Trace. Context. Continuity.
_{A local-first engineering memory layer and MCP server that provides AI coding tools with a shared, persistent context architecture.}

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Table of Contents

• Overview
• The Context Fragmentation Problem
• Core Architecture & Design
• Installation Guide
• Initialization & Configuration
• Command Line Interface Reference
  • Ingestion Commands
  • Exploration Commands
  • Maintenance Commands
• Terminal User Interface (TUI)
• Agent Integration (MCP)
  • Supported AI Environments
  • Manual MCP Configuration
• Advanced Subsystems
  • Semantic Vector Search
  • Context Window Compression (Dense Format)
• Security & Privacy
• Troubleshooting & FAQ
• Roadmap
• Contributing
• License

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Overview

traz is a local-first engineering memory layer designed specifically for AI-augmented development.

By capturing debugging history, architectural decisions, file modifications, and workflow traces as you code, traz establishes a highly searchable, persistent context timeline. This timeline is securely stored in a local SQLite vector database and automatically exposed via the Model Context Protocol (MCP). This enables any compatible AI agent (Claude Code, Cursor, Aider, Gemini CLI, OpenAI Codex) to instantly synchronize context without manual developer intervention or copy-pasting.

The Context Fragmentation Problem

Modern software development increasingly relies on multiple, specialized AI agents. A standard workflow might involve:

1. Triaging a stack trace in Claude Code.
2. Refactoring a dense component within Cursor IDE.
3. Generating unit tests via a terminal agent like Aider or Antigravity.

At every tool boundary, context is lost. The AI in the IDE does not inherently know what the AI in the terminal just fixed. Every new session starts as an isolated environment, forcing developers to manually rebuild the context window.

traz resolves this by acting as a persistent context plane. It ensures that subsequent AI sessions—regardless of the tool—inherit the context of previous decisions, mitigating context loss, reducing LLM token duplication, and preventing regression of thought.

Core Architecture & Design

The traz architecture is built upon three primary pillars:

1. Local Timeline Engine (SQLite): A zero-dependency, local-first storage mechanism. It tracks event schemas across sessions, ensuring that proprietary source code and architectural context never leave the local machine. There are no cloud accounts, no sync delays, and no vendor lock-in.
2. Hybrid Vector Search: Leverages the fastembed-rs library to execute ONNX-accelerated embedding generation locally. It implements Reciprocal Rank Fusion (RRF) to intelligently merge exact keyword matches (FTS5) with semantic vector similarity, ensuring high-fidelity context retrieval even when vocabulary drifts.
3. MCP Server Integration: Implements the open Model Context Protocol to serve context transparently to supported AI agents. By utilizing an engineered "Dense Output Format," it compresses context payload sizes by up to 75%, preserving valuable context window capacity for the LLM's actual reasoning process.

graph TD
    A[Claude Code] -->|stdio / MCP| C(traz MCP Server)
    B[Cursor IDE] -->|stdio / MCP| C
    D[CLI / Shell Hooks] -->|Commands| C
    C --> E[(SQLite Vector DB)]
    C --> F[fastembed-rs ONNX]
    E --> G[Timeline Engine]

For an in-depth review of the underlying schema, migration logic, and RRF mathematics, please refer to the Architecture Documentation.

Installation Guide

Select your preferred installation method:

1. Via NPM (Cross-Platform)

npm install -g @traz-dev/traz

2. Via Homebrew (macOS & Linux)

brew tap mithilgirish/traz
brew install traz

3. Standalone Installers (No dependencies required)

• macOS & Linux (Shell):

  curl --proto '=https' --tlsv1.2 -LsSf https://github.com/mithilgirish/traz/releases/latest/download/traz-installer.sh | sh
  

• Windows (PowerShell):

  irm https://github.com/mithilgirish/traz/releases/latest/download/traz-installer.ps1 | iex
  

4. Via Cargo (Build from source)

Requires the Rust toolchain (v1.75.0 or higher):

cargo install --git https://github.com/mithilgirish/traz.git traz

Build from Source manually

For contributors or users requiring specific branch deployments:

git clone https://github.com/mithilgirish/traz.git
cd traz
cargo build --release

# Optional: Add to PATH systematically
sudo cp target/release/traz /usr/local/bin/

Verification

Ensure the binary is correctly linked and the embedding model engine is accessible.

traz doctor

Initialization & Configuration

Initialize traz within your repository to establish the local timeline directory (.traz/). This directory acts as the nexus for your project's history.

cd your-project
traz init

Running traz init performs the following automated steps:

1. Creates the .traz/ local directory.
2. Initializes the SQLite schema (traz.db) and vector index.
3. Automatically appends .traz/ to your .gitignore to prevent committing the database to remote version control.

Command Line Interface Reference

The CLI is designed to be highly composable, scriptable for CI/CD integration, and readable for daily workflow monitoring.

Ingestion Commands

Log Manual Context: Append a manual note, architectural decision, or debugging trace directly to the timeline.

traz log "traced root cause of memory leak to unbounded queue growth in the worker pool"

Add Structured Events: Useful for shell aliases or git hooks.

traz add --tool cursor --event-type bug_fix --title "Fixed auth race condition"

Exploration Commands

View Recent Activity: Fetch a chronological list of the most recent events across all tools.

$ traz recent --limit 5

[claude-code · 2h ago] fixed websocket reconnect issue
[cursor · 5h ago] updated auth middleware
[warp · 1d ago] traced memory leak in queue worker
[aider · 2d ago] reverted broken cache optimization

Search Engineering History: Perform keyword searches against the timeline.

