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refactor(runner): rename openrouter package to llmclient

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pj committed 2026-06-12 10:52:14 +05:30
1 parent 8eabaaa842
commit 3ca0e9a5b9
3 files changed
+23 -22
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internal/runner/llm_source.go
+13 -13
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@@ -13,7 +13,7 @@ import (
"log/slog"
"strings"
"github.com/priyanshujain/sanderling/internal/openrouter"
"github.com/priyanshujain/sanderling/internal/llmclient"
"github.com/priyanshujain/sanderling/internal/trace"
"github.com/priyanshujain/sanderling/internal/verifier"
)
@@ -36,14 +36,14 @@ const (
// candidates the system already enumerated; it never invents actions.
const llmSystemPrompt = "You are exploring this app to surface bugs. Choose the most useful next action from the numbered candidates. Avoid repeating recent actions; prefer progress into new screens. Return only your ranked choices."
// llmSource selects each step's action with an OpenRouter model instead of the
// seeded random pick. It replaces ONLY the pick: the candidate list, the input
// llmSource selects each step's action with an OpenAI-compatible vision model
// instead of the seeded random pick. It replaces ONLY the pick: the candidate list, the input
// values, and action execution are all reused unchanged. The spec's JS setup
// still runs first each tick (setup precedence), and the LLM drives once setup
// yields nothing.
type llmSource struct {
verifier *verifier.Verifier
client *openrouter.Client
client *llmclient.Client
model string
logger *slog.Logger
history *actionHistory
@@ -126,17 +126,17 @@ func (s *llmSource) selectViaLLM(ctx context.Context) (verifier.Action, string,
// buildRequest assembles the one-shot multimodal request: a system frame, the
// numbered candidate list plus recent-action memory, and the downscaled
// screenshot. The strict json_schema response format pins the ranked output.
func (s *llmSource) buildRequest(candidates []verifier.ActionCandidate) openrouter.Request {
userParts := []openrouter.ContentPart{openrouter.TextPart(s.userPrompt(candidates))}
func (s *llmSource) buildRequest(candidates []verifier.ActionCandidate) llmclient.Request {
userParts := []llmclient.ContentPart{llmclient.TextPart(s.userPrompt(candidates))}
if screenshot := s.verifier.Screenshot(); len(screenshot) > 0 {
if dataURL, ok := screenshotDataURL(screenshot, llmMaxImageEdge); ok {
userParts = append(userParts, openrouter.ImagePart(dataURL))
userParts = append(userParts, llmclient.ImagePart(dataURL))
}
}
return openrouter.Request{
return llmclient.Request{
Model: s.model,
Messages: []openrouter.Message{
{Role: "system", Content: []openrouter.ContentPart{openrouter.TextPart(llmSystemPrompt)}},
Messages: []llmclient.Message{
{Role: "system", Content: []llmclient.ContentPart{llmclient.TextPart(llmSystemPrompt)}},
{Role: "user", Content: userParts},
},
ResponseFormat: rankedResponseFormat(),
@@ -166,10 +166,10 @@ func (s *llmSource) userPrompt(candidates []verifier.ActionCandidate) string {
// rankedResponseFormat is the strict structured-output schema: a short
// reasoning string and a ranked list of candidate indices.
func rankedResponseFormat() *openrouter.ResponseFormat {
return &openrouter.ResponseFormat{
func rankedResponseFormat() *llmclient.ResponseFormat {
return &llmclient.ResponseFormat{
Type: "json_schema",
JSONSchema: openrouter.JSONSchema{
JSONSchema: llmclient.JSONSchema{
Name: "ranked_actions",
Strict: true,
Schema: map[string]any{