Shodh Memory

Shodh-Memory

Persistent cognitive memory for AI agents and robots — with no LLM in the loop. Remembers what matters, forgets what doesn't, gets smarter with use.

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AI agents forget everything between sessions. Robots lose context between missions. They repeat mistakes, miss patterns, and treat every interaction like the first one.

Shodh-Memory fixes this. It's persistent memory that actually learns — memories you use often become easier to find, old irrelevant context fades automatically, and recalling one thing brings back related things. Works for chat agents (MCP/HTTP), robots (Zenoh/ROS2), and edge devices. No API keys. No cloud. No external databases. No LLM in the loop. One binary.

Why Not Just Use mem0 / Cognee / Zep?

	Shodh	mem0	Cognee	Zep
LLM calls to store a memory	0	2+ per add	3+ per cognify	2+ per episode
External services needed	None	OpenAI + vector DB	OpenAI + Neo4j + vector DB	OpenAI + Neo4j
Time to store a memory	55ms	~20 seconds	seconds	seconds
Learns from usage	Yes (Hebbian)	No	No	No
Forgets irrelevant data	Yes (decay)	No	No	Temporal only
Runs fully offline	Yes	No	No	No
Robotics / ROS2 native	Yes (Zenoh)	No	No	No
Binary size	~17MB	pip install + API keys	pip install + API keys + Neo4j	Cloud only

Every other memory system delegates intelligence to LLM API calls — that's why they're slow, expensive, and can't work offline.

No LLM in the Loop

Storing a memory makes zero LLM calls. Recalling makes zero LLM calls. Entity extraction, relation typing, knowledge-graph construction, causal tracing, ranking, decay, consolidation — all of it runs locally as algorithms, not API round-trips:

• Local embeddings — MiniLM (22MB, INT8) via ONNX Runtime, on-device semantic search
• Local reranking — a cross-encoder (ms-marco-MiniLM-L6, 23 MB int8, ONNX) reads the query and each of the top 30 candidates together, where the embedder sees them apart. On by default in the server: +16.5pp precision@1 and +8.7pp recall@10 on LoCoMo (1,531 held-out questions). It is a 22M-parameter ranking model, not an LLM, and it runs on CPU
• Local NER — GLiNER bi-edge-v2 span typer (ONNX, schema-driven: 141 fine / 18 coarse entity types), auto-downloaded on first run from the pinned release, with a rule-based fallback
• Typed relation extraction without an LLM — directed lexical cues + exemplar-matched semantic typing build a typed knowledge graph (LocatedIn, WorksAt, Causes…) from plain text
• Causal lineage — "what was the root cause of X?" is answered by walking typed causal edges backward through the graph, not by asking a model
• Mathematical memory dynamics — Hebbian strengthening, exponential→power-law decay, spreading activation, long-term potentiation

What that buys you: fully offline operation, millisecond latency instead of multi-second API calls, zero inference cost at any scale, deterministic, testable behavior, and data that never leaves the machine. Your agent's LLM does the reasoning — its memory doesn't need one.

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