Today's digest

# rohitg00/agentmemory **URL:** https://github.com/rohitg00/agentmemory **One-liner:** Agentmemory provides persistent memory for AI coding agents via MCP, hooks, and a REST API, with confidence scoring, knowledge graphs, and hybrid search. **Relevance to apollo:** medium (60/100) **Integration:** cleanroom-rebuild ## Summary Persistent memory for AI coding agents with MCP support. ## Why it's useful here Apollo is an autonomous interceptor agent that could benefit from persistent memory for mission context, learned threat profiles, and past engagement outcomes. Agentmemory's knowledge graph and confidence scoring could improve decision-making. ## Suggested use Run the agentmemory MCP server as a sidecar and use REST calls from Apollo to store/retrieve memory. Alternatively, study and cleanroom-rebuild the core algorithm in Python. ## Novelty / why now Combines Karpathy's LLM Wiki pattern with production-grade features (confidence scoring, lifecycle, knowledge graphs) and zero external database dependencies. ## Risks Language mismatch (TypeScript vs Python) requires running a separate server. The MCP server may have dependencies not suitable for embedded systems. Single maintainer, new project. ## Safety scan - Risk level: **low** - Stars: 6575 (age 77d, 85.39 stars/day) - Last push: 0 days ago - Contributors: 13 - License: Apache-2.0 - Postinstall hooks: none - Suspicious patterns: none - Notes: (none) ### Reviewer safety notes Low risk - no suspicious patterns, no postinstall hooks, Apache-2.0 license. However, the repo is very new (77 days) with rapid star growth (6.5k), which could indicate hype; evaluate stability and long-term maintenance.