Inspiration
In Indonesia, many beginner investors are trapped in “quick money” cycles: FOMO buys, panic decisions, and poor risk awareness.
We built Gampang Nyaham to shift behavior from impulsive action to disciplined, safer routines.
What it does
Gampang Nyaham is an autonomous behavioral-finance agent that:
- detects risky intent from user prompts,
- scores risk deterministically (
risk_score,risk_band), - applies strict guardrails (including paper-first mode),
- returns transparent decision evidence (
tool_calls,decision_flags,limitations).
The goal is not to predict profit. The goal is to prevent harmful decision patterns.
How we built it
We built a full agent runtime with:
- OpenClaw-based autonomous loop:
plan -> retrieve -> reason -> act -> verify -> fallback - FastAPI Bridge API for orchestration (
/agent/run,/health,/about, memory endpoints) - mem9 integration for session memory context
- Hybrid market data strategy (live-first with deterministic fallback)
- Deterministic policy engine for guardrails, recommendations, and next-step actions
- Next.js web dashboard for demo and interaction
- Dockerized deployment on VPS + web deployment on Vercel
Challenges we ran into
- Keeping outputs stable and safe even when dependencies degrade
- Managing provider auth/rate-limit issues while preserving user safety
- Balancing explainability (tool traces) with a simple, user-friendly UI
- Enforcing “paper-first” constraints without breaking agent autonomy
What we learned
- Reliable agent systems need deterministic guardrails, not only LLM responses
- Structured output contracts are critical for trust and debugging
- Fallback design is a core product feature, not just error handling
- Safety-first UX matters as much as model quality
What's next
- Deeper personalization from long-term behavior memory
- More real-time market adapters and better resilience controls
- Integration path to brokerage/payment rails with hard policy gates
- Multilingual coaching and better behavioral nudges for daily use
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