diff --git a/agent.py b/agent.py
deleted file mode 100644
index 43c056c..0000000
--- a/agent.py
+++ /dev/null
@@ -1,69 +0,0 @@
-"""Agent implementation using LangChain 1.x.
-
-The agent uses a simple tool‑based architecture.  The LLM is a local
-`ChatOllama` model (llama3).  Two tools are available:
-
-* ``search_local_kb`` – semantic search in Qdrant.
-* ``web_search`` – web search via Tavily.
-
-The system prompt instructs the LLM to decide which tool to use based on the
-question.  The response always contains a marker indicating the source
-(`chromadb`/`tavily`).  The marker is added by the LLM itself.
-"""
-
-from __future__ import annotations
-
-from typing import List
-
-from langchain_ollama import ChatOllama
-from langchain_core.prompts import ChatPromptTemplate
-from langchain_core.runnables import Runnable
-from langchain_core.tools import BaseTool
-
-# Import tools without relative import to allow top‑level import
-import tools
-
-# ---------------------------------------------------------------------------
-# Helper: create a tool list
-# ---------------------------------------------------------------------------
-
-def get_tools() -> List[BaseTool]:
-    """Return the list of tools used by the agent."""
-    return [tools.search_local_kb, tools.web_search]
-
-# ---------------------------------------------------------------------------
-# System prompt
-# ---------------------------------------------------------------------------
-SYSTEM_PROMPT = (
-    "You are an AI assistant that can answer questions using two sources: "
-    "1) a local knowledge base (Qdrant) and 2) the web via Tavily. "
-    "If the answer can be found in the local KB, use the `search_local_kb` tool. "
-    "If the answer requires up‑to‑date information, use the `web_search` tool. "
-    "Return the answer followed by a source marker on a new line: "
-    "`Source: chromadb` or `Source: tavily`."
-)
-
-# ---------------------------------------------------------------------------
-# Agent construction
-# ---------------------------------------------------------------------------
-
-def create_agent() -> Runnable:
-    """Create a runnable agent.
-
-    The agent is a simple chain: system prompt → user message → tool calls → LLM
-    response.  It uses the default tool‑calling behaviour of LangChain 1.x.
-    """
-    llm = ChatOllama(model="llama3", temperature=0.0)
-
-    # AgentExecutor can accept a system message via agent_kwargs
-    from langchain.agents import AgentExecutor
-
-    agent = AgentExecutor.from_llm_and_tools(
-        llm=llm,
-        tools=get_tools(),
-        verbose=True,
-        # Provide the system prompt so the LLM knows how to behave
-        agent_kwargs={"system_message": SYSTEM_PROMPT},
-    )
-
-    return agent
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