From b7e8c6ffd3232c6ba304a6ccfdf9c029e3e8f896 Mon Sep 17 00:00:00 2001 From: Ethan Ruhe Date: Fri, 16 Jan 2026 16:31:06 -0500 Subject: [PATCH] Add Braintrust deep research example --- braintrust/README.md | 532 +++++++++++ braintrust/activities/invoke_model.py | 126 +++ braintrust/agents/config.py | 10 + braintrust/agents/research_planning.py | 42 + .../agents/research_query_generation.py | 56 ++ .../agents/research_report_synthesis.py | 84 ++ braintrust/agents/research_web_search.py | 54 ++ braintrust/agents/shared.py | 57 ++ braintrust/pyproject.toml | 20 + braintrust/start_workflow.py | 70 ++ braintrust/tests/test_e2e.py | 110 +++ braintrust/uv.lock | 895 ++++++++++++++++++ braintrust/worker.py | 43 + .../workflows/deep_research_workflow.py | 86 ++ 14 files changed, 2185 insertions(+) create mode 100644 braintrust/README.md create mode 100644 braintrust/activities/invoke_model.py create mode 100644 braintrust/agents/config.py create mode 100644 braintrust/agents/research_planning.py create mode 100644 braintrust/agents/research_query_generation.py create mode 100644 braintrust/agents/research_report_synthesis.py create mode 100644 braintrust/agents/research_web_search.py create mode 100644 braintrust/agents/shared.py create mode 100644 braintrust/pyproject.toml create mode 100644 braintrust/start_workflow.py create mode 100644 braintrust/tests/test_e2e.py create mode 100644 braintrust/uv.lock create mode 100644 braintrust/worker.py create mode 100644 braintrust/workflows/deep_research_workflow.py diff --git a/braintrust/README.md b/braintrust/README.md new file mode 100644 index 00000000..7e436217 --- /dev/null +++ b/braintrust/README.md @@ -0,0 +1,532 @@ + + +# Deep Research + +Deep research systems combine multiple agents with information retrieval from +the web or other sources to produce evidence-based reports on specific topics. +Commercial implementations include +[Anthropic Research](https://www.anthropic.com/engineering/multi-agent-research-system), +[OpenAI Deep Research](https://openai.com/index/introducing-deep-research/), and +[Google Gemini Deep Research](https://gemini.google/overview/deep-research/). + +This recipe demonstrates a simple deep research system embodying the standard +deep research architecture. Deep research spans the following four phases: + +- **Planning**. Task decomposition and research strategy formulation. This + involves identifying separate aspects of the research problem that can be + worked on independently. +- **Question Development/Query Generation**. Designing queries for each of the + research questions. +- **Web Exploration/Information Retrieval**. Searching the web to retrieve + documents relevant to the research question. Extracting and summarizing + relevant information. +- **Report Generation/Synthesis**. Synthesizing findings into comprehensive, + well-cited reports. + +Deep research tasks can involve dozens of searches and process hundreds of +documents. This creates many possible failure modes that durable execution helps +protect against. + +This recipe uses OpenAI's Responses API, which includes a tool for web search. +It also uses OpenAI's +[Structured Outputs API](https://platform.openai.com/docs/guides/structured-outputs), +which asks the model to generate outputs corresponding to desired data +structures. + +## Prerequisites + +Set the required environment variables: + +```bash +export OPENAI_API_KEY="your-openai-api-key" +export BRAINTRUST_API_KEY="your-braintrust-api-key" +export BRAINTRUST_PROJECT_NAME="your-project-name" # optional, defaults to "deep-research" +``` + +## Running + +This example requires three terminal tabs. + +**Terminal 1** - Start the Temporal Dev Server: + +```bash +temporal server start-dev +``` + +**Terminal 2** - Run the worker: + +```bash +uv run python -m worker +``` + +**Terminal 3** - Start execution: + +```bash +uv run python -m start_workflow "What's the best restaurant in San Francisco?" +``` + +## Create the data structures + +We will use Python classes to ensure information passes between agents in a +structured way. + +The Planning Agent creates a `ResearchPlan`, which includes a research question, +a list of `ResearchAspects`, expected sources, a search strategy, and success +criteria. ResearchAspects include an aspect name, a priority, and a description. + +```python +class ResearchPlan(BaseModel): + research_question: str + key_aspects: List[ResearchAspect] + expected_sources: List[str] + search_strategy: str + success_criteria: List[str] +``` + +```python +class ResearchAspect(BaseModel): + aspect: str + priority: int + description: str +``` + +The Query Generation Agent creates a `QueryPlan`, and generates a list of +`SearchQueries`. + +```python +class QueryPlan(BaseModel): + queries: List[SearchQuery] +``` + +```python +class SearchQuery(BaseModel): + query: str + rationale: str + expected_info_type: str + priority: int +``` + +The Web Search Agent creates a `SearchResult`, which includes a query, a list of +sources, a key finding, a relevance score, and a list of citations. + +```python +class SearchResult(BaseModel): + query: str + sources: List[str] + key_findings: str + relevance_score: float + citations: List[str] +``` + +Finally, the Report Synthesis Agent creates a `ResearchReport`, which includes +an executive summary, a detailed analysis, a list of key findings, a confidence +assessment, a list of citations, and a list of follow-up questions. + +```python +class ResearchReport(BaseModel): + executive_summary: str + detailed_analysis: str + key_findings: List[str] + confidence_assessment: str + citations: List[str] + follow_up_questions: List[str] +``` + +## Create the Agents + +The deep research system uses four specialized agents, each implemented as +Temporal activities. In this implementation, each agent is implemented as a +single call to the OpenAI Responses API. + +This is possible because we are using structured outputs, which guarantee the +response will be in the correct format, eliminating the need for retries. + +The web search agent also requires only a single API call because OpenAI +integrates the web search tool into the Responses API. + +These agents run in the Workflow and use the `invoke_model` activity to make +OpenAI API calls. It is critical to set the `start_to_close_timeout` for these +activities to a value that is long enough to complete the task. If it is too +short, the activity will fail with a timeout error, causing a retry loop that +never completes. Response times for reasoning models such as `GPT-5` can vary +significantly depending on the nature of the request. Web search times also vary +depending on the size and content of the documents located by the search. + +### Research Planning Agent + +Analyzes research queries and creates comprehensive research strategies. Takes +an unstructured question and decomposes it into specific research aspects with +priorities, identifies expected source types, and defines success criteria. + +*File: agents/research_planning.py* + +```python +from .models import ResearchPlan +from .config import COMPLEX_REASONING_MODEL +from activities.invoke_model import invoke_model, InvokeModelRequest +from temporalio import workflow +from datetime import timedelta + +RESEARCH_PLANNING_INSTRUCTIONS = """ +You are a research planning specialist who creates focused research strategies. + +CORE RESPONSIBILITIES: +1. Decompose the user's question into 3-7 key research aspects +2. Identify required sources and evidence types +3. Design a practical search strategy +4. Set clear success criteria + +OUTPUT REQUIREMENTS: +- research_question: Clarified version of the original query +- key_aspects: Specific areas requiring investigation, each with: + - aspect: The research area name + - priority: 1-5 ranking (5 highest priority) + - description: What needs to be investigated +- expected_sources: Types of sources likely to contain relevant information +- search_strategy: High-level approach for information gathering +- success_criteria: Specific indicators of research completeness +""" + + +async def plan_research(query: str) -> ResearchPlan: + result = await workflow.execute_activity( + invoke_model, + InvokeModelRequest( + model=COMPLEX_REASONING_MODEL, + instructions=RESEARCH_PLANNING_INSTRUCTIONS, + input=f"Research query: {query}", + response_format=ResearchPlan, + ), + start_to_close_timeout=timedelta(seconds=300), + summary="Planning research", + ) + return result.response +``` + +### Query Generation Agent + +Converts research plans into optimized web search queries. Creates 3-5 diverse +queries that target different information types (factual data, expert analysis, +case studies, recent news) with varied search styles and temporal modifiers. + +*File: agents/research_query_generation.py* + +```python +from .models import QueryPlan, ResearchPlan +from .config import EFFICIENT_PROCESSING_MODEL +from activities.invoke_model import invoke_model, InvokeModelRequest +from temporalio import workflow +from datetime import timedelta + +QUERY_GENERATION_INSTRUCTIONS = """ +You are a search query specialist who crafts effective web searches. + +CORE RESPONSIBILITIES: +1. Generate 3-5 diverse search queries based on the research plan +2. Balance specificity with discoverability +3. Target different information types (factual, analytical, recent, historical) + +APPROACH: +- Vary query styles: direct questions, topic + keywords, source-specific searches +- Include temporal modifiers when relevant (recent, 2024, historical) +- Use domain-specific terminology appropriately + +OUTPUT REQUIREMENTS: +- queries: Search queries, each with: + - query: The actual search string + - rationale: Why this query addresses research needs + - expected_info_type: One of "factual_data", "expert_analysis", "case_studies", "recent_news" + - priority: 1-5 (5 highest priority) +""" + + +async def generate_queries(research_plan: ResearchPlan) -> QueryPlan: + # Prepare input with research plan context + plan_context = f""" +Research Question: {research_plan.research_question} + +Key Aspects to Research: +{chr(10).join([f"- {aspect.aspect} (Priority: {aspect.priority}): {aspect.description}" for aspect in research_plan.key_aspects])} + +Expected Sources: {", ".join(research_plan.expected_sources)} +Search Strategy: {research_plan.search_strategy} +Success Criteria: {", ".join(research_plan.success_criteria)} +""" + + result = await workflow.execute_activity( + invoke_model, + InvokeModelRequest( + model=EFFICIENT_PROCESSING_MODEL, + instructions=QUERY_GENERATION_INSTRUCTIONS, + input=plan_context, + response_format=QueryPlan, + ), + start_to_close_timeout=timedelta(seconds=300), + summary="Generating search queries", + ) + + return result.response +``` + +### Web Search Agent + +Executes searches using OpenAI's web search tool and analyzes results. +Prioritizes authoritative sources, extracts key findings, assesses relevance, +and provides proper citations with reliability assessments. + +*File: agents/research_web_search.py* + +```python +from .models import SearchResult, SearchQuery +from .config import EFFICIENT_PROCESSING_MODEL +from