memcache-hybrid
Python bindings for memcache_hybrid implemented with pybind11
Decision gist · record as of 2026-08-14
Yes, with conditions. Install if you are deploying LLM or graph reasoning inference on Ascend or Kunpeng hardware and need distributed KV cache pooling with multi-tier memory support. Do not install if: (1) your organization cannot adopt Mulan PSL v2 licensing, (2) you require production-grade stability (the package is 6 months old with no long-term track record), (3) you lack the infrastructure to deploy and manage MetaService and LocalService components, or (4) you are not targeting Ascend/Kunpeng hardware where the performance benefits are realized.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires MemFabric (memfabric_hybrid) as a runtime dependency; MetaService and LocalService processes must be deployed and running before client code can connect; Linux x86_64 or aarch64 with manylinux_2_26+ or manylinux_2_27+ required.
- Medium install friction due to compiled wheels for specific Python versions and Linux architectures.
- Package is very new (first release February 2026, latest August 2026) with active maintenance status, but the nascent release history and reliance on memfabric_hybrid as a runtime dependency warrant caution in production environments.
License · maintenance · safety
Mulan PSL v2 (unclear) — Licensed under Mulan PSL v2, which is a permissive open-source license. License treatment is marked unclear in the fact sheet, so review the actual LICENSE file and your organization's policy before adopting in proprietary or commercial contexts.
last release 2026-08-11 (3 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 711,014 downloads/mo, #5,258 on PyPI
Alternatives
Verify before relying
pip install memcache-hybrid
import memcache_hybrid
# Requires MetaService and LocalService components running; see docs for deployment- Whether Mulan PSL v2 is compatible with your organization's licensing requirements (marked unclear in metadata).
- Performance characteristics and scalability limits for your specific workload size and cluster topology.
- Stability and breaking-change policy given the package's very recent release (first release Feb 2026).
- Whether memfabric_hybrid is available and compatible in your target deployment environment.
What it is and what it does
memcache-hybrid is a Python wrapper around a distributed key-value cache engine built for LLM and graph reasoning inference acceleration. It exposes object-oriented APIs for batch and non-batch put/get/exist/remove operations on KV blocks, backed by a multi-tier memory pool spanning HBM, DDR, and SSD with automatic data migration between tiers. The package depends on memfabric_hybrid for low-latency, high-bandwidth cross-device and cross-machine data access using hardware-specific paths (RDMA, SDMA, shared memory) on Ascend and Kunpeng processors.
The system is architected around two core components: MetaService, which manages cluster-wide memory allocation and node membership, and LocalService, which provides memory regions and acts as the client-side library. MetaService supports both single-node and Kubernetes-based HA deployments. The package is extremely new—first released in February 2026—and targets inference workloads where KV cache pooling and efficient multi-node memory access are critical bottlenecks.
Use it for
- Accelerate LLM inference by pooling KV cache across multiple nodes with direct cross-device memory access.
- Implement vLLM-Ascend backend KV pool storage for large-language-model serving.
- Enable prefix-cache acceleration for inference workloads on Ascend hardware.
- Build distributed graph reasoning inference systems requiring high-bandwidth, low-latency KV storage.
- Manage multi-tier memory (HBM/DDR/SSD) for inference workloads with automatic hot/cold data migration.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, with conditions.
Install if you are deploying LLM or graph reasoning inference on Ascend or Kunpeng hardware and need distributed KV cache pooling with multi-tier memory support. Do not install if: (1) your organization cannot adopt Mulan PSL v2 licensing, (2) you require production-grade stability (the package is 6 months old with no long-term track record), (3) you lack the infrastructure to deploy and manage MetaService and LocalService components, or (4) you are not targeting Ascend/Kunpeng hardware where the performance benefits are realized.
Install
memcache-hybrid on PyPI
Before you install
Medium install friction due to compiled wheels for specific Python versions and Linux architectures. Package is very new (first release February 2026, latest August 2026) with active maintenance status, but the nascent release history and reliance on memfabric_hybrid as a runtime dependency warrant caution in production environments.
Requires MemFabric (memfabric_hybrid) as a runtime dependency; MetaService and LocalService processes must be deployed and running before client code can connect; Linux x86_64 or aarch64 with manylinux_2_26+ or manylinux_2_27+ required.
License in practice
Licensed under Mulan PSL v2, which is a permissive open-source license. License treatment is marked unclear in the fact sheet, so review the actual LICENSE file and your organization's policy before adopting in proprietary or commercial contexts.
Quickstart
pip install memcache-hybrid
import memcache_hybrid
# Requires MetaService and LocalService components running; see docs for deploymentVerify before relying
- Whether Mulan PSL v2 is compatible with your organization's licensing requirements (marked unclear in metadata).
- Performance characteristics and scalability limits for your specific workload size and cluster topology.
- Stability and breaking-change policy given the package's very recent release (first release Feb 2026).
- Whether memfabric_hybrid is available and compatible in your target deployment environment.
Package facts
| License | Mulan PSL v2 unclear |
| Python support | Supports the current Python release >=3.8 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 1 packagememfabric_hybrid |
| Maintenance | Actively maintained 3 days since the last release |
| First released | |
| Downloads | 711,014 / month, #5,258 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: memcache_hybrid-1.2.0-cp310-cp310-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl; memcache_hybrid-1.2.0-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl; memcache_hybrid-1.2.0-cp311-cp311-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl; memcache_hybrid-1.2.0-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl; memcache_hybrid-1.2.0-cp312-cp312-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl; memcache_hybrid-1.2.0-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl; memcache_hybrid-1.2.0-cp313-cp313-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl; memcache_hybrid-1.2.0-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl
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See also mooncake-transfer-engine · mooncake-transfer-engine-cuda13 · lmcache · deepspeed · google-cloud-memcache · diskcache · vllm · ipex-llm · diskcache-weave · vllm-tpu