[{"_1":2,"_3":-5,"_4":-5},"loaderData",{"_5":6,"_7":8,"_9":10},"actionData","errors","root",{"_105":106},"routes/modules.$creator.$moduleId",{"_14":15,"_16":17,"_18":19,"_20":21},"routes/modules.$creator.$moduleId.endpoints",{"_11":12,"_13":-5},"error","Module not found","tools","module",{"_20":22,"_23":24,"_25":26,"_27":28,"_29":30,"_31":32,"_33":34,"_35":26,"_36":37,"_38":-5,"_39":-5,"_40":-5,"_41":42,"_43":44,"_45":46,"_47":48,"_49":50,"_51":52,"_53":54,"_55":-5,"_56":48,"_57":58,"_59":60,"_61":-5,"_62":-5,"_63":-5,"_64":-5,"_65":66,"_67":68,"_69":70,"_71":72,"_73":74,"_75":-5,"_76":-5,"_77":-5,"_78":48,"_79":-5,"_80":81,"_82":83,"_84":85,"_86":87,"_88":89,"_90":91},"apiUrl","https://odel.app/api","creator","mauriceisrael","moduleId","smartmemory","@mauriceisrael/smartmemory","displayName","SmartMemory","tagline","Neuro-symbolic memory for LLMs (POC)","iconKey","https://avatars.githubusercontent.com/u/1738778?v=4","category","local","status","published","visibility","public","metaDescription","deploymentType","external","workerName","bindingName","mcpConfig","pricingModel","free","creatorUserId","system:external-import","createdAt",1783335613000,"updatedAt",1788775902000,"sourceRegistry","official-mcp-registry","sourceUrl","https://registry.modelcontextprotocol.io/v0/servers?search=io.github.MauriceIsrael%2FSmartMemory","sourceVerified",true,"sourceMissingSince","importedAt","upstreamStars",3,"upstreamUpdatedAt",1782566155000,"detectedTransport","detectedAuthType","probeStatus","lastProbedAt","githubRepoUrl","https://github.com/MauriceIsrael/SmartMemory","githubRepoFullName","MauriceIsrael/SmartMemory","releaseId","ext-mauriceisrael-smartmemory","version","1.0.1","descriptionMarkdown","# 🧠 SmartMemory\n\n**Give your LLM structured, verifiable memory** — turn conversations into knowledge graphs your AI can reason over.\n\n

\n An MCP server that teaches AI assistants business rules through natural dialogue.\n

\n\n

\n $\"License:$ \n $\"Python$ \n $\"Model$ \n $\"Neuro-symbolic\"$ \n $\"Status:$ \n $\"PRs$ \n

\n\n> [!CAUTION]\n> **Proof of Concept.** SmartMemory is an experimental implementation of a neuro-symbolic architecture, built to explore how LLMs can interact with knowledge graphs to learn and apply rules. It is **not intended for production use** — treat it as a research and learning playground.\n\n\n\n---\n\n## Why SmartMemory?\n\nLLMs are brilliant talkers with no real memory. Across a conversation they **forget**, they **can't explain *why*** they concluded something, and they happily state things that were never verified.\n\nSmartMemory adds the missing half: a **symbolic brain**.\n\n- Facts you state are stored in an **auditable knowledge graph** (RDF), each with its provenance.\n- Logic is captured as **explicit, inspectable rules** (SPARQL/OWL) — not hidden in weights.\n- New conclusions are **derived, traceable, and reversible** — and ambiguous ones are sent back to *you* for validation.\n\nThe result is an assistant that doesn't just *sound* right — it can **show its reasoning**.\n\n---\n\n## What it can do\n\nSmartMemory turns your AI assistant into a domain expert that supports:\n\n- **Asynchronous reasoning** — deductions run in the background (`InferenceManager`) without slowing the conversation.\n- **Uncertainty handling** — ambiguous facts trigger a *human-in-the-loop* validation workflow.\n- **Smart NLP extraction** — handles complex sentences, coreferences, and direct Turtle notation.\n- **Provenance & audit** — every stored fact keeps its origin (UUID, source, timestamp).\n- **Dynamic rule engine** — learns and applies new SPARQL rules on the fly.\n\n---\n\n## How it works\n\n```mermaid\nflowchart LR\n A[\"Natural-language
