agentmemory is an open-source AI developer tool for giving AI coding agents shared persistent memory across sessions and clients. It is published from the rohitg00/agentmemory GitHub repository, so builders can inspect the source, run it locally, and adapt the workflow instead of depending on a closed SaaS dashboard. The project is most useful when a team wants a practical starting point today: clone the repo, follow the README, and test the workflow against real files rather than a demo prompt.
The core workflow centers on a global npm package and npx path for starting the memory server; integrations for Claude Code, Cursor, Gemini CLI, Codex CLI, Hermes, OpenClaw, pi, OpenCode, and generic MCP or HTTP clients; hooks, MCP support, REST access, a viewer, and filesystem connector notes in the README; benchmark claims around retrieval accuracy published by the project. That makes agentmemory a fit for developers who repeatedly re-explain repo architecture, coding preferences, bugs, and project context to AI coding agents. The tool is not positioned as a hosted model or a paid API. It is a repository-first project with setup instructions, code, examples, and issue history in the open. Teams should treat the GitHub repository as the source of truth for version, install steps, security notes, and current limitations.
Setup starts with installing @agentmemory/agentmemory globally with npm, running agentmemory to start the server, and connecting a supported agent such as claude-code or cursor. After installation, users should run the sample flow from the README, confirm the required local dependencies, and keep credentials in local environment files when the project supports optional providers. For production use, review the license, pinned package versions, and open issues before handing it sensitive data. Open-source AI tools move quickly; a recent push date and active issue queue are good signs, but they do not replace a local security review.
Pricing is simple: agentmemory is free to use as open-source software. Costs come from the developer's own environment, such as model API keys, local GPU time, storage, or any optional third-party service connected during setup. This matters for budget planning because the software itself can be free while inference or generation providers still charge separately.
Why it stands out: it focuses on one painful workflow gap: making coding agents remember project context and decisions across sessions instead of relying only on short static rules files. It belongs in a builder-facing AI toolkit because it gives agents, coding assistants, or creative workflows a concrete surface to act on. The strongest users will be technical teams comfortable reading README files, running local commands, and evaluating output quality themselves. Non-technical buyers looking for a managed support contract should wait for a hosted product or commercial wrapper.