activities.invoke_model import invoke_model, InvokeModelRequest +from temporalio import workflow +from datetime import timedelta + +WEB_SEARCH_INSTRUCTIONS = """ +You are a web research specialist who finds and evaluates information from web sources. + +CORE RESPONSIBILITIES: +1. Execute web searches using the web search tool +2. Prioritize authoritative sources: academic, government, established research organizations, prominent news outlets, primary sources +3. Extract key information relevant to the research question +4. Provide proper citations and assess reliability + +APPROACH: +- Focus on information directly relevant to the research question +- Extract specific facts, data points, and evidence +- Note conflicting information and limitations +- Flag questionable or unverified claims + +OUTPUT REQUIREMENTS: +- query: The search query that was executed +- sources: URLs and source descriptions consulted +- key_findings: Synthesized information relevant to research question (2-4 paragraphs) +- relevance_score: 0.0-1.0 assessment of how well results address the query +- citations: Formatted sources with URLs +""" + + +async def search_web(query: SearchQuery) -> SearchResult: + search_input = f""" +Search Query: {query.query} +Query Rationale: {query.rationale} +Expected Information Type: {query.expected_info_type} +Priority Level: {query.priority} + +Please search for information using the provided query and analyze the results according to the instructions. +""" + result = await workflow.execute_activity( + invoke_model, + InvokeModelRequest( + model=EFFICIENT_PROCESSING_MODEL, + instructions=WEB_SEARCH_INSTRUCTIONS, + input=search_input, + response_format=SearchResult, + tools=[{"type": "web_search"}], + ), + start_to_close_timeout=timedelta(seconds=300), + summary="Searching web for information", + ) + return result.response +``` + +### Report Synthesis Agent + +Directs the agent to synthesize all research findings into comprehensive, +well-cited reports. These should include structured narratives with executive +summaries, detailed analysis, key findings, confidence assessments, and +follow-up research questions. + +*File: agents/research_report_synthesis.py* + +```python +from typing import List +from temporalio import workflow +from datetime import timedelta +from .models import ResearchReport, ResearchPlan, SearchResult +from .config import COMPLEX_REASONING_MODEL +from activities.invoke_model import invoke_model, InvokeModelRequest + +REPORT_SYNTHESIS_INSTRUCTIONS = """ +You are a research synthesis expert who creates comprehensive research reports. + +CORE RESPONSIBILITIES: +1. Synthesize all research into a coherent narrative +2. Structure information logically with evidence support +3. Provide comprehensive citations +4. Assess confidence levels and acknowledge limitations +5. Generate follow-up questions for deeper research + +REPORT STRUCTURE: +1. **Executive Summary**: Core findings and conclusions (1-2 paragraphs) +2. **Detailed Analysis**: Examination organized by themes with evidence +3. **Key Findings**: Bullet-point list of important discoveries +4. **Confidence Assessment**: Rate findings as High/Medium/Low/Uncertain +5. **Citations**: Complete source list with URLs +6. **Follow-up Questions**: Up to 5 areas for additional research, as warranted + +APPROACH: +- Address contradictory findings transparently +- Weight authoritative sources more heavily +- Distinguish facts from expert opinions +- Be explicit about information limitations + +OUTPUT REQUIREMENTS: +- executive_summary: 1-2 paragraph summary of core findings +- detailed_analysis: Multi-paragraph analysis organized by themes +- key_findings: Bullet-point discoveries +- confidence_assessment: Assessment of finding reliability +- citations: All sources referenced +- follow_up_questions: 3-5 specific questions for further research +""" + + +async def generate_synthesis( + original_query: str, research_plan: ResearchPlan, search_results: List[SearchResult] +) -> ResearchReport: + # Prepare comprehensive input with all research context + synthesis_input = f""" +ORIGINAL RESEARCH QUERY: {original_query} + +RESEARCH PLAN: +Research Question: {research_plan.research_question} +Key Aspects Investigated: { + ", ".join([aspect.aspect for aspect in research_plan.key_aspects]) + } +Search Strategy Used: {research_plan.search_strategy} +Success Criteria: {", ".join(research_plan.success_criteria)} + +SEARCH RESULTS TO SYNTHESIZE: +{ + chr(10).join( + [ + f"Query: {result.query}{chr(10)}Findings: {result.key_findings}{chr(10)}Relevance: {result.relevance_score}{chr(10)}Sources: {', '.join(result.sources)}{chr(10)}Citations: {', '.join(result.citations)}{chr(10)}" + for result in search_results + ] + ) + } + +Please synthesize all this information into a comprehensive research report following the specified structure and quality standards. +""" + result = await workflow.execute_activity( + invoke_model, + InvokeModelRequest( + model=COMPLEX_REASONING_MODEL, + instructions=REPORT_SYNTHESIS_INSTRUCTIONS, + input=synthesis_input, + response_format=ResearchReport, + ), + start_to_close_timeout=timedelta(seconds=300), + summary="Generating research report synthesis", + ) + + return result.response +``` + +## Create the Workflow + +The `DeepResearchWorkflow` orchestrates the four-phase research process with +built-in resilience and error handling: + +First, planning and query generation agents are run sequentially. Then, the +workflow executes searches concurrently. For robustness, the workflow continues +with partial results if