# Engram: Persistent Memory System for AI Coding Agents in Go This repository profile is provided by osrepos.com, an open source repository discovery platform. Source: osrepos.com Repository profile: https://osrepos.com/repo/gentleman-programming-engram Generated for open source discovery and AI-assisted research. Engram is an agent-agnostic, persistent memory system designed for AI coding agents. Built as a single Go binary, it leverages SQLite with FTS5 for efficient storage and retrieval of agent memories. It offers various interfaces including a CLI, HTTP API, MCP server, and a TUI, ensuring broad compatibility and ease of use for any agent supporting the Memory Protocol. GitHub: https://github.com/Gentleman-Programming/engram OSRepos URL: https://osrepos.com/repo/gentleman-programming-engram ## Summary Engram is an agent-agnostic, persistent memory system designed for AI coding agents. Built as a single Go binary, it leverages SQLite with FTS5 for efficient storage and retrieval of agent memories. It offers various interfaces including a CLI, HTTP API, MCP server, and a TUI, ensuring broad compatibility and ease of use for any agent supporting the Memory Protocol. ## Topics - Go - AI - AgentMemory - SQLite - FTS5 - DeveloperTools - CLI - OpenSource ## Repository Information Last analyzed by OSRepos: Tue Jun 02 2026 01:59:45 GMT+0100 (Western European Summer Time) Detail views: 12 GitHub clicks: 20 ## Safety Notice OSRepos shares public repositories for knowledge and discovery only. Review source code, dependencies, licenses, and security implications before running or installing anything. ## Content ## Introduction Engram, developed by Gentleman-Programming, provides a crucial missing piece for AI coding agents: persistent memory. Often, AI agents lose all context and learned information at the end of a session. Engram solves this by giving agents a "brain" where they can store, retrieve, and manage their observations and decisions across sessions. This system is implemented as a lightweight, single Go binary with zero external dependencies, making it highly portable and easy to deploy. It uses SQLite with FTS5 for robust full-text search capabilities, allowing agents to quickly find relevant past memories. Engram is agent-agnostic, designed to work with any AI coding agent that supports the Memory Protocol (MCP), such as Claude Code, OpenCode, Gemini CLI, and others. ## Installation Getting started with Engram is straightforward. For macOS users, you can install it via Homebrew: bash brew install gentleman-programming/tap/engram Detailed installation instructions for Windows, Linux, and other platforms are available in the official documentation: [Installation Guide](https://github.com/Gentleman-Programming/engram/blob/main/docs/INSTALLATION.md). ## Examples Engram integrates seamlessly with various AI agents. Here are a few quick setup examples: * **Claude Code**: `claude plugin marketplace add Gentleman-Programming/engram && claude plugin install engram` * **Pi**: `engram setup pi` * **OpenCode**: `engram setup opencode` * **VS Code (Copilot)**: `code --add-mcp '{"name":"engram","command":"engram","args":["mcp"]}'` Once set up, agents can save memories using tools like `mem_save` and retrieve context with `mem_search`. For instance, an agent might save a memory after completing a significant task: 1. Agent completes significant work (bugfix, architecture decision, etc.) 2. Agent calls mem_save ? title, type, What/Why/Where/Learned 3. Engram persists to SQLite with FTS5 indexing 4. Next session: agent searches memory, gets relevant context Engram also features a powerful Terminal User Interface (TUI) for human interaction, allowing you to browse, search, and manage memories directly from your terminal: bash engram tui For sharing memories across machines, Engram offers Git Sync, which uses compressed chunks to avoid merge conflicts and manage file sizes: bash engram sync # Export new memories as compressed chunk git add .engram/ && git commit -m "sync engram memories" engram sync --import # On another machine: import new chunks ## Why Use Engram Engram stands out as a robust solution for AI agent memory due to several key advantages: * **Agent-Agnostic**: Works with any agent supporting the Memory Protocol (MCP), providing a universal memory layer. * **Single Binary, Zero Dependencies**: Extremely easy to install and run, eliminating complex setup or dependency management. * **Persistent & Searchable**: Utilizes SQLite with FTS5 for reliable storage and fast, full-text search capabilities, ensuring agents can always access relevant past information. * **Comprehensive Toolset**: Offers a rich set of MCP tools for saving, updating, searching, and managing memories, along with session lifecycle management and conflict surfacing. * **Flexible Interfaces**: Provides a CLI for scripting, an HTTP API for programmatic access, an MCP server for agent communication, and an interactive TUI for human oversight. * **Cloud Integration (Opt-In)**: Supports optional cloud replication for sharing memories across teams and machines, with local SQLite remaining the source of truth. * **Memory Conflict Surfacing**: Beta features allow agents to detect and semantically judge conflicting memories, enhancing the quality of stored knowledge. ## Links * **GitHub Repository**: [https://github.com/Gentleman-Programming/engram](https://github.com/Gentleman-Programming/engram "Engram GitHub Repository" target="_blank") * **Installation Guide**: [https://github.com/Gentleman-Programming/engram/blob/main/docs/INSTALLATION.md](https://github.com/Gentleman-Programming/engram/blob/main/docs/INSTALLATION.md "Engram Installation Guide" target="_blank") * **Agent Setup**: [https://github.com/Gentleman-Programming/engram/blob/main/docs/AGENT-SETUP.md](https://github.com/Gentleman-Programming/engram/blob/main/docs/AGENT-SETUP.md "Engram Agent Setup" target="_blank") * **Full Documentation**: [https://github.com/Gentleman-Programming/engram/blob/main/DOCS.md](https://github.com/Gentleman-Programming/engram/blob/main/DOCS.md "Engram Full Documentation" target="_blank")