Create agents.py

agents.py

@@ -0,0 +1,111 @@
+from __future__ import annotations
+
+from langchain_core.output_parsers import PydanticOutputParser
+from langchain_core.prompts import ChatPromptTemplate
+
+from schemas import CodeAnalysis, FeedbackSignal, Spec, TestCaseList, TestPlan
+
+
+def build_spec_agent(llm):
+    parser = PydanticOutputParser(pydantic_object=Spec)
+    prompt = ChatPromptTemplate.from_messages(
+        [
+            (
+                "system",
+                "You extract structured requirements from problem statements. "
+                "Return JSON only that matches the schema. No markdown.",
+            ),
+            (
+                "human",
+                "Problem statement:\n{problem}\n\n"
+                "User-provided description:\n{description}\n\n"
+                "User-provided constraints:\n{constraints}\n\n"
+                "Language: {language}\n\n"
+                "{format_instructions}",
+            ),
+        ]
+    )
+    return prompt, parser
+
+
+def build_code_analysis_agent(llm):
+    parser = PydanticOutputParser(pydantic_object=CodeAnalysis)
+    prompt = ChatPromptTemplate.from_messages(
+        [
+            (
+                "system",
+                "Analyze code behavior and risks. Return JSON only. No markdown.",
+            ),
+            (
+                "human",
+                "Language: {language}\n\nCode:\n{code}\n\n{format_instructions}",
+            ),
+        ]
+    )
+    return prompt, parser
+
+
+def build_test_plan_agent(llm):
+    parser = PydanticOutputParser(pydantic_object=TestPlan)
+    prompt = ChatPromptTemplate.from_messages(
+        [
+            (
+                "system",
+                "Create a structured testing plan with targets per category. "
+                "Return JSON only. No markdown.",
+            ),
+            (
+                "human",
+                "Spec:\n{spec}\n\nCode analysis:\n{analysis}\n\n"
+                "Known issues from previous iteration:\n{issues}\n\n"
+                "Required categories: Basic cases, boundary cases, random cases, "
+                "stress cases, invalid/robustness cases, bug-targeted cases.\n"
+                "Desired per-category count: {per_category}\n\n"
+                "{format_instructions}",
+            ),
+        ]
+    )
+    return prompt, parser
+
+
+def build_feedback_agent(llm):
+    parser = PydanticOutputParser(pydantic_object=FeedbackSignal)
+    prompt = ChatPromptTemplate.from_messages(
+        [
+            (
+                "system",
+                "Assess test plan quality. Return JSON only. No markdown.",
+            ),
+            (
+                "human",
+                "Spec:\n{spec}\n\nPlan:\n{plan}\n\n"
+                "Detected issues so far:\n{issues}\n\n"
+                "{format_instructions}",
+            ),
+        ]
+    )
+    return prompt, parser
+
+
+def build_test_generator_agent(llm):
+    parser = PydanticOutputParser(pydantic_object=TestCaseList)
+    prompt = ChatPromptTemplate.from_messages(
+        [
+            (
+                "system",
+                "Generate concrete test cases. Use plan.targets for counts. "
+                "Return JSON only. No markdown. "
+                "Do not use code, expressions, or functions. "
+                "All values must be literal JSON (strings, numbers, arrays, objects). "
+                "Do not use repeat or operators. "
+                "Keep any single string length <= 200 characters.",
+            ),
+            (
+                "human",
+                "Spec:\n{spec}\n\nPlan:\n{plan}\n\n"
+                "Student index: {student_id}\n\n"
+                "{format_instructions}",
+            ),
+        ]
+    )
+    return prompt, parser