Remove rich, merge display into orchestrator, drop execute_batch

ARCHITECTURE.md

@@ -2,58 +2,48 @@
 
 ## Overview
 
-The application follows an **orchestrator pattern** where a central coordinator manages the turn lifecycle, delegating to stateless workers for LLM interaction and tool execution.
+The application is structured as two modules: `orchestrator.py` handles the conversation loop, LLM streaming, and cancellation; `tools.py` handles API calls, retry logic, and response formatting.
 
 ```mermaid
 graph TD
-    A[main.py] -->|creates, configures logging, wires signals| B[Orchestrator]
-    B -->|OpenAI SDK streaming| F[LLM API]
-    B -->|dispatches tool calls| D[ToolExecutor]
-    B -->|renders output| E[Display]
-    D -->|httpx async + tenacity retry| G[Elyos Weather API]
-    D -->|httpx async + tenacity retry| H[Elyos Research API]
+    A[orchestrator.py] -->|entry point, turn loop, streaming| B[AsyncOpenAI]
+    A -->|dispatches tool calls| C[ToolExecutor]
+    B -->|OpenAI SDK| D[LLM API]
+    C -->|httpx async + retry loop| E[Elyos Weather API]
+    C -->|httpx async + retry loop| F[Elyos Research API]
 ```
 
 ## Module Responsibilities
 
-| Module           | Role                                                    | Stateful? |
-| ---------------- | ------------------------------------------------------- | --------- |
-| `main.py`        | Entry point, logging config, asyncio loop, SIGINT wiring | No        |
-| `orchestrator.py`| Turn lifecycle, conversation history, LLM streaming, cancellation | Yes       |
-| `tools.py`       | API calls, tenacity retry, cancellable requests          | No        |
-| `models.py`      | Pydantic models for API responses, settings, tool results| No        |
-| `display.py`     | Themed output (rich), spinners, tool indicators          | No        |
+| Module           | Role                                                        | Stateful? |
+| ---------------- | ----------------------------------------------------------- | --------- |
+| `orchestrator.py`| Entry point, logging, turn lifecycle, LLM streaming, cancel | Yes       |
+| `tools.py`       | API calls, retry loop, cancellable requests, formatting     | No        |
 
 ## Turn Lifecycle
 
 ```mermaid
 sequenceDiagram
     participant U as User
-    participant O as Orchestrator
-    participant L as LLM API
+    participant O as _process_turn
+    participant S as _stream_response
     participant T as ToolExecutor
-    participant D as Display
 
     U->>O: input text
-    O->>L: stream(history)
+    O->>S: stream(history)
     loop streaming chunks
-        L-->>O: content delta / tool_call delta
-        O->>D: print_streaming_token()
+        S-->>O: content delta / tool_call delta
+        O-->>U: sys.stdout.write(token)
     end
     alt tool calls detected
-        O->>D: print_tool_call()
-        O->>D: tool_spinner()
         O->>T: execute(tool_call, cancel_event)
-        T-->>O: ToolResult
-        O->>D: print_tool_result_ok/error()
-        O->>L: stream(history + tool results)
+        T-->>O: result dict
+        O->>S: stream(history + tool results)
         loop streaming final response
-            O->>D: print_assistant_header()
-            L-->>O: content delta
-            O->>D: print_streaming_token()
+            S-->>O: content delta
+            O-->>U: sys.stdout.write(token)
         end
     end
-    O->>D: finish_streaming()
 ```
 
 ## Cancellation Flow
@@ -73,7 +63,7 @@ The signal handler is context-dependent:
 
 - **During input**: No custom SIGINT handler — `loop.add_reader(stdin)` races against `cancel_event.wait()`. Ctrl+C sets the cancel event, `_read_input()` returns `None`, and the app exits.
 - **During processing**: Custom handler is installed. 1st Ctrl+C sets `cancel_event`; 2nd sets `should_exit`.
-- **During HTTP requests**: `_cancellable_request()` races the httpx coroutine against `cancel_event` via `asyncio.wait(FIRST_COMPLETED)`, so cancellation is instant even during slow API calls.
+- **During HTTP requests**: `_race_with_cancel()` races the httpx coroutine against `cancel_event` via `asyncio.wait(FIRST_COMPLETED)`, so cancellation is instant even during slow API calls.
 - **On cleanup**: Signal handler is removed before `asyncio.run()` shutdown to avoid stale handlers.
 