$ traz search auth

[2d ago] claude-code
Fixed JWT refresh race condition

[5h ago] cursor
Updated auth middleware retry logic

View Workflow Timeline: Render a bulleted workflow trace of operations, useful for generating PR descriptions or commit summaries.

$ traz timeline

• created websocket handler
• debugged reconnect issue
• added retry backoff
• verified with local tests

Time-Bounded AI Recap: Generate a time-bounded summary, commonly used for daily standups or morning synchronization.

$ traz recap --hours 24

Maintenance Commands

Context Checkpointing: Save a named snapshot of the current state. Useful when switching branches or finalizing a massive refactor.

traz checkpoint --message "completed auth migration"

Backfill Embeddings: Generate missing vector embeddings for older events ingested prior to vector support or during offline periods.

traz backfill-embeddings

Terminal User Interface (TUI)

For an interactive, visually structured view of the repository's history and AI traces, traz ships with a native TUI.

traz tui

The TUI provides:

• Split-pane navigation of historical checkpoints.
• Interactive filtering by AI tool, event type, or timestamp.
• Detailed inspection of specific debug logs or diffs associated with an event.

Agent Integration (MCP)

traz acts as an MCP stdio server. It features auto-detecting setup workflows to register itself across major AI tools effortlessly.

Supported AI Environments

Run the setup command corresponding to your primary tool. The wizard will automatically locate the tool's configuration file and inject the necessary MCP routing logic.

traz setup claude   # Configures Claude Code
traz setup cursor   # Modifies ~/.cursor/mcp.json
traz setup opencode # Configures OpenCode
traz setup codex    # Configures OpenAI Codex CLI
traz setup gemini   # Configures Gemini CLI
traz setup agy      # Configures Antigravity CLI

Manual MCP Configuration

For tools not supported by the interactive wizard (such as Aider or custom agents), configure your agent to execute traz with the mcp subcommand.

Example mcp.json structure:

{
  "mcpServers": {
    "traz": {
      "command": "traz",
      "args": ["mcp"]
    }
  }
}

Once configured, the AI tool will automatically query the traz server on initialization, retrieve the latest checkpoints, and restore context natively. For further details, refer to the Agent Integration Guide.

Advanced Subsystems

Semantic Vector Search

By default, traz generates local embeddings for all ingested events using an ONNX-accelerated MiniLM model (fastembed-rs). This enables semantic retrieval, allowing you to find contextually relevant history even when exact keywords are omitted.

$ traz search "database connection pooling"

[semantic search] Search: "database connection pooling" (2 results)
─────────────────────────────────────────────
 1. Added pg_bouncer for connections (72%)
    Tool: cursor       Type: commit     Age: 3d ago     Tags: #db

 2. Re-architected connection lifecycle (64%)
    Tool: gemini       Type: refactor   Age: 1w ago     Tags: #db #performance

Context Window Compression (Dense Format)

Long-running projects accumulate extensive, verbose timelines. Standard JSON serialization of this data rapidly depletes an LLM's context window.

traz implements a --dense formatting protocol over MCP. Instead of passing nested JSON arrays to the agent, the server parses the historical data into an ultra-compact plaintext format, stripping redundant schema keys and whitespace. This methodology consistently compresses context payload sizes by 60% to 75%.

Security & Privacy

Security is foundational to traz.

• Zero Telemetry: The CLI binary contains no telemetry, analytics, or remote tracking systems.
• Local Isolation: The SQLite database is stored locally in your project folder (.traz/traz.db) and is strictly .gitignored. No data is transmitted to an external server.
• Embedding Generation: Semantic vector generation runs completely on-device using ONNX execution providers. Your proprietary commit messages and architecture plans are never sent to external embedding APIs.

Troubleshooting & FAQ

Q: traz setup cursor fails to detect my configuration. A: Ensure you have initialized Cursor at least once so the ~/.cursor configuration directory exists. You can manually append the JSON block defined in the Manual MCP Configuration section.

Q: Semantic search is returning errors or panicking in CI environments. A: In headless CI environments missing certain runtime libraries, fastembed-rs may fail to generate embeddings. Set the TRAZ_DISABLE_EMBEDDINGS=1 environment variable during automated testing to bypass vector generation.

Q: How do I clear my context timeline? A: You can purge the local memory by simply deleting the database file: rm -rf .traz/. Re-run traz init to start fresh.

Roadmap

v0.1 (Current)

• Local timeline storage (SQLite)
• CLI commands (recent, search, timeline, log, recap)
• Context and history retrieval system
• Native MCP server implementation
• Automatic git hook integrations
• Interactive Tool Setup Adapters
• Semantic search via local ONNX embeddings
• Terminal UI (TUI) Dashboard
• Token-optimized Dense output formats
• Workflow Checkpoints

Future Proposals

• VSCode and JetBrains native extensions
• Network-level team synchronization (Opt-in)
• Advanced Graph visualizations

Contributing

We welcome contributions from the community. If you plan to introduce significant architectural changes or core database migrations, please open an issue first to discuss the proposed design.

Development Workflow

# Clone the repository
git clone https://github.com/mithilgirish/traz.git
cd traz

# Run test suite
cargo test

# Ensure formatting and linting pass
cargo fmt --all -- --check
cargo clippy -- -D warnings

Please review the CONTRIBUTING.md for strict code style guidelines and testing protocols prior to submitting Pull Requests.

License

This project is licensed under the MIT License. All contributions are subject to these licensing terms.

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_{Built for developers who switch tools, not context.}