fix: replace litellm with tiktoken for token counting

- litellm had dependency conflicts with pydantic-ai
- tiktoken is lighter and already required by pydantic-ai
- Updated documentation (README.md, architecture.md)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

webber-api/docs/architecture.md

@@ -24,18 +24,30 @@ webber/
 ├── src/
 │   ├── main.py              # App entry point (NO routes)
 │   │
+│   ├── db/                  # Database layer
+│   │   ├── __init__.py      # Exports: Database, get_database, get_session
+│   │   ├── database.py      # SQLAlchemy async engine, session factory
+│   │   └── models.py        # Base declarative model
+│   │
 │   ├── shared/              # Cross-cutting concerns
 │   │   ├── base.py          # BaseController, BaseSchema
 │   │   ├── config.py        # Pydantic Settings
 │   │   ├── logging.py       # @logged decorator, trace_span
 │   │   ├── exceptions.py    # Custom exception hierarchy
 │   │   ├── auth.py          # API key validation
-│   │   └── context.py       # UserProvider singleton
+│   │   ├── context.py       # UserProvider singleton
+│   │   └── tokens.py        # Token counting utilities (litellm)
 │   │
 │   └── domains/             # Feature domains
 │       ├── router.py        # Root router (composes all)
 │       ├── health/          # Health endpoints
 │       ├── auth/            # Authentication
+│       ├── conversations/   # Multi-turn conversation memory
+│       │   ├── models.py    # Conversation, Message SQLAlchemy models
+│       │   ├── schemas.py   # Pydantic request/response models
+│       │   ├── service.py   # ConversationService business logic
+│       │   ├── router.py    # REST API endpoints
+│       │   └── summarize.py # Context summarization logic
 │       ├── agents/          # Agent orchestration
 │       │   ├── explore/     # Codebase navigation
 │       │   ├── plan/        # Implementation design
@@ -201,6 +213,10 @@ All settings via environment variables or `.env`:
 | ALLOWED_PATHS | [] | Paths accessible to tools |
 | SESSION_TTL_HOURS | 24 | Session expiry |
 | MAX_CONTEXT_TOKENS | 128000 | Max context window |
+| DATABASE_URL | sqlite+aiosqlite:///./webber.db | Database connection URL |
+| SUMMARIZATION_THRESHOLD | 0.8 | Summarize at N% of max tokens |
+| SUMMARIZATION_TARGET_TOKENS | 500 | Target summary size |
+| KEEP_RECENT_MESSAGES | 6 | Messages to keep unsummarized |
 
 ---
 
@@ -250,6 +266,53 @@ Tools are sandboxed operations agents can invoke:
 
 ---
 
+## Database Layer
+
+SQLAlchemy 2.0 async with lazy initialization pattern.
+
+### Supported Databases
+- **Development**: SQLite via `aiosqlite`
+- **Production**: PostgreSQL via `asyncpg`
+
+### Pattern
+```python
+from src.db import get_session
+from sqlalchemy.ext.asyncio import AsyncSession
+
+async def my_endpoint(session: AsyncSession = Depends(get_session)):
+    # Session auto-commits on success, rollbacks on exception
+    result = await session.execute(query)
+```
+
+Tables are created lazily on first `get_session()` call.
+
+---
+
+## Conversation API
+
+Multi-turn conversation memory with automatic context summarization.
+
+### Endpoints
+| Endpoint | Method | Description |
+|----------|--------|-------------|
+| `/conversations/` | POST | Create new conversation |
+| `/conversations/` | GET | List user's conversations |
+| `/conversations/{id}` | GET | Get conversation with history |
+| `/conversations/{id}/messages` | POST | Add message, triggers agent |
+| `/conversations/{id}` | DELETE | Delete conversation |
+
+### Models
+- **Conversation**: User session with agent type, working directory
+- **Message**: Individual messages with role, content, token count
+
+### Context Summarization
+When total tokens exceed 80% of `MAX_CONTEXT_TOKENS`:
+1. Keep last 6 messages intact
+2. Summarize older messages into a single summary message
+3. Mark old messages as summarized (soft delete)
+
+---
+
 ## Authentication Flow
 
 1. Client sends `X-API-Key` header