 The cancel event is cleared at the start of each new turn.
@@ -91,58 +81,41 @@ Using `asyncio.to_thread(input)` spawns a thread that blocks on `input()`. When
 ```mermaid
 flowchart TD
     REQ[API Request] --> CHECK{Response throttled?}
-    CHECK -->|No| PARSE[Parse response]
+    CHECK -->|No| PARSE[Parse + format response]
     CHECK -->|Yes| RETRY{Attempts < 3?}
     RETRY -->|Yes| WAIT[Wait retry_after_seconds] --> REQ
-    RETRY -->|No| ERR[_RateLimitError]
+    RETRY -->|No| ERR[RuntimeError]
 ```
 
-Retry is handled declaratively via a tenacity `@_throttle_retry` decorator:
+Retry is handled by `_request_with_retry`, a simple loop:
 
-- **Trigger**: `_ThrottledError` raised when API returns `status: "throttled"`
-- **Wait**: Dynamic — reads `retry_after_seconds` from the throttled response (capped at 15s)
+- **Trigger**: API returns `status: "throttled"` in JSON (HTTP 200)
+- **Wait**: Reads `retry_after_seconds` from the throttled response (capped at 15s)
 - **Stop**: After 3 attempts
-- **On exhaustion**: `retry_error_callback` converts to `_RateLimitError`
-- **Logging**: `before_sleep` callback logs each retry with attempt count and wait time
-
-Both `_get_weather` and `_research_topic` use the same decorator. The method bodies contain only the happy path + throttle guard.
+- **On exhaustion**: Raises `RuntimeError` with retry guidance
+- **Cancellation**: Each wait is cancellable via `_wait_or_cancel`
 
 ## Data Flow
 
 ```mermaid
 flowchart LR
     subgraph API Responses
-        W1[Flat weather JSON] -->|from_api| WR[WeatherResponse]
-        W2[Array weather JSON] -->|from_api| WR
-        R1[Research JSON] --> RR[ResearchResponse]
-        T1[Throttled JSON] --> TE[_ThrottledError]
+        W1[Flat weather JSON] -->|_format_weather| S[String for LLM]
+        W2[Array weather JSON] -->|_format_weather| S
+        R1[Research JSON] -->|_format_research| S
+        T1[Throttled JSON] -->|_request_with_retry| W1
+        T1 -->|_request_with_retry| R1
         HTML[HTML error] --> DE[DecodingError]
     end
-    WR -->|display| S[String for LLM]
-    RR -->|display| S
-    TE -->|@_throttle_retry| W1
-    TE -->|@_throttle_retry| R1
     DE -->|error result| S
 ```
 
-## Display Theme
-
-| Element    | Style       | Usage                                    |
-| ---------- | ----------- | ---------------------------------------- |
-| User       | Bold cyan   | `You:` prompt label                      |
-| Assistant  | Bold green  | `Assistant:` header before streamed text |
-| Tool call  | Yellow      | `⚡ tool_name(args)` indicator           |
-| Tool OK    | Green       | `✓ tool_name completed`                  |
-| Tool error | Bold red    | `✗ tool_name: message`                   |
-| Separator  | Dim         | `────` rule between turns                |
-| Meta       | Dim         | `[cancelled]`, `Goodbye!`                |
-
 ## Key Design Decisions
 
