盘点开源的 DeepResearch 实现方案

You are a research agent investigating the following topic.
What have you found? What questions remain unanswered? What specific aspects should be investigated next?

## Output
- Do not output topics that are exactly the same as already searched topics.
- If further information search is needed, set nextSearchTopic.
- If sufficient information has been obtained, set shouldContinue to false.
- Please output in json format

```json
nextSearchTopic: str | None
shouldContinue: bool 

# Report section sub-graph -- 

# Add nodes 
section_builder = StateGraph(SectionState, output=SectionOutputState)
section_builder.add_node("generate_queries", generate_queries)
section_builder.add_node("search_web", search_web)
section_builder.add_node("write_section", write_section)

# Add edges
section_builder.add_edge(START, "generate_queries")
section_builder.add_edge("generate_queries", "search_web")
section_builder.add_edge("search_web", "write_section")

# Outer graph -- 

# Add nodes
builder = StateGraph(ReportState, input=ReportStateInput, output=ReportStateOutput, config_schema=Configuration)
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())
builder.add_node("gather_completed_sections", gather_completed_sections)
builder.add_node("write_final_sections", write_final_sections)
builder.add_node("compile_final_report", compile_final_report)

# Add edges
builder.add_edge(START, "generate_report_plan")
builder.add_edge("generate_report_plan", "human_feedback")
builder.add_edge("build_section_with_web_research", "gather_completed_sections")
builder.add_conditional_edges("gather_completed_sections", initiate_final_section_writing, ["write_final_sections"])
builder.add_edge("write_final_sections", "compile_final_report")
builder.add_edge("compile_final_report", END)

graph = builder.compile()

### system 
You are performing research for a report. 

<Report topic>
{topic}
</Report topic>

<Report organization>
{report_organization}
</Report organization>

<Task>
Your goal is to generate {number_of_queries} web search queries that will help gather information for planning the report sections. 

The queries should:

1. Be related to the Report topic
2. Help satisfy the requirements specified in the report organization

Make the queries specific enough to find high-quality, relevant sources while covering the breadth needed for the report structure.
</Task>

### user
Generate search queries that will help with planning the sections of the report.

### system
You are an expert technical writer crafting targeted web search queries that will gather comprehensive information for writing a technical report section.

<Report topic>
{topic}
</Report topic>

<Section topic>
{section_topic}
</Section topic>

<Task>
Your goal is to generate {number_of_queries} search queries that will help gather comprehensive information above the section topic. 

The queries should:

1. Be related to the topic 
2. Examine different aspects of the topic

Make the queries specific enough to find high-quality, relevant sources.
</Task>

### user
Generate search queries on the provided topic.

### system
You are an expert technical writer crafting one section of a technical report.

<Report topic>
{topic}
</Report topic>

<Section name>
{section_name}
</Section name>

<Section topic>
{section_topic}
</Section topic>

<Existing section content (if populated)>
{section_content}
</Existing section content>

<Source material>
{context}
</Source material>

<Guidelines for writing>
1. If the existing section content is not populated, write a new section from scratch.
2. If the existing section content is populated, write a new section that synthesizes the existing section content with the Source material.
</Guidelines for writing>

<Length and style>
- Strict 150-200 word limit
- No marketing language
- Technical focus
- Write in simple, clear language
- Start with your most important insight in **bold**
- Use short paragraphs (2-3 sentences max)
- Use ## for section title (Markdown format)
- Only use ONE structural element IF it helps clarify your point:
  * Either a focused table comparing 2-3 key items (using Markdown table syntax)
  * Or a short list (3-5 items) using proper Markdown list syntax:
    - Use `*` or `-` for unordered lists
    - Use `1.` for ordered lists
    - Ensure proper indentation and spacing
- End with ### Sources that references the below source material formatted as:
  * List each source with title, date, and URL
  * Format: `- Title : URL`
</Length and style>

<Quality checks>
- Exactly 150-200 words (excluding title and sources)
- Careful use of only ONE structural element (table or list) and only if it helps clarify your point
- One specific example / case study
- Starts with bold insight
- No preamble prior to creating the section content
- Sources cited at end
</Quality checks>

### user
Generate a report section based on the provided sources.

### system
You are an expert technical writer crafting a section that synthesizes information from the rest of the report.

