enyal 0.1.0

Persistent, queryable memory for AI coding agents

Enyal

Persistent, queryable memory for AI coding agents.

Enyal gives AI agents like Claude Code durable context that survives session restarts. Every conversation becomes accumulated institutional knowledge—facts, preferences, decisions, and conventions that persist and grow.

Features

• Persistent Memory: Context survives restarts, crashes, and process termination
• Semantic Search: Find relevant context using natural language queries
• Hierarchical Scoping: Global → workspace → project → file context inheritance
• Fully Offline: Zero network calls during operation
• Cross-Platform: macOS (Intel + Apple Silicon) and Windows 10/11
• MCP Compatible: Works with any MCP client (Claude Desktop, Claude Code, etc.)

Installation

# Using uv (recommended)
uv add enyal

# Using pip
pip install enyal

Quick Start

As an MCP Server

Add to your Claude Desktop configuration (~/Library/Application Support/Claude/claude_desktop_config.json):

{
  "mcpServers": {
    "enyal": {
      "command": "python",
      "args": ["-m", "enyal.mcp"],
      "env": {
        "ENYAL_DB_PATH": "~/.enyal/context.db"
      }
    }
  }
}

Available Tools

• enyal_remember: Store new context with metadata
• enyal_recall: Semantic search for relevant context
• enyal_forget: Remove or deprecate context
• enyal_stats: Usage and health metrics

As a Python Library

from enyal import ContextStore, ContextType, ScopeLevel

# Initialize store
store = ContextStore("~/.enyal/context.db")

# Remember something
entry_id = store.remember(
    content="Always use pytest for testing in this project",
    content_type=ContextType.CONVENTION,
    scope_level=ScopeLevel.PROJECT,
    scope_path="/Users/dev/myproject",
    tags=["testing", "pytest"]
)

# Recall relevant context
results = store.recall(
    query="how should I write tests?",
    limit=5,
    min_confidence=0.5
)

for result in results:
    print(f"{result.score:.2f}: {result.entry.content}")

Architecture

Enyal uses a unified SQLite database with:

• Relational storage for metadata and attributes
• sqlite-vec for vector similarity search
• FTS5 for keyword search
• WAL mode for concurrent access

See docs/ARCHITECTURE.md for detailed design decisions.

Configuration

Environment variables:

Variable	Default	Description
ENYAL_DB_PATH	~/.enyal/context.db	Database file location
ENYAL_PRELOAD_MODEL	false	Pre-load embedding model at startup
ENYAL_LOG_LEVEL	INFO	Logging level

Development

# Clone repository
git clone https://github.com/seancorkum/enyal.git
cd enyal

# Install with dev dependencies
uv sync --all-extras

# Run tests
uv run pytest

# Type checking
uv run mypy src/enyal

# Linting
uv run ruff check src/enyal

Performance

Benchmarked on Intel Mac with Python 3.12:

Metric	Target (p95)	Measured (p95)	Status
Cold start (model load + first query)	<2000ms	~1500ms	✓
Warm query latency	<50ms	~34ms	✓
Write latency	<50ms	~34ms	✓
Concurrent reads (4 threads)	<150ms	~85ms	✓
Memory (100k entries estimated)	<500MB	~35MB	✓

Run benchmarks:

uv run python benchmarks/benchmark_performance.py

License

MIT