diff --git a/CLAUDE.md b/CLAUDE.md new file mode 100644 index 0000000..c58a851 --- /dev/null +++ b/CLAUDE.md @@ -0,0 +1,94 @@ +# Open Deep Research + +## About Open Deep Research + +Open Deep Research is an experimental, fully open-source research assistant that automates deep research and produces comprehensive reports on any topic. It's designed to help researchers, analysts, and curious individuals generate detailed, well-sourced reports without manual research overhead. + +### Key Features +- **Automated Research**: Searches multiple sources (web, academic papers, specialized databases) +- **Comprehensive Reports**: Generates structured markdown reports with proper citations +- **Multiple Search APIs**: Supports Tavily, Perplexity, Exa, ArXiv, PubMed, DuckDuckGo, and more +- **Flexible Models**: Compatible with any LLM that supports the `init_chat_model()` API +- **Quality Evaluation**: Built-in evaluation systems to assess report quality + +## Two Research Implementations + +Open Deep Research offers two distinct approaches to automated research, each with unique advantages: + +### 1. Graph-based Workflow Implementation + +The **graph-based implementation** (`src/open_deep_research/graph.py`) follows a structured plan-and-execute workflow: + +**Characteristics:** +- **Interactive Planning**: Uses a planner model to generate a structured report outline +- **Human-in-the-Loop**: Allows review and feedback on the report plan before execution +- **Sequential Process**: Creates sections one by one with reflection between iterations +- **Quality Focus**: Emphasizes report accuracy and structure through iterative refinement + +**Best for:** +- High-stakes research where accuracy is critical +- Reports requiring specific structure or customization +- Situations where you want control over the research process +- Academic or professional research contexts + +### 2. Multi-Agent Implementation + +The **multi-agent implementation** (`src/open_deep_research/multi_agent.py`) uses a supervisor-researcher architecture: + +**Characteristics:** +- **Supervisor Agent**: Manages overall research process and assembles final report +- **Parallel Research**: Multiple researcher agents work simultaneously on different sections +- **Speed Optimized**: Significantly faster due to parallel processing +- **Tool Specialization**: Each agent has specific tools for their role + +**Best for:** +- Quick research and rapid report generation +- Exploratory research where speed matters +- Situations with less need for human oversight +- Business intelligence and market research + +## Quality Evaluation + +This guide explains how to quickly test and evaluate the quality of reports generated by Open Deep Research using the pytest evaluation system. The pytest evaluation system provides an easy way to: +- Test both research agent implementations (multi-agent and graph-based) +- Get immediate visual feedback with rich console output +- Verify report quality against 9 comprehensive criteria +- Compare different model configurations +- Track results in LangSmith for analysis + +### Test Specific Agent +```bash +# Test only the multi-agent implementation +python tests/run_test.py --agent multi_agent + +# Test only the graph-based implementation +python tests/run_test.py --agent graph +``` + +## Understanding the Output + +### Console Output +The evaluation provides rich visual feedback including: + +1. **Test Configuration Panel**: Shows which agent and search API are being tested +2. **Model Configuration Table**: Displays all model settings in a formatted table +3. **Report Generation Status**: Real-time feedback during report creation +4. **Generated Report Display**: Full report rendered in markdown format +5. **Evaluation Results**: + - **PASSED/FAILED** status in color-coded panel + - **Report Structure Analysis**: Table showing section headers + - **Evaluation Justification**: Detailed explanation from the evaluator + +### What Gets Evaluated + +The system checks reports against 9 quality criteria: + +1. **Topic Relevance (Overall)**: Does the report address the input topic thoroughly? +2. **Section Relevance (Critical)**: Are all sections directly relevant to the main topic? +3. **Structure and Flow**: Do sections flow logically and create a cohesive narrative? +4. **Introduction Quality**: Does the introduction provide context and scope? +5. **Conclusion Quality**: Does the conclusion summarize key findings? +6. **Structural Elements**: Proper use of tables, lists, etc. +7. **Section Headers**: Correct Markdown formatting (# for title, ## for sections) +8. **Citations**: Proper source citation in each main body section +9. **Overall Quality**: Well-researched, accurate, and professionally written \ No newline at end of file diff --git a/README.md b/README.md index fb10b3f..f58c0f0 100644 --- a/README.md +++ b/README.md @@ -153,6 +153,17 @@ The multi-agent implementation uses a supervisor-researcher architecture: This implementation focuses on efficiency and parallelization, making it ideal for faster report generation with less direct user involvement. +You can customize the multi-agent implementation through several parameters: + +- `supervisor_model`: Model for the supervisor agent (default: "anthropic:claude-3-5-sonnet-latest") +- `researcher_model`: Model for researcher agents (default: "anthropic:claude-3-5-sonnet-latest") +- `number_of_queries`: Number of search queries to generate per section (default: 2) +- `search_api`: API to use for web searches (default: "tavily", options include "duckduckgo", "none") +- `ask_for_clarification`: Whether the supervisor should ask clarifying questions before research (default: false) - **Important**: Set to `true` to enable the Question tool for the supervisor agent +- `mcp_server_config`: Configuration for MCP servers (optional) +- `mcp_prompt`: Additional instructions for using MCP tools (optional) +- `mcp_tools_to_include`: Specific MCP tools to include (optional) + ## MCP (Model Context Protocol) Support The multi-agent implementation (`src/open_deep_research/multi_agent.py`) supports MCP servers to extend research capabilities beyond web search. MCP tools are available to research agents alongside or instead of traditional search tools, enabling access to local files, databases, APIs, and other data sources. @@ -211,7 +222,13 @@ MCP server config: MCP prompt: ``` -Step 1: Use the `list_allowed_directories` tool to get the list of allowed directories. Step 2: Use the `read_file` tool to read files in the allowed directory. +CRITICAL: You MUST follow this EXACT sequence when using filesystem tools: + +1. FIRST: Call `list_allowed_directories` tool to discover allowed directories +2. SECOND: Call `list_directory` tool on a specific directory from step 1 to see available files +3. THIRD: Call `read_file` tool to read specific files found in step 2 + +DO NOT call `list_directory` or `read_file` until you have first called `list_allowed_directories`. You must discover the allowed directories before attempting to browse or read files. ``` MCP tools: @@ -221,7 +238,7 @@ list_directory read_file ``` -Example test case that you can provide: +Example test topic and follow-up feedback that you can provide that will reference the included file: Topic: ``` @@ -297,14 +314,41 @@ groq.APIError: Failed to call a function. Please adjust your prompt. See 'failed (7) For working with local models via Ollama, see [here](https://github.com/langchain-ai/open_deep_research/issues/65#issuecomment-2743586318). -## Testing Report Quality +## Evaluation Systems + +Open Deep Research includes two comprehensive evaluation systems to assess report quality and performance: -To compare the quality of reports generated by both implementations: +### 1. Pytest-based Evaluation System +A developer-friendly testing framework that provides immediate feedback during development and testing cycles. + +#### **Features:** +- **Rich Console Output**: Formatted tables, progress indicators, and color-coded results +- **Binary Pass/Fail Testing**: Clear success/failure criteria for CI/CD integration +- **LangSmith Integration**: Automatic experiment tracking and logging +- **Flexible Configuration**: Extensive CLI options for different testing scenarios +- **Real-time Feedback**: Live output during test execution + +#### **Evaluation Criteria:** +The system evaluates reports against 9 comprehensive quality dimensions: +- Topic relevance (overall and section-level) +- Structure and logical flow +- Introduction and conclusion quality +- Proper use of structural elements (headers, citations) +- Markdown formatting compliance +- Citation quality and source attribution +- Overall research depth and accuracy + +#### **Usage:** ```bash -# Test with default Anthropic models +# Run all agents with default settings python tests/run_test.py --all +# Test specific agent with custom models +python tests/run_test.py --agent multi_agent \ + --supervisor-model "anthropic:claude-3-7-sonnet-latest" \ + --search-api tavily + # Test with OpenAI o3 models python tests/run_test.py --all \ --supervisor-model "openai:o3" \ @@ -317,7 +361,68 @@ python tests/run_test.py --all \ --search-api "tavily" ``` -The test results will be logged to LangSmith, allowing you to compare the quality of reports generated by each implementation with different model configurations. +#### **Key Files:** +- `tests/run_test.py`: Main test runner with rich CLI interface +- `tests/test_report_quality.py`: Core test implementation +- `tests/conftest.py`: Pytest configuration and CLI options + +### 2. LangSmith Evaluate API System + +A comprehensive batch evaluation system designed for detailed analysis and comparative studies. + +#### **Features:** +- **Multi-dimensional Scoring**: Four specialized evaluators with 1-5 scale ratings +- **Weighted Criteria**: Detailed scoring with customizable weights for different quality aspects +- **Dataset-driven Evaluation**: Batch processing across multiple test cases +- **Performance