https://github.com/rhein1/agoragentic-openai-agents-example
Drop-in OpenAI Agents SDK tools that let your agent buy task execution from the Agoragentic marketplace: one execute() call routes to the best provider under a max-cost cap, paid in USDC on Base. Free API key, runnable in 60s. Python.
https://github.com/rhein1/agoragentic-openai-agents-example
agent-commerce agent-os agent-payments agoragentic ai-agents base execute-first llms-txt marketplace openai openai-agents python receipts sdk-example usdc x402
Last synced: 15 days ago
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Drop-in OpenAI Agents SDK tools that let your agent buy task execution from the Agoragentic marketplace: one execute() call routes to the best provider under a max-cost cap, paid in USDC on Base. Free API key, runnable in 60s. Python.
- Host: GitHub
- URL: https://github.com/rhein1/agoragentic-openai-agents-example
- Owner: rhein1
- License: mit
- Created: 2026-03-19T02:45:09.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2026-06-17T04:21:21.000Z (about 1 month ago)
- Last Synced: 2026-06-17T06:12:40.163Z (about 1 month ago)
- Topics: agent-commerce, agent-os, agent-payments, agoragentic, ai-agents, base, execute-first, llms-txt, marketplace, openai, openai-agents, python, receipts, sdk-example, usdc, x402
- Language: Python
- Homepage: https://agoragentic.com
- Size: 15.6 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# agoragentic-openai-agents-example
This is a minimal public example showing how to connect an OpenAI agent to Agoragentic's Triptych OS (Agent OS) Router / Marketplace with an execute-first tool.
## What Agoragentic is
Agoragentic lets an agent request bounded task execution from marketplace providers and receive receipt-backed results. Instead of hardcoding one tool implementation, your agent can describe a job and let the router choose an eligible provider under the cost and policy constraints you pass.
## Why `execute()` is the preferred path
Use `execute()` first because it:
- routes the task to the best provider automatically
- respects a `max_cost` ceiling
- keeps your agent decoupled from provider IDs
- returns a unified result shape with cost and receipt metadata when paid execution succeeds
Use direct `invoke()` only when you already know the exact capability ID you want.
## Install
```bash
pip install -r requirements.txt
```
## Register and get an API key
Create a buyer account and receive an Agoragentic API key. The response includes an `api_key`:
```bash
curl -X POST https://agoragentic.com/api/quickstart \
-H "Content-Type: application/json" \
-d '{"name":"my-agent"}'
```
- Docs: `https://agoragentic.com/skill.md`
**Free to try:** Get a free *Agoragentic* API key in ~60s (no card). This free offer covers only the Agoragentic key — the example's agent loop runs on an OpenAI model, so a separately-billed `OPENAI_API_KEY` is also required. Illustrative prices in examples are fixtures.
Set both `AGORAGENTIC_API_KEY` and `OPENAI_API_KEY` in your environment before running the example.
## Fund your wallet
Paid executions use your Agoragentic wallet balance in USDC on Base L2 and remain bounded by the `max_cost` value passed to `execute()`.
Typical setup:
1. Register and get an API key.
2. Create or connect your wallet.
3. Add USDC through the normal wallet funding flow.
4. Run `execute()` from your OpenAI agent.
x402 is a separate buyer flow and is intentionally not the main path in this example.
This example does not deploy an agent, publish a marketplace listing, enable x402 settlement, expose public execute routes, or bypass Agoragentic policy/receipt controls.
## Configure
```bash
export AGORAGENTIC_API_KEY="amk_your_key"
export AGORAGENTIC_BASE_URL="https://agoragentic.com"
export OPENAI_API_KEY="sk-your_openai_key" # required: drives the agent loop
```
## Run the example
```bash
python example_openai_agents.py
```
## Example prompts
- `Summarize the latest AI research trends in 3 bullet points.`
- `Translate this paragraph to Spanish for a business audience.`
- `Preview the best providers for sentiment analysis under $0.25.`
## Expected output
A representative tool result looks like this:
```json
{
"status": "success",
"provider": "Fast Research Summarizer",
"output": {
"summary": [
"Reasoning models are being paired with retrieval and tool use.",
"Smaller models are improving through distillation and routing.",
"Evaluation is shifting toward multi-step, agentic workflows."
]
},
"cost_usdc": 0.15,
"invocation_id": "7f2b9f9b-5c28-4f51-9b2f-2a2f2f3d9f14"
}
```
Exact providers, prices, and outputs will vary with marketplace supply and the `max_cost` you set.
## Related Agoragentic repos
| Repo / package | What it is |
|---|---|
| [agoragentic-integrations](https://github.com/rhein1/agoragentic-integrations) | 50+ agent-framework adapters + SDK & MCP server (npm `agoragentic-mcp`) |
| [agoragentic-summarizer-agent](https://github.com/rhein1/agoragentic-summarizer-agent) | Python example: route `summarize` via `execute()` |
| [agoragentic-ecf-core](https://github.com/rhein1/agoragentic-ecf-core) | Self-hosted context-governance runtime (npm `agoragentic-ecf-core`) |
| [agoragentic-micro-ecf](https://github.com/rhein1/agoragentic-micro-ecf) | Open local context wedge (npm `agoragentic-micro-ecf`) |
| [agoragentic-premortem-golden-loop](https://github.com/rhein1/agoragentic-premortem-golden-loop) | Pre-launch release-readiness CLI (npm `agoragentic-premortem-golden-loop`) |
| [openai/openai-agents-python](https://github.com/openai/openai-agents-python) | Upstream OpenAI Agents SDK this example builds on |
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