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https://github.com/fabro-sh/fabro.git
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e557774b0b
fabro-llm is now a thin integration crate: catalog construction from the lithos built-ins, the Fabro policy layer, and operator overlays; client construction from catalog plus credentials; a Fabro `ModelResolver` that enforces `metadata.fabro` policy; model selection; a server gateway adapter; attachment inlining middleware; reasoning normalization; one-shot structured output; probe wiring; and catalog API views. The in-house codecs, transports, providers, tool loop, retry, cost, and token-count code are deleted along with the wire snapshots that covered them. fabro-agent consumes lithos `StreamEvent`s and `Response`s directly. Retry is split: lithos's retry middleware handles failures before any visible output, and the agent replays the turn after. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
106 lines
3.7 KiB
Rust
106 lines
3.7 KiB
Rust
use std::path::Path;
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use std::sync::Arc;
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use fabro_agent::{AgentProfile, LocalSandbox, OpenAiProfile, Session, SessionOptions};
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use fabro_llm::test_support::client_from_env;
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use fabro_llm::{Client, ClientOptions};
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use fabro_test::{TwinScenario, TwinScenarios, TwinToolCall, twin_openai};
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use tokio::fs::read_to_string;
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const MODEL: &str = "gpt-5.4-mini";
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#[expect(
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clippy::disallowed_methods,
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reason = "e2e_openai! expands live-mode environment lookups even for twin-only tests"
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)]
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#[fabro_macros::e2e_test(twin)]
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async fn openai_twin_compaction_preserves_tool_call_pairs() {
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let tmp = tempfile::tempdir().expect("failed to create tempdir");
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let (base_url, api_key) = fabro_test::e2e_openai!();
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load_compaction_scenarios(&api_key).await;
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let mut session = make_openai_session(tmp.path(), base_url, api_key).await;
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session.initialize().await.unwrap();
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let result = session
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.process_input(
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"Trigger the compaction regression by writing four small files, then say done.",
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)
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.await;
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assert!(
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result.is_ok(),
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"session should complete without sending an orphaned function_call_output: {result:?}"
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);
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assert_eq!(
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read_to_string(tmp.path().join("four.txt"))
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.await
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.expect("four.txt should be written"),
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"four"
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);
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}
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async fn make_openai_session(cwd: &Path, base_url: String, api_key: String) -> Session {
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let client = openai_client(base_url, api_key).await;
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let profile: Arc<dyn AgentProfile> = Arc::new(OpenAiProfile::new(MODEL));
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let sandbox = Arc::new(LocalSandbox::new(cwd.to_path_buf()));
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let options = SessionOptions {
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enable_context_compaction: true,
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compaction_threshold_percent: 80,
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compaction_preserve_turns: 6,
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..SessionOptions::default()
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};
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Session::new(client, profile, sandbox, options, None)
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}
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async fn load_compaction_scenarios(namespace: &str) {
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TwinScenarios::new(namespace.to_string())
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.scenario(
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TwinScenario::responses(MODEL)
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.stream(true)
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.input_contains("Trigger the compaction regression")
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.tool_call(TwinToolCall::write_file("one.txt", "one")),
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)
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.scenario(
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TwinScenario::responses(MODEL)
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.stream(true)
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.tool_call(TwinToolCall::write_file("two.txt", "two")),
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)
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.scenario(
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TwinScenario::responses(MODEL)
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.stream(true)
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.tool_call(TwinToolCall::write_file("three.txt", "three")),
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)
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.scenario(
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TwinScenario::responses(MODEL)
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.stream(true)
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.tool_call(TwinToolCall::write_file("four.txt", "four"))
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.usage(180_000, 5),
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)
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.scenario(
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TwinScenario::responses(MODEL)
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.stream(false)
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.input_contains("Here is the conversation to summarize")
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.text("short summary"),
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)
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.scenario(TwinScenario::responses(MODEL).stream(true).text("Done."))
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.load(twin_openai().await)
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.await;
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}
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/// A client whose `openai` provider points at `base_url` and authenticates
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/// with `api_key`, the way the twin expects.
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async fn openai_client(base_url: String, api_key: String) -> Client {
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let catalog = fabro_llm::build_catalog(&fabro_config::LlmLayer::default(), &move |name| {
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(name == fabro_static::EnvVars::OPENAI_BASE_URL).then(|| base_url.clone())
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})
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.expect("catalog should build");
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client_from_env(
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catalog,
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move |name| (name == fabro_static::EnvVars::OPENAI_API_KEY).then(|| api_key.clone()),
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ClientOptions::standard(),
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)
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.await
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}
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