# Reach

- **Event:** [The Persistent Context Sprint Hackathon | Live at MongoDB .Local Build Fest](https://cerebralvalley.ai/e/persistent-context-sprint-hackathon)
- **When:** Thu, Aug 13 at 12:30 – 8:00 PM (PDT)
- **Where:** Pier 48, San Francisco, CA
- **Team:** [Nikhil Marudai](https://cerebralvalley.ai/u/Nikhil_M)
- **GitHub:** https://github.com/NikhilMarudai/reach-agent
- **Demo video:** https://youtu.be/crCtjtYt0uo
- **Gallery:** https://cerebralvalley.ai/e/persistent-context-sprint-hackathon/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/persistent-context-sprint-hackathon/hackathon/gallery/52

Most people don't abandon their goals out of laziness. They abandon them because nothing adapts. The plan that made sense in week one goes stale by week three, the app keeps sending the same generic reminder, and nobody notices that you always fail on Sundays. Habit apps track behavior; they don't understand it.

Ridge is what it looks like when an accountability app gets a memory. It's an agentic layer we built for REACH, our shipped social-accountability app, running entirely outside it over its public API. It learns from a signal almost no product has: on REACH, your peers verify photographic proof of every rep, so Ridge's memory is built from what real humans confirmed you did, not what you told a chatbot.

What that unlocks: plans that reshape around your actual failure modes (it discovered, unprompted, that our demo user only breaks streaks on week edges, and rebuilt her week around it). Encouragement that cites your real history: "three weeks in, don't stop now," never "you missed a post." Recovery challenges created for you at the moment your pattern warrants one. A companion that gets sharper the longer you use it.

How the stack delivers that: MongoDB Atlas holds every layer of the memory. Observations carry mandatory evidence pointers (evidence-free memories are refused at the storage layer), beliefs are distilled from them, and per-user narrative stories plus the full conversation transcript live alongside. LangGraph runs the deliberation loop (observe, remember, decide, act) and checkpoints every step to Atlas. Kill the agent mid-thought, rerun it, and it resumes where it stopped. No cold start. A purpose-built MCP server exposes REACH's real API as tools, so the agent acts through the same doors as the mobile app, under the same rules. OpenRouter routes each job to the right model. ElevenLabs gives the agent a voice and a personality: Ridge.

One rule, enforced in code: Ridge never verifies proof and never posts it. Humans judge; the agent plans. Built for REACH, and modular by construction: the entire app-specific surface is one MCP server and one seam file.

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Markdown version of https://cerebralvalley.ai/e/persistent-context-sprint-hackathon/hackathon/gallery/52. Site index for agents: https://cerebralvalley.ai/llms.txt · full text: https://cerebralvalley.ai/llms-full.txt