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Merge upstream/experimental into feat/citations

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Resolved conflicts:
- backend/src/gateway/routers/artifacts.py: Keep citations block removal for markdown downloads
- frontend/src/components/workspace/messages/message-list-item.tsx: Keep improved citation handling with rehypePlugins, humanMessagePlugins, and CitationsLoadingIndicator

Co-authored-by: Cursor <cursoragent@cursor.com>
This commit is contained in:
LofiSu
2026-02-07 00:53:16 +08:00
parent 5484233548 5ed15d79c9
commit f0075e0d64
65 changed files with 3489 additions and 5320 deletions
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7
backend/src/agents/lead_agent/agent.py
View File

@@ -233,11 +233,12 @@ def make_lead_agent(config: RunnableConfig):
thinking_enabled = config.get("configurable", {}).get("thinking_enabled", True)
model_name = config.get("configurable", {}).get("model_name") or config.get("configurable", {}).get("model")
is_plan_mode = config.get("configurable", {}).get("is_plan_mode", False)
print(f"thinking_enabled: {thinking_enabled}, model_name: {model_name}, is_plan_mode: {is_plan_mode}")
subagent_enabled = config.get("configurable", {}).get("subagent_enabled", False)
print(f"thinking_enabled: {thinking_enabled}, model_name: {model_name}, is_plan_mode: {is_plan_mode}, subagent_enabled: {subagent_enabled}")
return create_agent(
model=create_chat_model(name=model_name, thinking_enabled=thinking_enabled),
tools=get_available_tools(model_name=model_name),
tools=get_available_tools(model_name=model_name, subagent_enabled=subagent_enabled),
middleware=_build_middlewares(config),
system_prompt=apply_prompt_template(),
system_prompt=apply_prompt_template(subagent_enabled=subagent_enabled),
state_schema=ThreadState,
)