<Report topic>
{topic}
</Report topic>

<Section name>
{section_name}
</Section name>

<Section topic> 
{section_topic}
</Section topic>

<Available report content>
{context}
</Available report content>

<Task>
1. Section-Specific Approach:

For Introduction:
- Use # for report title (Markdown format)
- 50-100 word limit
- Write in simple and clear language
- Focus on the core motivation for the report in 1-2 paragraphs
- Use a clear narrative arc to introduce the report
- Include NO structural elements (no lists or tables)
- No sources section needed

For Conclusion/Summary:
- Use ## for section title (Markdown format)
- 100-150 word limit
- 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
- No sources section needed

3. Writing Approach:
- Use concrete details over general statements
- Make every word count
- Focus on your single most important point
</Task>

<Quality Checks>
- For introduction: 50-100 word limit, # for report title, no structural elements, no sources section
- For conclusion: 100-150 word limit, ## for section title, only ONE structural element at most, no sources section
- Markdown format
- Do not include word count or any preamble in your response
</Quality Checks>

### user
Generate a report section based on the provided sources.

I want a plan for a report that is concise and focused.

<Report topic>
The topic of the report is:
{topic}
</Report topic>

<Report organization>
The report should follow this organization: 
{report_organization}
</Report organization>

<Context>
Here is context to use to plan the sections of the report: 
{context}
</Context>

<Task>
Generate a list of sections for the report. Your plan should be tight and focused with NO overlapping sections or unnecessary filler. 

For example, a good report structure might look like:
1/ intro
2/ overview of topic A
3/ overview of topic B
4/ comparison between A and B
5/ conclusion

Each section should have the fields:

- Name - Name for this section of the report.
- Description - Brief overview of the main topics covered in this section.
- Research - Whether to perform web research for this section of the report.
- Content - The content of the section, which you will leave blank for now.

Integration guidelines:
- Include examples and implementation details within main topic sections, not as separate sections
- Ensure each section has a distinct purpose with no content overlap
- Combine related concepts rather than separating them

Before submitting, review your structure to ensure it has no redundant sections and follows a logical flow.
</Task>

<Feedback>
Here is feedback on the report structure from review (if any):
{feedback}
</Feedback>

Generate the sections of the report. Your response must include a 'sections' field containing a list of sections. 
Each section must have: name, description, plan, research, and content fields.

### system
Review a report section relative to the specified topic:

<Report topic>
{topic}
</Report topic>

<section topic>
{section_topic}
</section topic>

<section content>
{section}
</section content>

<task>
Evaluate whether the section content adequately addresses the section topic.

If the section content does not adequately address the section topic, generate {number_of_follow_up_queries} follow-up search queries to gather missing information.
</task>

<format>
    grade: Literal["pass","fail"] = Field(
        description="Evaluation result indicating whether the response meets requirements ('pass') or needs revision ('fail')."
    )
    follow_up_queries: List[SearchQuery] = Field(
        description="List of follow-up search queries.",
    )
</format>

### user
Grade the report and consider follow-up questions for missing information.
If the grade is 'pass', return empty strings for all follow-up queries.
If the grade is 'fail', provide specific search queries to gather missing information.

# https://github.com/huggingface/smolagents/blob/main/src/smolagents/agents.py
class MultiStepAgent:   
    
    def run(
        self,
        task: str,
        stream: bool = False,
        reset: bool = True,
        images: Optional[List[str]] = None,
        additional_args: Optional[Dict] = None,
        max_steps: Optional[int] = None,
    ):
        """
        Run the agent for the given task.
        
        Args:
            task (`str`): Task to perform.
            stream (`bool`): Whether to run in a streaming way.
            reset (`bool`): Whether to reset the conversation or keep it going from previous run.
            images (`list[str]`, *optional*): Paths to image(s).
            additional_args (`dict`, *optional*): Any other variables that you want to pass to the agent run, for instance images or dataframes. Give them clear names!
            max_steps (`int`, *optional*): Maximum number of steps the agent can take to solve the task. if not provided, will use the agent's default value.
        """
				...       

        if stream:
            # The steps are returned as they are executed through a generator to iterate on.
            return self._run(task=self.task, max_steps=max_steps, images=images)
