// the find
varun29ankuS/shodh-memory
Cognitive memory for AI agents — learns from use, forgets what's irrelevant, strengthens what matters. Single binary, fully offline.
Shodh-Memory is a Rust binary that gives AI agents (and robots) persistent memory without any cloud dependency or LLM API calls. It stores memories in a three-tier RocksDB structure — working, session, long-term — with Hebbian reinforcement and mathematical decay borrowed from neuroscience literature. Ships as a single binary with MCP, REST, Python/npm/Rust SDKs, and a Zenoh transport layer for ROS2 robotics.
- Zero LLM calls on the hot path is a real differentiator. Competitors like mem0 and Cognee burn 2–3 OpenAI calls per store; Shodh uses local MiniLM embeddings and algorithmic scoring, which is why the 55ms store latency is plausible and the 763ns entity lookup is believable.
- The neuroscience grounding is not decorative. Cowan's working memory model and Wixted's decay functions are directly reflected in the three-tier architecture — memories don't just accumulate forever, they actually fade or strengthen based on usage frequency, which solves a real retrieval-quality problem.
- The robotics integration is genuinely novel — 26 mission-specific payload fields (geo_location, heading, reward, terrain_type, etc.), haversine spatial recall, and first-class Zenoh transport mean a Spot or PX4 drone can query 'what happened near this GPS coordinate last mission' without any cloud round-trip.
- Actually shipped to every major registry: crates.io, PyPI, npm, Docker Hub, MCP Registry, Homebrew — which suggests the release pipeline is real, not vaporware.
- The repo has junk committed to it: `mcp-publisher.exe`, `full_log.txt`, `job_log.txt`, `bhashini-authorization-letter.html`, `STPI_PITCH_DECK.md`, `Shodh_memory_IITD.pdf`. These are files from a grant application or pitch process. A project disciplined enough to implement DiskANN-variant indexes should know not to commit pitch decks to its source tree.
- The Python and npm SDKs are thin HTTP wrappers, not native bindings — users still need the Rust binary running as a sidecar. The README buries this; someone reading `pip install shodh-memory` will assume they get a self-contained library.
- The custom vector DB (`src/vector_db/vamana.rs`, `spann.rs`, `pq.rs`) reimplements DiskANN/SPANN in a side project. These are complex algorithms with subtle correctness requirements; the risk that this implementation has silent recall quality bugs is non-trivial at 217 stars with no published recall accuracy numbers against a standard benchmark.
- No schema migration story. The RocksDB storage format is undocumented and the codebase has a `bugs-remedies/` directory with dated audit files suggesting the storage layer has already had correctness issues. Upgrading the binary without losing your memory data is an open question.