conversation\"] -->|LLM extraction| B[\"Facts\"]\n B --> C[(\"Knowledge Graph
RDF / Turtle\")]\n C -->|SPARQL / OWL rules| D[\"Inference engine\"]\n D -->|new deductions| C\n D -->|ambiguous?| E[\"Human-in-the-loop
validation\"]\n E -->|approve rule / fact| C\n C -->|provenance + audit| F[\"Verifiable answers\"]\n```\n\nThe LLM is the **language cortex** (understanding and extraction); the knowledge graph and rule engine are the **symbolic memory** (storage, logic, proof). Neither alone is enough — together they are *neuro-symbolic*.\n\n---\n\n## Two ways to use it\n\n| | 💬 **Conversational Mode** — *the \"Brain\"* | 🏗️ **Supervision Mode** — *the \"Factory\"* |\n|---|---|---|\n| **For** | Individuals using an LLM client (Claude Desktop, etc.) | Teams, developers, heavy users |\n| **Goal** | Let your assistant remember facts and learn logic as you chat | Extract thousands of rules from documents (PDFs) and visualize the graph |\n| **How** | Configure it as an MCP server | Deploy the full dashboard via Docker |\n| **Setup** | [Jump to setup ↓](#mode-1--conversational-setup-mcp) | [Jump to setup ↓](#mode-2--supervision-setup-docker) |\n\n---\n\n## Quick start\n\n| I want to… | Go to |\n|---|---|\n| Get running in 5 minutes | [Quick Start Guide](https://github.com/MauriceIsrael/SmartMemory/blob/master/QUICKSTART.md) |\n| Try the advanced demo | [Demo Procedure](https://github.com/MauriceIsrael/SmartMemory/blob/master/docs/demonstration_procedure.md) |\n| Understand the internals | [Architecture](https://github.com/MauriceIsrael/SmartMemory/blob/master/docs/architecture.md) · [Neuro-symbolic principles](https://github.com/MauriceIsrael/SmartMemory/blob/master/docs/neuro-symbolic.md) |\n| Configure a provider | [Configuration reference](https://github.com/MauriceIsrael/SmartMemory/blob/master/CONFIGURATION.md) |\n| Fix a problem | [Troubleshooting](https://github.com/MauriceIsrael/SmartMemory/blob/master/TROUBLESHOOTING.md) |\n| Browse all docs | [Documentation index](https://github.com/MauriceIsrael/SmartMemory/blob/master/docs/INDEX.md) |\n\n---\n\n## Mode 1 — Conversational Setup (MCP)\n\nGives your LLM long-term memory and logical deduction.\n\n### Option A — Docker (recommended) 🐳\n\nNo Python required. The image is published on GitHub Container Registry.\n\n**Claude Desktop** — edit `~/Library/Application Support/Claude/claude_desktop_config.json`:\n\n```json\n{\n \"mcpServers\": {\n \"smart-memory\": {\n \"command\": \"docker\",\n \"args\": [\"run\", \"--rm\", \"-i\", \"ghcr.io/mauriceisrael/smart-memory:latest\"]\n }\n }\n}\n```\n\nThe same block works for any MCP client (e.g. Cline) — just point it at your client's `mcp_settings.json`. Restart the client and you're done. ✅\n\n### Option B — Local server (from source) 🔒\n\nBest for developers and privacy-conscious users.\n\n```bash\ngit clone https://github.com/MauriceIsrael/SmartMemory\ncd SmartMemory\npython3 -m venv venv\nsource venv/bin/activate\npip install -e .\n```\n\nThen point Claude Desktop at your local install:\n\n```json\n{\n \"mcpServers\": {\n \"smartmemory\": {\n \"command\": \"/absolute/path/to/SmartMemory/venv/bin/python\",\n \"args\": [\"-m\", \"smart_memory.server\"]\n }\n }\n}\n```\n\nRestart Claude and try: *\"I know Bob. He goes to work by car. Can he vote?\"* — see the [demo](#interactive-demo--from-facts-to-rules) below.\n\n---\n\n## Mode 2 — Supervision Setup (Docker)\n\nRuns the **web dashboard** and **API server** — ideal for visualizing the knowledge graph, extracting rules from PDFs, and hosting a shared memory for a team.