KisanMind

A smallholder farmer deciding what to plant needs soil, weather, mandi prices and scheme eligibility at once, and that information lives in four places and two languages. Most of the people who need it do not type; they talk. HackWarts gave us 24 hours. I took the AI service end to end: the agents, the graph, the voice chain, the tests, and the frontend wiring that put agent output on the dashboard. Ved and Nimit took the Next.js pages and auth, Parth the Node backend and MongoDB.

The AI service is FastAPI with LangGraph: five nodes over one shared TypedDict state, seven endpoints (/orchestrate, /crop/recommend, /market/analyze, /financial/analyze, /chat/follow-up, /chat/voice, /health), four pluggable LLM providers behind one llm_service, and 152 test functions in nine files, sixty of them for voice and language handling.

State, not messages. The graph carries one SharedState: farmer_profile, then weather_data, crop_output, market_output, financial_output, insights_output, final_output. Each node reads what it needs and writes exactly one key, so a failing node leaves a typed hole rather than a corrupted conversation, and the tests can assert on the state after any single node.

The crop node is deliberately not an LLM call. It loads a catalogue of 25 crops, eliminates by four hard rules (water, season, soil, budget) and records a reason for every crop it drops, scores the survivors on five weighted factors, and only then asks Gemini to re-rank the top three with reasoning. The reply is parsed against a schema; a bad reply is retried once and then replaced by the rule-based ranking, so the endpoint always answers. Rejection reasons are attached after the model call, so the model cannot invent them. That is what makes the recommendation explainable, and what makes it testable.

Market and financial. The market node pulls mandi prices, compares with MSP, computes a trend and a selling window, and picks a buyer channel before the LLM writes the summary. The financial node is rule-matched eligibility for PM-KISAN, PMFBY, KCC and PM-Kusum with a rough ROI and credit need. The insights node reads all of it, looks for contradictions between agents (a crop the market node says will not sell), and writes the action plan with a confidence.

Chat. Follow-up questions go through a keyword router in English and Hindi (crop, financial, market, “what if” profile updates, insights, general) and a session store that keeps the crop ranking, so “what about the second one” works.

The crop node’s first two steps, elimination and scoring, run here with the same rules, weights and catalogue as the repository. Change the farm and watch which crops get rejected and why, and which three would reach the model.

Browser audio goes to /chat/voice, is transcribed by Sarvam Saaras (22 Indian languages) with Groq Whisper as fallback, runs through the same chat handler as text, and comes back as Sarvam Bulbul speech with ElevenLabs as fallback, as base64 audio. Every one of those hops broke at least once on the second day.

Fifteen tests were added for audio validation alone: MIME handling, file extension safety, TTS truncation, and boolean parsing of form fields.

Evaluation. The pipeline has tests but no measured quality: no agreement rate against an agronomy reference, no word-error rate on Hindi speech. The schemes are rule-matched from a static list, not live. Until a 30-profile evaluation set exists and its results are published here, this page does not carry a measured mark. That is the next piece of work on this project, and it is the same discipline I apply to the evaluation lab on this site.