### Search Results Across All Your Agents
*Three-pane layout with semantic styling: filter bar with pills, results list with color-coded agents and score tiers, and syntax-highlighted detail preview with tab navigation*

---
### Rich Conversation Detail View
*Full conversation rendering with markdown formatting, code blocks, headers, and structured content*

---
### Quick Start & Keyboard Reference
*Built-in help screen (press `F1` or `?`) with all shortcuts, filters, modes, and navigation tips*
---
## ๐ก Why This Exists
### The Problem
AI coding agents are transforming how we write software. Claude Code, Codex, Cursor, Copilot, Aider, Pi-Agent; each creates a trail of conversations, debugging sessions, and problem-solving attempts. But this wealth of knowledge is **scattered and unsearchable**:
- **Fragmented storage**: Each agent stores data differentlyโJSONL files, SQLite databases, markdown logs, proprietary JSON formats
- **No cross-agent visibility**: Solutions discovered in Cursor are invisible when you're using Claude Code
- **Lost context**: That brilliant debugging session from two weeks ago? Good luck finding it by scrolling through files
- **No semantic search by default**: File-based grep doesn't understand natural language queries; cass can add optional local ML search when model files are installed
### The Solution
`cass` treats your coding agent history as a **unified knowledge base**. It:
1. **Normalizes** disparate formats into a common schema
2. **Indexes** everything with a purpose-built full-text search engine
3. **Surfaces** relevant past conversations in milliseconds
4. **Respects** your privacyโeverything stays local, nothing phones home
### Who Benefits
- **Individual developers**: Find that solution you know you've seen before
- **Teams**: Share institutional knowledge across different tool preferences
- **AI agents themselves**: Let your current agent learn from all your past agents (via robot mode)
- **Power users**: Build workflows that leverage your complete coding history
---
## โจ Key Features
### โก Instant Search (Sub-60ms Latency)
- **"Search-as-you-type"**: Results update instantly with every keystroke.
- **Edge N-Gram Indexing**: We frontload the work by pre-computing prefix matches (e.g., "cal" -> "calculate") during indexing, trading disk space for O(1) lookup speed at query time.
- **Smart Tokenization**: Handles `snake_case` ("my_var" matches "my" and "var"), hyphenated terms, and code symbols (`c++`, `foo.bar`) correctly.
- **Zero-Stall Updates**: The background indexer commits changes atomically; `reader.reload()` ensures new messages appear in the search bar immediately without restarting.
### ๐ง Optional Semantic Search (Local Inference, No Network at Query Time)
- **Local inference**: Uses a FastEmbed embedder running ONNX on-device. Once the chosen model is installed, no network traffic is required to answer queries.
- **Opt-in acquisition**: `cass models install` downloads the requested embedder from Hugging Face on explicit request and verifies SHA256 checksums. Nothing is fetched until you run the install command. Three embedders are supported:
- `all-minilm-l6-v2` โ `cass models install --model all-minilm-l6-v2` (alias: `minilm`). 384-dim. ~90 MB. The default; fastest. Best for general English semantic similarity.
- `snowflake-arctic-s` โ `cass models install --model snowflake-arctic-s`. 384-dim. ~120 MB. Stronger MTEB scores than MiniLM at similar cost; good drop-in replacement for code-heavy corpora.
- `nomic-embed` โ `cass models install --model nomic-embed` (alias: `nomic-embed-text-v1.5`). 768-dim. ~270 MB. Highest recall on long-context queries; trade off larger index footprint.
Removal mirrors install: `cass models remove --model