some searches fail. Finally, the report synthesis agent +pulls together the findings into a comprehensive report. + +*File: workflows/deep_research_workflow.py* + +```python +from temporalio import workflow +from temporalio.exceptions import ApplicationError +import asyncio +from typing import List + +from agents.research_planning import plan_research +from agents.research_query_generation import generate_queries +from agents.research_web_search import search_web +from agents.research_report_synthesis import generate_synthesis +from agents.models import SearchResult + + +@workflow.defn +class DeepResearchWorkflow: + @workflow.run + async def run(self, query: str) -> str: + # Step 1: Research Planning + research_plan = await plan_research(query) + + # Step 2: Query Generation + query_plan = await generate_queries(research_plan) + + # Step 3: Web Search (parallel execution with resilience) + search_results = await self._execute_searches(query_plan.queries) + + # Ensure we have at least one successful search result + if not search_results: + raise ApplicationError( + "All web searches failed - cannot generate report", + "NO_SEARCH_RESULTS", + non_retryable=True, + ) + + # Step 4: Report Synthesis + final_report = await generate_synthesis(query, research_plan, search_results) + + # Format the final output + formatted_report = self._format_final_report(query, final_report) + return formatted_report + + async def _execute_searches(self, search_queries) -> List[SearchResult]: + """Execute web searches in parallel with resilience to individual failures""" + + # Create individual search coroutines + async def execute_single_search(search_query): + try: + return await search_web(search_query) + except Exception as e: + workflow.logger.exception( + f"Search failed for query '{search_query.query}': {e}" + ) + return None + + # Execute all searches in parallel + search_tasks = [execute_single_search(query) for query in search_queries] + results = await asyncio.gather(*search_tasks) + + # Filter out None results + return [result for result in results if result is not None] + + def _format_final_report(self, original_query, report) -> str: + """Format the final report for display""" + return f""" +# Deep Research Report + +**Research Query:** {original_query} + +## Executive Summary +{report.executive_summary} + +## Detailed Analysis +{report.detailed_analysis} + +## Key Findings +{chr(10).join([f"• {finding}" for finding in report.key_findings])} + +## Confidence Assessment +{report.confidence_assessment} + +## Sources and Citations +{chr(10).join([f"• {citation}" for citation in report.citations])} + +## Recommended Follow-up Questions +{chr(10).join([f"• {question}" for question in report.follow_up_questions])} + +""" + +``` diff --git a/braintrust/activities/invoke_model.py b/braintrust/activities/invoke_model.py new file mode 100644 index 00000000..f5e5c028 --- /dev/null +++ b/braintrust/activities/invoke_model.py @@ -0,0 +1,126 @@ +from temporalio import activity +from openai import AsyncOpenAI +import braintrust +from braintrust import wrap_openai +from typing import Optional, List, cast, Any, TypeVar, Generic +from typing_extensions import Annotated +from pydantic import BaseModel +from pydantic.functional_validators import BeforeValidator +from pydantic.functional_serializers import PlainSerializer + +import importlib +import os + +T = TypeVar("T", bound=BaseModel) + + +def _coerce_class(v: Any) -> type[Any]: + """Pydantic validator: convert string path to class during deserialization.""" + if isinstance(v, str): + mod_path, sep, qual = v.partition(":") + if not sep: # support "package.module.Class" + mod_path, _, qual = v.rpartition(".") + module = importlib.import_module(mod_path) + obj = module + for attr in qual.split("."): + obj = getattr(obj, attr) + return cast(type[Any], obj) + elif isinstance(v, type): + return v + else: + raise ValueError(f"Cannot coerce {v} to class") + + +def _dump_class(t: type[Any]) -> str: + """Pydantic serializer: convert class to string path during serialization.""" + return f"{t.__module__}:{t.__qualname__}" + + +# Custom type that automatically handles class <-> string conversion in Pydantic serialization +ClassReference = Annotated[ + type[T], + BeforeValidator(_coerce_class), + PlainSerializer(_dump_class, return_type=str), +] + + +class InvokeModelRequest(BaseModel, Generic[T]): + model: str + instructions: str # Fallback if Braintrust prompt unavailable + input: str + prompt_slug: Optional[str] = None # Braintrust prompt slug (e.g., "report-synthesis") + response_format: Optional[ClassReference[T]] = None + tools: Optional[List[dict]] = None + + +class InvokeModelResponse(BaseModel, Generic[T]): + # response_format records the type of the response model + response_format: Optional[ClassReference[T]] = None + response_model: Any + + @property + def response(self) -> T: + """Reconstruct the original response type if response_format was provided.""" + if self.response_format: + model_cls = self.response_format + return model_cls.model_validate(self.response_model) + return self.response_model + + +@activity.defn +async def invoke_model(request: InvokeModelRequest[T]) -> InvokeModelResponse[T]: + instructions = request.instructions + + # Load prompt from Braintrust if slug provided + if request.prompt_slug: + try: + prompt = braintrust.load_prompt( + project=os.environ.get("BRAINTRUST_PROJECT", "deep-research"), + slug=request.prompt_slug, + ) + # Extract system message content only + # NOTE: Other params (temperature, max_tokens, model) are NOT used + built = prompt.build() + for msg in built.get("messages", []): + if msg.get("role") == "system": + instructions = msg["content"] + activity.logger.info( + f"Loaded prompt '{request.prompt_slug}' from Braintrust" + ) + break + except Exception as e: + # Log warning but continue with fallback + activity.logger.warning( + f"Failed to load prompt '{request.prompt_slug}': {e}. " + "Using hardcoded fallback." + ) + + client = wrap_openai(AsyncOpenAI(max_retries=0)) + + kwargs: dict[str, Any] = { + "model": request.model, + "instructions": instructions, + "input": request.input, + } + + if request.response_format: + kwargs["text_format"] = request.response_format + + if request.tools: + kwargs["tools"] = request.tools + + # Use responses API consistently + resp = await client.responses.parse(**kwargs) + + if request.response_format: + # Convert structured response to dict for managed serialization. + # This allows us to reconstruct the original response type while maintaining type safety. + parsed_model = cast(BaseModel, resp.output_parsed) + return InvokeModelResponse( + response_model=parsed_model.model_dump(), + response_format=request.response_format, + ) + else: + return InvokeModelResponse( + response_model=resp.output_text, response_format=None + ) diff --git a/braintrust/agents/config.py b/braintrust/agents/config.py new file mode 100644 index 00000000..c1a85ae8 --- /dev/null +++ b/braintrust/agents/config.py @@ -0,0 +1,10 @@ +"""Configuration constants for the deep research system.""" + +# Model configuration constants +# Change these values to switch models globally +COMPLEX_REASONING_MODEL = "gpt-4o" # For planning and synthesis tasks +EFFICIENT_PROCESSING_MODEL = "gpt-4o-mini" # For query generation and search analysis + +# Alternative model options (uncomment to use): +# COMPLEX_REASONING_MODEL = "gpt-5" +# EFFICIENT_PROCESSING_MODEL = "gpt-5-mini" diff --git a/braintrust/agents/research_planning.py b/braintrust/agents/research_planning.py new file mode 100644 index 00000000..d6544ee3 --- /dev/null +++ b/braintrust/agents/research_planning.py @@ -0,0 +1,42 @@ +from .shared import ResearchPlan, today_str +from .config import COMPLEX_REASONING_MODEL +from activities.invoke_model import invoke_model, InvokeModelRequest +from temporalio import workflow +from datetime import timedelta + +RESEARCH_PLANNING_INSTRUCTIONS = f""" +You are a research planning specialist who creates focused research strategies. + +CORE RESPONSIBILITIES: +1. Decompose the user's question into 3-7 key research aspects +2. Identify required sources and evidence types +3. Design a practical search strategy +4. Set clear success criteria + +OUTPUT REQUIREMENTS: +- research_question: Clarified version of the original query +- key_aspects: Specific areas requiring investigation, each with: + - aspect: The research area name + - priority: 1-5 ranking (5 highest priority) + - description: What needs to be investigated +- expected_sources: Types of sources likely to contain relevant information +- search_strategy: High-level approach for information gathering +- success_criteria: Specific indicators of research completeness + +TODAY'S DATE: {today_str()} +""" + + +async def plan_research(query: str) -> ResearchPlan: + result = await workflow.execute_activity( + invoke_model, + InvokeModelRequest( + model=COMPLEX_REASONING_MODEL, + instructions=RESEARCH_PLANNING_INSTRUCTIONS, + input=f"Research query: {query}", + response_format=ResearchPlan, + ), + start_to_close_timeout=timedelta(seconds=300), + summary="Planning research", + ) + return result.response diff --git a/braintrust/agents/research_query_generation.py b/braintrust/agents/research_query_generation.py new file mode 100644 index 00000000..121c7eea --- /dev/null +++ b/braintrust/agents/research_query_generation.py @@ -0,0 +1,56 @@ +from .shared import QueryPlan, ResearchPlan, today_str +from .config import EFFICIENT_PROCESSING_MODEL +from activities.invoke_model import invoke_model, InvokeModelRequest +from temporalio import workflow +from datetime import timedelta + +QUERY_GENERATION_INSTRUCTIONS = f""" +You are a search query specialist who crafts effective web searches. + +CORE RESPONSIBILITIES: +1. Generate 3-5 diverse search queries based on the research plan +2. Balance specificity with discoverability +3. Target different information types (factual, analytical, recent, historical) + +APPROACH: +- Vary query styles: direct questions, topic + keywords, source-specific searches +- Include temporal modifiers when relevant (recent, 2024, historical) +- Use domain-specific terminology appropriately + +OUTPUT REQUIREMENTS: +- queries: Search queries, each with: + - query: The actual search string + - rationale: Why this query addresses research needs + - expected_info_type: One of "factual_data", "expert_analysis", "case_studies", "recent_news" + - priority: 1-5 (5 highest priority) + +TODAY'S DATE: {today_str()} +""" + + +async def generate_queries(research_plan: ResearchPlan) -> QueryPlan: + # Prepare input with research plan context + plan_context = f""" +Research Question: {research_plan.research_question} + +Key Aspects to Research: +{chr(10).join([f"- {aspect.aspect} (Priority: {aspect.priority}): {aspect.description}" for aspect in research_plan.key_aspects])} + +Expected Sources: {", ".join(research_plan.expected_sources)} +Search Strategy: {research_plan.search_strategy} +Success Criteria: {", ".join(research_plan.success_criteria)} +""" + + result = await