Optimization**: Caching with extended TTL for evaluator prompts +- **Professional Reporting**: Structured analysis with improvement recommendations + +#### **Evaluation Dimensions:** + +1. **Overall Quality** (7 weighted criteria): + - Research depth and source quality (20%) + - Analytical rigor and critical thinking (15%) + - Structure and organization (20%) + - Practical value and actionability (10%) + - Balance and objectivity (15%) + - Writing quality and clarity (10%) + - Professional presentation (10%) + +2. **Relevance**: Section-by-section topic relevance analysis with strict criteria + +3. **Structure**: Assessment of logical flow, formatting, and citation practices + +4. **Groundedness**: Evaluation of alignment with retrieved context and sources + +#### **Usage:** +```bash +# Run comprehensive evaluation on LangSmith datasets +python tests/evals/run_evaluate.py +``` + +#### **Key Files:** +- `tests/evals/run_evaluate.py`: Main evaluation script +- `tests/evals/evaluators.py`: Four specialized evaluator functions +- `tests/evals/prompts.py`: Detailed evaluation prompts for each dimension +- `tests/evals/target.py`: Report generation workflows + +### When to Use Each System + +**Use Pytest System for:** +- Development and debugging cycles +- CI/CD pipeline integration +- Quick model comparison experiments +- Interactive testing with immediate feedback +- Gate-keeping before production deployments + +**Use LangSmith System for:** +- Comprehensive model evaluation across datasets +- Research and analysis of system performance +- Detailed performance profiling and benchmarking +- Comparative studies between different configurations +- Production monitoring and quality assurance + +Both evaluation systems complement each other and provide comprehensive coverage for different use cases and development stages. ## UX diff --git a/pyproject.toml b/pyproject.toml index 818d1aa..00e06c9 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -12,7 +12,7 @@ dependencies = [ "langgraph>=0.2.55", "langchain-community>=0.3.9", "langchain-openai>=0.3.7", - "langchain-anthropic>=0.3.9", + "langchain-anthropic>=0.3.15", "langchain-mcp-adapters>=0.1.6", "langchain-deepseek>=0.1.2", "langchain-tavily", @@ -33,7 +33,11 @@ dependencies = [ "markdownify>=0.11.6", "azure-identity>=1.21.0", "azure-search>=1.0.0b2", - "azure-search-documents>=11.5.2" + "azure-search-documents>=11.5.2", + "rich>=13.0.0", + "langgraph-cli[inmem]>=0.3.1", + "langsmith>=0.3.37", + "langchain-core>=0.3.64", ] [project.optional-dependencies] @@ -44,10 +48,11 @@ requires = ["setuptools>=73.0.0", "wheel"] build-backend = "setuptools.build_meta" [tool.setuptools] -packages = ["open_deep_research"] +packages = ["open_deep_research", "tests"] [tool.setuptools.package-dir] "open_deep_research" = "src/open_deep_research" +"tests" = "tests" [tool.setuptools.package-data] "*" = ["py.typed"] diff --git a/src/open_deep_research/configuration.py b/src/open_deep_research/configuration.py index 587a31f..92a46d2 100644 --- a/src/open_deep_research/configuration.py +++ b/src/open_deep_research/configuration.py @@ -29,49 +29,68 @@ class SearchAPI(Enum): NONE = "none" @dataclass(kw_only=True) -class Configuration: - """The configurable fields for the chatbot.""" +class WorkflowConfiguration: + """Configuration for the workflow/graph-based implementation (graph.py).""" # Common configuration - report_structure: str = DEFAULT_REPORT_STRUCTURE # Defaults to the default report structure - search_api: SearchAPI = SearchAPI.TAVILY # Default to TAVILY + report_structure: str = DEFAULT_REPORT_STRUCTURE + search_api: SearchAPI = SearchAPI.TAVILY search_api_config: Optional[Dict[str, Any]] = None process_search_results: Literal["summarize", "split_and_rerank"] | None = None - # Summarization model for summarizing search results - # will be used if summarize_search_results is True summarization_model_provider: str = "anthropic" summarization_model: str = "claude-3-5-haiku-latest" - # Whether to include search results string in the agent output state - # This is used for evaluation purposes only include_source_str: bool = False - # Graph-specific configuration + # Workflow-specific configuration number_of_queries: int = 2 # Number of search queries to generate per iteration max_search_depth: int = 2 # Maximum number of reflection + search iterations - planner_provider: str = "anthropic" # Defaults to Anthropic as provider - planner_model: str = "claude-3-7-sonnet-latest" # Defaults to claude-3-7-sonnet-latest - planner_model_kwargs: Optional[Dict[str, Any]] = None # kwargs for planner_model - writer_provider: str = "anthropic" # Defaults to Anthropic as provider - writer_model: str = "claude-3-5-sonnet-latest" # Defaults to claude-3-5-sonnet-latest - writer_model_kwargs: Optional[Dict[str, Any]] = None # kwargs for writer_model + planner_provider: str = "anthropic" + planner_model: str = "claude-3-7-sonnet-latest" + planner_model_kwargs: Optional[Dict[str, Any]] = None + writer_provider: str = "anthropic" + writer_model: str = "claude-3-7-sonnet-latest" + writer_model_kwargs: Optional[Dict[str, Any]] = None + + @classmethod + def from_runnable_config( + cls, config: Optional[RunnableConfig] = None + ) -> "WorkflowConfiguration": + """Create a WorkflowConfiguration instance from a RunnableConfig.""" + configurable = ( + config["configurable"] if config and "configurable" in config else {} + ) + values: dict[str, Any] = { + f.name: os.environ.get(f.name.upper(), configurable.get(f.name)) + for f in fields(cls) + if f.init + } + return cls(**{k: v for k, v in values.items() if v}) + +@dataclass(kw_only=True) +class MultiAgentConfiguration: + """Configuration for the multi-agent implementation (multi_agent.py).""" + # Common configuration + search_api: SearchAPI = SearchAPI.TAVILY + search_api_config: Optional[Dict[str, Any]] = None + process_search_results: Literal["summarize", "split_and_rerank"] | None = None + summarization_model_provider: str = "anthropic" + summarization_model: str = "claude-3-5-haiku-latest" # Multi-agent specific configuration - supervisor_model: str = "openai:gpt-4.1" # Model for supervisor agent in multi-agent setup - researcher_model: str = "openai:gpt-4.1" # Model for research agents in multi-agent setup + number_of_queries: int = 2 # Number of search queries to generate per section + supervisor_model: str = "anthropic:claude-3-7-sonnet-latest" + researcher_model: str = "anthropic:claude-3-7-sonnet-latest" + final_report_model: str = "openai:gpt-4.1" ask_for_clarification: bool = False # Whether to ask for clarification from the user - # MCP server configuration for multi-agent setup - # see examples here: https://github.com/langchain-ai/langchain-mcp-adapters#client-1 + # MCP server configuration mcp_server_config: Optional[Dict[str, Any]] = None - # optional prompt to append to the researcher agent prompt mcp_prompt: Optional[str] = None - # optional list of MCP tool names to include in the researcher agent - # if not set, all MCP tools across all servers in the config will be included mcp_tools_to_include: Optional[list[str]] = None @classmethod def from_runnable_config( cls, config: Optional[RunnableConfig] = None - ) -> "Configuration": - """Create a Configuration instance from a RunnableConfig.""" + ) -> "MultiAgentConfiguration": + """Create a MultiAgentConfiguration instance from a RunnableConfig.""" configurable = ( config["configurable"] if config and "configurable" in config else {} ) @@ -81,3 +100,6 @@ def from_runnable_config( if f.init } return cls(**{k: v for k, v in values.items() if v}) + +# Keep the old Configuration class for backward compatibility +Configuration = WorkflowConfiguration diff --git a/src/open_deep_research/graph.py b/src/open_deep_research/graph.py index d1c9a7a..44e70e1 100644 --- a/src/open_deep_research/graph.py +++ b/src/open_deep_research/graph.py @@ -29,7 +29,7 @@ section_writer_inputs ) -from open_deep_research.configuration import Configuration +from open_deep_research.configuration import WorkflowConfiguration from open_deep_research.utils import ( format_sections, get_config_value, @@ -67,7 +67,7 @@ async def generate_report_plan(state: ReportState, config: RunnableConfig): feedback = " /// ".join(feedback_list) if feedback_list else "" # Get configuration - configurable = Configuration.from_runnable_config(config) + configurable = WorkflowConfiguration.from_runnable_config(config) report_structure = configurable.report_structure number_of_queries = configurable.number_of_queries search_api = get_config_value(configurable.search_api) @@ -210,7 +210,7 @@ async def generate_queries(state: SectionState, config: RunnableConfig): section = state["section"] # Get configuration - configurable = Configuration.from_runnable_config(config) + configurable = WorkflowConfiguration.from_runnable_config(config) number_of_queries = configurable.number_of_queries # Generate queries @@ -252,7 +252,7 @@ async def search_web(state: SectionState, config: RunnableConfig): search_queries = state["search_queries"] # Get configuration - configurable = Configuration.from_runnable_config(config) + configurable = WorkflowConfiguration.from_runnable_config(config) search_api = get_config_value(configurable.search_api) search_api_config = configurable.search_api_config or {} # Get the config dict, default to empty params_to_pass = get_search_params(search_api, search_api_config) # Filter parameters @@ -289,7 +289,7 @@ async def write_section(state: SectionState, config: RunnableConfig) -> Command[ source_str = state["source_str"] # Get configuration - configurable = Configuration.from_runnable_config(config) + configurable = WorkflowConfiguration.from_runnable_config(config) # Format system instructions section_writer_inputs_formatted = section_writer_inputs.format(topic=topic, @@ -368,7 +368,7 @@ async def write_final_sections(state: SectionState, config: RunnableConfig): """ # Get configuration - configurable = Configuration.from_runnable_config(config) + configurable = WorkflowConfiguration.from_runnable_config(config) # Get state topic = state["topic"] @@ -430,7 +430,7 @@ def compile_final_report(state: ReportState, config: RunnableConfig): """ # Get configuration - configurable = Configuration.from_runnable_config(config) + configurable = WorkflowConfiguration.from_runnable_config(config) # Get sections sections = state["sections"] @@ -484,7 +484,7 @@ def initiate_final_section_writing(state: ReportState): # Outer graph for initial report plan compiling results from each section -- # Add nodes -builder = StateGraph(ReportState, input=ReportStateInput, output=ReportStateOutput, config_schema=Configuration) +builder = StateGraph(ReportState, input=ReportStateInput, output=ReportStateOutput, config_schema=WorkflowConfiguration) builder.add_node("generate_report_plan", generate_report_plan) builder.add_node("human_feedback", human_feedback) builder.add_node("build_section_with_web_research", section_builder.compile()) diff --git a/src/open_deep_research/multi_agent.py b/src/open_deep_research/multi_agent.py index f5fa935..72aeb04 100644 --- a/src/open_deep_research/multi_agent.py +++ b/src/open_deep_research/multi_agent.py @@ -4,6 +4,7 @@ import warnings from langchain.chat_models import init_chat_model +from langchain_core.messages import ToolMessage, filter_messages from langchain_core.tools import tool, BaseTool from langchain_core.runnables import RunnableConfig from langchain_mcp_adapters.client import MultiServerMCPClient @@ -12,21 +13,37 @@ from langgraph.types import Command, Send from langgraph.graph import START, END, StateGraph -from open_deep_research.configuration import Configuration +from open_deep_research.configuration import MultiAgentConfiguration from open_deep_research.utils import ( get_config_value, tavily_search, duckduckgo_search, get_today_str, - load_mcp_server_config ) - -from open_deep_research.prompts import SUPERVISOR_INSTRUCTIONS, RESEARCH_INSTRUCTIONS +from langchain_core.messages import BaseMessage + +from open_deep_research.prompts import SUPERVISOR_INSTRUCTIONS, RESEARCH_INSTRUCTIONS, FINAL_REPORT_WRITING_INSTRUCTIONS + +def extract_research_content(messages: List[BaseMessage]) -> str: + """Extract all tool call content from research agent messages.""" + research_content = [] + for message in messages: + if isinstance(message, ToolMessage): + # Extract tool call results (search results, MCP results, etc.) + content = message.content + tool_name = message.tool_call_id + if tool_name != "FinishResearch" and content.strip(): # Exclude FinishResearch and empty content + research_content.append(f"=== {tool_name} Results ===\n{content}\n") + + if not research_content: + return "No research content was collected." + + return "\n".join(research_content) ## Tools factory - will be initialized based on configuration def get_search_tool(config: RunnableConfig): """Get the appropriate search tool based on configuration""" - configurable = Configuration.from_runnable_config(config) + configurable = MultiAgentConfiguration.from_runnable_config(config) search_api = get_config_value(configurable.search_api) # Return None if no search tool is requested @@ -67,24 +84,6 @@ class Sections(BaseModel): description="Sections of the report.", ) -class Introduction(BaseModel): - """Introduction to the report.""" - name: str = Field( - description="Name for the report.", - ) - content: str = Field( - description="The content of the introduction, giving an overview of the report." - ) - -class Conclusion(BaseModel): - """Conclusion to the report.""" - name: str = Field( - description="Name for the conclusion of the report.", - ) - content: str = Field( - description="The content of the conclusion, summarizing the report." - ) - class Question(BaseModel): """Ask a follow-up question to clarify the report scope.""" question: str = Field( @@ -100,40 +99,28 @@ class FinishReport(BaseModel): """Finish the report.""" ## State -class ReportStateOutput(TypedDict): +class ReportStateOutput(MessagesState): final_report: str # Final report # for evaluation purposes only - # this is included only if configurable.include_source_str is True - source_str: str # String of formatted source content from web search + source_str: list[str] # String of formatted source content from web search class ReportState(MessagesState): sections: list[str] # List of report sections - completed_sections: Annotated[list[Section], operator.add] # Send() API key final_report: str # Final report - question_asked: bool # Track if a clarifying question has been asked - # for evaluation purposes only - # this is included only if configurable.include_source_str is True - source_str: Annotated[str, operator.add] # String of formatted source content from web search + source_str: Annotated[list[str], operator.add] # String of formatted source content from web search class SectionState(MessagesState): section: str # Report section - completed_sections: list[Section] # Final key we duplicate in outer state for Send() API - # for evaluation purposes only - # this is included only if configurable.include_source_str is True - source_str: str # String of formatted source content from web search + source_str: list[str] # Final key we duplicate in outer state for Send() API class SectionOutputState(TypedDict): - completed_sections: list[Section] # Final key we duplicate in outer state for Send() API - # for evaluation purposes only - # this is included only if configurable.include_source_str is True - source_str: str # String of formatted source content from web search - + source_str: list[str] # Final key we duplicate in outer state for Send() API async def _load_mcp_tools( config: RunnableConfig, existing_tool_names: set[str], ) -> list[BaseTool]: - configurable = Configuration.from_runnable_config(config) + configurable = MultiAgentConfiguration.from_runnable_config(config) if not configurable.mcp_server_config: return [] @@ -160,22 +147,25 @@ async def _load_mcp_tools( # Tool lists will be built dynamically based on configuration -def get_supervisor_tools(config: RunnableConfig) -> list[BaseTool]: +async def get_supervisor_tools(config: RunnableConfig) -> list[BaseTool]: """Get supervisor tools based on configuration""" - configurable = Configuration.from_runnable_config(config) + configurable = MultiAgentConfiguration.from_runnable_config(config) search_tool = get_search_tool(config) - tools = [tool(Sections), tool(Introduction), tool(Conclusion), tool(FinishReport)] + tools = [tool(Sections), tool(FinishReport)] if configurable.ask_for_clarification: tools.append(tool(Question)) if search_tool is not None: tools.append(search_tool) # Add search tool, if available + existing_tool_names = {cast(BaseTool, tool).name for tool in tools} + mcp_tools = await _load_mcp_tools(config, existing_tool_names) + tools.extend(mcp_tools) return tools async def get_research_tools(config: RunnableConfig) -> list[BaseTool]: """Get research tools based on configuration""" search_tool = get_search_tool(config) - tools = [tool(Section), tool(FinishResearch)] + tools = [tool(FinishResearch)] if search_tool is not None: tools.append(search_tool) # Add search tool, if available existing_tool_names = {cast(BaseTool, tool).name for tool in tools} @@ -191,23 +181,21 @@ async def supervisor(state: ReportState, config: RunnableConfig): messages = state["messages"] # Get configuration - configurable = Configuration.from_runnable_config(config) + configurable = MultiAgentConfiguration.from_runnable_config(config) supervisor_model = get_config_value(configurable.supervisor_model) - + # Initialize the model llm = init_chat_model(model=supervisor_model) - # If sections have been completed, but we don't yet have the final report, then we need to initiate writing the introduction and conclusion - if state.get("completed_sections") and not state.get("final_report"): - research_complete_message = {"role": "user", "content": "Research is complete. Now write the introduction and conclusion for the report. Here are the completed main body sections: \n\n" + "\n\n".join([s.content for s in state["completed_sections"]])} + # If research has been completed, but we don't yet have the final report, then we need to initiate writing the final report + if state.get("source_str") and not state.get("final_report"): + # Join all research results from all agents into a single string + all_research = "\n\n".join(state["source_str"]) if isinstance(state["source_str"], list) else state["source_str"] + research_complete_message = {"role": "user", "content": f"Research is now complete. Now write a final report based using the following research results: {all_research}."