| title | Examples |
|---|
Complete applications built on the Agents API guide, each running against a real Core installation with a Project API key.
A conversation workspace built on Core. Assemble Agents from a resource library and an environment template, then talk to them in a local Web chat or through Feishu bots that act as the Agent's identity. Both entries create ordinary public Sessions.
Source: example/parsar.
Run and configure it with the example README, which also owns its supported features, storage and validation.
Each feature maps to one part of the API. Read the code next to the guide section:
| Feature | API used | Guide | Code |
|---|---|---|---|
| Server-side key and headers | Bearer key, OpenAI-Beta, route allowlist |
Before you start | server.mjs |
| Agents with a Harness and model | x_agents_core.harness, model_provider |
Core extensions | server/sessions.mjs |
| Web and Feishu conversations | Public Session creation, input events, Turns and Items | Send input | server/sessions.mjs, server/feishu.mjs |
| Hosted Environments from templates | openai_hosted with packages, files, env and setup_commands |
Create a Session | server/runtime-profile.mjs |
| Crash-safe Session creation | Idempotency-Key, recovery from a lost response |
Idempotency | server/sessions.mjs |
| Live replies | Event stream, text deltas, reconnect and reconcile | Stream events | src/lib/live-session.ts |
| Follow-ups and cancel | Input events with idempotency keys | Send input | src/Composer.tsx |
| History | Paging Turns and Items | Pagination | src/lib/api.ts |
| Skills and versions | /skills, default version |
Skills | src/Skills.tsx |
A Python script that runs batches of tasks as hosted Sessions, with any Harness and model, and grades each answer outside the sandbox. It shows Core as a rollout engine for RL training and evaluation. Each rollout is one Session with no network, task files as initial files and the answer as an Artifact.
Source: example/rl-eval.
Run and configure it with the example README, which also owns its output format, task format and training setup.
| Feature | API used | Guide | Code |
|---|---|---|---|
| One Session per rollout | Inline input, metadata and openai_hosted |
Create a Session | rl_eval.py |
| Task files without network | Inline files and network.access: disabled |
Initial files | rl_eval.py |
| Harness and model matrix | x_agents_core.harness, model, model_provider |
Core extensions | rl_eval.py |
| Wait, time out and cancel | Turn list polling, agent.session.input.cancel |
Cancel | rl_eval.py |
| Answer outside the sandbox | Artifacts under outputs/ |
Artifacts | rl_eval.py |
| Trajectories and usage | Items and Turn usage |
Turns and Items | rl_eval.py |
To contribute an example, follow the application example boundary.