workflow.execute_activity( + invoke_model, + InvokeModelRequest( + model=EFFICIENT_PROCESSING_MODEL, + instructions=QUERY_GENERATION_INSTRUCTIONS, + input=plan_context, + response_format=QueryPlan, + ), + start_to_close_timeout=timedelta(seconds=300), + summary="Generating search queries", + ) + + return result.response diff --git a/braintrust/agents/research_report_synthesis.py b/braintrust/agents/research_report_synthesis.py new file mode 100644 index 00000000..34e92b79 --- /dev/null +++ b/braintrust/agents/research_report_synthesis.py @@ -0,0 +1,84 @@ +from typing import List +from temporalio import workflow +from datetime import timedelta +from .shared import ResearchReport, ResearchPlan, SearchResult, today_str +from .config import COMPLEX_REASONING_MODEL +from activities.invoke_model import invoke_model, InvokeModelRequest + +REPORT_SYNTHESIS_INSTRUCTIONS = f""" +You are a research synthesis expert who creates comprehensive research reports. + +CORE RESPONSIBILITIES: +1. Synthesize all research into a coherent narrative +2. Structure information logically with evidence support +3. Provide comprehensive citations +4. Assess confidence levels and acknowledge limitations +5. Generate follow-up questions for deeper research + +REPORT STRUCTURE: +1. **Executive Summary**: Core findings and conclusions (1-2 paragraphs) +2. **Detailed Analysis**: Examination organized by themes with evidence +3. **Key Findings**: Bullet-point list of important discoveries +4. **Confidence Assessment**: Rate findings as High/Medium/Low/Uncertain +5. **Citations**: Complete source list with URLs +6. **Follow-up Questions**: Up to 5 areas for additional research, as warranted + +APPROACH: +- Address contradictory findings transparently +- Weight authoritative sources more heavily +- Distinguish facts from expert opinions +- Be explicit about information limitations + +OUTPUT REQUIREMENTS: +- executive_summary: 1-2 paragraph summary of core findings +- detailed_analysis: Multi-paragraph analysis organized by themes +- key_findings: Bullet-point discoveries +- confidence_assessment: Assessment of finding reliability +- citations: All sources referenced +- follow_up_questions: 3-5 specific questions for further research + +TODAY'S DATE: {today_str()} +""" + + +async def generate_synthesis( + original_query: str, research_plan: ResearchPlan, search_results: List[SearchResult] +) -> ResearchReport: + # Prepare comprehensive input with all research context + synthesis_input = f""" +ORIGINAL RESEARCH QUERY: {original_query} + +RESEARCH PLAN: +Research Question: {research_plan.research_question} +Key Aspects Investigated: { + ", ".join([aspect.aspect for aspect in research_plan.key_aspects]) + } +Search Strategy Used: {research_plan.search_strategy} +Success Criteria: {", ".join(research_plan.success_criteria)} + +SEARCH RESULTS TO SYNTHESIZE: +{ + chr(10).join( + [ + f"Query: {result.query}{chr(10)}Findings: {result.key_findings}{chr(10)}Relevance: {result.relevance_score}{chr(10)}Sources: {', '.join(result.sources)}{chr(10)}Citations: {', '.join(result.citations)}{chr(10)}" + for result in search_results + ] + ) + } + +Please synthesize all this information into a comprehensive research report following the specified structure and quality standards. +""" + result = await workflow.execute_activity( + invoke_model, + InvokeModelRequest( + model=COMPLEX_REASONING_MODEL, + instructions=REPORT_SYNTHESIS_INSTRUCTIONS, # Fallback + input=synthesis_input, + prompt_slug="report-synthesis", # Load from Braintrust if available + response_format=ResearchReport, + ), + start_to_close_timeout=timedelta(seconds=300), + summary="Generating research report synthesis", + ) + + return result.response diff --git a/braintrust/agents/research_web_search.py b/braintrust/agents/research_web_search.py new file mode 100644 index 00000000..b302b879 --- /dev/null +++ b/braintrust/agents/research_web_search.py @@ -0,0 +1,54 @@ +from .shared import SearchResult, SearchQuery, today_str +from .config import EFFICIENT_PROCESSING_MODEL +from activities.invoke_model import invoke_model, InvokeModelRequest +from temporalio import workflow +from datetime import timedelta + +WEB_SEARCH_INSTRUCTIONS = f""" +You are a web research specialist who finds and evaluates information from web sources. + +CORE RESPONSIBILITIES: +1. Execute web searches using the web search tool +2. Prioritize authoritative sources: academic, government, established research organizations, prominent news outlets, primary sources +3. Extract key information relevant to the research question +4. Provide proper citations and assess reliability + +APPROACH: +- Focus on information directly relevant to the research question +- Extract specific facts, data points, and evidence +- Note conflicting information and limitations +- Flag questionable or unverified claims + +OUTPUT REQUIREMENTS: +- query: The search query that was executed +- sources: URLs and source descriptions consulted +- key_findings: Synthesized information relevant to research question (2-4 paragraphs) +- relevance_score: 0.0-1.0 assessment of how well results address the query +- citations: Formatted sources with URLs + +TODAY'S DATE: {today_str()} +""" + + +async def search_web(query: SearchQuery) -> SearchResult: + search_input = f""" +Search Query: {query.query} +Query Rationale: {query.rationale} +Expected Information Type: {query.expected_info_type} +Priority Level: {query.priority} + +Please search for information using the provided query and analyze the results according to the instructions. +""" + result = await