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dukaan-agent

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Dukaan Agent

An open agentic-commerce agent for D2C merchants with WhatsApp-first customers — built on MCP + Razorpay test-mode APIs.

Python FastMCP Razorpay Mode License

What this is

Tier-2 D2C merchants take orders on WhatsApp by hand and send payment links one message at a time. Dukaan Agent exposes a merchant's catalog and checkout as MCP tools so any AI agent (Claude Desktop, etc.) can search products, honour a customer's spend mandate, and complete a UPI checkout autonomously — no human clicking a link, every money action bounded and logged.

Razorpay demoed exactly this loop for Zomato at FTX'26 on the Claude Agent SDK. This is the open version any WhatsApp-first D2C merchant can plug in — no enterprise contract — with a real delegation model, an append-only audit trail, and measured results across 50 sessions.

Mock-first: the whole project runs offline with zero credentials. Razorpay orders and Hinglish parsing fall back to deterministic mocks; adding real keys flips on the live path with no code changes.

Related MCP server: Cashfree CLI

Architecture

   Customer (WhatsApp-style chat)
        │  "ek blue shirt chahiye 800 se kam mein"
        ▼
   Mock Chat UI  (ui/index.html — demo surface)
        │
        ▼
   MCP Server (FastMCP)  ── server/mcp_server.py
   ├── Tools
   │     search_catalog · get_product_details · create_order · get_session_audit
   ├── Intent parsing      server/intent_parser.py   (Claude Haiku ▸ mock fallback)
   ├── Catalog             server/catalog.py          (SQLite + fuzzy search)
   ├── Delegation          server/delegation.py       (spend mandates)
   ├── Guardrails          server/guardrails.py       (velocity · allowlist · auth · fraction)
   └── Audit trail         server/audit.py            (append-only JSONL)
        │
        ▼
   Razorpay Test-Mode API  ── server/razorpay_client.py  (live ▸ mock fallback)
        │
        ▼
   UPI deep link + QR      ── server/upi.py   →  upi://pay?pa=…&am=…&cu=INR

How it works

  1. Customer sends a message — English or Hinglish ("ek blue shirt chahiye 800 se kam mein").

  2. Intent is parsed into {category, keywords, max_price_inr, quantity, color} (Claude Haiku, or the built-in deterministic parser offline).

  3. search_catalog fuzzy-matches the SQLite catalog and returns in-stock products (out-of-stock items are flagged, never silently ordered).

  4. create_order runs all guardrails — velocity → merchant allowlist → mandate authorization → single-order fraction — before any money moves.

  5. Razorpay order + UPI QR are generated; the agent hands back a upi://pay deep link and a scannable QR — no human in the checkout loop.

  6. Every step is written to an append-only audit log, retrievable as a timestamped timeline via get_session_audit.

Guardrails — the 3-layer delegation model

A spend cap is one if-statement; this is a delegation model. A customer pre-authorizes an agent with a mandate (total cap, per-category allowlist, expiry). Every order clears four checks — all run (no short-circuit) so the audit log records the full picture, and the first failure is reported as the blocking reason.

Layer

Check

Blocks when

Reason code

Velocity

≤ 3 orders / hour / customer

4th order within the hour

VELOCITY_EXCEEDED

Anomaly

Merchant on pre-approved allowlist

Merchant not recognised

SUSPICIOUS_MERCHANT

Authorization

Within mandate: not expired, total cap, category allowlist

Cap / category / expiry breached

CAP_EXCEEDED · CATEGORY_LIMIT_EXCEEDED · MANDATE_EXPIRED · NO_MANDATE

Single-order

No order > 40% of remaining balance

One order too large

SINGLE_ORDER_TOO_LARGE

Batch results

Seeded 50 synthetic shopping sessions (English + Hinglish) across four customer mandates, run fully offline. Reproduce with make batch.

18/50 sessions completed successfully · avg 3.0 tool calls to checkout
· guardrails fired correctly in 21/21 attempted violations

Outcome

Count

Successful orders

18

Blocked by guardrails

21 (cap 6 · category 7 · velocity 5 · expired 3)

Errors (out-of-stock / ambiguous)

11

Accuracy vs expected outcome

50 / 50 (100%)

Full breakdown is written to batch/report.txt.

Setup

Everything runs without any API keys (mock mode). Keys are optional and only enable the live path.

# 1. Clone and enter
git clone <your-repo-url> dukaan-agent && cd dukaan-agent

# 2. Install dependencies
pip install -r requirements.txt          # or: make setup

# 3. (Optional) add real keys
cp .env.example .env                      # fill in Razorpay test + Anthropic keys

# 4. Seed the catalog
python -m server.catalog                  # or: make seed

# 5. Run the 50-session batch + report
python batch/runner.py && python batch/report.py    # or: make batch

# 6. Run the tests
pytest tests/ -v                          # or: make test

# 7. Open the demo chat UI
open ui/index.html

Connect to Claude Desktop (requires Python 3.10+ for FastMCP)

python -m server.mcp_server               # or: make serve

Add to claude_desktop_config.json:

{
  "mcpServers": {
    "dukaan-agent": {
      "command": "python",
      "args": ["-m", "server.mcp_server"],
      "cwd": "/absolute/path/to/dukaan-agent",
      "env": {
        "RAZORPAY_KEY_ID": "your_key_here",
        "RAZORPAY_KEY_SECRET": "your_secret_here",
        "ANTHROPIC_API_KEY": "your_key_here"
      }
    }
  }
}

Note: The MCP server (make serve) needs Python 3.10+ (FastMCP). The offline pipeline — make seed, make test, make batch — runs on Python 3.9+.

Project structure

dukaan-agent/
├── server/
│   ├── mcp_server.py       # FastMCP server — 4 tools, 3 resources (entry point)
│   ├── catalog.py          # SQLite catalog + token-level fuzzy search
│   ├── delegation.py       # spend mandates (cap · category allowlist · expiry)
│   ├── guardrails.py       # velocity · allowlist · authorization · single-order
│   ├── audit.py            # append-only JSONL audit trail
│   ├── intent_parser.py    # Hinglish/English parser (Claude Haiku ▸ mock)
│   ├── razorpay_client.py  # Razorpay test-mode wrapper (live ▸ mock)
│   └── upi.py              # UPI deep link + QR code
├── batch/
│   ├── intents.json        # 50 synthetic shopping intents
│   ├── runner.py           # batch runner (direct calls, offline)
│   └── report.py           # stats + headline metric → report.txt
├── ui/index.html           # mock WhatsApp-style demo chat
├── tests/                  # catalog · delegation · guardrails (pytest)
├── data/                   # SQLite DB, audit log, QR PNGs (auto-created)
├── requirements.txt
├── Makefile                # setup · seed · serve · test · batch · clean
└── .env.example

Why this matters

India is building NPCI's Unified Agent Protocol (UAP) — national infrastructure for AI agents to make UPI payments under one-time consent with per-merchant spend limits, extending UPI Circle's delegation framework. Dukaan Agent is a working prototype of that direction: consent-bounded, category-limited, auditable agentic checkout over UPI — the shape of Indian agentic commerce before the rails ship.

License

MIT

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