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Tianyi Cui
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packages/llm-pi-ai/README.md
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# @deepseek-ai/dsh-llm-pi-ai
DeepSeek adapter for the harness LLM seam backed by
[`@earendil-works/pi-ai`](https://www.npmjs.com/package/@earendil-works/pi-ai)
(the LLM library behind the pi agent).
DeepSeek adapter for the harness LLM seam backed by [`@earendil-works/pi-ai`](https://www.npmjs.com/package/@earendil-works/pi-ai) (the LLM library behind the pi agent).
## Why a second adapter exists
`@deepseek-ai/dsh-llm-deepseek` already talks to the same endpoint. This
package is its **design-verification twin**: same models, same wire
protocol, completely different internals — a unified LLM library with its
own event vocabulary versus hand-rolled fetch/SSE. Anything the harness
`StreamChunk` protocol cannot express for BOTH implementations is a
core-vocabulary bug. The differences it exercised on purpose:
`@deepseek-ai/dsh-llm-deepseek` already talks to the same endpoint. This package is its **design-verification twin**: same models, same wire protocol, completely different internals — a unified LLM library with its own event vocabulary versus hand-rolled fetch/SSE. Anything the harness `StreamChunk` protocol cannot express for BOTH implementations is a core-vocabulary bug. The differences it exercised on purpose:
- pi-ai hands back tool-call `arguments` as **parsed objects**; the harness
keeps raw JSON strings (re-stringified at `block-end`).
- pi-ai reports failures as **in-stream error events** (it never throws
mid-stream); these map to `finish {kind:'error'|'aborted'}` chunks — the
protocol's other sanctioned error path besides throwing (which
llm-deepseek uses).
- pi-ai folds reasoning tokens into `usage.output`; there is no separate
reasoning count to map.
- pi-ai's options omit stop sequences; `GenerateOptions.stop` is injected
via its `onPayload` hook.
- pi-ai hands back tool-call `arguments` as **parsed objects**; the harness keeps raw JSON strings (re-stringified at `block-end`).
- pi-ai reports failures as **in-stream error events** (it never throws mid-stream); these map to `finish {kind:'error'|'aborted'}` chunks — the protocol's other sanctioned error path besides throwing (which llm-deepseek uses).
- pi-ai folds reasoning tokens into `usage.output`; there is no separate reasoning count to map.
- pi-ai's options omit stop sequences; `GenerateOptions.stop` is injected via its `onPayload` hook.
## Config
Same shape as llm-deepseek (one-line swap in cordis.yml), with pi-ai's
thinking-level vocabulary:
Same shape as llm-deepseek (one-line swap in cordis.yml), with pi-ai's thinking-level vocabulary:
```yaml
- id: llm
@@ -41,20 +27,12 @@ thinking-level vocabulary:
## Dependency weight
pi-ai declares the openai/anthropic/google/mistral/AWS SDKs as install-time
dependencies. They are lazy-loaded — only the openai SDK actually loads for
this adapter — but they do land in `node_modules`. Accepted for a package
whose purpose is design verification.
pi-ai declares the openai/anthropic/google/mistral/AWS SDKs as install-time dependencies. They are lazy-loaded — only the openai SDK actually loads for this adapter — but they do land in `node_modules`. Accepted for a package whose purpose is design verification.
## Limitations
Same MVP contract as llm-deepseek: `prefill` throws `UNSUPPORTED`, images
are not representable, `tool_choice` is not mapped.
Same MVP contract as llm-deepseek: `prefill` throws `UNSUPPORTED`, images are not representable, `tool_choice` is not mapped.
## Testing
Unit suites run against a local `node:http` mock SSE server (pi-ai's openai
SDK happily talks to any base URL). Real-API coverage in
`tests/adapter.e2e.ts` (`yarn test:e2e`, key-gated): V4 Flash + V4 Pro across
all exposed reasoning levels (off/high/xhigh), the thinking+tools round trip,
and a cross-adapter structural-equivalence check against llm-deepseek.
Unit suites run against a local `node:http` mock SSE server (pi-ai's openai SDK happily talks to any base URL). Real-API coverage in `tests/adapter.e2e.ts` (`yarn test:e2e`, key-gated): V4 Flash + V4 Pro across all exposed reasoning levels (off/high/xhigh), the thinking+tools round trip, and a cross-adapter structural-equivalence check against llm-deepseek.