} messages = messages + [research_complete_message] # Get tools based on configuration - supervisor_tool_list = get_supervisor_tools(config) - - # Remove Question tool if a question has already been asked - if state.get("question_asked", False): - supervisor_tool_list = [tool for tool in supervisor_tool_list if tool.name != "Question"] + supervisor_tool_list = await get_supervisor_tools(config) llm_with_tools = ( llm @@ -218,6 +206,12 @@ async def supervisor(state: ReportState, config: RunnableConfig): tool_choice="any" ) ) + + # Get system prompt + system_prompt = SUPERVISOR_INSTRUCTIONS.format(today=get_today_str()) + if configurable.mcp_prompt: + system_prompt += f"\n\n{configurable.mcp_prompt}" + # Invoke return { "messages": [ @@ -225,7 +219,7 @@ async def supervisor(state: ReportState, config: RunnableConfig): [ { "role": "system", - "content": SUPERVISOR_INSTRUCTIONS.format(today=get_today_str()) + "content": system_prompt } ] + messages @@ -233,24 +227,15 @@ async def supervisor(state: ReportState, config: RunnableConfig): ] } -async def supervisor_tools(state: ReportState, config: RunnableConfig) -> Command[Literal["supervisor", "research_team", "__end__"]]: +async def supervisor_tools(state: ReportState, config: RunnableConfig) -> Command[Literal["supervisor", "research_team", "finish_report"]]: """Performs the tool call and sends to the research agent""" - configurable = Configuration.from_runnable_config(config) - + result = [] sections_list = [] - intro_content = None - conclusion_content = None - source_str = "" # Get tools based on configuration - supervisor_tool_list = get_supervisor_tools(config) + supervisor_tool_list = await get_supervisor_tools(config) supervisor_tools_by_name = {tool.name: tool for tool in supervisor_tool_list} - search_tool_names = { - tool.name - for tool in supervisor_tool_list - if tool.metadata is not None and tool.metadata.get("type") == "search" - } # First process all tool calls to ensure we respond to each one (required for OpenAI) for tool_call in state["messages"][-1].tool_calls: @@ -273,70 +258,46 @@ async def supervisor_tools(state: ReportState, config: RunnableConfig) -> Comma # Question tool was called - return to supervisor to ask the question question_obj = cast(Question, observation) result.append({"role": "assistant", "content": question_obj.question}) - return Command(goto=END, update={"messages": result, "question_asked": True}) + return Command(goto=END, update={"messages": result}) elif tool_call["name"] == "Sections": sections_list = cast(Sections, observation).sections - elif tool_call["name"] == "Introduction": - # Format introduction with proper H1 heading if not already formatted - observation = cast(Introduction, observation) - if not observation.content.startswith("# "): - intro_content = f"# {observation.name}\n\n{observation.content}" - else: - intro_content = observation.content - elif tool_call["name"] == "Conclusion": - # Format conclusion with proper H2 heading if not already formatted - observation = cast(Conclusion, observation) - if not observation.content.startswith("## "): - conclusion_content = f"## {observation.name}\n\n{observation.content}" - else: - conclusion_content = observation.content - elif tool_call["name"] in search_tool_names and configurable.include_source_str: - source_str += cast(str, observation) + elif tool_call["name"] == "FinishReport": + return Command(goto="finish_report") + # After processing all tool calls, decide what to do next if sections_list: # Send the sections to the research agents - return Command(goto=[Send("research_team", {"section": s}) for s in sections_list], update={"messages": result}) - elif intro_content: - # Store introduction while waiting for conclusion - # Append to messages to guide the LLM to write conclusion next - result.append({"role": "user", "content": "Introduction written. Now write a conclusion section."}) - state_update = { - "final_report": intro_content, - "messages": result, - } - elif conclusion_content: - # Get all sections and combine in proper order: Introduction, Body Sections, Conclusion - intro = state.get("final_report", "") - body_sections = "\n\n".join([s.content for s in state["completed_sections"]]) - - # Assemble final report in correct order - complete_report = f"{intro}\n\n{body_sections}\n\n{conclusion_content}" - - # Append to messages to indicate completion - result.append({"role": "user", "content": "Report is now complete with introduction, body sections, and conclusion."}) - - state_update = { - "final_report": complete_report, - "messages": result, - } + return Command(goto=[Send("research_team", {"section": s}) for s in sections_list], update={"messages": result, "sections": sections_list}) + else: # Default case (for search tools, etc.) state_update = {"messages": result} - # Include source string for evaluation - if configurable.include_source_str and source_str: - state_update["source_str"] = source_str - return Command(goto="supervisor", update=state_update) +async def finish_report(state: ReportState, config: RunnableConfig) -> str: + """Extract the final report from the messages""" + configurable = MultiAgentConfiguration.from_runnable_config(config) + model = get_config_value(configurable.final_report_model) + llm = init_chat_model(model=model) + final_report = await llm.ainvoke(FINAL_REPORT_WRITING_INSTRUCTIONS.format( + sources_str="\n\n".join(state["source_str"]), + sections="\n\n".join(state["sections"]), + messages="\n\n".join([ + m.pretty_repr() + for m in filter_messages(state["messages"], exclude_tool_calls=True) + ]) + )) + return {"final_report": final_report.content} + async def supervisor_should_continue(state: ReportState) -> str: """Decide if we should continue the loop or stop based upon whether the LLM made a tool call""" messages = state["messages"] last_message = messages[-1] - # End because the supervisor asked a question or is finished - if not last_message.tool_calls or (len(last_message.tool_calls) == 1 and last_message.tool_calls[0]["name"] == "FinishReport"): + # End because the supervisor asked a question or FinishReport was called + if not last_message.tool_calls or (isinstance(last_message, ToolMessage) and last_message.name == "FinishReport"): # Exit the graph return END @@ -347,7 +308,7 @@ async def research_agent(state: SectionState, config: RunnableConfig): """LLM decides whether to call a tool or not""" # Get configuration - configurable = Configuration.from_runnable_config(config) + configurable = MultiAgentConfiguration.from_runnable_config(config) researcher_model = get_config_value(configurable.researcher_model) # Initialize the model @@ -363,6 +324,11 @@ async def research_agent(state: SectionState, config: RunnableConfig): if configurable.mcp_prompt: system_prompt += f"\n\n{configurable.mcp_prompt}" + # Ensure we have at least one user message (required by Anthropic) + messages = state.get("messages", []) + if not messages: + messages = [{"role": "user", "content": f"Please research and write the section: {state['section']}"}] + return { "messages": [ # Enforce tool calling to either perform more search or call the Section tool to write the section @@ -376,28 +342,20 @@ async def research_agent(state: SectionState, config: RunnableConfig): "content": system_prompt } ] - + state["messages"] + + messages ) ] } async def research_agent_tools(state: SectionState, config: RunnableConfig): """Performs the tool call and route to supervisor or continue the research loop""" - configurable = Configuration.from_runnable_config(config) - + result = [] - completed_section = None - source_str = "" # Get tools based on configuration research_tool_list = await get_research_tools(config) research_tools_by_name = {tool.name: tool for tool in research_tool_list} - search_tool_names = { - tool.name - for tool in research_tool_list - if tool.metadata is not None and tool.metadata.get("type") == "search" - } - + # Process all tool calls first (required for OpenAI) for tool_call in state["messages"][-1].tool_calls: # Get the tool @@ -413,24 +371,14 @@ async def research_agent_tools(state: SectionState, config: RunnableConfig): "content": observation, "name": tool_call["name"], "tool_call_id": tool_call["id"]}) - - # Store the section observation if a Section tool was called - if tool_call["name"] == "Section": - completed_section = cast(Section, observation) - - # Store the source string if a search tool was called - if tool_call["name"] in search_tool_names and configurable.include_source_str: - source_str += cast(str, observation) # After processing all tools, decide what to do next - state_update = {"messages": result} - if completed_section: - # Write the completed section to state and return to the supervisor - state_update["completed_sections"] = [completed_section] - if configurable.include_source_str and source_str: - state_update["source_str"] = source_str + return {"messages": result} + +async def finish_research(state: SectionState, config: RunnableConfig): + # Extract all research content from the complete message history (including current results) + return {"source_str": [extract_research_content(state["messages"])]} - return state_update async def research_agent_should_continue(state: SectionState) -> str: """Decide if we should continue the loop or stop based upon whether the LLM made a tool call""" @@ -440,29 +388,32 @@ async def research_agent_should_continue(state: SectionState) -> str: if last_message.tool_calls[0]["name"] == "FinishResearch": # Research is done - return to supervisor - return END + return "finish_research" else: return "research_agent_tools" """Build the multi-agent workflow""" # Research agent workflow -research_builder = StateGraph(SectionState, output=SectionOutputState, config_schema=Configuration) +research_builder = StateGraph(SectionState, output=SectionOutputState, config_schema=MultiAgentConfiguration) research_builder.add_node("research_agent", research_agent) research_builder.add_node("research_agent_tools", research_agent_tools) +research_builder.add_node("finish_research", finish_research) research_builder.add_edge(START, "research_agent") research_builder.add_conditional_edges( "research_agent", research_agent_should_continue, - ["research_agent_tools", END] + ["research_agent_tools", "finish_research"] ) research_builder.add_edge("research_agent_tools", "research_agent") +research_builder.add_edge("finish_research", END) # Supervisor workflow -supervisor_builder = StateGraph(ReportState, input=MessagesState, output=ReportStateOutput, config_schema=Configuration) +supervisor_builder = StateGraph(ReportState, input=MessagesState, output=ReportStateOutput, config_schema=MultiAgentConfiguration) supervisor_builder.add_node("supervisor", supervisor) supervisor_builder.add_node("supervisor_tools", supervisor_tools) supervisor_builder.add_node("research_team", research_builder.compile()) +supervisor_builder.add_node("finish_report", finish_report) # Flow of the supervisor agent supervisor_builder.add_edge(START, "supervisor") @@ -472,5 +423,6 @@ async def research_agent_should_continue(state: SectionState) -> str: ["supervisor_tools", END] ) supervisor_builder.add_edge("research_team", "supervisor") +supervisor_builder.add_edge("finish_report", END) graph = supervisor_builder.compile() \ No newline at end of file diff --git a/src/open_deep_research/prompts.py b/src/open_deep_research/prompts.py index cd2b88b..0a475d1 100644 --- a/src/open_deep_research/prompts.py +++ b/src/open_deep_research/prompts.py @@ -223,6 +223,7 @@ - 50-100 word limit - Write in simple and clear language - Focus on the core motivation for the report in 1-2 paragraphs +- Preview the specific content covered in the main body sections (mention key examples, case studies, or findings) - Use a clear narrative arc to introduce the report - Include NO structural elements (no lists or tables) - No sources section needed @@ -230,6 +231,8 @@ For Conclusion/Summary: - Use ## for section title (Markdown format) - 100-150 word limit +- Synthesize and tie together the key themes, findings, and insights from the main body sections +- Reference specific examples, case studies, or data points covered in the report - For comparative reports: * Must include a focused comparison table using Markdown table syntax * Table should distill insights from the report @@ -241,7 +244,7 @@ - Use `*` or `-` for unordered lists - Use `1.