Agentic Coding

This is a GitHub repository page for **agentmemory**, a persistent memory system for AI coding agents. Based on your user intent, here's what's relevant: Agentmemory is a persistent memory engine that works with Claude Code, Cursor, Codex CLI, GitHub Copilot CLI, and 20+ other coding agents. It automatically captures tool usage from each session and compresses observations into searchable, structured memory so agents retain context across sessions without re-explanation. The system uses a 4-tier memory consolidation model (working, episodic, semantic, procedural) with hybrid search combining BM25 keyword matching, vector embeddings, and knowledge graph traversal. For solo founders using Claude Code and Cursor daily, it solves the core pain point: agents no longer forget project architecture, auth implementations, testing patterns, or your preferences between sessions—everything gets indexed and injected at SessionStart. The platform includes 54 MCP tools, 17 skills, real-time memory viewer, and costs roughly $10/year in LLM tokens versus $500+ for LLM-summarized approaches, with local embeddings available free. Installation is one command (npx @agentmemory/agentmemory@latest), and it integrates via hooks, MCP, or REST API across any agent supporting those protocols.

This guide covers five core rules for agentic coding development at startups using Claude Code: everyone ships (democratizing coding to non-technical team members), automate the tedium (agents handling routine 80% of work so engineers focus on judgment calls), trust but verify (establishing monitoring and verification frameworks), build for rebuilding (constantly iterating as model capabilities evolve), and prototype-dogfood-productionize (using internal agents to inform customer-facing products). Featured startups report significant productivity gains: ClickHouse shipped 30% more features, Omni achieved 2-3x engineering productivity, Artemis Security runs 6,000+ PRs weekly, and Clay automated 100% of bug triage. Key advanced practices include using Skills (reusable instruction files) to encode team standards and onboard new developers, implementing Code Review and custom agents for automated testing and CI/CD, connecting Claude to tools via MCP for expanded context, using CLAUDE.md files for architecture invariants, and maintaining evaluation "golden sets" to prevent drift. The guide emphasizes combining agent autonomy with deterministic safeguards through hooks and dynamic workflows, especially for regulated industries, and leveraging git worktrees for safe parallel rebuilds when refactoring for new model capabilities.

Hermes Agent is a self-improving AI agent with a built-in learning loop that creates and refines skills from experience, searches past conversations, and builds persistent user models across sessions. It supports multiple LLM providers (Nous Portal, OpenRouter, OpenAI, custom endpoints) with no lock-in, runs on various infrastructure from $5 VPS to serverless platforms, and offers multi-platform access via Telegram, Discord, Slack, WhatsApp, Signal, and CLI. Key features for agentic coding include autonomous skill creation and self-improvement during use, FTS5 session search with LLM-powered cross-session recall, subagent spawning for parallel workflows, and integration with the agentskills.io open standard for procedural memory and skill management.