", "fallback reply")
+        q = channels.pending_replies["cli-bob"]
+        assert await q.get() == "fallback reply"
+
+    async def test_deliver_unknown_channel_does_not_raise(self):
+        """Missing callback must not raise an exception."""
+        await channels.deliver("cli-x", "ghost_channel", "msg")
+
+    async def test_deliver_creates_queue_if_absent(self):
+        channels.register("cli", AsyncMock())
+        assert "cli-new" not in channels.pending_replies
+        await channels.deliver("cli-new", "cli", "hi")
+        assert "cli-new" in channels.pending_replies
+
+    async def test_deliver_reuses_existing_queue(self):
+        """Second deliver to the same session appends to the same queue."""
+        channels.register("cli", AsyncMock())
+        await channels.deliver("cli-alvis", "cli", "first")
+        await channels.deliver("cli-alvis", "cli", "second")
+        q = channels.pending_replies["cli-alvis"]
+        assert await q.get() == "first"
+        assert await q.get() == "second"
+
+    async def test_deliver_telegram_sends_to_callback(self):
+        sent = []
+
+        async def fake_tg(session_id, text):
+            sent.append((session_id, text))
+
+        channels.register("telegram", fake_tg)
+        await channels.deliver("tg-999", "telegram", "test message")
+        assert sent == [("tg-999", "test message")]
+
+
+# ── register_defaults ──────────────────────────────────────────────────────────
+
+class TestRegisterDefaults:
+    def test_registers_telegram_and_cli(self):
+        channels.register_defaults()
+        assert "telegram" in channels._callbacks
+        assert "cli" in channels._callbacks
+
+    async def test_cli_callback_is_noop(self):
+        """CLI send callback does nothing (replies are handled via SSE queue)."""
+        channels.register_defaults()
+        cb = channels._callbacks["cli"]
+        # Should not raise and should return None
+        result = await cb("cli-alvis", "some reply")
+        assert result is None
+
+    async def test_telegram_callback_chunks_long_messages(self):
+        """Telegram callback splits messages > 4000 chars into chunks."""
+        channels.register_defaults()
+        cb = channels._callbacks["telegram"]
+        long_text = "x" * 9000  # > 4000 chars → should produce 3 chunks
+        with patch("channels.httpx.AsyncClient") as mock_client_cls:
+            mock_client = AsyncMock()
+            mock_client.__aenter__ = AsyncMock(return_value=mock_client)
+            mock_client.__aexit__ = AsyncMock(return_value=False)
+            mock_client.post = AsyncMock()
+            mock_client_cls.return_value = mock_client
+            await cb("tg-123", long_text)
+            # 9000 chars / 4000 per chunk = 3 POST calls
+            assert mock_client.post.await_count == 3

tests/unit/test_router.py

@@ -0,0 +1,200 @@
+"""Unit tests for router.py — Router, _parse_tier, _format_history, _LIGHT_PATTERNS."""