\n\n```bash\n# Dashboard mode — example with Mistral\ndocker run -p 8080:8080 \\\n -e LLM_PROVIDER=mistral \\\n -e LLM_MODEL=mistral-large-latest \\\n -e LLM_API_KEY=your-api-key \\\n -v $(pwd)/brain:/app/data \\\n ghcr.io/mauriceisrael/smart-memory:latest dashboard\n```\n\n```bash\n# Dashboard mode — example with a local model (Ollama)\ndocker run -p 8080:8080 \\\n -e LLM_PROVIDER=ollama \\\n -e LLM_MODEL=llama3 \\\n -e LLM_BASE_URL=http://172.17.0.1:11434 \\\n -v $(pwd)/brain:/app/data \\\n ghcr.io/mauriceisrael/smart-memory:latest dashboard\n```\n\n> Add `dashboard` to start the web server; without it the container starts in MCP mode. The `-v` volume persists your knowledge graph and rules. Open the dashboard at `http://localhost:8080`.\n\n---\n\n## LLM configuration\n\nSmartMemory uses an LLM to extract facts and rules from natural language and documents. Configure it via the **dashboard Admin page** or via **environment variables** (`-e LLM_PROVIDER=…`).\n\n| Provider | Example models | Notes |\n|---|---|---|\n| **Mistral** | `mistral-large-latest`, `mistral-small-latest` | European, [La Plateforme](https://mistral.ai/) API |\n| **Ollama** (local, free) | `llama3`, `qwen2.5-coder`, `mistral` | Runs offline |\n| **OpenAI** | `gpt-4`, `gpt-3.5-turbo` | |\n| **Anthropic** | `claude-3-5-sonnet` | |\n| **Google** | `gemini-1.5-pro` | |\n\n→ [Full configuration guide](https://github.com/MauriceIsrael/SmartMemory/blob/master/CONFIGURATION.md)\n\n### Extracting rules from documents\n\n1. **Upload a PDF** (e.g. `Company_Policy.pdf`).\n2. **Pick a provider** — the server needs an API key (or a local Ollama) to read the document.\n3. **Review & approve** — the system proposes rules; you accept them in bulk from the dashboard.\n\n---\n\n## Interactive demo — from facts to rules\n\nWhat happens in **Conversational Mode**:\n\n```text\n> I know Bob\nLLM: ✦ I've recorded the fact: I know Bob.\n\n> He goes to work by car\nLLM: ✦ Noted: Bob goes to work by car.\n\n> Can Bob vote?\nLLM: ✦ I can't conclude yet — but since he drives, he is likely an adult.\n May I add the rule \"Drivers are adults\"?\n\n> yes\nLLM: ✨ Rule 'drivers_are_adults' added.\n May I also add \"Adults can vote\"?\n\n> yes\nLLM: ✨ Rule 'adults_can_vote' added.\n ✦ Therefore, yes — Bob can vote. (derived from 2 rules)\n```\n\nEvery step is stored, attributed, and replayable — that's the point.\n\n---\n\n## Tech stack\n\n- **Backend:** Python 3.11+, RDFLib, FastAPI\n- **Frontend:** SvelteKit, TypeScript, TailwindCSS\n- **Reasoning:** Neuro-symbolic (LLM + SPARQL / OWL)\n- **Protocol:** Model Context Protocol (MCP)\n- **Packaging & deploy:** Docker, GitHub Container Registry, Google Cloud Run\n\n---\n\n## Roadmap\n\n- [ ] Broaden document ingestion (DOCX, HTML, web pages)\n- [ ] Richer graph visualization and rule-conflict detection\n- [ ] First tagged release (`v0.1.0`)\n\nIdeas and contributions welcome — see [CONTRIBUTING.md](https://github.com/MauriceIsrael/SmartMemory/blob/master/CONTRIBUTING.md).\n\n---\n\n## License\n\nMIT — see [LICENSE](https://github.com/MauriceIsrael/SmartMemory/blob/master/LICENSE).\n","changelogMarkdown","videoKey","requiredSecretsJson","publishedAt","priceCents","trialDays",0,"upstream",{"_94":95,"_96":97,"_98":99,"_100":101,"_102":-5,"_103":104},"requiredSecrets",[],"tags",[],"assets",[],"stats",{"_92":81,"_93":81},"ratingAvg","reviewCount","topics",[],"license","MIT","language","Python","registryName","io.github.MauriceIsrael/SmartMemory","homepage","packages",[],"ENV",{"_107":108,"_109":110,"_111":112},"ENVIRONMENT","production","TURNSTILE_SITE_KEY","0x4AAAAAAB8-rEI2gNDf_QhU","SENTRY_DSN","https://45fd1229f78089d5c3ee78be128030e1@o4510583261757440.ingest.de.sentry.io/4510584253448272"]