workflow.execute_activity( + invoke_model, + InvokeModelRequest( + model=EFFICIENT_PROCESSING_MODEL, + instructions=WEB_SEARCH_INSTRUCTIONS, + input=search_input, + response_format=SearchResult, + tools=[{"type": "web_search"}], + ), + start_to_close_timeout=timedelta(seconds=300), + summary="Searching web for information", + ) + return result.response diff --git a/braintrust/agents/shared.py b/braintrust/agents/shared.py new file mode 100644 index 00000000..fd422266 --- /dev/null +++ b/braintrust/agents/shared.py @@ -0,0 +1,57 @@ +from pydantic import BaseModel +from typing import List +from datetime import datetime + + +class ResearchAspect(BaseModel): + aspect: str + priority: int + description: str + + +class ResearchPlan(BaseModel): + research_question: str + key_aspects: List[ResearchAspect] + expected_sources: List[str] + search_strategy: str + success_criteria: List[str] + + +class SearchQuery(BaseModel): + query: str + rationale: str + expected_info_type: str + priority: int + + +class QueryPlan(BaseModel): + queries: List[SearchQuery] + + +class SearchResult(BaseModel): + query: str + sources: List[str] + key_findings: str + relevance_score: float + citations: List[str] + + +class ResearchReport(BaseModel): + executive_summary: str + detailed_analysis: str + key_findings: List[str] + confidence_assessment: str + citations: List[str] + follow_up_questions: List[str] + + +def today_str() -> str: + # Use Temporal workflow time when running inside a workflow. + # Fall back to the system clock when outside a workflow context. + try: + from temporalio import workflow # type: ignore + + now = workflow.now() + except Exception: + now = datetime.now() + return f"{now:%a} {now:%b} {now.day}, {now:%Y}" diff --git a/braintrust/pyproject.toml b/braintrust/pyproject.toml new file mode 100644 index 00000000..badbb0c3 --- /dev/null +++ b/braintrust/pyproject.toml @@ -0,0 +1,20 @@ +[project] +name = "deep-research-basic-openai-python" +version = "0.1" +description = "Deep research example using OpenAI Responses API with Temporal workflows" +authors = [{ name = "Temporal Technologies Inc", email = "sdk@temporal.io" }] +requires-python = ">=3.10" +readme = "README.md" +license = "MIT" +dependencies = ["temporalio>=1.15.0,<2", "openai>=1.40.0", "pydantic>=2.0.0", "braintrust[temporal]"] + +[dependency-groups] +dev = [ + "pytest>=8.4.2", + "pytest-asyncio>=0.24.0", + "pytest-timeout>=2.3.1", + +] + +[tool.ruff] +target-version = "py39" \ No newline at end of file diff --git a/braintrust/start_workflow.py b/braintrust/start_workflow.py new file mode 100644 index 00000000..67489892 --- /dev/null +++ b/braintrust/start_workflow.py @@ -0,0 +1,70 @@ +import asyncio +import sys +import uuid +import os +from datetime import datetime + +from braintrust import init_logger, start_span +from braintrust.contrib.temporal import BraintrustPlugin + +init_logger(project=os.environ.get("BRAINTRUST_PROJECT", "deep-research")) + +from temporalio.client import Client +from temporalio.contrib.pydantic import pydantic_data_converter + +from workflows.deep_research_workflow import DeepResearchWorkflow + + +async def main(): + # Connect to Temporal server with matching data converter + client = await Client.connect( + "localhost:7233", + data_converter=pydantic_data_converter, + plugins=[BraintrustPlugin()], + ) + + # Get research query from command line or use default + if len(sys.argv) > 1: + research_query = " ".join(sys.argv[1:]) + else: + research_query = "What are the latest developments in renewable energy policy and their economic impacts?" + + print(f"🔍 Starting Deep Research for: {research_query}") + print("=" * 80) + + # Generate unique workflow ID (or use environment variable if set) + workflow_id = os.environ.get("WORKFLOW_ID") + if not workflow_id: + workflow_id = f"deep-research-{datetime.now().strftime('%Y%m%d-%H%M%S')}-{uuid.uuid4().hex[:8]}" + + print(f"📋 Workflow ID: {workflow_id}") + + try: + # Execute the deep research workflow with Braintrust tracing + with start_span(name="deep-research-request", type="task") as span: + span.log(input={"query": research_query, "workflow_id": workflow_id}) + + result = await client.execute_workflow( + DeepResearchWorkflow.run, + research_query, + id=workflow_id, + task_queue="deep-research-task-queue", + ) + + span.log(output={"result": result}) + + print("📋 RESEARCH COMPLETED!") + print("=" * 80) + print(result) + + except Exception as e: + print(f"❌ Research failed: {e}") + print("Please check that:") + print("1. Temporal server is running (temporal server start-dev)") + print("2. Worker is running (uv run python -m worker)") + print("3. OpenAI API key is configured") + sys.exit(1) + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/braintrust/tests/test_e2e.py b/braintrust/tests/test_e2e.py new file mode 100644 index 00000000..d68b601a --- /dev/null +++ b/braintrust/tests/test_e2e.py @@ -0,0 +1,110 @@ +import asyncio +import subprocess +import os +import pytest + + +class IntegrationTestRunner: + """Helper class to manage worker and workflow execution""" + + def __init__(self): + self.worker_process = None + + async def start_worker(self): + """Start the worker process""" + print("Starting worker process...") + self.worker_process = subprocess.Popen( + ["uv", "run", "python", "worker.py"], + stdout=subprocess.PIPE, + stderr=subprocess.PIPE, + text=True, + ) + # Give worker time to start up + await asyncio.sleep(3) + + # Check if worker started successfully + if self.worker_process.poll() is not None: + stdout, stderr = self.worker_process.communicate() + raise RuntimeError( + f"Worker failed to start. stdout: {stdout}, stderr: {stderr}" + ) + + def