mnemon-dev/mnemon — LLM-supervised persistent memory for AI agents — graph-based recall, cross-session knowledge, single…

Description and use

**Brief Summary (2‑3 sentences)**
mnemon-dev/mnemon is an open‑source Go library that gives AI agents a persistent, graph‑based memory store, enabling cross‑session recall and tool‑driven workflows. It works out‑of‑the‑box with Claude Code, OpenClaw, and any CLI‑based agent, turning ad‑hoc prompts into repeatable, orchestrated pipelines. With 135 ⭐, recent commits and solid ecosystem signals, it’s ready for serious pilot projects.

**Value**

• **Persistent, structured recall** – Agents can store and retrieve knowledge as nodes and edges, so information learned in one session is instantly available in the next.
• **Workflow orchestration** – By exposing a simple API/CLI/SDK, mnemon lets you stitch together multiple agents, tools, and external services into a single, repeatable pipeline.
• **Tool‑agnostic integration** – Because it is language‑agnostic and only requires HTTP/CLI calls, it can be dropped into existing Claude Code, OpenClaw, or custom CLI agents with minimal code changes.
• **Single‑binary deployment** – The whole memory engine runs as one compiled binary, simplifying packaging, scaling, and security hardening.

**Practical Adoption Path**

1. **Prototype** – Clone the repo, run the provided binary locally, and call the REST API from a small Claude‑Code script to store and retrieve a few facts.
2. **Integrate** – Add the Go client (or any HTTP wrapper) to your existing agent codebase; replace ad‑hoc prompt‑based “remember” logic with mnemon’s AddNode, Link, and Query calls.
3. **Orchestrate** – Use the CLI to chain multiple agents (e.g., a research agent → summarizer → executor) and persist intermediate results in the graph.
4. **Scale** – Deploy the binary behind a service mesh or as a sidecar in Kubernetes; configure persistent storage (SQLite, PostgreSQL, or cloud blob) to retain the graph across pods.
5. **Monitor & Harden** – Leverage the built‑in health endpoints and logs, apply standard Go security best practices, and audit the open‑source license before production rollout.

**Production Readiness**

• **Activity & Adoption** – Recent commits (as of 2026‑05‑11), 135 stars, 21 forks, and usage in the Claude Code and OpenClaw ecosystems indicate an active community.
• **Stability** – The project is written in Go, compiled to a single binary, and provides stable API/CLI contracts, reducing runtime dependencies.
• **Ecosystem Fit** – Fits naturally into orchestration, MCP, and RAG stacks; the 20 GitHub topics show broad relevance.
• **Risks** – Licensing and long‑term maintainer commitment still need a final check, and a formal security audit is recommended before handling sensitive data.

Overall, mnemon offers a high‑value, low‑friction way to give AI agents durable memory and workflow orchestration, and it is mature enough for a production pilot after standard security and licensing vetting.

Problem

mnemon-dev/mnemon helps turn isolated prompts and tools into repeatable agent workflows.

Outcome

High for an OSS candidate: recent activity, adoption, and ecosystem signals are strong enough for a serious pilot.

Workflow

• coordinate multi-agent workflows
• add tool-use pipelines
• standardize agent memory

Evidence

LLM-supervised persistent memory for AI agents — graph-based recall, cross-session knowledge, single binary. Works with Claude Code, OpenClaw, and any CLI agent.

Source