NeumAI
Framework to manage creation and synchronization of vector embeddings at large scale
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Decision brief
NeumAI stands out in the space of managing large-scale vector embeddings, offering tools tailored for operations such as retrieval-augmented generation (RAG). Users looking to self-host embeddings management with an open
Good fit when
- When you require robust and scalable infrastructure specifically designed for creating and synchronizing vector embeddings at scale.
- If your project involves implementing a retrieval-augmented generation pipeline, where efficient embedding management is critical.
Avoid when
- When your project requires customization beyond what the provided architecture allows, without the support expected from commercial offerings or competitive open-source frameworks.
- If your needs are simpler and don't demand large-scale operations, NeumAI’s capabilities focused on handling vast vector sets may be excessive for smaller projects.
- Pricing:
- freemium - Offers an open-source model under the Apache-2.0 license, potentially featuring a free-tier with premium/support options.
- Requirements:
- Requires Python and compatibility with infrastructure that supports its backend architecture.; Contact their team at founders@tryneum.com for self-hosting.
Observed Jul 11, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Dormant (948d since push)
- As of today
- Provenance
- Not a fork · Organization account
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- Security (OSV)
- No lockfile
- As of 1mo
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Install
pip install NeumAIHow it fits your stack(7)
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Evidence and technical details
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Overview
Neum AI provides tools for managing the generation, storage, and synchronization of vector embeddings in data engineering environments. It includes support for pipeline operations, embedding retrieval, and integration with various machine learning workflows.
Capability facts
- Languages
- python
Source: github.language · Aug 21, 2026
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README
Self-host
If you are interested in deploying Neum AI to your own cloud contact us at founders@tryneum.com.
We have a sample backend architecture published on GitHub which you can use as a starting point.
For agents
This page has a .md twin and JSON over the API.