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LinkingMem - v0.3.0
LinkingMem — Graph-native RAG Engine
A high-performance Rust + Python engine for graph-based RAG, unifying vector search, graph traversal, and LLM reasoning in a single system. Query → Embedding → HNSW retrieval → Graph expansion (BFS) → Ranking → LLM answer LinkingMem combines vector search and graph traversal in one tightly integrated pipeline, enabling fast multi-hop reasoning, efficient memory usage, and production-ready scalability.
Quick update from the LinkingMem team — we're back with v0.3.0, and this one's a big jump from what we launched before.
The short version of what's new:
🖼️ Multimodal, for real this time — text and image nodes now share the same vector space. Query the graph with a text question or an image URL, and it works the same way under the hood. Two embedding modes to pick from: Vision-LLM captioning (works out of the box, no extra setup) or CLIP embeddings (true visual similarity, no LLM call needed per image).
🐛 Two nasty bugs squashed — one where importing a graph could silently corrupt your data directory on the next merge, another where re-ingesting text in separate calls could quietly produce duplicate/colliding entity IDs. Both found through actual end-to-end testing against a running stack, not just unit tests — details in the https://github.com/khapu2906/Lin... if you're curious how deep that rabbit hole went.
📚 Docs you can actually trust — went through every doc, every env var, every API endpoint and made sure it matches what the code actually does. If you tried LinkingMem before and hit a doc that lied to you, that should be fixed now.
🐳 Docker images are live — khapu2906/linkingmem:v0.3.0-full (all-in-one) and v0.3.0-engine (bring your own plugin), both on Docker Hub.
If you checked us out at launch and bounced off a rough edge, this is a good time to give it another look. And if you're new here: LinkingMem is a Rust + Python engine that does vector search + graph traversal + LLM reasoning in one system, instead of stitching together separate vector/graph DBs.
Would love to hear what you think, especially if you're testing the image/multimodal side — that's the part I'm most excited about in this release. 🚀
About LinkingMem - v0.3.0 on Product Hunt
“LinkingMem — Graph-native RAG Engine”
LinkingMem - v0.3.0 was submitted on Product Hunt and earned 11 upvotes and 11 comments, placing #16 on the daily leaderboard. A high-performance Rust + Python engine for graph-based RAG, unifying vector search, graph traversal, and LLM reasoning in a single system. Query → Embedding → HNSW retrieval → Graph expansion (BFS) → Ranking → LLM answer LinkingMem combines vector search and graph traversal in one tightly integrated pipeline, enabling fast multi-hop reasoning, efficient memory usage, and production-ready scalability.
On the analytics side, LinkingMem - v0.3.0 competes within Productivity, Open Source, GitHub and Database — topics that collectively have 767.7k followers on Product Hunt. The dashboard above tracks how LinkingMem - v0.3.0 performed against the three products that launched closest to it on the same day.
Who hunted LinkingMem - v0.3.0?
LinkingMem - v0.3.0 was hunted by Kent Phung. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of LinkingMem - v0.3.0 including community comment highlights and product details, visit the product overview.
Kent Phung