Sisyphus agent recreated in LangChain to figure out how it works and how to use it

2026-04-15 12:47:38 -06:00
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+"""
+Oracle agent node — the high-IQ architecture and debugging advisor.
+
+Loki equivalent: assets/agents/oracle/config.yaml + tools.sh
+
+In Loki, the oracle is a READ-ONLY advisor spawned for:
+    - Architecture decisions and multi-system tradeoffs
+    - Complex debugging (after 2+ failed fix attempts)
+    - Code/design review
+    - Risk assessment
+
+It uses temperature 0.2 (slightly higher than explore/coder for more creative
+reasoning) and ends with "ORACLE_COMPLETE".
+
+In LangGraph, oracle is a node that receives the full message history, reasons
+about the problem, and writes structured advice back.  It has read-only tools
+only — it never modifies files.
+
+Key Loki→LangGraph mapping:
+    - Loki oracle triggers (the "MUST spawn oracle when..." rules in sisyphus)
+      become routing conditions in the supervisor node.
+    - Oracle's structured output format (Analysis/Recommendation/Reasoning/Risks)
+      is enforced via the system prompt, same as in Loki.
+"""
+
+from __future__ import annotations
+
+from langchain_core.messages import SystemMessage
+from langchain_openai import ChatOpenAI
+
+from sisyphus_langchain.state import SisyphusState
+from sisyphus_langchain.tools.filesystem import (
+    list_directory,
+    read_file,
+    search_content,
+    search_files,
+)
+
+# ---------------------------------------------------------------------------
+# System prompt — faithfully mirrors oracle/config.yaml
+# ---------------------------------------------------------------------------
+ORACLE_SYSTEM_PROMPT = """\
+You are Oracle — a senior architect and debugger consulted for complex decisions.
+
+## Your Role
+
+You are READ-ONLY. You analyze, advise, and recommend. You do NOT implement.
+
+## When You're Consulted
+
+1. **Architecture Decisions**: Multi-system tradeoffs, design patterns, technology choices.
+2. **Complex Debugging**: After 2+ failed fix attempts, deep analysis needed.
+3. **Code Review**: Evaluating proposed designs or implementations.
+4. **Risk Assessment**: Security, performance, or reliability concerns.
+
+## Your Process
+
+1. **Understand**: Read relevant code, understand the full context.
+2. **Analyze**: Consider multiple angles and tradeoffs.
+3. **Recommend**: Provide clear, actionable advice.
+4. **Justify**: Explain your reasoning.
+
+## Output Format
+
+Structure your response as:
+
+## Analysis
+[Your understanding of the situation]
+
+## Recommendation
+[Clear, specific advice]
+
+## Reasoning
+[Why this is the right approach]
+
+## Risks/Considerations
+[What to watch out for]
+
+## Rules
+
+1. Never modify files — you advise, others implement.
+2. Be thorough — read all relevant context before advising.
+3. Be specific — general advice isn't helpful.
+4. Consider tradeoffs — there are rarely perfect solutions.
+5. Stay focused — answer the specific question asked.
+"""
+
+# Read-only tools — same set as explore (oracle never writes)
+ORACLE_TOOLS = [read_file, search_content, search_files, list_directory]
+
+
+def create_oracle_node(model_name: str = "gpt-4o", temperature: float = 0.2):
+    """
+    Factory that returns an oracle node function.
+
+    Oracle uses a more expensive model than explore because it needs deeper
+    reasoning.  In Loki, the model is inherited from the global config unless
+    overridden in oracle/config.yaml.
+
+    Args:
+        model_name: Model identifier (use a strong reasoning model).
+        temperature: LLM temperature (Loki oracle uses 0.2).
+    """
+    llm = ChatOpenAI(model=model_name, temperature=temperature).bind_tools(ORACLE_TOOLS)
+
+    def oracle_node(state: SisyphusState) -> dict:
+        """
+        LangGraph node: run the oracle agent.
+
+        Reads conversation history, applies the oracle system prompt,
+        invokes the LLM, and returns structured advice.
+        """
+        response = llm.invoke(
+            [SystemMessage(content=ORACLE_SYSTEM_PROMPT)] + state["messages"]
+        )
+        return {
+            "messages": [response],
+            "agent_outputs": {
+                **state.get("agent_outputs", {}),
+                "oracle": response.content,
+            },
+        }
+
+    return oracle_node