MemoryGraph

# MemoryGraph Setup for Gemini CLI Get persistent memory working with Google's Gemini CLI. ## Prerequisites - **Gemini CLI** installed ([Installation Guide](https://github.com/google-gemini/gemini-cli)) - **Google AI API key** or Google Cloud credentials - Python 3.10+ - pipx installed (`pip install --user pipx && pipx ensurepath`) ## Choose Your Backend MemoryGraph supports two backend options: | Feature | Local (SQLite) | Cloud | |---------|---------------|-------| | **Setup** | Zero-config | API key required | | **Data Location** | `~/.memorygraph/` | memorygraph.dev | | **Multi-device** | No | Yes | | **Team sharing** | No | Yes | | **Offline** | Yes | No | | **Cost** | Free | Free tier available | **New users**: We recommend starting with **Cloud** for multi-device sync, or **Local** for single-machine use. --- ## Quick Start (Local Backend) ### 1. Install MemoryGraph ```bash pipx install memorygraphMCP ``` Verify installation: ```bash memorygraph --version ``` ### 2. Configure Gemini CLI Gemini CLI supports MCP servers via configuration file. **Create or edit** `~/.gemini/config.json`: ```json { "mcpServers": { "memorygraph": { "command": "memorygraph", "args": [] } } } ``` ### 3. Verify Setup Start Gemini CLI and check for MCP tools: ```bash gemini ``` Then ask: ``` What MCP tools do you have available? ``` ## First Memory Store a memory: ``` Store this for later: The main database is PostgreSQL on port 5432 ``` Retrieve it: ``` What database does this project use? ``` --- ## Quick Start (Cloud Backend) Cloud backend syncs memories across all your devices and enables team collaboration. ### 1. Get Your API Key 1. Sign up at [app.memorygraph.dev](https://app.memorygraph.dev) 2. Copy your API key (starts with `mg_`) ### 2. Install MemoryGraph ```bash pipx install memorygraphMCP ``` ### 3. Configure Gemini CLI with Cloud Backend Edit `~/.gemini/config.json`: ```json { "mcpServers": { "memorygraph": { "command": "memorygraph", "args": ["--backend", "cloud"], "env": { "MEMORYGRAPH_API_KEY": "mg_your_api_key_here" } } } } ``` ### 4. Verify Setup Start Gemini CLI and ask about available memory tools. --- ## Migrating from Local to Cloud Already using local SQLite and want to switch to cloud? ### Step 1: Export Local Memories ```bash memorygraph export --output memories-backup.json ``` ### Step 2: Import to Cloud ```bash export MEMORYGRAPH_API_KEY=mg_your_key_here memorygraph import --backend cloud --input memories-backup.json ``` ### Step 3: Update Gemini CLI Configuration Update `~/.gemini/config.json`: ```json { "mcpServers": { "memorygraph": { "command": "memorygraph", "args": ["--backend", "cloud"], "env": { "MEMORYGRAPH_API_KEY": "mg_your_api_key_here" } } } } ``` ### Step 4: Restart Gemini CLI Restart to apply the new configuration. See [CLOUD_BACKEND.md](../CLOUD_BACKEND.md) for detailed migration options. --- ## Configuration Options ### Extended Mode (More Tools) ```json { "mcpServers": { "memorygraph": { "command": "memorygraph", "args": ["--profile", "extended"] } } } ``` ### Custom Database Path ```json { "mcpServers": { "memorygraph": { "command": "memorygraph", "env": { "MEMORY_SQLITE_PATH": "/path/to/memory.db" } } } } ``` ### Full Path (Recommended) ```json { "mcpServers": { "memorygraph": { "command": "/Users/yourname/.local/bin/memorygraph", "args": [] } } } ``` Find your path: `which memorygraph` ## Troubleshooting ### MCP Server Not Loading 1. **Check Gemini CLI version** - Ensure you have MCP support 2. **Verify config location**: `~/.gemini/config.json` 3. **Check JSON syntax** - Use a JSON validator ### Command Not Found ```bash # Check if memorygraph is installed which memorygraph # If not found pipx ensurepath # Restart terminal ``` ### Test Server Manually ```bash echo '{"jsonrpc":"2.0","method":"initialize","params":{"protocolVersion":"2024-11-05","capabilities":{},"clientInfo":{"name":"test","version":"1.0.0"}},"id":1}' | memorygraph ``` ### View Logs Check Gemini CLI output for MCP-related errors: ```bash gemini --verbose ``` ## Tips for Gemini CLI Users 1. **Leverage the 1M context window** - Gemini can process large codebases 2. **Store key insights** - Remember important findings from large file analysis 3. **Track patterns** - Use memory to build up project knowledge 4. **Query before analysis** - Check what you already know about a topic ## Recommended: Memory Protocol Add this to `GEMINI.md` or your project's instructions file for automatic memory usage: ```markdown ## Memory Protocol ### REQUIRED: Before Starting Work You MUST use `recall_memories` before any task. Query by project, tech, or task type. ### REQUIRED: Automatic Storage Triggers Store memories on ANY of: - **Git commit** → what was fixed/added - **Bug fix** → problem + solution - **Version release** → summarize changes - **Architecture decision** → choice + rationale - **Pattern discovered** → reusable approach ### Timing Mode (default: on-commit) `memory_mode: immediate | on-commit | session-end` ### Memory Fields - **Type**: solution | problem | code_pattern | fix | error | workflow - **Title**: Specific, searchable (not generic) - **Content**: Accomplishment, decisions, patterns - **Tags**: project, tech, category (REQUIRED) - **Importance**: 0.8+ critical, 0.5-0.7 standard, 0.3-0.4 minor - **Relationships**: Link related memories when they exist Do NOT wait to be asked. Memory storage is automatic. ``` **File locations:** - **Project-specific**: `GEMINI.md` in project root - **Global**: `~/.gemini/instructions.md` ## Gemini-Specific Use Cases Gemini CLI's large context window pairs well with MemoryGraph: ### Large Codebase Analysis ``` Analyze all the files in src/ and store key architectural patterns you find ``` ### Cross-File Patterns ``` Search the codebase for authentication patterns and remember what you find ``` ### Incremental Learning ``` What have we learned about this codebase so far? ``` ## Combining with Gemini's Strengths | Gemini Strength | MemoryGraph Enhancement | |-----------------|------------------------| | 1M token context | Store insights from large analyses | | Fast inference | Quick memory queries | | Code understanding | Persist code patterns | | Multi-modal | Store context about diagrams/images discussed | ## Configuration File Location Default location: `~/.gemini/config.json` Alternative locations may be supported - check Gemini CLI documentation for your version. ## Limitations - MCP support in Gemini CLI may vary by version - Check Google's documentation for current MCP capabilities - Some features may require specific Gemini API tiers ## Next Steps - [Full Configuration Reference](../CONFIGURATION.md) - [All Tool Profiles](../TOOL_PROFILES.md) - [Troubleshooting Guide](../TROUBLESHOOTING.md) - [Gemini CLI Documentation](https://github.com/google-gemini/gemini-cli) --- **Works with**: Gemini CLI (with MCP support) **Transport**: stdio **Profiles**: core (9 tools), extended (11 tools) **Note**: MCP support may vary by Gemini CLI version