mnemo — A local-first learning agent that remembers, without leaking

A developer built **mnemo**, a local-first learning-management agent that tracks topics, sequences them by prerequisites, and schedules spaced-repetition reviews using the SM-2 algorithm, all running entirely on-device through a Hermes-3 agent orchestration loop. The system addresses privacy and safety concerns in AI tutoring by storing all data locally in SQLite and implementing a two-gate approval system in the schema—resources start with `approved = 0` and are further filtered through a `source_allowlist` table at the SQL layer—preventing unverified content from reaching young learners. The agent exposes seven MCP tools for Hermes-3 to drive adaptive study sessions, with 69 passing tests covering the SM-2 algorithm, integration, API endpoints, and safety scenarios.

This is a submission for the Hermes Agent Challenge: Build With Hermes Agent mnemo is a learning-management agent for kids and self-directed learners. It tracks topics, sequences them by prerequisites, schedules spaced-repetition reviews SM-2 , generates quizzes, and adapts the plan over time through a Hermes-driven feedback loop. The problem: hosted AI tutors send a child's mistakes, struggles, and reading level to someone else's servers. Browser-only chat loops forget everything the moment the tab closes — no cross-session adaptation, no long-term plan. And LLMs happily hallucinate URLs and quiz answers, so nothing stops an unreviewed link from reaching a 6th-grader. mnemo's answer is a three-part stance baked into the schema, not bolted on: events table. resource starts approved = 0 in the schema. A second gate joins a source allowlist table at the SQL layer — even an approved resource is suppressed if its origin isn't a trusted source khan-academy , ck12 , internal-curriculum . Both gates have dedicated scenario tests.Pitch deck single-file Reveal.js, no build step : pitch.html — 6 slides covering the problem, the Hermes differentiator, architecture, the two-gate safety model, and a realistic dashboard mockup of the shipped UI. Quick local run: python3 -m venv .venv && source .venv/bin/activate pip install -r requirements.txt sqlite3 learning.db < migrations/001 init.sql sqlite3 learning.db < migrations/002 quiz bank.sql sqlite3 learning.db < migrations/003 source allowlist.sql python -m uvicorn webapp.app:app --reload --port 8000 Open http://localhost:8000 for the dashboard, or run a Hermes session end-to-end: ollama pull hermes3 curl -X POST http://localhost:8000/api/agent/session \ -H "Content-Type: application/json" \ -d '{"learner id": 1, "backend": "ollama"}' The dashboard streams each tool call the agent makes, then refreshes progress and the review queue automatically. Repository: gitea.com/rohithtp/mnemo https://gitea.com/rohithtp/mnemo replace with actual URL on publish Key directories: webapp/ — FastAPI REST + SSE + static dashboard 25+ endpoints mcp server/ — stdio MCP server exposing 7 tools to Hermes src/agent/ — Hermes session orchestrator, allowlist, memory, insights src/learning/ — SM-2 algorithm, sequencing logic, safety/privacy gates migrations/ — 3 SQL files, schema-version tracked tests/ — 69 passing tests SM-2 unit + integration + API + scenario docs/ — full build plan, decisions log, 25-risk register, two-profile docs| Layer | Choice | |---|---| | Database | SQLite WAL mode on-device · libSQL/Turso documented as forward path | | Backend | FastAPI + uvicorn + SSE streaming | | Agent runtime | Hermes-3 via Ollama local · HuggingFace Inference API fallback | | Tool surface | stdio MCP Hermes + WebMCP progressive enhancement Chrome 146+ | | Frontend | Vanilla JS + plain CSS — no framework, no build step | | Spaced repetition | SM-2 algorithm, implemented as pure functions | | Tests | pytest — 15 SM-2 unit + 18 integration + 31 API + 5 scenario = 69 passing | | Deployment | Dockerfile + systemd unit | | Language | Python 3.11+ 3.14 tested | All Python deps are pinned in requirements.txt . The Hermes-3 model digest is pinned by SHA256 and asserted at startup so a silent upstream change can't drift the agent's behaviour mid-session R02 mitigation . Hermes-3 is the orchestration brain for the whole adaptive loop. The reason it had to be Hermes specifically, not a stateless prompt loop, comes down to four capabilities: 1. Tool-calling over a real schema. src/agent/session.py runs a tool-calling loop where Hermes drives a study session by calling the 7 MCP tools get progress summary , get ready topics , start topic , get next review items , record review result , recommend resources , generate quiz . Each call is logged to the events table with arguments and result. The agent isn't generating advice in prose — it's writing to SQLite through audited tools. 2. Cross-session memory. Every session writes a memory note event summarising patterns the agent noticed. The next session's system prompt is injected with the most recent notes for that learner, so Hermes "remembers" that this kid struggles with unlike denominators across sessions, not just within one tab. Memory notes are per-learner and stay on-device. 3. Pattern analysis + human-approvable proposals. Phase 6 added POST /api/agent/insights/{learner id} . Hermes reads the event trail across a configurable window, computes avg quality , days active , resource-type variety, and emits structured proposals like {"type": "pace", "direction": "increase", "reason": "avg quality above 4.0"} . Each proposal is stored with resolved at = NULL and surfaces in the dashboard with Approve / Dismiss buttons. The agent never silently applies pace changes — a human resolves each proposal. 4. Allowlist-enforced tool boundary. src/agent/allowlist.py defines AGENT ALLOWLIST and AGENT DENY LIST as constants. set learner preferences is on the deny-list, so even if the model is jailbroken into trying to mutate preferences directly, the session orchestrator raises PermissionError before the tool runs. This is the difference between "the UI hides it" and "the agent cannot do it." The combination — local model + persistent memory + audited tool calls + human-in-the-loop on every state-mutating proposal — is what makes mnemo trustable as a coach for a child, not just for an adult tinkerer. That posture is what the Hermes agent makes practical; a hosted stateless chatbot can't enforce any of it.