MemoryGraph

# Workplan 0: Marketing & Distribution **Priority**: HIGH - Critical for adoption **Status**: READY TO EXECUTE **Goal**: Maximize discoverability and adoption of MemoryGraph MCP server --- ## Parallel Execution Guide This workplan can be executed with **3-4 parallel agents** for maximum efficiency. ### Dependency Graph ``` Prerequisites (verify first) │ ├──► Section 1: Primary Discovery ──┐ │ (MCPB, MCP Repo, Awesome List) │ │ ├──► Section 3: Monitoring ├──► Section 2: Launch Announcements │ │ │ (Reddit, Twitter, HN) │ ▼ │ │ Section 4: Secondary └──► Section 6: Content Marketing ───┘ (Post-launch) (Blog, Video) ``` ### Parallel Work Units | Agent | Section | Dependencies | Can Run With | |-------|---------|--------------|--------------| | **Agent 1** | 1.1 MCPB Bundle | Prerequisites | Agents 2, 3, 4 | | **Agent 2** | 1.2 MCP Repo PR | Prerequisites | Agents 1, 3, 4 | | **Agent 3** | 1.3 Awesome List PR | Prerequisites | Agents 1, 2, 4 | | **Agent 4** | 2.1-2.3 Launch Posts | Prerequisites | Agents 1, 2, 3 | | **Agent 5** | 3.1-3.3 Monitoring Setup | After launches | Solo | | **Agent 6** | 4.1-4.3 Secondary Distribution | After primary | Solo | | **Agent 7** | 6.1-6.2 Content Marketing | Any time | Any agent | ### Recommended Execution Order **Phase A** (4 agents parallel): - Agent 1: MCPB bundle creation and submission - Agent 2: MCP servers repo PR - Agent 3: Awesome-mcp-servers PR - Agent 4: Reddit/Twitter/HN posts draft **Phase B** (2 agents parallel): - Agent 5: Set up monitoring and issue templates - Agent 6: Secondary distribution PRs **Phase C** (1 agent, ongoing): - Agent 7: Blog posts and video content ### Notes for Parallel Execution - All Section 1 tasks can run simultaneously (different repos) - Section 2 posts should be timed (Tuesday-Thursday 9am EST) - Section 3 monitoring should begin after first posts are live - Section 6 content can be created anytime but publish after launch --- ## Prerequisites - [ ] Verify PyPI package is up-to-date (`pip install memorygraphMCP`) - [ ] Verify README has clear quick-start instructions - [ ] Verify GitHub repo is public and presentable --- ## 1. Primary Discovery Channels **Priority: CRITICAL** - Must complete for successful launch ### 1.1 Anthropic Connectors Directory (MCPB Bundle) - [x] Create manifest.json for MCPB bundle specification v0.3 - [x] Create build script (scripts/build_mcpb.py) - [ ] Create icon assets (16x16, 32x32, 128x128 PNG) - [ ] Build .mcpb bundle: `python scripts/build_mcpb.py` - [ ] Test bundle installation in Claude Desktop - [ ] Submit to Anthropic Connectors Directory - [ ] Monitor submission for feedback, respond within 24 hours **Why critical**: Official Anthropic directory for Claude Desktop extensions. **Reference**: https://support.claude.com/en/articles/12922929-building-desktop-extensions-with-mcpb ### 1.2 Official MCP Repository - [ ] Submit PR to https://github.com/modelcontextprotocol/servers - [ ] Add to community servers section - [ ] Use PR template from Section 5 below - [ ] Monitor PR for feedback, respond within 24 hours **Why critical**: Official Anthropic repository, highest trust and visibility. ### 1.3 Top Awesome List - [ ] Submit PR to https://github.com/appcypher/awesome-mcp-servers (7000+ stars) - [ ] Add under "Memory" or "Knowledge Graph" section - [ ] Use PR template from Section 5 below - [ ] Monitor PR for feedback **Why critical**: Most starred awesome list, high developer visibility. --- ## 2. Launch Announcements **Priority: HIGH** - Important for initial momentum ### 2.1 Reddit Posts - [ ] Post to r/ClaudeAI - **Title**: "I built a graph-based memory server for Claude Code (zero-config SQLite)" - **Content**: Quick start guide, PyPI link, GitHub link - **Emphasis**: One-line install, works in 30 seconds - **Best time**: Tuesday-Thursday, 9am-12pm EST - [ ] Post to r/mcp - **Focus**: Technical advantages (graph vs vector memory) - **Audience**: Technical MCP developers - **Content**: Compare with other memory servers - [ ] Post to r/LocalLLaMA (if applicable) - **Focus**: Open-source, self-hosted memory solution - **Audience**: Privacy-focused developers ### 2.2 Twitter/X - [ ] Create announcement thread - Tag @AnthropicAI - Hashtags: #MCP #ClaudeCode #AIAgents #GraphDatabase - Include demo GIF or screenshot - Best time: Tuesday-Thursday, 9-11am EST ### 2.3 Hacker News - [ ] Prepare "Show HN" post - **Title**: "Show HN: MemoryGraph – Graph-based memory for Claude Code (MCP server)" - **Best time**: Tuesday 9am EST or Wednesday 9am EST - **Content**: Focus on zero-config, graph relationships, developer productivity --- ## 3. Monitoring & Support **Priority: HIGH** - Critical for user