+
+import pytest
+from unittest.mock import AsyncMock, MagicMock, patch
+
+from router import Router, _parse_tier, _format_history, _LIGHT_PATTERNS
+
+
+# ── _LIGHT_PATTERNS regex ──────────────────────────────────────────────────────
+
+class TestLightPatterns:
+    @pytest.mark.parametrize("text", [
+        "hi", "Hi", "HI",
+        "hello", "hey", "yo", "sup",
+        "good morning", "good evening", "good night", "good afternoon",
+        "bye", "goodbye", "see you", "cya", "later", "ttyl",
+        "thanks", "thank you", "thx", "ty",
+        "ok", "okay", "k", "cool", "great", "awesome", "perfect",
+        "sounds good", "got it", "nice", "sure",
+        "how are you", "how are you?", "how are you doing today?",
+        "what's up",
+        "what day comes after Monday?",
+        "what day follows Friday?",
+        "what comes after summer?",
+        "what does NASA stand for?",
+        "what does AI stand for?",
+        # with trailing punctuation
+        "hi!", "hello.", "thanks!",
+    ])
+    def test_matches(self, text):
+        assert _LIGHT_PATTERNS.match(text.strip()), f"Expected light match for: {text!r}"
+
+    @pytest.mark.parametrize("text", [
+        "what is the capital of France",
+        "tell me about bitcoin",
+        "what is 2+2",
+        "write me a poem",
+        "search for news about the election",
+        "what did we talk about last time",
+        "what is my name",
+        "/think compare these frameworks",
+        "how do I install Python",
+        "explain machine learning",
+        "",  # empty string doesn't match the pattern
+    ])
+    def test_no_match(self, text):
+        assert not _LIGHT_PATTERNS.match(text.strip()), f"Expected NO light match for: {text!r}"
+
+
+# ── _parse_tier ────────────────────────────────────────────────────────────────
+
+class TestParseTier:
+    @pytest.mark.parametrize("raw,expected", [
+        ("light", "light"),
+        ("Light", "light"),
+        ("LIGHT\n", "light"),
+        ("medium", "medium"),
+        ("Medium.", "medium"),
+        ("complex", "complex"),
+        ("Complex!", "complex"),
+        # descriptive words → light
+        ("simplefact", "light"),
+        ("trivial question", "light"),
+        ("basic", "light"),
+        ("easy answer", "light"),
+        ("general knowledge", "light"),
+        # unknown → medium
+        ("unknown_category", "medium"),
+        ("", "medium"),
+        ("I don't know", "medium"),
+        # complex only if 'complex' appears in first 60 chars
+        ("this is a complex query requiring search", "complex"),
+        # _parse_tier checks "complex" before "medium", so complex wins even if medium appears first
+        ("medium complexity, not complex", "complex"),
+    ])
+    def test_parse_tier(self, raw, expected):
+        assert _parse_tier(raw) == expected
+
+
+# ── _format_history ────────────────────────────────────────────────────────────
+
+class TestFormatHistory:
+    def test_empty(self):
+        assert _format_history([]) == "(none)"
+
+    def test_single_user_message(self):
+        history = [{"role": "user", "content": "hello there"}]
+        result = _format_history(history)
+        assert "user: hello there" in result
+
+    def test_multiple_turns(self):
+        history = [
+            {"role": "user", "content": "What is Python?"},
+            {"role": "assistant", "content": "Python is a programming language."},
+        ]
+        result = _format_history(history)
+        assert "user: What is Python?" in result
+        assert "assistant: Python is a programming language." in result
+
+    def test_truncates_long_content(self):
+        long_content = "x" * 300
+        history = [{"role": "user", "content": long_content}]
+        result = _format_history(history)
+        # content is truncated to 200 chars in _format_history
+        assert len(result) < 250
+
+    def test_missing_keys_handled(self):
+        # Should not raise — uses .get() with defaults
+        history = [{"role": "user"}]  # no content key
+        result = _format_history(history)
+        assert "user:" in result
+
+
+# ── Router.route() ─────────────────────────────────────────────────────────────
+
+class TestRouterRoute:
+    def _make_router(self, classify_response: str, reply_response: str = "Sure!") -> Router:
+        """Return a Router with a mock model that returns given classification and reply."""
+        model = MagicMock()
+        classify_msg = MagicMock()
+        classify_msg.content = classify_response
+        reply_msg = MagicMock()
+        reply_msg.content = reply_response
+        # First ainvoke call → classification; second → reply
+        model.ainvoke = AsyncMock(side_effect=[classify_msg, reply_msg])
+        return Router(model=model)
+
+    async def test_force_complex_bypasses_classification(self):
+        router = self._make_router("medium")
+        tier, reply = await router.route("some question", [], force_complex=True)
+        assert tier == "complex"
+        assert reply is None
+        # Model should NOT have been called
+        router.model.ainvoke.assert_not_called()
+
+    async def test_regex_light_skips_llm_classification(self):
+        # Regex match bypasses classification entirely; the only ainvoke call is the reply.
+        model = MagicMock()
+        reply_msg = MagicMock()
+        reply_msg.content = "I'm doing great!"
