--- title: "stock-rnn vs mempalace" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/lilianweng-stock-rnn-vs-mempalace-mempalace" tools: ["lilianweng-stock-rnn", "mempalace-mempalace"] --- # stock-rnn vs mempalace *GraphCanon updated Jul 11, 2026* ## Verdict Pick stock-rnn when tags unique to stock-rnn: embeddings, lstm, python, rnn-tensorflow; pick mempalace when tags unique to mempalace: ai, chromadb, llm, memory. [stock-rnn](https://lilianweng.github.io/lil-log) reports 2.0k GitHub stars, 673 forks, and 24 open issues, last pushed Jul 28, 2022. [mempalace](http://mempalaceofficial.com/) has 57k stars, 7.4k forks, and 616 open issues, last pushed Jul 10, 2026. Figures are from public GitHub metadata via [stock-rnn's repository](https://github.com/lilianweng/stock-rnn) and [mempalace's repository](https://github.com/MemPalace/mempalace). | | [stock-rnn](/tools/lilianweng-stock-rnn.md) | [mempalace](/tools/mempalace-mempalace.md) | | --- | --- | --- | | Tagline | Predict stock market prices using RNN model with multilayer LSTM cells + optional multi-stock embeddings. | The best-benchmarked open-source AI memory system. | | Stars | 1,976 | 57,215 | | Forks | 673 | 7,387 | | Open issues | 24 | 616 | | Language | Python | Python | | Adopt for | - | MemPalace is an advanced open-source AI memory system that integrates with ChromaDB to optimize machine learning model memories and enhance data retrieval efficiency. | | Persona | - | - | | Runtime | - | - | | License | - | MIT | | Categories | Model Training, Vector Databases | Model Training, Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [stock-rnn](/tools/lilianweng-stock-rnn.md) | [mempalace](/tools/mempalace-mempalace.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Very active (96%) | | Days since push | 1444d | 0d | | Open issues (now) | 24 | 616 | | Owner type | User | Organization | | Security scan | No lockfile | No MCP manifest | | Full report | [trust report](/tools/lilianweng-stock-rnn/trust.md) | [trust report](/tools/mempalace-mempalace/trust.md) | ## Decision facts: mempalace - **Adopt for:** MemPalace is an advanced open-source AI memory system that integrates with ChromaDB to optimize machine learning model memories and enhance data retrieval efficiency. ## Choose when ### Choose stock-rnn if… - Tags unique to stock-rnn: embeddings, lstm, python, rnn-tensorflow. - Leaner open-issue backlog (24). ### Choose mempalace if… - Tags unique to mempalace: ai, chromadb, llm, memory. - mempalace ships Docker support for self-hosted deployment. - When you need a highly benchmarked solution for managing AI model memories, MemPalace can provide superior performance due to its optimization features integrated specifically around ML model needs. ## When NOT to use stock-rnn - Last GitHub push was 1445 days ago (dormant maintenance, Jul 28, 2022). Validate activity before betting a new project on stock-rnn. - Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. - Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ## When NOT to use mempalace - Avoid if requiring a proprietary system where full transparency or customization of the memory management layer may not be necessary, since MemPalace is open source and might involve deeper technical啃 - "如果你的应用场景对内存管理层的完全透明或定制化需求不高，因为MemPalace是开源的，可能需要更深的技术介入来满足特定需求。" - If your project strictly adheres to non-MIT licenses, then MemPalace might not be suitable due to its MIT license which may conflict with licensing requirements. ## Common questions ### What is the difference between stock-rnn and mempalace? stock-rnn: Predict stock market prices using RNN model with multilayer LSTM cells + optional multi-stock embeddings.. mempalace: The best-benchmarked open-source AI memory system.. See the comparison table for live GitHub stats and shared categories. ### When should I choose stock-rnn over mempalace? Choose stock-rnn over mempalace when Tags unique to stock-rnn: embeddings, lstm, python, rnn-tensorflow; Leaner open-issue backlog (24). ### When should I choose mempalace over stock-rnn? Choose mempalace over stock-rnn when Tags unique to mempalace: ai, chromadb, llm, memory; mempalace ships Docker support for self-hosted deployment; When you need a highly benchmarked solution for managing AI model memories, MemPalace can provide superior performance due to its optimization features integrated specifically around ML model needs. ### When should I avoid stock-rnn? Last GitHub push was 1445 days ago (dormant maintenance, Jul 28, 2022). Validate activity before betting a new project on stock-rnn. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ### When should I avoid mempalace? Avoid if requiring a proprietary system where full transparency or customization of the memory management layer may not be necessary, since MemPalace is open source and might involve deeper technical啃 "如果你的应用场景对内存管理层的完全透明或定制化需求不高，因为MemPalace是开源的，可能需要更深的技术介入来满足特定需求。" If your project strictly adheres to non-MIT licenses, then MemPalace might not be suitable due to its MIT license which may conflict with licensing requirements. ### Is stock-rnn or mempalace more popular on GitHub? mempalace has more GitHub stars (57,215 vs 1,976). Stars measure visibility, not whether either tool fits your constraints. ### Are stock-rnn and mempalace open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to stock-rnn or mempalace? GraphCanon lists graph-backed alternatives at [stock-rnn alternatives](/tools/lilianweng-stock-rnn/alternatives) and [mempalace alternatives](/tools/mempalace-mempalace/alternatives) ([stock-rnn markdown twin](/tools/lilianweng-stock-rnn/alternatives.md), [mempalace markdown twin](/tools/mempalace-mempalace/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/lilianweng-stock-rnn-vs-mempalace-mempalace.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, stock-rnn or mempalace? stock-rnn: Dormant. mempalace: Very active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for stock-rnn and mempalace? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [stock-rnn trust report](/tools/lilianweng-stock-rnn/trust); [mempalace trust report](/tools/mempalace-mempalace/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=lilianweng-stock-rnn`](/api/graphcanon/graph?tool=lilianweng-stock-rnn) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_