Please provide the basic information of the app
App name(Required)
MemPalace
Project URL(Required, Github preferred)
https://github.com/MemPalace/mempalace
Container image URL(Optional but helpful)
[To be determined — Python-based app installable via uv tool install mempalace; containerization would require packaging ChromaDB + SQLite dependencies]
Reasons(Optional)
MemPalace is an open-source AI memory system that aligns well with Olares' local-first philosophy. It would complement existing coding agents (OpenClaw, Hermes, Claude Code, Codex, Copilot CLI, Droid) by providing persistent conversation memory and context across sessions.
Why it fits Olares:
- Fully local and offline — no API calls, runs entirely on your Olares instance with ChromaDB backend
- MCP server integration — 29 MCP tools for memory reads/writes, knowledge graphs, and agent diaries that work with existing Olares agents
- Privacy-first architecture — stores conversation history locally with semantic search, no external dependencies
- Agent ecosystem support — includes Claude Code hooks, Codex plugin support, and OpenClaw integrations
- Pluggable backend — ChromaDB default but supports alternative vector stores for future flexibility
Use cases on Olares:
- Persistent memory across agent sessions (continue conversations from days/weeks ago with full context)
- Cross-agent knowledge sharing (agents can access a shared memory palace)
- Local knowledge graphs for personal/team wikis with temporal entity tracking
- Agent diary feature for tracking what agents learned and accomplished over time
Additional context
Technical implementation:
- MIT licensed, Python-based with 1,008 commits and 82 active contributors as of May 2026
- Local SQLite knowledge graph with entity-relationship tracking
- Supports both verbatim retrieval and AAAK compression modes
- Straightforward containerization path (Python app + ChromaDB + SQLite)
Credibility note (transparency):
The project has drawn criticism in the community for alleged inauthentic GitHub star growth and benchmark methodology concerns. Independent analysis suggests the headline 96.6% LongMemEval score was measured on raw ChromaDB retrieval rather than the full "palace" architecture, and some reviewers question production readiness beyond demo use cases.
However, the core technical features (local-first operation, MCP integration, agent hooks) are legitimate and well-aligned with Olares' values. The BEC Lab team should evaluate the codebase and architecture independently rather than relying on benchmark marketing claims.
Alternative consideration:
If credibility concerns are a blocker, alternatives like Mem0 or custom ChromaDB/Qdrant integrations might provide similar functionality with less controversy.
Community voting & Contribution
📊Voting matters
If you want this update, vote with 👍 (thumbs up) on the issue. We prioritize requests with higher community interest.
🙌You can help!
If you are willing and able to work on porting this app, please leave a comment stating so. You can request to be assigned to this issue.
Please provide the basic information of the app
App name(Required)
MemPalace
Project URL(Required, Github preferred)
https://github.com/MemPalace/mempalace
Container image URL(Optional but helpful)
[To be determined — Python-based app installable via
uv tool install mempalace; containerization would require packaging ChromaDB + SQLite dependencies]Reasons(Optional)
MemPalace is an open-source AI memory system that aligns well with Olares' local-first philosophy. It would complement existing coding agents (OpenClaw, Hermes, Claude Code, Codex, Copilot CLI, Droid) by providing persistent conversation memory and context across sessions.
Why it fits Olares:
Use cases on Olares:
Additional context
Technical implementation:
Credibility note (transparency):
The project has drawn criticism in the community for alleged inauthentic GitHub star growth and benchmark methodology concerns. Independent analysis suggests the headline 96.6% LongMemEval score was measured on raw ChromaDB retrieval rather than the full "palace" architecture, and some reviewers question production readiness beyond demo use cases.
However, the core technical features (local-first operation, MCP integration, agent hooks) are legitimate and well-aligned with Olares' values. The BEC Lab team should evaluate the codebase and architecture independently rather than relying on benchmark marketing claims.
Alternative consideration:
If credibility concerns are a blocker, alternatives like Mem0 or custom ChromaDB/Qdrant integrations might provide similar functionality with less controversy.
Community voting & Contribution
📊Voting matters
If you want this update, vote with 👍 (thumbs up) on the issue. We prioritize requests with higher community interest.
🙌You can help!
If you are willing and able to work on porting this app, please leave a comment stating so. You can request to be assigned to this issue.