stop_worker(self): + """Stop the worker process""" + if self.worker_process: + print("Stopping worker process...") + self.worker_process.terminate() + try: + self.worker_process.wait(timeout=5) + except subprocess.TimeoutExpired: + self.worker_process.kill() + self.worker_process.wait() + + async def run_workflow(self, query: str | None = None): + """Run the workflow and return the exit code""" + if query is None: + query = "What is the capital of France?" + + print(f"Running workflow with query: {query}") + + process = subprocess.Popen( + ["uv", "run", "python", "start_workflow.py", query], + stdout=subprocess.PIPE, + stderr=subprocess.PIPE, + text=True, + ) + + try: + stdout, stderr = process.communicate(timeout=60) # Increased timeout + except subprocess.TimeoutExpired: + process.kill() + try: + stdout, stderr = process.communicate(timeout=2) + except subprocess.TimeoutExpired: + stdout, stderr = "", "" + return -1, stdout or "", stderr or "Process timed out" + + print(f"Workflow stdout: {stdout}") + if stderr: + print(f"Workflow stderr: {stderr}") + + return process.returncode, stdout, stderr + + +@pytest.mark.asyncio +@pytest.mark.timeout(180) # 3 minute timeout for integration test +async def test_worker_and_workflow_integration(): + """Integration test that starts worker and runs workflow to completion""" + # Require OpenAI API key - fail if not available + if not os.environ.get("OPENAI_API_KEY"): + raise RuntimeError( + "OPENAI_API_KEY environment variable is required for integration test" + ) + + runner = IntegrationTestRunner() + + try: + # Start the worker + await runner.start_worker() + + # Give worker extra time to fully initialize + await asyncio.sleep(2) + + # Run the workflow with a simple query + exit_code, stdout, stderr = await runner.run_workflow("What is 2+2?") + + # Validate successful execution + assert exit_code == 0, ( + f"Workflow failed with exit code {exit_code}. stderr: {stderr}" + ) + 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temporalio.worker import Worker +from temporalio.contrib.pydantic import pydantic_data_converter + +from workflows.deep_research_workflow import DeepResearchWorkflow +from activities import invoke_model + + +async def main(): + # Connect to Temporal server with pydantic data converter for our data classes + client = await Client.connect( + "localhost:7233", + data_converter=pydantic_data_converter, + ) + + # Create worker with all workflows and activities + worker = Worker( + client, + task_queue="deep-research-task-queue", + workflows=[ + DeepResearchWorkflow, + ], + activities=[ + invoke_model.invoke_model, + ], + plugins=[BraintrustPlugin()], + ) + + print("Starting Deep Research Worker...") + + await worker.run() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/braintrust/workflows/deep_research_workflow.py b/braintrust/workflows/deep_research_workflow.py new file mode 100644 index 00000000..1ccb789c --- /dev/null +++ b/braintrust/workflows/deep_research_workflow.py @@ -0,0 +1,86 @@ +from temporalio import workflow +from temporalio.exceptions import ApplicationError +import asyncio +from typing import List + +from agents.research_planning import plan_research +from agents.research_query_generation import generate_queries +from agents.research_web_search import search_web +from agents.research_report_synthesis import generate_synthesis +from agents.shared import SearchResult + + +@workflow.defn +class DeepResearchWorkflow: + @workflow.run + async def run(self, query: str) -> str: + # Step 1: Research Planning + research_plan = await plan_research(query) + + # Step 2: Query Generation + query_plan = await generate_queries(research_plan) + + # Step 3: Web Search (parallel execution with resilience) + search_results = await self._execute_searches(query_plan.queries) + + # Ensure we have at least one successful search result + if not search_results: + raise ApplicationError( + "All web searches failed - cannot generate report", + "NO_SEARCH_RESULTS", + non_retryable=True, + ) + + # Step 4: Report Synthesis + final_report = await generate_synthesis(query, research_plan, search_results) + + # Format the final output + formatted_report = self._format_final_report(query, final_report) + return formatted_report + + async def _execute_searches(self, search_queries) -> List[SearchResult]: + """Execute web searches in parallel with resilience to individual failures""" + + # Create individual search coroutines + async def execute_single_search(search_query): + try: + return await search_web(search_query) + except Exception as e: + workflow.logger.exception( + f"Search failed for query '{search_query.query}': {e}" + ) + return None + + # Execute all searches in parallel + search_tasks = [execute_single_search(query) for query in search_queries] + results = await asyncio.gather(*search_tasks) + + # Filter out None results + return [result for result in results if result is not None] + + def _format_final_report(self, original_query, report) -> str: + """Format the final report for display""" + return f""" +# Deep Research Report + +**Research Query:** {original_query} + +## Executive Summary +{report.executive_summary} + +## Detailed Analysis +{report.detailed_analysis} + +## Key Findings +{chr(10).join([f"• {finding}" for finding in report.key_findings])} + +## Confidence Assessment +{report.confidence_assessment} + +## Sources and Citations +{chr(10).join([f"• {citation}" for citation in report.citations])} + +## Recommended Follow-up Questions +{chr(10).join([f"• {question}" for question in report.follow_up_questions])} + +"""