` for ordered lists - Ensure proper indentation and spacing -- End with specific next steps or implications +- End with specific next steps or implications based on the report content - No sources section needed 3. Writing Approach: @@ -262,7 +265,8 @@ SUPERVISOR_INSTRUCTIONS = """ You are scoping research for a report based on a user-provided topic. -### Your responsibilities: + +**CRITICAL: You MUST follow this EXACT sequence of tool calls. Do NOT skip any steps or call tools out of order.** 1. **Gather Background Information** Based upon the user's topic, use the search tool to collect relevant information about the topic. @@ -282,7 +286,7 @@ - Do not proceed to defining sections until you have asked this clarifying question and received the user's response 3. **Define Report Structure** - Only after completing both research AND clarification with the user: + Only after completing both initial research AND clarification with the user: - You MUST use the `Sections` tool to define a list of report sections - Each section should be a written description with: a section name and a section research plan - Do not include sections for introductions or conclusions (We'll add these later) @@ -291,22 +295,7 @@ - Format your sections as a list of strings, with each string having the scope of research for that section. 4. **Assemble the Final Report** - When all sections are returned: - - IMPORTANT: First check your previous messages to see what you've already completed - - If you haven't created an introduction yet, use the `Introduction` tool to generate one - - Set content to include report title with a single # (H1 level) at the beginning - - Example: "# [Report Title]\n\n[Introduction content...]" - - After the introduction, use the `Conclusion` tool to summarize key insights - - Set content to include conclusion title with ## (H2 level) at the beginning - - Example: "## Conclusion\n\n[Conclusion content...]" - - Only use ONE structural element IF it helps distill the points made in the report: - - Either a focused table comparing items present in the report (using Markdown table syntax) - - Or a short list using proper Markdown list syntax: - - Use `*` or `-` for unordered lists - - Use `1.` for ordered lists - - Ensure proper indentation and spacing - - Do not call the same tool twice - check your message history - - Once you're done, call `FinishReport` tool to finish the report + - REQUIRED: Call the `FinishReport` tool. This will call a tool to write the final report based off of the research you have conducted and the sections that you have decided upon. ### Additional Notes: - You are a reasoning model. Think through problems step-by-step before acting. @@ -317,82 +306,190 @@ Today is {today} """ +FINAL_REPORT_WRITING_INSTRUCTIONS = """**Follow these Instructions To Assemble the Final Report** + +You are writing a report to answer the user's question. This is the back and forth between you and the user. + +{messages} + +Your report needs to be based off of the following sources: {sources_str} + +And your report should follow this structured list of sections: {sections} + +Before starting with the sections, you should first write an introduction. For the introduction: +- Use # for report title (Markdown format) +- 50-100 word limit +- Write in simple and clear language +- Give an outline of what the report will cover in 1-2 paragraphs, as well as the main takeaways +- Use a clear narrative arc to introduce the report +- Include NO structural elements (no lists or tables) +- No sources section needed +- You should not speak about the report's motivation or purpose, just get in to the answer. + +Once your introduction is complete, you should write each of the main body sections. For each section: +1. Review the report topic, section name, and section topic carefully. +2. Then, look at the provided Source material. +3. Decide the sources that you will use it to write a report section. +4. Write the report section and list your sources. +5. Write the sections keeping in mind what you have written already in the introduction and above sections. +6. The report needs to be cohesive and follow a logical flow. + +Each Section should follow these guidelines: +- Strict 150-200 word limit +- Use simple, clear language +- Use short paragraphs (2-3 sentences max) +- Use ## for section title (Markdown format) + +Each Section should follow these citation rules: +- Assign each unique URL a single citation number in your text +- End with ### Sources that lists each source with corresponding numbers +- IMPORTANT: Number sources sequentially without gaps (1,2,3,4...) in the final list regardless of which sources you choose +- Example format: + [1] Source Title: URL + [2] Source Title: URL + +For each section, make sure to: +1. Verify that EVERY claim is grounded in the provided Source material +2. Confirm each URL appears ONLY ONCE in the Source list +3. Verify that sources are numbered sequentially (1,2,3...) without any gaps + +Finally, you should write a conclusion. For the conclusion: +- Use ## for section title (Markdown format) +- 100-150 word limit +- Synthesize and tie together the key themes, findings, and insights from the main body sections +- Reference specific examples, case studies, or data points covered in the report +- For comparative reports: + * Must include a focused comparison table using Markdown table syntax + * Table should distill insights from the report + * Keep table entries clear and concise +- For non-comparative reports: + * Only use ONE structural element IF it helps distill the points made in the report: + * Either a focused table comparing items present in the report (using Markdown table syntax) + * Or a short list using proper Markdown list syntax: + - Use `*` or `-` for unordered lists + - Use `1.` for ordered lists + - Ensure proper indentation and spacing +- End with specific next steps or implications based on the report content +- No sources section needed + +Your report will be evaluated based on the following criteria - keep these in mind when writing the report! + +** Research Depth and Comprehensiveness (20% weight)** + - Ensure thorough analysis of the topic + - Cover all relevant aspects related to the research question + - Demonstrate deep understanding of the subject matter + - Provide sufficient background context + +**Source Quality and Methodology (15% weight)** + - Incorporate information from authoritative sources + - Use a diverse range of source types (e.g., academic papers, news articles, industry reports) + - Properly cite and integrate sources throughout the report + - Clearly explain the research methodology used + +**Analytical Rigor (20% weight)** + - Conduct sophisticated analysis of the information + - Critically evaluate source material + - Identify and discuss nuances and limitations of the research + +**Structure and Organization (10% weight)** + - Ensure logical flow and coherence throughout the report + - Organize content into clear, well-defined sections + - Use appropriate headings and formatting + - Create smooth transitions between concepts and sections + +**Practical Value and Actionability (15% weight)** + - Provide clear insights and actionable recommendations + - Include specific examples and use cases to illustrate key points + +**Balance and Objectivity (10% weight)** + - Present multiple perspectives on the topic + - Acknowledge limitations and trade-offs in the research + - Clearly distinguish between facts and opinions + - Maintain an unbiased tone throughout the report + +**Writing Quality and Clarity (10% weight)** + - Use clear, professional language + - Employ appropriate terminology consistently + - Maintain a consistent tone and style + - Ensure the report is engaging and readable + + + +Enhance the analytical depth by: +- Identifying patterns, trends, or relationships in the data +- Discussing implications of the findings +- Addressing potential counterarguments or alternative interpretations + +Strengthen the methodology and source quality by: +- Clearly explaining the research approach +- Integrating citations smoothly into the text +- Using a mix of primary and secondary sources, if applicable + +Incorporate visual elements (e.g., tables) where appropriate to illustrate complex ideas or data. + +Review and refine the report, paying special attention to: +- Logical flow and coherence +- Clarity and conciseness of language +- Proper formatting and citation style +- Balance and objectivity in presenting information + +Finally, make sure to: +- Ensure the report's depth matches the complexity of the topic +- Tailor the content and style to the intended audience +- Address all critical aspects of the topic comprehensively +- Incorporate quantitative data, case studies, and concrete examples where relevant +- Use consistent formatting for headings, subheadings, and body text + +Remember to maintain a professional, objective tone throughout the report while showcasing deep expertise and insight on the subject matter. Your goal is to produce a report that not only synthesizes the provided information but also adds value through thoughtful analysis and clear, actionable recommendations. +""" + RESEARCH_INSTRUCTIONS = """ -You are a researcher responsible for completing a specific section of a report. +You are a researcher responsible for gathering comprehensive information about a specific section topic for a report. -### Your goals: +### Your Role: +You are NOT responsible for writing the actual report section. Your job is to collect thorough, high-quality research information that will later be used by the supervisor to write the final report. -1. **Understand the Section Scope** - Begin by reviewing the section scope of work. This defines your research focus. Use it as your objective. +### Your Research Focus:
{section_description}
-2. **Strategic Research Process** - Follow this precise research strategy: - - a) **First Search**: Begin with well-crafted search queries for a search tool that directly addresses the core of the section topic. - - Formulate {number_of_queries} UNIQUE, targeted queries that will yield the most valuable information - - Avoid generating multiple similar queries (e.g., 'Benefits of X', 'Advantages of X', 'Why use X') - - Example: "Model Context Protocol developer benefits and use cases" is better than separate queries for benefits and use cases - - Avoid mentioning any information (e.g., specific entities, events or dates) that might be outdated in your queries, unless explicitly provided by the user or included in your instructions - - Example: "LLM provider comparison" is better than "openai vs anthropic comparison" - - If you are unsure about the date, use today's date - - b) **Analyze Results Thoroughly**: After receiving search results: - - Carefully read and analyze ALL provided content - - Identify specific aspects that are well-covered and those that need more information - - Assess how well the current information addresses the section scope - - c) **Follow-up Research**: If needed, conduct targeted follow-up searches: - - Create ONE follow-up query that addresses SPECIFIC missing information - - Example: If general benefits are covered but technical details are missing, search for "Model Context Protocol technical implementation details" - - AVOID redundant queries that would return similar information - - d) **Research Completion**: Continue this focused process until you have: - - Comprehensive information addressing ALL aspects of the section scope - - At least 3 high-quality sources with diverse perspectives - - Both breadth (covering all aspects) and depth (specific details) of information - -3. **REQUIRED: Two-Step Completion Process** - You MUST complete your work in exactly two steps: - - **Step 1: Write Your Section** - - After gathering sufficient research information, call the Section tool to write your section - - The Section tool parameters are: - - `name`: The title of the section - - `description`: The scope of research you completed (brief, 1-2 sentences) - - `content`: The completed body of text for the section, which MUST: - - Begin with the section title formatted as "## [Section Title]" (H2 level with ##) - - Be formatted in Markdown style - - Be MAXIMUM 200 words (strictly enforce this limit) - - End with a "### Sources" subsection (H3 level with ###) containing a numbered list of URLs used - - Use clear, concise language with bullet points where appropriate - - Include relevant facts, statistics, or expert opinions - -Example format for content: -``` -## [Section Title] +### Strategic Research Process: -[Body text in markdown format, maximum 200 words...] +1. **Initial Research**: Begin with well-crafted search queries that directly address the core of the section topic. + - Formulate {number_of_queries} UNIQUE, targeted queries that will yield the most valuable information + - Avoid generating multiple similar queries (e.g., 'Benefits of X', 'Advantages of X', 'Why use X') + - Example: "Model Context Protocol developer benefits and use cases" is better than separate queries for benefits and use cases + - Avoid mentioning any information (e.g., specific entities, events or dates) that might be outdated in your queries, unless explicitly provided by the user or included in your instructions + - Example: "LLM provider comparison" is better than "openai vs anthropic comparison" + - If you are unsure about the date, use today's date -### Sources -1. [URL 1] -2. [URL 2] -3. [URL 3] -``` +2. **Analyze Results Thoroughly**: After receiving search results: + - Carefully read and analyze ALL provided content + - Identify specific aspects that are well-covered and those that need more information + - Assess how well the current information addresses the section scope - **Step 2: Signal Completion** - - Immediately after calling the Section tool, call the FinishResearch tool - - This signals that your research work is complete and the section is ready - - Do not skip this step - the FinishResearch tool is required to properly complete your work +3. **Follow-up Research**: If needed, conduct targeted follow-up searches: + - Create targeted follow-up queries that address SPECIFIC missing information + - Example: If general benefits are covered but technical details are missing, search for "Model Context Protocol technical implementation details" + - AVOID redundant queries that would return similar information ---- +4. **Research Completion**: Continue this focused process until you have: + - Comprehensive information addressing ALL aspects of the section scope + - Multiple high-quality sources with diverse perspectives + - Both breadth (covering all aspects) and depth (specific details) of information -### Research Decision Framework +### Completion Process: -Before each search query or when writing the section, think through: +**REQUIRED**: When you have gathered sufficient research information: +- Call the `FinishResearch` tool to signal that your research work is complete +- This indicates to the supervisor that you have collected comprehensive information about your assigned section topic +- The supervisor will use all the research information you've gathered to write the final report + +### Research Decision Framework: + +Before each search query, think through: 1. **What information do I already have?** - Review all information gathered so far @@ -404,18 +501,13 @@ 3. **What is the most effective next action?** - Determine if another search is needed (and what specific aspect to search for) - - Or if enough information has been gathered to write a comprehensive section - ---- - -### Notes: -- **CRITICAL**: You MUST call the Section tool to complete your work - this is not optional -- Focus on QUALITY over QUANTITY of searches -- Each search should have a clear, distinct purpose -- Do not write introductions or conclusions unless explicitly part of your section -- Keep a professional, factual tone -- Always follow markdown formatting -- Stay within the 200 word limit for the main content + - Or if enough comprehensive information has been gathered to finish research + +### Key Points: +- **Focus on INFORMATION GATHERING** - you are not writing the final section +- **Quality over quantity** - ensure each search has a clear, distinct purpose +- **Be thorough** - the supervisor depends on your research to write a comprehensive report +- **Signal completion** - use FinishResearch when you have sufficient information Today is {today} """ diff --git a/src/open_deep_research/utils.py b/src/open_deep_research/utils.py index 904f2fd..e0de2c0 100644 --- a/src/open_deep_research/utils.py +++ b/src/open_deep_research/utils.py @@ -1357,7 +1357,8 @@ def perform_search(): TAVILY_SEARCH_DESCRIPTION = ( "A search engine optimized for comprehensive, accurate, and trusted results. " - "Useful for when you need to answer questions about current events." + "Useful for when you need to answer questions about current events. " + "Even if you only have a single query, make sure it is passed in as a list, not a string! " ) @tool(description=TAVILY_SEARCH_DESCRIPTION) diff --git a/tests/evals/run_evaluate.py b/tests/evals/run_evaluate.py new file mode 100644 index 0000000..7ce2b64 --- /dev/null +++ b/tests/evals/run_evaluate.py @@ -0,0 +1,85 @@ +from langsmith import Client +from tests.evals.evaluators import eval_overall_quality, eval_relevance, eval_structure +from tests.evals.target import generate_report_multi_agent +from dotenv import load_dotenv +import os +import asyncio +from typing import Literal +from langchain_core.messages import MessageLikeRepresentation +from open_deep_research.multi_agent import supervisor_builder + +load_dotenv("../.env") + +client = Client() + +dataset_name = "ODR: Multi Agent Examples" +evaluators = [eval_overall_quality, eval_relevance, eval_structure] +# TODO: Configure these variables +process_search_results = "summarize" +include_source = False +summarization_model = "gpt-4.1" +summarization_model_provider = "openai" +supervisor_model = "openai:gpt-4.1" +researcher_model = "openai:gpt-4.1" +final_report_model = "openai:gpt-4.1" + + +async def generate_report_multi_agent( + messages: list[MessageLikeRepresentation], + process_search_results: Literal["summarize", "split_and_rerank"] | None = None, + include_source: bool = True, + summarization_model: str = summarization_model, + summarization_model_provider: str = summarization_model_provider, + supervisor_model: str = supervisor_model, + researcher_model: str = researcher_model, + final_report_model: str = final_report_model, +): + """Generate a report using the open deep research multi-agent architecture""" + graph = supervisor_builder.compile() + config = {"configurable": {}} + if include_source: + config["configurable"]["include_source_str"] = True + if process_search_results: + config["configurable"]["process_search_results"] = process_search_results + config["configurable"]["summarization_model"] = summarization_model + config["configurable"]["summarization_model_provider"] = summarization_model_provider + config["configurable"]["supervisor_model"] = supervisor_model + config["configurable"]["researcher_model"] = researcher_model + config["configurable"]["final_report_model"] = final_report_model + + final_state = await graph.ainvoke( + # this is a hack + # TODO: Find workaround at some point + {"messages": messages + [{"role": "user", "content": "Generate the report now and don't ask any more follow-up questions"}]}, + config + ) + return { + "messages": [ + {"role": "assistant", "content": final_state["final_report"]} + ] + } + +async def target(inputs: dict): + return await generate_report_multi_agent( + inputs["messages"], + process_search_results, + include_source, + summarization_model, + summarization_model_provider, + supervisor_model, + researcher_model + ) + +async def main(): + return await client.aevaluate( + target, + data=dataset_name, + evaluators=evaluators, + experiment_prefix=f"ODR: Multi Agent - PSR:{process_search_results}, IS:{include_source}", + max_concurrency=1, + metadata={"process_search_results": process_search_results, "include_source": include_source}, + ) + +if __name__ == "__main__": + results = asyncio.run(main()) + print(results) \ No newline at end of file diff --git a/tests/run_test.py b/tests/run_test.py index 49226ac..b1344bd 100644 --- a/tests/run_test.py +++ b/tests/run_test.py @@ -3,38 +3,25 @@ import subprocess import sys import argparse +from rich.console import Console +from rich.panel import Panel +from rich.rule import Rule -''' -Example w/ o3 -- -python tests/run_test.py --all \ - --supervisor-model "openai:o3" \ - --researcher-model "openai:o3" \ - --planner-provider "openai" \ - --planner-model "o3" \ - --writer-provider "openai" \ - --writer-model "o3" \ - --eval-model "openai:o3" \ - --search-api "tavily" +console = Console() -Example w/ gpt-4.1 -- -python tests/run_test.py --all \ - --supervisor-model "openai:gpt-4.1" \ - --researcher-model "openai:gpt-4.1" \ - --planner-provider "openai" \ - --planner-model "gpt-4.1" \ - --writer-provider "openai" \ - --writer-model "gpt-4.1" \ - --eval-model "openai:o3" \ - --search-api "tavily" -''' +""" +Simplified test runner for Open Deep Research with rich console output. -def main(): - # LangSmith project name for report quality testing - langsmith_project = "Open Deep Research: Report Quality Testing" +Example usage: +python tests/run_test.py --all # Run all agents with rich output +python tests/run_test.py --agent multi_agent --supervisor-model "anthropic:claude-3-7-sonnet-latest" +python tests/run_test.py --agent graph --search-api tavily +""" +def main(): # Parse command line arguments - parser = argparse.ArgumentParser(description="Run report quality tests for different agents") - parser.add_argument("--rich-output", action="store_true", help="Show rich output in terminal") + parser = argparse.ArgumentParser(description="Run tests for Open Deep Research with rich console output") + parser.add_argument("--rich-output", action="store_true", default=True, help="Show rich output in terminal (default: True)") parser.add_argument("--experiment-name", help="Name for the LangSmith experiment") parser.add_argument("--agent", choices=["multi_agent", "graph"], help="Run tests for a specific agent") parser.add_argument("--all", action="store_true", help="Run tests for all agents") @@ -46,7 +33,8 @@ def main(): parser.add_argument("--planner-model", help="Model for planner in graph-based agent (e.g., 'claude-3-7-sonnet-latest')") parser.add_argument("--writer-provider", help="Provider for writer model (e.g., 'anthropic')") parser.add_argument("--writer-model", help="Model for writer in graph-based agent (e.g., 'claude-3-5-sonnet-latest')") - parser.add_argument("--eval-model", help="Model for evaluating report quality (default: openai:gpt-4-turbo)") + parser.add_argument("--eval-model", help="Model for evaluating report quality (default: openai:claude-3-7-sonnet-latest)") + parser.add_argument("--max-search-depth", help="Maximum search depth for graph agent") # Search API configuration parser.add_argument("--search-api", choices=["tavily", "duckduckgo"], @@ -54,111 +42,125 @@ def main(): args = parser.parse_args() - # Base pytest options - base_pytest_options = ["-v", "--disable-warnings", "--langsmith-output"] - if args.rich_output: - base_pytest_options.append("--rich-output") - - # Define available agents - agents = ["multi_agent", "graph"] + # Define available agents and their test configurations + agents = { + "multi_agent": { + "test": "tests/test_report_quality.py::test_response_criteria_evaluation", + "topic": "Model Context Protocol", + "description": "Testing multi_agent with a full MCP report", + "needs_research_agent_param": True, + }, + "graph": { + "test": "tests/test_report_quality.py::test_response_criteria_evaluation", + "topic": "Model Context Protocol", + "description": "Testing graph agent with a full MCP report", + "needs_research_agent_param": True, + } + } # Determine which agents to test if args.agent: if args.agent in agents: agents_to_test = [args.agent] else: - print(f"Error: Unknown agent '{args.agent}'") - print(f"Available agents: {', '.join(agents)}") + console.print(f"[red]Error: Unknown agent '{args.agent}'[/red]") + console.print(f"Available agents: {', '.join(agents.keys())}") return 1 elif args.all: - agents_to_test = agents + agents_to_test = list(agents.keys()) else: # Default to testing all agents - agents_to_test = agents + agents_to_test = list(agents.keys()) # Run tests for each agent for agent in agents_to_test: - print(f"\nRunning tests for {agent}...") + console.print(Rule(f"[bold blue]Testing {agent.upper()} Agent[/bold blue]")) + + agent_config = agents[agent] # Set up LangSmith environment for this agent - os.environ["LANGSMITH_PROJECT"] = langsmith_project - os.environ["LANGSMITH_TEST_SUITE"] = langsmith_project + project_name = f"ODR: Pytest" + os.environ["LANGSMITH_PROJECT"] = project_name + os.environ["LANGSMITH_TEST_SUITE"] = project_name # Ensure tracing is enabled os.environ["LANGCHAIN_TRACING_V2"] = "true" - # Create a fresh copy of the pytest options for this run - pytest_options = base_pytest_options.copy() - - # We're now using direct pytest command line arguments instead of environment variables - # No need to set environment variables for test parameters - - # Test file path - test_file = "tests/test_report_quality.py" - # Set up experiment name - experiment_name = args.experiment_name if args.experiment_name else f"Report Quality Test | Agent: {agent}" - print(f" Project: {langsmith_project}") - print(f" Experiment: {experiment_name}") - + experiment_name = args.experiment_name if args.experiment_name else f"{agent_config['topic']}" os.environ["LANGSMITH_EXPERIMENT"] = experiment_name - print(f"\nℹ️ Test results for {agent} are being logged to LangSmith") - - # Run the test with direct pytest arguments instead of environment variables - cmd = ["python", "-m", "pytest", test_file] + pytest_options + [ - f"--research-agent={agent}" - ] - - # Add model configurations if provided - if args.supervisor_model: - cmd.append(f"--supervisor-model={args.supervisor_model}") - if args.researcher_model: - cmd.append(f"--researcher-model={args.researcher_model}") - if args.planner_provider: - cmd.append(f"--planner-provider={args.planner_provider}") - if args.planner_model: - cmd.append(f"--planner-model={args.planner_model}") - if args.writer_provider: - cmd.append(f"--writer-provider={args.writer_provider}") - if args.writer_model: - cmd.append(f"--writer-model={args.writer_model}") - if args.eval_model: - cmd.append(f"--eval-model={args.eval_model}") - if args.search_api: - cmd.append(f"--search-api={args.search_api}") - - print(f"Running command: {' '.join(cmd)}") - - result = subprocess.run(cmd, capture_output=True, text=True) - - # Print test output with highlighting for section relevance analysis - stdout = result.stdout - - # Highlight section relevance information with colors if supported - if "POTENTIAL IRRELEVANT SECTIONS DETECTED" in stdout: - # Extract and highlight the section relevance analysis - import re - section_analysis = re.search(r'⚠️ POTENTIAL IRRELEVANT SECTIONS DETECTED:.*?(?=\n\n|\Z)', - stdout, re.DOTALL) - if section_analysis: - analysis_text = section_analysis.group(0) - # Use ANSI color codes for highlighting (red for irrelevant sections) - highlighted_analysis = f"\033[1;31m{analysis_text}\033[0m" - stdout = stdout.replace(analysis_text, highlighted_analysis) + console.print(f"[dim]Project: {project_name}[/dim]") + console.print(f"[dim]Test: {agent_config['description']}[/dim]") + console.print(f"[dim]Experiment: {experiment_name}[/dim]") - print(stdout) - - if result.stderr: - print("Errors/warnings:") - print(result.stderr) - - # Print a summary of section relevance issues - if "POTENTIAL IRRELEVANT SECTIONS DETECTED" in result.stdout: - print("\n\033[1;33m==== SECTION RELEVANCE ISSUES DETECTED ====\033[0m") - print("Some sections may not be relevant to the main topic.") - print("Review the detailed analysis in the test output above.") - print("Consider updating the prompts to improve section relevance.") - + # Run the test + console.print(f"\n[green]Running test for {agent} agent...[/green]") + run_test(agent, agent_config, args) + + console.print(Rule("[bold green]All tests complete[/bold green]")) + +def run_test(agent, agent_config, args): + """Run the pytest with rich console formatting.""" + # Base pytest options (added -s to disable output capturing) + base_pytest_options = ["-v", "-s", "--disable-warnings", "--langsmith-output"] + + # Build the command + cmd = ["python", "-m", "pytest", agent_config["test"]] + base_pytest_options + + # Add research agent parameter if needed + if agent_config["needs_research_agent_param"]: + cmd.append(f"--research-agent={agent}") + + # Add model configurations if provided + add_model_configs(cmd, args) + + # Display command in a nice panel + console.print(Panel( + f"[bold]Running Command:[/bold]\n[dim]{' '.join(cmd)}[/dim]", + style="blue", + title="pytest execution" + )) + + # Run the command with real-time output (no capture) + console.print(f"\n[yellow]Starting test execution...