retention ### 3.1 Issue Management - [ ] Set up GitHub issue templates (bug, feature request, question) - [ ] Monitor GitHub issues daily (first 2 weeks) - [ ] Respond to installation problems within 24 hours - [ ] Fix critical bugs in patch releases - [ ] Track common questions for FAQ updates ### 3.2 Analytics Tracking - [ ] Monitor PyPI download statistics weekly - [ ] Track GitHub stars/forks growth - [ ] Collect user testimonials and feedback - [ ] Document common use cases from community ### 3.3 Community Engagement - [ ] Respond to Reddit comments within 24 hours - [ ] Respond to Twitter mentions - [ ] Engage with issues and discussions on GitHub --- ## 4. Secondary Distribution (Post-Launch) **Priority: MEDIUM** - Execute after primary channels ### 4.1 Additional Awesome Lists - [ ] Submit to https://github.com/wong2/awesome-mcp-servers - [ ] Submit to https://github.com/punkpeye/awesome-mcp-servers - [ ] Submit to any new awesome-mcp lists that emerge ### 4.2 Developer Directories - [ ] Add to devhunt.org (if applicable) - [ ] Add to producthunt.com (consider timing) - [ ] Add to alternativeto.net ### 4.3 Documentation Sites - [ ] Ensure docs are indexed by search engines - [ ] Consider dev.to blog post - [ ] Consider hashnode blog post --- ## 5. PR Template for Awesome Lists Use this template when submitting to GitHub awesome lists: ```markdown ## Add MemoryGraph to Memory/Knowledge Graph section ### Description MemoryGraph is a graph-based MCP memory server that provides intelligent, relationship-aware memory for Claude Code and other MCP clients. Unlike vector-based memory, it uses graph databases (Neo4j, Memgraph, FalkorDB, or SQLite) to capture how information connects. **NEW in v0.11**: Python SDK (`memorygraphsdk`) for LlamaIndex, LangChain, CrewAI, and AutoGen integration! ### Key Features - **Zero-config installation**: `pip install memorygraphMCP` with SQLite default - **🆕 Python SDK**: Native integrations for popular AI agent frameworks - LlamaIndex: `MemoryGraphChatMemory`, `MemoryGraphRetriever` - LangChain: `MemoryGraphMemory` with session support - CrewAI: `MemoryGraphCrewMemory` for multi-agent crews - AutoGen: `MemoryGraphAutoGenHistory` - **Cloud Backend**: Sync across devices with memorygraph.dev - **Graph-based storage**: Captures relationships between memories - **Pattern recognition**: Learns from past solutions and decisions - **Automatic context extraction**: Extracts structure from natural language - **Multi-backend support**: SQLite (default), Neo4j, Memgraph, FalkorDB, Cloud - **Docker deployment**: One-command setup for all modes - **Comprehensive testing**: 1,200+ passing tests ### Why This Server? This server uses graph relationships to understand *how* information connects, enabling queries like: - "What solutions worked for similar problems?" - "What decisions led to this outcome?" - "What patterns exist across my projects?" Perfect for developers using Claude Code who want persistent, intelligent memory that learns from context and understands relationships. ### Links - Repository: https://github.com/gregorydickson/memory-graph - PyPI (MCP): https://pypi.org/project/memorygraphMCP/ - PyPI (SDK): https://pypi.org/project/memorygraphsdk/ - Documentation: See README and docs/ folder - Installation: `pip install memorygraphMCP` or `pip install memorygraphsdk` - Quick start: `memorygraph` CLI for setup ``` --- ## 6. Content Marketing (Optional) **Priority: LOW** - Execute when bandwidth allows ### 6.1 Blog Posts - [ ] Write launch blog post: "Why Graph Memory Beats Vector Memory for AI Agents" - [ ] Write tutorial: "Building Persistent Memory for Claude Code in 5 Minutes" - [ ] Write case study: "How MemoryGraph Improved My Development Workflow" ### 6.2 Video Content - [ ] Create 2-minute demo video showing installation and usage - [ ] Create longer tutorial video (10-15 min) for YouTube --- ## Acceptance Criteria - [ ] PR submitted to official MCP servers repo - [ ] PR submitted to top awesome-mcp-servers list - [ ] At least 2 Reddit posts published - [ ] GitHub issues monitored and responded to - [ ] PyPI downloads tracked --- ## Success Metrics (First Month) | Metric | Target | |--------|--------| | GitHub Stars | 50+ | | PyPI Downloads | 500+ | | GitHub Issues Responded | 100% within 48h | | Reddit Post Engagement | 10+ upvotes per post | --- ## Notes - This workplan focuses on **developer reach**, not paid marketing - Timing matters: Tuesday-Thursday mornings are best for developer content - Quality > Quantity: Better to have 2 great posts than 5 mediocre ones - Be genuine: Engage with feedback, don't just broadcast - Iterate: Update messaging based on what resonates