-1. **Orchestrator owns all state** — ToolExecutor and Display are stateless workers. This makes the system easy to reason about and test.
-2. **Cancel via asyncio.Event** — shared between orchestrator and tool executor, checked cooperatively. HTTP requests are raced against the event for instant cancellation.
-3. **Pydantic normalization** — `WeatherResponse.from_api()` handles the non-deterministic API schemas at the boundary, so downstream code always sees a consistent model.
-4. **Tenacity for retry** — `@_throttle_retry` decorator with custom wait strategy reading `retry_after_seconds` from the API response. Keeps method bodies clean.
-5. **Content-type guard** — `_request()` checks for `application/json` before parsing, handling infrastructure-level HTML errors (e.g., unicode input → Cloud Run 400).
+1. **Flat functions over classes** — `_process_turn`, `_stream_response`, `_execute_tools` are plain async functions. State is just the `history` list and `cancel_event`, threaded through arguments.
+2. **Cancel via asyncio.Event** — shared across the call chain, checked cooperatively. HTTP requests are raced against the event for instant cancellation.
+3. **Dict-based formatting** — `_format_weather()` handles both flat and array API schemas with dict access. No Pydantic models needed for two simple response shapes.
+4. **Simple retry loop** — `_request_with_retry` reads `retry_after_seconds` from the throttled response, sleeps (cancellably), and retries. Three attempts, capped at 15s wait.
+5. **Content-type guard** — `_request()` checks for `application/json` before parsing, handling infrastructure-level HTML errors (e.g., unicode input causing Cloud Run 400).
 6. **History integrity on cancel** — stub tool results ensure the conversation history is always valid, preventing LLM 400 errors after interrupted tool calls.
-7. **File-only logging** — comprehensive DEBUG-level logs to timestamped session files, with third-party loggers silenced to WARNING. No console noise.
+7. **File-only logging** — DEBUG-level logs to timestamped session files, with third-party loggers silenced to WARNING. No console noise.

CLAUDE.md

@@ -7,28 +7,28 @@ Take-home interview project: CLI chat app with streaming LLM + tool calling agai
 ## Quick reference
 
 - `make install` → `make run` to use
-- `make check` runs lint + test (21 e2e tests)
-- API keys in `.env` (LLM_API_KEY, ELYOS_API_KEY; optional LLM_BASE_URL, LLM_MODEL)
+- `make check` runs lint + test (20 e2e tests)
+- Env vars: LLM_API_KEY, ELYOS_API_KEY (required); LLM_BASE_URL, LLM_MODEL (optional). `uv run` auto-loads `.env` if present.
 - Logs: `cli_chat_<timestamp>_<uuid>.log` per session
 - Design docs: README.md, ARCHITECTURE.md, DISCOVERIES.md
 
 ## Key decisions
 
-- Any OpenAI-compatible endpoint for LLM, configurable via LLM_BASE_URL and LLM_MODEL in .env
+- Any OpenAI-compatible endpoint for LLM, configurable via LLM_BASE_URL and LLM_MODEL env vars
 - httpx async for external API calls, with `asyncio.wait` racing requests against cancel events
-- Pydantic for all data models + settings
-- Tenacity `@retry` decorator for throttle retry (custom wait from `retry_after_seconds`)
-- Orchestrator pattern: separates turn lifecycle from chat/tool execution
+- Plain dicts for API responses, formatted by `_format_weather` / `_format_research` helpers
+- Simple retry loop for throttle handling (reads `retry_after_seconds`, max 3 attempts)
+- Flat async functions: `_process_turn` → `_stream_response` → `_execute_tools`
 - Async stdin via `loop.add_reader` (not `asyncio.to_thread(input)`) — avoids dangling threads
 - Cancellation adds stub tool results to keep conversation history valid for the LLM
-- Styled display via rich: cyan user, green assistant, yellow tools, red errors
+- Rich spinner for tool call pending state
 - File-only logging (DEBUG level) with timestamped + UUID session files
 
 ## Ctrl+C behavior
 
 - During input: exits cleanly
 - During processing: first Ctrl+C cancels current operation, second exits
-- During HTTP requests: instant cancellation via `_cancellable_request` (asyncio.wait race)
+- During HTTP requests: instant cancellation via `_race_with_cancel` (asyncio.wait race)
 