+        model.ainvoke = AsyncMock(return_value=reply_msg)
+        router = Router(model=model)
+        tier, reply = await router.route("how are you", [], force_complex=False)
+        assert tier == "light"
+        assert reply == "I'm doing great!"
+        # Exactly one model call — no classification step
+        assert router.model.ainvoke.call_count == 1
+
+    async def test_llm_classifies_medium(self):
+        router = self._make_router("medium")
+        tier, reply = await router.route("what is the bitcoin price?", [], force_complex=False)
+        assert tier == "medium"
+        assert reply is None
+
+    async def test_llm_classifies_light_generates_reply(self):
+        router = self._make_router("light", "Paris is the capital of France.")
+        tier, reply = await router.route("what is the capital of France?", [], force_complex=False)
+        assert tier == "light"
+        assert reply == "Paris is the capital of France."
+
+    async def test_llm_classifies_complex_downgraded_to_medium(self):
+        # Without /think prefix, complex classification → downgraded to medium
+        router = self._make_router("complex")
+        tier, reply = await router.route("compare React and Vue", [], force_complex=False)
+        assert tier == "medium"
+        assert reply is None
+
+    async def test_llm_error_falls_back_to_medium(self):
+        model = MagicMock()
+        model.ainvoke = AsyncMock(side_effect=Exception("connection error"))
+        router = Router(model=model)
+        tier, reply = await router.route("some question", [], force_complex=False)
+        assert tier == "medium"
+        assert reply is None
+
+    async def test_light_reply_empty_falls_back_to_medium(self):
+        """If the light reply comes back empty, router returns medium instead."""
+        router = self._make_router("light", "")  # empty reply
+        tier, reply = await router.route("what is 2+2", [], force_complex=False)
+        assert tier == "medium"
+        assert reply is None
+
+    async def test_strips_think_tags_from_classification(self):
+        """Router strips <think>...</think> from model output before parsing tier."""
+        model = MagicMock()
+        classify_msg = MagicMock()
+        classify_msg.content = "<think>Hmm let me think...</think>medium"
+        reply_msg = MagicMock()
+        reply_msg.content = "I'm fine!"
+        model.ainvoke = AsyncMock(side_effect=[classify_msg, reply_msg])
+        router = Router(model=model)
+        tier, _ = await router.route("what is the news?", [], force_complex=False)
+        assert tier == "medium"
+
+    async def test_think_prefix_forces_complex(self):
+        """/think prefix is already stripped by agent.py; force_complex=True is passed."""
+        router = self._make_router("medium")
+        tier, reply = await router.route("analyse this", [], force_complex=True)
+        assert tier == "complex"
+        assert reply is None

tests/unit/test_vram_manager.py

@@ -0,0 +1,164 @@
+"""Unit tests for vram_manager.py — VRAMManager flush/poll/prewarm logic."""
+
+import asyncio
+import pytest
+from unittest.mock import AsyncMock, MagicMock, patch
+
+from vram_manager import VRAMManager
+
+
+BASE_URL = "http://localhost:11434"
+
+
+def _make_manager() -> VRAMManager:
+    return VRAMManager(base_url=BASE_URL)
+
+
+def _mock_client(get_response=None, post_response=None):
+    """Return a context-manager mock for httpx.AsyncClient."""
