docs: add comprehensive README

README.md

@@ -1,7 +1,97 @@
 # Mnemosyne
 
-Object-addressed context memory for LLM agents. Built on [Pichay](https://github.com/fsgeek/pichay)'s demand paging foundation.
+Mnemosyne is a transparent HTTP proxy designed to manage LLM context memory. It sits between LLM clients and providers like Anthropic or OpenAI, providing semantic segmentation, multi-fidelity compression, and live monitoring to reduce context costs and improve agent performance.
 
-## Status
+## Overview
+
+Mnemosyne acts as a drop-in proxy. It intercepts API requests, segments content into labeled blocks, and applies multi-fidelity compression to manage the context window. As context grows, Mnemosyne automatically degrades content fidelity to keep the most relevant information available while minimizing token usage.
+
+## Architecture
+
+```text
+Client → Mnemosyne Proxy → Provider API
+             |
+      +------+------+
+      |             |
+  BlockStore    PageStore
+      |             |
+  FidelityManager   |
+      |             |
+ SSECleanupFilter   |
+      |             |
+  ObjectStore <-----+
+```
+
+## Features
+
+* **Transparent Proxy**: Operates as a drop-in replacement for LLM API endpoints. No client-side changes are required.
+* **Semantic Segmentation**: Automatically labels conversation content with tensor/block IDs, allowing the model to reference specific segments.
+* **Multi-Fidelity Compression**: Supports five levels of fidelity (L0-L4):
+    * L0: Full content (no compression)
+    * L1: Detailed summary (~30% size)
+    * L2: Compact summary (~5% size)
+    * L3: Metadata stub (~50-100 tokens)
+    * L4: Evicted (not in context)
+* **Pressure-Based Degradation**: Automatically downgrades object fidelity based on context window pressure zones (Normal, Caution, Warning, Critical, Emergency).
+* **SSE Cleanup Filter**: Intercepts streaming responses to strip internal memory management tags (e.g., `<memory_cleanup>`, `<yuyay-response>`) and executes cleanup operations in real-time.
+* **Live Monitoring**: Includes an embedded dashboard at `/dashboard` providing real-time metrics on context reduction, fidelity distribution, admission control, and latency.
+* **REST API**: Exposes endpoints for session management, benchmarking, and memory state inspection.
+* **OpenCode Integration**: Includes a plugin for OpenCode to enable memory-aware context injection and status reporting.
+
+## Installation
+
+Mnemosyne is a Python project managed with `uv`.
+
+1. Clone the repository:
+   ```bash
+   git clone https://github.com/jyapayne/mnemosyne
+   cd mnemosyne
+   ```
+2. Install dependencies:
+   ```bash
+   uv sync
+   ```
+
+## Configuration
+
+Mnemosyne is configured via environment variables:
+
+* `ANTHROPIC_API_KEY`: API key for the provider.
+* `MNEMOSYNE_PORT`: Port for the proxy server (default: 8080).
+* `MNEMOSYNE_HOST`: Host for the proxy server (default: 127.0.0.1).
+
+## Usage
+
+Run the proxy server:
+
+```bash
+uv run mnemosyne
+```
+
+Point your LLM client (e.g., Claude Code) to the proxy URL (e.g., `http://127.0.0.1:8080/v1`).
+
+## API Reference
+
+* `/api/sessions`: List active conversation sessions.
+* `/api/benchmark`: Retrieve performance and token usage metrics.
+* `/api/memory`: Inspect the current state of the object store.
+* `/api/blocks`: View tracked conversation blocks.
+
+## How It Works
+
+### Request Lifecycle
+
+1. **Inbound**:
+    * `label_messages`: Injects `[block:xxxx]` markers into message content.
+    * `apply_to_messages`: Drops or collapses blocks based on cleanup operations.
+    * `compact_messages`: Compresses remaining content.
+    * `apply_fidelity`: Adjusts content based on current fidelity levels.
+2. **Outbound**:
+    * `SSECleanupFilter`: Strips cleanup tags from streaming responses, executes operations, and forwards the clean stream.
+3. **Post-Response**:
+    * Updates fidelity pressure, schedules summaries, and records benchmarks.
+
+## License
+
+MIT
 
-Work in progress. See [ARCHITECTURE.md](ARCHITECTURE.md) and [ROADMAP.md](ROADMAP.md) for design details.