Update deep research to run faster with fewer results

Author: xuro-langchain

langchain/deep-research/src/agent/configuration.py

@@ -44,7 +44,7 @@ class Configuration(BaseModel):
         metadata={
             "x_oap_ui_config": {
                 "type": "number",
-                "default": 3,
+                "default": 2,
                 "min": 1,
                 "max": 10,
                 "description": "Maximum number of retries for structured output calls from models"
@@ -96,7 +96,7 @@ class Configuration(BaseModel):
         metadata={
             "x_oap_ui_config": {
                 "type": "slider",
-                "default": 6,
+                "default": 4,
                 "min": 1,
                 "max": 10,
                 "step": 1,
@@ -119,11 +119,11 @@ class Configuration(BaseModel):
     )
     # Model Configuration
     summarization_model: str = Field(
-        default="openai:gpt-4.1-mini",
+        default="openai:gpt-4.1-nano",
         metadata={
             "x_oap_ui_config": {
                 "type": "text",
-                "default": "openai:gpt-4.1-mini",
+                "default": "openai:gpt-4.1-nano",
                 "description": "Model for summarizing research results from Tavily search results"
             }
         }
@@ -151,11 +151,11 @@ class Configuration(BaseModel):
         }
     )
     research_model: str = Field(
-        default="openai:gpt-4.1",
+        default="openai:gpt-4.1-nano",
         metadata={
             "x_oap_ui_config": {
                 "type": "text",
-                "default": "openai:gpt-4.1",
+                "default": "openai:gpt-4.1-nano",
                 "description": "Model for conducting research. NOTE: Make sure your Researcher Model supports the selected search API."
             }
         }
@@ -171,11 +171,11 @@ class Configuration(BaseModel):
         }
     )
     compression_model: str = Field(
-        default="openai:gpt-4.1",
+        default="openai:gpt-4.1-nano",
         metadata={
             "x_oap_ui_config": {
                 "type": "text",
-                "default": "openai:gpt-4.1",
+                "default": "openai:gpt-4.1-nano",
                 "description": "Model for compressing research findings from sub-agents. NOTE: Make sure your Compression Model supports the selected search API."
             }
         }
@@ -191,11 +191,11 @@ class Configuration(BaseModel):
         }
     )
     final_report_model: str = Field(
-        default="openai:gpt-4.1",
+        default="openai:gpt-4.1-nano",
         metadata={
             "x_oap_ui_config": {
                 "type": "text",
-                "default": "openai:gpt-4.1",
+                "default": "openai:gpt-4.1-nano",
                 "description": "Model for writing the final report from all research findings"
             }
         }

langchain/deep-research/src/agent/prompts.py

@@ -52,9 +52,10 @@
 You will return a single research question that will be used to guide the research.
 
 Guidelines:
-1. Maximize Specificity and Detail
+1. Maximize Specificity but ENSURE CONCISENESS
 - Include all known user preferences and explicitly list key attributes or dimensions to consider.
 - It is important that all details from the user are included in the instructions.
+- Critically, avoid repeating any information and prioritize conciseness in your generation.
 
 2. Fill in Unstated But Necessary Dimensions as Open-Ended
 - If certain attributes are essential for a meaningful output but the user has not provided them, explicitly state that they are open-ended or default to no specific constraint.
@@ -142,7 +143,7 @@
 
 <Available Tools>
 You have access to two main tools:
-1. **tavily_search**: For conducting web searches to gather information
+1. **tavily_search**: For conducting web searches to gather information. Supply max 3 queries to this tool.
 2. **think_tool**: For reflection and strategic planning during research
 {mcp_prompt}
 
@@ -245,10 +246,11 @@
 
 Please create a detailed answer to the overall research brief that:
 1. Is well-organized with proper headings (# for title, ## for sections, ### for subsections)
-2. Includes specific facts and insights from the research
-3. References relevant sources using [Title](URL) format
-4. Provides a balanced, thorough analysis. Be as comprehensive as possible, and include all information that is relevant to the overall research question. People are using you for deep research and will expect detailed, comprehensive answers.
-5. Includes a "Sources" section at the end with all referenced links
+2. Is concise and to the point. Avoid repeating information.
+3. Includes specific facts and insights from the research
+4. References relevant sources using [Title](URL) format
+5. Provides a balanced, thorough analysis. Be as comprehensive as possible, and include all information that is relevant to the overall research question. People are using you for deep research and will expect detailed, comprehensive answers.
+6. Includes a "Sources" section at the end with all referenced links
 
 You can structure your report in a number of different ways. Here are some examples:
 

langchain/deep-research/src/agent/utils.py

@@ -43,7 +43,7 @@
 @tool(description=TAVILY_SEARCH_DESCRIPTION)
 async def tavily_search(
     queries: List[str],
-    max_results: Annotated[int, InjectedToolArg] = 5,
+    max_results: Annotated[int, InjectedToolArg] = 2,
     topic: Annotated[Literal["general", "news", "finance"], InjectedToolArg] = "general",
     config: RunnableConfig = None
 ) -> str:
@@ -137,7 +137,7 @@ async def noop():
 
 async def tavily_search_async(
     search_queries, 
-    max_results: int = 5, 
+    max_results: int = 2, 
     topic: Literal["general", "news", "finance"] = "general", 
     include_raw_content: bool = True, 
     config: RunnableConfig = None