+    client = AsyncMock()
+    client.__aenter__ = AsyncMock(return_value=client)
+    client.__aexit__ = AsyncMock(return_value=False)
+    if get_response is not None:
+        client.get = AsyncMock(return_value=get_response)
+    if post_response is not None:
+        client.post = AsyncMock(return_value=post_response)
+    return client
+
+
+# ── _flush ─────────────────────────────────────────────────────────────────────
+
+class TestFlush:
+    async def test_sends_keep_alive_zero(self):
+        client = _mock_client(post_response=MagicMock())
+        with patch("vram_manager.httpx.AsyncClient", return_value=client):
+            mgr = _make_manager()
+            await mgr._flush("qwen3:4b")
+        client.post.assert_awaited_once()
+        _, kwargs = client.post.await_args
+        body = kwargs.get("json") or client.post.call_args[1].get("json") or client.post.call_args[0][1]
+        assert body["model"] == "qwen3:4b"
+        assert body["keep_alive"] == 0
+
+    async def test_posts_to_correct_endpoint(self):
+        client = _mock_client(post_response=MagicMock())
+        with patch("vram_manager.httpx.AsyncClient", return_value=client):
+            mgr = _make_manager()
+            await mgr._flush("qwen3:8b")
+        url = client.post.call_args[0][0]
+        assert url == f"{BASE_URL}/api/generate"
+
+    async def test_ignores_exceptions_silently(self):
+        client = AsyncMock()
+        client.__aenter__ = AsyncMock(return_value=client)
+        client.__aexit__ = AsyncMock(return_value=False)
+        client.post = AsyncMock(side_effect=Exception("connection refused"))
+        with patch("vram_manager.httpx.AsyncClient", return_value=client):
+            mgr = _make_manager()
+            # Should not raise
+            await mgr._flush("qwen3:4b")
+
+
+# ── _prewarm ───────────────────────────────────────────────────────────────────
+
+class TestPrewarm:
+    async def test_sends_keep_alive_300(self):
+        client = _mock_client(post_response=MagicMock())
+        with patch("vram_manager.httpx.AsyncClient", return_value=client):
+            mgr = _make_manager()
+            await mgr._prewarm("qwen3:4b")
+        _, kwargs = client.post.await_args
+        body = kwargs.get("json") or client.post.call_args[1].get("json") or client.post.call_args[0][1]
+        assert body["keep_alive"] == 300
+        assert body["model"] == "qwen3:4b"
+
+    async def test_ignores_exceptions_silently(self):
+        client = AsyncMock()
+        client.__aenter__ = AsyncMock(return_value=client)
+        client.__aexit__ = AsyncMock(return_value=False)
+        client.post = AsyncMock(side_effect=Exception("timeout"))
+        with patch("vram_manager.httpx.AsyncClient", return_value=client):
+            mgr = _make_manager()
+            await mgr._prewarm("qwen3:4b")
+
+
+# ── _poll_evicted ──────────────────────────────────────────────────────────────
+
+class TestPollEvicted:
+    async def test_returns_true_when_models_absent(self):
+        resp = MagicMock()
+        resp.json.return_value = {"models": [{"name": "some_other_model"}]}
+        client = _mock_client(get_response=resp)
+        with patch("vram_manager.httpx.AsyncClient", return_value=client):
+            mgr = _make_manager()
+            result = await mgr._poll_evicted(["qwen3:4b", "qwen2.5:1.5b"], timeout=5)
+        assert result is True
+
+    async def test_returns_false_on_timeout_when_model_still_loaded(self):
+        resp = MagicMock()
+        resp.json.return_value = {"models": [{"name": "qwen3:4b"}]}
+        client = _mock_client(get_response=resp)
+        with patch("vram_manager.httpx.AsyncClient", return_value=client):
+            mgr = _make_manager()
+            result = await mgr._poll_evicted(["qwen3:4b"], timeout=0.1)
+        assert result is False
+
+    async def test_returns_true_immediately_if_already_empty(self):
+        resp = MagicMock()
+        resp.json.return_value = {"models": []}
+        client = _mock_client(get_response=resp)
+        with patch("vram_manager.httpx.AsyncClient", return_value=client):
+            mgr = _make_manager()
+            result = await mgr._poll_evicted(["qwen3:4b"], timeout=5)
+        assert result is True
+
+    async def test_handles_poll_error_and_continues(self):
+        """If /api/ps errors, polling continues until timeout."""
