https://devpost.com/software/traid-h6db5a

Text a photo + a casual instruction ("Sell this keyboard, has a scratch, want it gone quickly") to your Poke assistant. trAId identifies the item, researches eBay with a real logged-in browser, texts you the comparable listings it found (title + price), prices it, drafts a listing, asks you to approve over text, publishes to eBay, then autonomously negotiates with buyers — only interrupting you when a sale closes or a listing goes stale. It can also buy: give it an item and a price cap and it hunts, haggles, and closes under budget.

Built for CalHacks AI Hackathon 2026 (24h). See PRD-trAId.md for the full spec and docs/demo_runbook.md for the demo.

Every Claude call — product ID, pricing bullets, copy, revision parsing, negotiation decisions — goes through AgentSpan's native model layer (app/agents/llm.py). Nothing in this repo imports the anthropic SDK directly. Stagehand uses its own internal LLM to drive the browser; that's the automation engine's requirement, separate from our reasoning layer.

Poke ──(MCP /mcp)──▶ FastAPI ──▶ AgentSpan sale workflow (durable, resumable)
  ▲                                identify→memory→eBay research→report comps→price→copy
  └──(Poke webhook: proactive)───  →propose→⏸approve→eBay publish→verify→notify
                                   │
            ┌──────────────────────┼───────────────────────────┐
            ▼                      ▼                            ▼
       AgentSpan(Claude)     Browserbase/Stagehand          Redis
                             (1 persistent context)    (RedisVL + Agent Memory
                                                        + state keys + lock)

Post-publish: a sell-side negotiation monitor + a buy-side purchase flow hang off
the same Redis state, the shared Browserbase context, and the Poke path.

app/
  main.py               FastAPI: mounts MCP at /mcp, runs the negotiation monitor
  mcp_server.py         Poke-facing MCP tools
  poke_client.py        outbound proactive Poke messages
  config.py  models.py  settings + Pydantic contracts
  agents/   llm.py (AgentSpan Claude chokepoint), identify, pricing, copywriter,
            revision_parser, negotiator (hard floor/cap guardrails in code)
  browser/  session (one shared context), research(+ebay_research),
            publish(+ebay_publish), threads(+ebay_offers)
  memory/   comparables_index (RedisVL), seller_memory (Agent Memory)
  state/    redis_state (keys + publish lock), negotiation_state (policy+scheduler)
  workflow/ sale, management, negotiation, purchase
scripts/    seed_seller_memory
docs/       demo_runbook.md

uv venv && source .venv/bin/activate
uv pip install -r requirements.txt
cp .env.example .env          # fill in keys

# In the Browserbase console: create a Context, open it, log into eBay by hand
# once, then paste its id into BROWSERBASE_CONTEXT_ID in .env. (No login code —
# the session cookies persist in that one context across every run.)

# prerequisites: Redis Stack, Redis Agent Memory Server, AgentSpan local server
python -m scripts.seed_seller_memory        # seed demo seller prefs

uvicorn app.main:app --port 8000
npx poke@latest tunnel http://localhost:8000/mcp -n "trAId"

Grounded against live docs on 2026-06-20; pin exact signatures on Day 1:

• AgentSpan: Agent/AgentRuntime/AgentHandle kwargs, multimodal vision message shape in agents/llm.py, runtime.start/handle.approve return types.
• MCP SDK: FastMCP.streamable_http_app() mount path + session_manager.run().
• RedisVL: SearchIndex, VectorQuery, SemanticCache import paths.
• Redis Agent Memory client: search_long_term_memory / create_long_term_memory signatures (wrapped defensively in memory/seller_memory.py).
• Stagehand Python: StagehandConfig fields + page.act/extract schema arg. Each is isolated behind a thin adapter so a signature fix touches one file.