halkyon/packages/halkyon_llm/README.md
Tizian 「ティツィアーン」 6e63312bfa
docs: the assistant answering in Learn, as step 3 built it
Learn's AGENTS.md describes the field with the assistants answering in it
(ADR 0021, step 3): who answers, the scope, the conversation sheet and its
lookups, images, private studysets' warnings, kept conversations and the
tasks; and the testing trap of awaiting a finished subscription's cancel.
The shared packages' READMEs name providerTermsOf and onThisMachine.
Learn's README no longer says there is no end-to-end encryption, which
private studysets changed (ADR 0019).

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Signed-off-by: Tizian 「ティツィアーン」 <raisondetredev@pm.me>
2026-10-26 07:17:09 +01:00

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# halkyon_llm
Language models' APIs in pure Dart, streamed, over plain HTTP, no SDKs: Anthropic's
Messages API, OpenAI's chat completions (and every service compatible with them), and
OpenAI's Responses API, behind one interface of what they share: a system prompt and
messages, text as the model writes it, images, and tool calls. Halkyon's server forwards
the operator's assistants with it, and its apps call a person's own provider with it
(Halkyon Learn's ADR 0021, points 5 and 10).
## Asking
```dart
final client = LlmClient();
final endpoint = LlmEndpoint(
protocol: LlmProtocol.anthropic,
model: 'claude-sonnet-5-5',
apiKey: key,
);
final request = LlmRequest(
system: 'You help with learning Japanese.',
messages: [
LlmMessage.user([const LlmText('What is "dog" in Japanese?')]),
],
maxOutputTokens: 4096,
);
await for (final event in client.send(endpoint, request)) {
switch (event) {
case LlmTextDelta(:final text):
stdout.write(text);
case LlmFinished(:final message, :final stop, :final usage):
// The model's whole turn, to send back in the next request.
}
}
client.close();
```
`send` streams `LlmTextDelta`s as the model writes, then one `LlmFinished` with the
model's turn, why it stopped (`LlmStop`: `end`, `maxTokens`, `toolCalls`, `refused`,
`other`) and what the provider counted (`LlmUsage`, input with what was read from a
cache, output with the model's thinking). A failure ends the stream with an `LlmFailure`
of a kind a caller can act on (`key`, `rateLimited` with the provider's `retryAfter`,
`unavailable`, `request`, `provider`, `network`, `malformed`), the usage counted so far
and the provider's words, cut to 300 characters with anything that looks like a key
removed. Cancelling the subscription aborts the request.
### The protocols
| `LlmProtocol` | Request | Default base URL |
|---|---|---|
| `anthropic` | `POST {base}/v1/messages` | `https://api.anthropic.com` |
| `openai` | `POST {base}/chat/completions` | `https://api.openai.com/v1` |
| `openaiResponses` (`openai-responses`) | `POST {base}/responses` | `https://api.openai.com/v1` |
A base URL is `https`, but for a model on the same machine or network (`localhost`, a
private or link-local address, a single-label name such as a container's, `.local`,
`.internal`, `.home.arpa`), as Ollama and llama.cpp serve it; anything else is a
`FormatException` when the endpoint is made. `onThisMachine` says whether its requests stay on
this machine (a loopback address, or `localhost` as RFC 6761 reserves it), not merely on
the local network, which may be shared.
- **Chat completions** go to OpenAI, Mistral, Gemini's compatibility endpoint, OpenRouter
and local servers. OpenAI, Mistral and Gemini refuse fields they don't know and differ
in which they take, so each of those hosts, and OpenRouter, gets its own fields; every
other host gets what Ollama, vLLM and llama.cpp all take. Replies are read leniently:
usage at the end, with the last choice or as a running total; tool calls in pieces or
whole, with or without an index; errors as a status, in a chunk, or lost.
- **The Responses API** is stateless here (`store: false`): each request carries the
whole conversation, with the model's encrypted reasoning as it streamed it.
- **What goes back:** a model's turn holds, besides its text and calls, what its provider
wants returned with the tools' results, kept as it came: Anthropic's thinking blocks
and the Responses API's reasoning items as `LlmProviderBlock`s (sent to that protocol
alone), Gemini's thought signatures in `LlmToolCall.extra`, the Responses API's message
phase in `LlmText.extra`. Send the `LlmFinished.message` back unchanged.
- **The person's opaque ID** (`LlmRequest.personId`) goes where the provider takes one:
Anthropic's `metadata.user_id`, OpenAI's `safety_identifier`, OpenRouter's `user`.
## Testing with it
`package:halkyon_llm/testing.dart` has `FakeLlmClient`: it keeps what it was sent, and
answers with what a test queues (`replyText`, `reply` with events and a failure, `fail`),
so code that forwards or runs a tools' loop is tested without HTTP.
## How it is tested
- **Documented replies** (`test/fixtures/documented/<protocol>/`): each provider's stream
written event by event from its documentation, in our own words: text, tool calls,
errors mid-stream, the statuses, and the variants each compatible service streams.
Each is a `.http` file: `HTTP <status>`, the headers that matter, an empty line, the
body. The tests replay them in pieces of 1, 7 and 4,096 bytes, so a line or a character
cut in two is read whole.
- **Recorded replies** (`test/fixtures/recorded/<protocol>[@<host>]/`): real ones, which
`tool/record_llm.dart` writes with a key of one's own (four small requests: a text, a
tool call, the tool's result sent back with the model's whole turn, a wrong key). Their
words are the model's, so only what every such reply must hold is checked. Read them
before committing them.
```sh
cd packages/halkyon_llm
dart run tool/record_llm.dart --protocol anthropic
dart run tool/record_llm.dart --protocol openai-responses --model <a small model>
```
## Licence
BSD-3-Clause (`LICENSE`), as Serverpod's own and `halkyon_oidc`, so that others can use
it; the rest of Halkyon is AGPL. Not on pub.dev yet.