Mengram is an open-source memory system that provides OpenClaw agents with persistent memory across sessions. The tool addresses the problem where OpenClaw agents forget everything when they restart, losing memory of what worked, what failed, and what procedures they learned.

Memory types

• Episodic memory — stores what happened (conversations, actions, outcomes)
• Entity memory — tracks people, tools, projects and facts about them
• Procedural memory — automatically extracts "how to do X" procedures from repeated patterns. If your agent does a heartbeat check 3 times, Mengram creates a reusable procedure for it
• Smart archival — old/outdated facts get automatically superseded when new info contradicts them

Integration with OpenClaw

Here's how to use Mengram with OpenClaw agents:

from mengram import Mengram
m = Mengram(api_key="om-...")

Agent saves what it learned
m.add("Completed heartbeat check. Job queue has 18 pending jobs. No urgent alerts.")
Later, agent searches its memory
results = m.search("what's the heartbeat status?")
Procedures are auto-created from repeated patterns
procedures = m.get_procedures()
→ "Run HEARTBEAT.md strict-check", "Daily brief at 10:00 AM", etc.

The agent builds up procedural knowledge over time — it doesn't just remember facts, it learns how to do things and when to do them.

Technical stack

Mengram uses Python SDK, REST API, PostgreSQL + pgvector for embeddings, and works with any LLM.

The project is available on GitHub at github.com/alibaizhanov/mengram.