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lemmalog MCP Server

JordyZomer/lemmalog

Score: 86.2 ↓ 4.4% Rank #720 MCP Server
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A Datalog engine for LLM agent memory: stratified rules, provenance-tracked facts, incremental derivation, and an MCP server that lets your harness use it as a shared brain.

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Overview

JordyZomer/lemmalog is a Rust MCP server licensed under MIT. A Datalog engine for LLM agent memory: stratified rules, provenance-tracked facts, incremental derivation, and an MCP server that lets your harness use it as a shared brain. Topics: agent-memory, datalog, knowledge-graph, llm, llm-agents, mcp, neurosymbolic.

Ranked #720 out of 24842 indexed tools.

In the top 3% of all indexed tools.

Ecosystem

Rust MIT
agent-memorydatalogknowledge-graphllmllm-agentsmcpneurosymbolic

Score Breakdown

StarsFreshnessIssue HealthContributorsDependents
Stars 15% 330

330 stars → growing community

Freshness 25% 10d ago

Last commit 10d ago → recently updated

Issue Health 25% 83%

5/6 issues closed → responsive maintainer

Contributors 10% 5

5 contributors → small team

Dependents 25% 0

No dependents → no downstream usage

npm Downloads N/A
PyPI Downloads N/A
Forks 30
Description Detailed
License MIT

Weights: Freshness 20% · Issue Health 20% · Dependents 22% · Stars 10% · Contributors 8% · How we score →

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Matched Queries

"mcp server""mcp-server"topic:mcp

From the README

# Lemmalog

A Datalog engine for LLM agent memory. This repo contains the engine
(Rust crate, MCP server, REPL, agent skill) plus the design document
([`datalog-context-engine-design.md`](datalog-context-engine-design.md),
with an honest status log of what shipped).

The thesis: **an agent's memory should be a deductive database** — the
agent builds a *verifiable model of what it knows* and mechanically
reasons over how that knowledge changes, rather than "remembering
better" than a vector store. Base facts
are asserted at the ingestion boundary (LLM extraction); rules derive
closures, temporal projections, contradiction candidates, and relevance
diffusion; every fact carries provenance back to its source episodes; and
each conversation turn updates derived views incrementally instead of
re-deriving them (or worse, re-reasoning them in-context).

## What's implemented

| Design element | Status |
|---|---|
| Runtime-parsed, stratified Datalog (interpreter, not proc-macro) | ✅ |
| Negat
Read full README on GitHub →

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