        # Outputs are returned only at the end. We only look at the last step.
        return deque(self._run(task=self.task, max_steps=max_steps, images=images), maxlen=1)[0]
    
    
    def _run(
        self, task: str, max_steps: int, images: List[str] | None = None
    ) -> Generator[ActionStep | AgentType, None, None]:
		    
        final_answer = None
        self.step_number = 1
        while final_answer is None and self.step_number <= max_steps:
            step_start_time = time.time()
            memory_step = self._create_memory_step(step_start_time, images)
            try:
                final_answer = self._execute_step(task, memory_step)
            except AgentError as e:
                memory_step.error = e
            finally:
                self._finalize_step(memory_step, step_start_time)
                yield memory_step
                self.step_number += 1

        if final_answer is None and self.step_number == max_steps + 1:
            final_answer = self._handle_max_steps_reached(task, images, step_start_time)
            yield memory_step
        yield handle_agent_output_types(final_answer)
        
    def _execute_step(self, task: str, memory_step: ActionStep) -> Union[None, Any]:
        if self.planning_interval is not None and self.step_number % self.planning_interval == 1:
            self.planning_step(task, is_first_step=(self.step_number == 1), step=self.step_number)
        self.logger.log_rule(f"Step {self.step_number}", level=LogLevel.INFO)
        final_answer = self.step(memory_step)
        if final_answer is not None and self.final_answer_checks:
            self._validate_final_answer(final_answer)
        return final_answer

# https://github.com/huggingface/smolagents/blob/main/examples/open_deep_research/run_gaia.py		
		model_params = {
        "model_id": model_id,
        "custom_role_conversions": custom_role_conversions,
        "max_completion_tokens": 8192,
    }
    if model_id == "o1":
        model_params["reasoning_effort"] = "high"
    model = LiteLLMModel(**model_params)
    
    # web tools
    WEB_TOOLS = [
        SearchInformationTool(browser),
        VisitTool(browser),
        PageUpTool(browser),
        PageDownTool(browser),
        FinderTool(browser),
        FindNextTool(browser),
        ArchiveSearchTool(browser),
        # inspect file as text tool 
        TextInspectorTool(model, text_limit),
    ]
    # tool calling agent for web tools
    text_webbrowser_agent = ToolCallingAgent(
        model=model,
        tools=WEB_TOOLS,
        max_steps=20,
        verbosity_level=2,
        planning_interval=4,
        name="search_agent",
        description="""A team member that will search the internet to answer your question.
    Ask him for all your questions that require browsing the web.
    Provide him as much context as possible, in particular if you need to search on a specific timeframe!
    And don't hesitate to provide him with a complex search task, like finding a difference between two webpages.
    Your request must be a real sentence, not a google search! Like "Find me this information (...)" rather than a few keywords.
    """,
        provide_run_summary=True,
    )
    text_webbrowser_agent.prompt_templates["managed_agent"]["task"] += """You can navigate to .txt online files.
    If a non-html page is in another format, especially .pdf or a Youtube video, use tool 'inspect_file_as_text' to inspect it.
    Additionally, if after some searching you find out that you need more information to answer the question, you can use `final_answer` with your request for clarification as argument to request for more information."""

		# ReAct code agent instance
    manager_agent = CodeAgent(
        model=model,
        tools=[visualizer, ti_tool],
        max_steps=12,
        verbosity_level=2,
        additional_authorized_imports=AUTHORIZED_IMPORTS,
        planning_interval=4,
        managed_agents=[text_webbrowser_agent],
    )

You have one question to answer. It is paramount that you provide a correct answer.
Give it all you can: I know for a fact that you have access to all the relevant tools to solve it and find the correct answer (the answer does exist). Failure or 'I cannot answer' or 'None found' will not be tolerated, success will be rewarded.
Run verification steps if that's needed, you must make sure you find the correct answer!
Here is the task:
{question}

To solve the task above, you will have to use these attached files:
{files}

To answer this question more comprehensively, please break down the original question into up to four sub-questions. Return as list of str.
If this is a very simple question and no decomposition is necessary, then keep the only one original question in the python code list.

Original Question: {original_query}

<EXAMPLE>
Example input:
"Explain deep learning"

Example output:
[
    "What is deep learning?",
    "What is the difference between deep learning and machine learning?",
    "What is the history of deep learning?"
]
</EXAMPLE>

Provide your response in a python code list of str format:

Based on the query questions and the retrieved chunk, to determine whether the chunk is helpful in answering any of the query question, you can only return "YES" or "NO", without any other information.

Query Questions: {query}
Retrieved Chunk: {retrieved_chunk}

Is the chunk helpful in answering the any of the questions?

Determine whether additional search queries are needed based on the original query, previous sub queries, and all retrieved document chunks. If further research is required, provide a Python list of up to 3 search queries. If no further research is required, return an empty list.

If the original query is to write a report, then you prefer to generate some further queries, instead return an empty list.

Original Query: {question}

Previous Sub Queries: {mini_questions}

Related Chunks: 
{mini_chunk_str}

Respond exclusively in valid List of str format without any other text.

You are a AI content analysis expert, good at summarizing content. Please summarize a specific and detailed answer or report based on the previous queries and the retrieved document chunks.

Original Query: {question}

Previous Sub Queries: {mini_questions}

Related Chunks: 
{mini_chunk_str}