Merge pull request #2327 from arc53/research-agent

Research agent

Author: dartpain

.gitignore

@@ -2,6 +2,7 @@
 __pycache__/
 *.py[cod]
 *$py.class
+results.txt
 experiments/
 
 experiments

application/agents/agent_creator.py

@@ -1,7 +1,8 @@
 import logging
 
+from application.agents.agentic_agent import AgenticAgent
 from application.agents.classic_agent import ClassicAgent
-from application.agents.react_agent import ReActAgent
+from application.agents.research_agent import ResearchAgent
 from application.agents.workflow_agent import WorkflowAgent
 
 logger = logging.getLogger(__name__)
@@ -10,7 +11,9 @@
 class AgentCreator:
     agents = {
         "classic": ClassicAgent,
-        "react": ReActAgent,
+        "react": ClassicAgent,  # backwards compat: react falls back to classic
+        "agentic": AgenticAgent,
+        "research": ResearchAgent,
         "workflow": WorkflowAgent,
     }
 

application/agents/agentic_agent.py

@@ -0,0 +1,63 @@
+import logging
+from typing import Dict, Generator, Optional
+
+from application.agents.base import BaseAgent
+from application.agents.tools.internal_search import (
+    INTERNAL_TOOL_ID,
+    add_internal_search_tool,
+)
+from application.logging import LogContext
+
+logger = logging.getLogger(__name__)
+
+
+class AgenticAgent(BaseAgent):
+    """Agent where the LLM controls retrieval via tools.
+
+    Unlike ClassicAgent which pre-fetches docs into the prompt,
+    AgenticAgent gives the LLM an internal_search tool so it can
+    decide when, what, and whether to search.
+    """
+
+    def __init__(
+        self,
+        retriever_config: Optional[Dict] = None,
+        *args,
+        **kwargs,
+    ):
+        super().__init__(*args, **kwargs)
+        self.retriever_config = retriever_config or {}
+
+    def _gen_inner(
+        self, query: str, log_context: LogContext
+    ) -> Generator[Dict, None, None]:
+        tools_dict = self.tool_executor.get_tools()
+        add_internal_search_tool(tools_dict, self.retriever_config)
+        self._prepare_tools(tools_dict)
+
+        # 4. Build messages (prompt has NO pre-fetched docs)
+        messages = self._build_messages(self.prompt, query)
+
+        # 5. Call LLM — the handler manages the tool loop
+        llm_response = self._llm_gen(messages, log_context)
+
+        yield from self._handle_response(
+            llm_response, tools_dict, messages, log_context
+        )
+
+        # 6. Collect sources from internal search tool results
+        self._collect_internal_sources()
+
+        yield {"sources": self.retrieved_docs}
+        yield {"tool_calls": self._get_truncated_tool_calls()}
+
+        log_context.stacks.append(
+            {"component": "agent", "data": {"tool_calls": self.tool_calls.copy()}}
+        )
+
+    def _collect_internal_sources(self):
+        """Collect retrieved docs from the cached InternalSearchTool instance."""
+        cache_key = f"internal_search:{INTERNAL_TOOL_ID}:{self.user or ''}"
+        tool = self.tool_executor._loaded_tools.get(cache_key)
+        if tool and hasattr(tool, "retrieved_docs") and tool.retrieved_docs:
+            self.retrieved_docs = tool.retrieved_docs