 ## API quirks (see DISCOVERIES.md for details)
 

DISCOVERIES.md

@@ -40,7 +40,7 @@ The API returns two different response shapes **randomly for the same city**. Co
 }
 ```
 
-**Handling:** `WeatherResponse.from_api()` detects the shape and normalizes both into a consistent `conditions` list.
+**Handling:** `_format_weather` detects the shape — the flat case is wrapped into a single-element `conditions` list before rendering, so both shapes render identically.
 
 #### 2. Weather Also Rate-Limits (HTTP 200)
 
@@ -371,9 +371,9 @@ Two distinct behaviors:
 
 Some inputs (observed with whitespace-only `"   "` and XSS payloads) **occasionally return an empty `{}`** with HTTP 200. This is non-deterministic — the same input returns a normal response most of the time, but rarely returns `{}`.
 
-**Impact:** Without a guard, `ResearchResponse(**{})` raises a Pydantic `ValidationError` (missing required `topic` and `summary`).
+**Impact:** Formatting an empty dict would raise a `KeyError` on `summary`.
 
-**Handling:** `_research_topic()` checks for empty or incomplete responses (missing `topic` or empty `summary`) before Pydantic parsing, returning a graceful "no results" message instead of crashing.
+**Handling:** `_research_topic` checks for missing `topic` or empty `summary` before calling `_format_research`, returning a graceful "no results" message instead of crashing.
 
 #### 6. Standard Error Responses
 - `422` for missing `topic` param

README.md

@@ -6,23 +6,44 @@ A command-line chat application with streaming LLM responses and tool calling. B
 
 - Python 3.12+
 - [uv](https://docs.astral.sh/uv/)
-- API keys in `.env`:
+- Environment variables:
+  - `LLM_API_KEY` (required)
+  - `ELYOS_API_KEY` (required)
+  - `LLM_BASE_URL` (optional, default: OpenRouter)
+  - `LLM_MODEL` (optional, default: `openai/gpt-4o-mini`)
+
+  Set them however you prefer — exported in your shell, via CI secrets, or dropped into a local `.env` file, which `uv run` picks up automatically:
   ```
   LLM_API_KEY=sk-...
   ELYOS_API_KEY=...
   ```
-  Optional overrides: `LLM_BASE_URL` (default: OpenRouter), `LLM_MODEL` (default: `openai/gpt-4o-mini`)
 
 ## Setup
 
+### With uv (recommended)
+
 ```bash
-make install
+make install   # runs uv sync
+make run       # runs uv run cli-chat
+```
+
+### Without uv (bare Python)
+
+```bash
+python -m venv .venv
+source .venv/bin/activate   # Windows: .venv\Scripts\activate
+pip install .
 ```
 
 ## Usage
 
 ```bash
+# With uv
 make run
+
+# Without uv
+source .venv/bin/activate
+cli-chat
 ```
 
 **Example session:**
@@ -58,17 +79,14 @@ make check      # lint + test (CI gate)
 
 ## Design
 
-The application uses an **orchestrator pattern** — a central coordinator manages the conversation turn lifecycle, delegating to stateless workers for LLM streaming and tool execution. See [ARCHITECTURE.md](ARCHITECTURE.md) for diagrams and detailed design rationale.
+The application uses flat async functions to manage the conversation turn lifecycle, with a `ToolExecutor` class handling API calls. See [ARCHITECTURE.md](ARCHITECTURE.md) for diagrams and detailed design rationale.
 
 **API quirks** discovered during development are documented in [DISCOVERIES.md](DISCOVERIES.md).
 
 ### Project Structure
 
 ```
 src/cli_chat/
-├── main.py          # entry point, signal wiring
-├── orchestrator.py  # turn lifecycle, history, LLM streaming, cancellation
-├── tools.py         # API calls with retry + quirk handling
-├── models.py        # pydantic models (settings, responses)
-└── display.py       # streaming output, spinners
+├── orchestrator.py  # entry point, turn lifecycle, LLM streaming, cancellation
+└── tools.py         # API calls with retry, response formatting
 ```

pyproject.toml

@@ -6,15 +6,12 @@ requires-python = ">=3.12"
 dependencies = [
     "openai>=1.60.0",
     "httpx>=0.27.0",
-    "pydantic>=2.10.0",
-    "pydantic-settings>=2.7.0",
     "python-dotenv>=1.0.0",
     "rich>=13.9.0",
-    "tenacity>=9.0.0",
 ]
 
 [project.scripts]
-cli-chat = "cli_chat.main:main"
+cli-chat = "cli_chat.orchestrator:main"
 
 [build-system]
 requires = ["hatchling"]