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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. 🚀
the mmap-based storage approach for low-latency graph access is genuinely clever, especially paired with rust under the hood. curious how the python ai plugins handle serialization overhead in production pipelines.
Spent an afternoon with LinkingMem and the graph-plus-vector combo genuinely made multi-hop retrieval feel snappy. Loved that the Python plugin layer didn't choke on my custom embeddings.
The Rust plus Python hybrid setup is genuinely useful for teams like ours. One thing that would help a lot: built-in observability for the retrieval path itself, like per-hop latency, which entities got traversed, and why certain branches were pruned. Right now debugging multi-hop answers feels like guesswork, and having that trace baked in would save hours when something hallucinates.
Native query language support would be a great addition, something like Cypher or a GraphQL-style interface so users can explore the knowledge graph directly without writing custom traversal code. Would make debugging and ad-hoc analysis much smoother.
A built-in query debugger that visualizes the graph traversal path and intermediate retrieval scores for each multi-hop question would be incredibly useful for tuning relevance and catching hallucination sources.
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.
LinkingMem - v0.3.0 was featured in Productivity (655.7k followers), Open Source (68.6k followers), GitHub (41.3k followers) and Database (2.2k followers) on Product Hunt. Together, these topics include over 183.5k products, making this a competitive space to launch in.
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.
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Kent Phung