+        client = AsyncMock()
+        client.__aenter__ = AsyncMock(return_value=client)
+        client.__aexit__ = AsyncMock(return_value=False)
+        client.get = AsyncMock(side_effect=Exception("network error"))
+        with patch("vram_manager.httpx.AsyncClient", return_value=client):
+            mgr = _make_manager()
+            result = await mgr._poll_evicted(["qwen3:4b"], timeout=0.2)
+        assert result is False
+
+
+# ── enter_complex_mode / exit_complex_mode ─────────────────────────────────────
+
+class TestComplexMode:
+    async def test_enter_complex_mode_returns_true_on_success(self):
+        mgr = _make_manager()
+        mgr._flush = AsyncMock()
+        mgr._poll_evicted = AsyncMock(return_value=True)
+        result = await mgr.enter_complex_mode()
+        assert result is True
+
+    async def test_enter_complex_mode_flushes_medium_models(self):
+        mgr = _make_manager()
+        mgr._flush = AsyncMock()
+        mgr._poll_evicted = AsyncMock(return_value=True)
+        await mgr.enter_complex_mode()
+        flushed = {call.args[0] for call in mgr._flush.call_args_list}
+        assert "qwen3:4b" in flushed
+        assert "qwen2.5:1.5b" in flushed
+
+    async def test_enter_complex_mode_returns_false_on_eviction_timeout(self):
+        mgr = _make_manager()
+        mgr._flush = AsyncMock()
+        mgr._poll_evicted = AsyncMock(return_value=False)
+        result = await mgr.enter_complex_mode()
+        assert result is False
+
+    async def test_exit_complex_mode_flushes_complex_and_prewarms_medium(self):
+        mgr = _make_manager()
+        mgr._flush = AsyncMock()
+        mgr._prewarm = AsyncMock()
+        await mgr.exit_complex_mode()
+        # Must flush 8b
+        flushed = {call.args[0] for call in mgr._flush.call_args_list}
+        assert "qwen3:8b" in flushed
+        # Must prewarm medium models
+        prewarmed = {call.args[0] for call in mgr._prewarm.call_args_list}
+        assert "qwen3:4b" in prewarmed
+        assert "qwen2.5:1.5b" in prewarmed

tests/use_cases/apple_pie_research.md

@@ -0,0 +1,41 @@
+# Use Case: Apple Pie Research
+
+Verify that a deep research query triggers the complex tier, uses web search and
+page fetching, and produces a substantive, well-sourced recipe response.
+
+## Steps
+
+**1. Send the research query** (the `/think` prefix forces complex tier):
+
+```bash
+curl -s -X POST http://localhost:8000/message \
+  -H "Content-Type: application/json" \
+  -d '{"text": "/think what is the best recipe for an apple pie?", "session_id": "use-case-apple-pie", "channel": "cli", "user_id": "claude"}'
+```
+
+**2. Wait for the streaming reply** (complex tier can take up to 5 minutes):
+
+```bash
+curl -s -N --max-time 300 "http://localhost:8000/stream/use-case-apple-pie"
+```
+
+**3. Confirm tier and tool usage in agent logs:**
+
+```bash
+docker compose -f /home/alvis/adolf/docker-compose.yml logs deepagents \
+  --since=600s | grep -E "tier=complex|web_search|fetch_url|crawl4ai"
+```
+
+## Evaluate (use your judgment)
+
+Check each of the following:
+
+- **Tier**: logs show `tier=complex` for this session
+- **Tool use**: logs show `web_search` or `fetch_url` calls during the request
+- **Ingredients**: response lists specific apple pie ingredients (apples, flour, butter, sugar, etc.)
+- **Method**: response includes preparation or baking steps
+- **Sources**: response cites real URLs it fetched, not invented links
+- **Quality**: response is structured and practical — not a refusal, stub, or generic placeholder
+
+Report PASS only if all six criteria are met. For any failure, state which criterion
+failed and quote the relevant part of the response or logs.

tests/use_cases/cli_startup.md

@@ -0,0 +1,18 @@
+# Use Case: CLI Startup
+
+Verify the Adolf CLI container starts cleanly, shows the welcome banner,
+and exits without error when the user closes input.
+
+## Steps
+
+```bash
+echo "" | docker compose --profile tools run --rm -T cli \
+  python3 cli.py --url http://deepagents:8000 --session use-case-cli-startup
+echo "exit code: $?"
+```
+
+## Pass if
+
+- Output contains `Adolf CLI`
+- Output contains the session name and gateway URL
+- Exit code is 0