Type a coding task. The agent's only tools are `bash` (+ `bash_output` / `bash_kill` for background tasks): file reads, writes, searches, and test runs all happen through shell commands, each in a fresh `bash -c` (the system prompt tells the model to pass `workdir` instead of `cd`). Reasoning streams dimmed; tool calls/results render inline.
Each run starts a fresh session by default (its event log lands under `./.sessions/`). To **continue** a previous conversation, set `RESUME_SESSION_ID` to that session's id — the `main` agent then rehydrates the persisted log instead of starting fresh, so the model sees the earlier turns as history:
```sh
RESUME_SESSION_ID=<prior-session-id> pnpm run demo:coding
```
The id is wired through `cordis.yml` (`resumeSessionId: !!js process.env.RESUME_SESSION_ID`); unset, the agent starts a new session. A missing/unreadable id is non-fatal — it logs a warning and starts no `main` agent.
| `llm-deepseek` | real `LlmAdapter` via config (`!!js process.env.…` secrets); swap one line to `@deepseek-ai/dsh-llm-pi-ai` for the library-backed twin |
| `bash` (`dsh-bash-local`) + `tool-bash` | the executor seam + tool schemas as separate plugins |
| `agent-loop` | agent created from config with a coding system prompt |
| `src/stdio-chat.ts` | UI as a plugin; copied from echo-agent with reasoning-dimming and an exit-on-idle close handler for piped stdin. Example-local on purpose — extract a shared UI package when a third example needs it |
-`tests/full-loop.e2e.ts` — the canary: real model runs `echo e2e-ok` through the real bash tool; asserts `tool/call`/`tool/result` session events and the final answer.
-`tests/coding-task.e2e.ts` — the swebench-style smoke: a temp dir holds `add.js` (with `a - b` where `a + b` belongs) and a failing `add.test.js`; the agent must fix the bug and verify. The test re-runs `node add.test.js` ITSELF and inspects the files — agent claims are not trusted.
-`tests/resume.e2e.ts` — durable continuity across processes: run 1 tells the real model a secret code and persists the turn to a temp JSONL root, then the whole context is disposed; run 2 is a fresh context over the same root that RESUMES the session id and asks the model to recall the code. The recall can only come from the rehydrated log.