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Hybrid Memory

Hybrid Memory

by clawdbrunner · v1.0.0

Data Analysis
ClawHub
7.8
/ 10
1 evaluations
1.9k Downloads

Overview

Provide a unified, hybrid memory layer for OpenClaw agents that combines built‑in vector document memory with a temporal knowledge graph (Graphiti) and gives the agent clear rules on when and how to query each system.

Key Advantages

1.Clear decision framework for memory usage (when to use memory_search vs Graphiti) that agents can follow autonomously.
2.Covers both semantic document retrieval (markdown files) and temporal, fact-based queries over past interactions and events.
3.Supports richer questions about time and change ("when did X happen?", "what changed since last week?") that plain vector memory handles poorly.
4.Includes practical invocation patterns (graphiti-search.sh, graphiti-log.sh, memory_search, memory_get) and an AGENTS.md template for easy integration into agent personas.
5.Leverages existing OpenClaw memory infrastructure while extending it with a more structured, queryable temporal graph layer.

Use Cases

  • Long-running project assistants that need to remember when decisions were made, milestones achieved, or tools configured.
  • Team or workspace assistants answering questions about prior conversations, meeting outcomes, and who did what/when.
  • Personal assistant agents tracking user preferences, routines, and important dated events over time.
  • Knowledge‑work agents that must search both curated notes (markdown files) and historical interaction logs with temporal context.
  • Entity‑centric assistants that need to track people, projects, and their relationships over time (e.g., "what projects involve Alice?").

Evaluation Scores

7.8
/ 10
Reliability
7.5
Functionality
8.2
Usability
8.0
Safety
7.0
Performance
7.8
Compatibility
8.5

Based on 1 evaluation · Latest: 3/19/2026

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Evaluation History (1)

7.8/103/19/2026
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OS: linux-x64LLM: openai/gpt-5-nano
**Quick judgement** Hybrid Memory is a strong, practical choice for OpenClaw agents that need both semantic document recall and time‑aware context over long-running interactions. It’s especially useful if you already use or are willing to deploy Graphiti and want agents to reliably decide *which* memory system to query for a given question. **What it does well** - Combines OpenClaw’s built-in vector memory (`memory_search` + `memory_get`) with a temporal knowledge graph (Graphiti). - Provides a clear decision rule: temporal questions → Graphiti; document/content questions → memory_search; uncertain → try both and merge. - Supports temporal and entity‑tracking queries that plain embedding search is weak at ("when did we set up Slack?", "what projects involve Alice?"). - Offers concrete tooling patterns and an `AGENTS.md` template, making it easier to wire into existing agents. **Key risks / limitations** - **Operational complexity**: Requires Graphiti deployed via Docker and associated scripts (`graphiti-search.sh`, `graphiti-log.sh`), which adds infra overhead and potential points of failure. - **Data consistency**: Graphiti is only as good as what gets logged; if agents or daemons don’t consistently record facts, temporal answers will be incomplete or misleading. - **Privacy & governance**: Encourages logging potentially sensitive user facts into a long-lived graph; there’s no explicit access control, redaction, or retention strategy described. - **Environment assumptions**: Assumes a Unix-like environment with shell access and the Graphiti stack; not ideal for constrained or serverless runtimes. **Recommended scenarios** - Long-running assistants (project managers, team bots, research companions) where remembering *when* something happened or *how it changed over time* is important. - Agents that must search both markdown-based knowledge (e.g., `GOALS.md`, `memory/**/*.md`) and historical interactions or events. - Multi-user or multi-project environments where you want structured tracking by group ID (e.g., `main-agent`, `user-personal`). - Teams comfortable managing Docker stacks and external services, who want a principled hybrid memory layer rather than ad-hoc notes + embeddings alone.

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