[/yellow]\n") + result = subprocess.run(cmd) + + # Display results with rich formatting + console.print(f"\n[yellow]Test execution completed.[/yellow]") + if result.returncode == 0: + console.print(Panel( + f"[bold green]✅ Test for {agent} PASSED[/bold green]", + style="green", + title="Test Result" + )) + else: + console.print(Panel( + f"[bold red]❌ Test for {agent} FAILED[/bold red]\n[red]Return code: {result.returncode}[/red]", + style="red", + title="Test Result" + )) + +def add_model_configs(cmd, args): + """Add model configuration arguments to command.""" + if args.supervisor_model: + cmd.append(f"--supervisor-model={args.supervisor_model}") + if args.researcher_model: + cmd.append(f"--researcher-model={args.researcher_model}") + if args.planner_provider: + cmd.append(f"--planner-provider={args.planner_provider}") + if args.planner_model: + cmd.append(f"--planner-model={args.planner_model}") + if args.writer_provider: + cmd.append(f"--writer-provider={args.writer_provider}") + if args.writer_model: + cmd.append(f"--writer-model={args.writer_model}") + if args.eval_model: + cmd.append(f"--eval-model={args.eval_model}") + if args.search_api: + cmd.append(f"--search-api={args.search_api}") + if args.max_search_depth: + cmd.append(f"--max-search-depth={args.max_search_depth}") + if __name__ == "__main__": sys.exit(main() or 0) \ No newline at end of file diff --git a/tests/test_report_quality.py b/tests/test_report_quality.py index 62578e2..817eefc 100644 --- a/tests/test_report_quality.py +++ b/tests/test_report_quality.py @@ -7,6 +7,11 @@ from pydantic import BaseModel, Field from langchain.chat_models import init_chat_model from langsmith import testing as t +from rich.console import Console +from rich.panel import Panel +from rich.table import Table +from rich.text import Text +from rich.markdown import Markdown from langgraph.checkpoint.memory import MemorySaver from langgraph.types import Command @@ -15,6 +20,9 @@ from open_deep_research.graph import builder from open_deep_research.multi_agent import supervisor_builder +# Initialize rich console with force_terminal to ensure output even when pytest captures stdout +console = Console(force_terminal=True, width=120) + class CriteriaGrade(BaseModel): """Score the response against specific criteria.""" grade: bool = Field(description="Does the response meet the provided criteria?") @@ -26,14 +34,14 @@ def get_evaluation_llm(eval_model=None): Args: eval_model: Model identifier to use for evaluation - Format: "provider:model_name" (e.g., "openai:gpt-4-turbo") + Format: "provider:model_name" (e.g., "anthropic:claude-3-7-sonnet-latest") If None, it will use environment variable or default Returns: Structured LLM for generating evaluation grades """ # Use provided model, then environment variable, then default - model_to_use = eval_model or os.environ.get("EVAL_MODEL", "openai:gpt-4-turbo") + model_to_use = eval_model or os.environ.get("EVAL_MODEL", "anthropic:claude-3-7-sonnet-latest") criteria_eval_llm = init_chat_model(model_to_use) return criteria_eval_llm.with_structured_output(CriteriaGrade) @@ -85,7 +93,7 @@ def search_api(request): @pytest.fixture def eval_model(request): """Get the evaluation model from command line or environment variable.""" - return request.config.getoption("--eval-model") or os.environ.get("EVAL_MODEL", "openai:gpt-4-turbo") + return request.config.getoption("--eval-model") or os.environ.get("EVAL_MODEL", "anthropic:claude-3-7-sonnet-latest") @pytest.fixture def models(request, research_agent): @@ -131,9 +139,21 @@ def models(request, research_agent): @pytest.mark.langsmith def test_response_criteria_evaluation(research_agent, search_api, models, eval_model): """Test if a report meets the specified quality criteria.""" - print(f"Testing {research_agent} report generation with {search_api} search...") - print(f"Models: {models}") - print(f"Eval model: {eval_model}") + console.print(Panel.fit( + f"[bold blue]Testing {research_agent} report generation with {search_api} search[/bold blue]", + title="Test Configuration" + )) + + # Create a table for model configuration + models_table = Table(title="Model Configuration") + models_table.add_column("Parameter", style="cyan") + models_table.add_column("Value", style="green") + + for key, value in models.items(): + models_table.add_row(key, str(value)) + models_table.add_row("eval_model", eval_model) + + console.print(models_table) # Log inputs to LangSmith t.log_inputs({ @@ -149,8 +169,7 @@ def test_response_criteria_evaluation(research_agent, search_api, models, eval_m if research_agent == "multi_agent": # Initial messages - initial_msg = [{"role": "user", "content": "What is model context protocol?"}] - followup_msg = [{"role": "user", "content": "high-level overview of MCP, tell me about interesting specific MCP servers, developer audience, just focus on MCP. generate the report now and don't ask any more follow-up questions."}] + initial_msg = [{"role": "user", "content": "Give me a high-level overview of MCP (model context protocol). Keep the report to 3 main body sections. One section on the origins of MPC, one section on interesting examples of MCP servers, and one section on the future roadmap for MCP. Report should be written for a developer audience."}] # Checkpointer for the multi-agent approach checkpointer = MemorySaver() @@ -162,24 +181,24 @@ def test_response_criteria_evaluation(research_agent, search_api, models, eval_m "search_api": search_api, "supervisor_model": models.get("supervisor_model"), "researcher_model": models.get("researcher_model"), + "ask_for_clarification": False, # Don't ask for clarification from the user and proceed to write the report + "process_search_results": "summarize", # Optionally summarize } thread_config = {"configurable": config} # Run the workflow with asyncio asyncio.run(graph.ainvoke({"messages": initial_msg}, config=thread_config)) - asyncio.run(graph.ainvoke({"messages": followup_msg}, config=thread_config)) # Get the final state once both invocations are complete final_state = graph.get_state(thread_config) - print(f"Final state values: {final_state.values}") report = final_state.values.get('final_report', "No report generated") - print(f"Report length: {len(report)}") + console.print(f"[bold green]Report generated with length: {len(report)} characters[/bold green]") elif research_agent == "graph": # Topic query - topic_query = "What is model context protocol? high-level overview of MCP, tell me about interesting specific MCP servers, developer audience, just focus on MCP" + topic_query = "Give me a high-level overview of MCP (model context protocol). Keep the report to 3 main body sections. One section on the origins of MPC, one section on interesting examples of MCP servers, and one section on the future roadmap for MCP. Report should be written for a developer audience." # Checkpointer for the graph approach checkpointer = MemorySaver() @@ -202,10 +221,11 @@ async def run_graph_agent(thread): if '__interrupt__' in event: interrupt_value = event['__interrupt__'][0].value - # Pass True to approve the report plan + # Pass True to approve the report plan and proceed to write the report async for event in graph.astream(Command(resume=True), thread, stream_mode="updates"): - print(event) - print("\n") + # console.print(f"[dim]{event}[/dim]") + # console.print() + None final_state = graph.get_state(thread) report = final_state.values.get('final_report', "No report generated") @@ -226,10 +246,39 @@ async def run_graph_agent(thread): import re section_headers = re.findall(r'##\s+([^\n]+)', report) - # Print the evaluation results - print(f"Evaluation result: {'PASSED' if eval_result.grade else 'FAILED'}") - print(f"Report contains {len(section_headers)} sections: {', '.join(section_headers)}") - print(f"Justification: {eval_result.justification}") + # Display the generated report + console.print(Panel( + Markdown(report), + title="Generated Report", + border_style="blue" + )) + + # Create evaluation results display + result_color = "green" if eval_result.grade else "red" + result_text = "PASSED" if eval_result.grade else "FAILED" + + console.print(Panel.fit( + f"[bold {result_color}]{result_text}[/bold {result_color}]", + title="Evaluation Result" + )) + + # Create sections table + sections_table = Table(title="Report Structure Analysis") + sections_table.add_column("Section", style="cyan") + sections_table.add_column("Header", style="yellow") + + for i, header in enumerate(section_headers, 1): + sections_table.add_row(f"Section {i}", header) + + console.print(sections_table) + console.print(f"[bold]Total sections found: {len(section_headers)}[/bold]") + + # Display justification in a panel + console.print(Panel( + eval_result.justification, + title="Evaluation Justification", + border_style="yellow" + )) # Log outputs to LangSmith t.log_outputs({ diff --git a/uv.lock b/uv.lock index 735a371..d57e860 100644 --- a/uv.lock +++ b/uv.lock @@ -4,7 +4,8 @@ requires-python = ">=3.10" resolution-markers = [ "python_full_version >= '3.13'", "python_full_version >= '3.12.4' and python_full_version < '3.13'", - "python_full_version < '3.12.4'", + "python_full_version >= '3.11' and python_full_version < '3.12.4'", + "python_full_version < '3.11'", ] [[package]] @@ -18,7 +19,7 @@ wheels = [ [[package]] name = "aiohttp" -version = "3.11.18" +version = "3.12.12" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "aiohappyeyeballs" }, @@ -30,72 +31,76 @@ dependencies = [ { name = "propcache" }, { name = "yarl" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/63/e7/fa1a8c00e2c54b05dc8cb5d1439f627f7c267874e3f7bb047146116020f9/aiohttp-3.11.18.tar.gz", hash = "sha256:ae856e1138612b7e412db63b7708735cff4d38d0399f6a5435d3dac2669f558a", size = 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