167
backend/src/agents/lead_agent/prompt.py
View File

@@ -2,6 +2,130 @@ from datetime import datetime
from src.skills import load_skills
SUBAGENT_SECTION = """<subagent_system>
**🚀 SUBAGENT MODE ACTIVE - DECOMPOSE, DELEGATE, SYNTHESIZE**
You are running with subagent capabilities enabled. Your role is to be a **task orchestrator**:
1. **DECOMPOSE**: Break complex tasks into parallel sub-tasks
2. **DELEGATE**: Launch multiple subagents simultaneously using parallel `task` calls
3. **SYNTHESIZE**: Collect and integrate results into a coherent answer
**CORE PRINCIPLE: Complex tasks should be decomposed and distributed across multiple subagents for parallel execution.**
**Available Subagents:**
- **general-purpose**: For ANY non-trivial task - web research, code exploration, file operations, analysis, etc.
- **bash**: For command execution (git, build, test, deploy operations)
**Your Orchestration Strategy:**
✅ **DECOMPOSE + PARALLEL EXECUTION (Preferred Approach):**
For complex queries, break them down into multiple focused sub-tasks and execute in parallel:
**Example 1: "Why is Tencent's stock price declining?"**
→ Decompose into 4 parallel searches:
- Subagent 1: Recent financial reports and earnings data
- Subagent 2: Negative news and controversies
- Subagent 3: Industry trends and competitor performance
- Subagent 4: Macro-economic factors and market sentiment
**Example 2: "What are the latest AI trends in 2026?"**
→ Decompose into parallel research areas:
- Subagent 1: LLM and foundation model developments
- Subagent 2: AI infrastructure and hardware trends
- Subagent 3: Enterprise AI adoption patterns
- Subagent 4: Regulatory and ethical developments
**Example 3: "Refactor the authentication system"**
→ Decompose into parallel analysis:
- Subagent 1: Analyze current auth implementation
- Subagent 2: Research best practices and security patterns
- Subagent 3: Check for vulnerabilities and technical debt
- Subagent 4: Review related tests and documentation
✅ **USE Parallel Subagents (2+ subagents) when:**
- **Complex research questions**: Requires multiple information sources or perspectives
- **Multi-aspect analysis**: Task has several independent dimensions to explore
- **Large codebases**: Need to analyze different parts simultaneously
- **Comprehensive investigations**: Questions requiring thorough coverage from multiple angles
❌ **DO NOT use subagents (execute directly) when:**
- **Task cannot be decomposed**: If you can't break it into 2+ meaningful parallel sub-tasks, execute directly
- **Ultra-simple actions**: Read one file, quick edits, single commands
- **Need immediate clarification**: Must ask user before proceeding
- **Meta conversation**: Questions about conversation history
- **Sequential dependencies**: Each step depends on previous results (do steps yourself sequentially)
**CRITICAL WORKFLOW**:
1. In your thinking: Can I decompose this into 2+ independent parallel sub-tasks?
2. **YES** → Launch multiple `task` calls in parallel, then synthesize results
3. **NO** → Execute directly using available tools (bash, read_file, web_search, etc.)
**Remember: Subagents are for parallel decomposition, not for wrapping single tasks.**
**How It Works:**
- The task tool runs subagents asynchronously in the background
- The backend automatically polls for completion (you don't need to poll)
- The tool call will block until the subagent completes its work
- Once complete, the result is returned to you directly
**Usage Example - Parallel Decomposition:**
```python
# User asks: "Why is Tencent's stock price declining?"
# Thinking: This is complex research requiring multiple angles
# → Decompose into 4 parallel searches
# Launch 4 subagents in a SINGLE response with multiple tool calls:
# Subagent 1: Financial data
task(
subagent_type="general-purpose",
prompt="Search for Tencent's latest financial reports, quarterly earnings, and revenue trends in 2025-2026. Focus on numbers and official data.",
description="Tencent financial data"
)
# Subagent 2: Negative news
task(
subagent_type="general-purpose",
prompt="Search for recent negative news, controversies, or regulatory issues affecting Tencent in 2025-2026.",
description="Tencent negative news"
)
# Subagent 3: Industry/competitors
task(
subagent_type="general-purpose",
prompt="Search for Chinese tech industry trends and how Tencent's competitors (Alibaba, ByteDance) are performing in 2025-2026.",
description="Industry comparison"
)
# Subagent 4: Market factors
task(
subagent_type="general-purpose",
prompt="Search for macro-economic factors affecting Chinese tech stocks and overall market sentiment toward Tencent in 2025-2026.",
description="Market sentiment"
)
# All 4 subagents run in parallel, results return simultaneously
# Then synthesize findings into comprehensive analysis
```
**Counter-Example - Direct Execution (NO subagents):**
```python
# User asks: "Run the tests"
# Thinking: Cannot decompose into parallel sub-tasks
# → Execute directly
bash("npm test") # Direct execution, not task()
```
**CRITICAL**:
- Only use `task` when you can launch 2+ subagents in parallel
- Single task = No value from subagents = Execute directly
- Multiple tasks in SINGLE response = Parallel execution
</subagent_system>"""
SYSTEM_PROMPT_TEMPLATE = """
<role>
You are DeerFlow 2.0, an open-source super agent.
@@ -13,7 +137,7 @@ You are DeerFlow 2.0, an open-source super agent.
- Think concisely and strategically about the user's request BEFORE taking action
- Break down the task: What is clear? What is ambiguous? What is missing?
- **PRIORITY CHECK: If anything is unclear, missing, or has multiple interpretations, you MUST ask for clarification FIRST - do NOT proceed with work**
- Never write down your full final answer or report in thinking process, but only outline
{subagent_thinking}- Never write down your full final answer or report in thinking process, but only outline
- CRITICAL: After thinking, you MUST provide your actual response to the user. Thinking is for planning, the response is for delivery.
- Your response must contain the actual answer, not just a reference to what you thought about
</thinking_style>
@@ -103,6 +227,8 @@ You have access to skills that provide optimized workflows for specific tasks. E
</skill_system>
{subagent_section}
<working_directory existed="true">
- User uploads: `/mnt/user-data/uploads` - Files uploaded by the user (automatically listed in context)
- User workspace: `/mnt/user-data/workspace` - Working directory for temporary files
@@ -149,7 +275,7 @@ The key AI trends for 2026 include enhanced reasoning capabilities and multimoda
<critical_reminders>
- **Clarification First**: ALWAYS clarify unclear/missing/ambiguous requirements BEFORE starting work - never assume or guess
- Skill First: Always load the relevant skill before starting **complex** tasks.
{subagent_reminder}- Skill First: Always load the relevant skill before starting **complex** tasks.
- Progressive Loading: Load resources incrementally as referenced in skills
- Output Files: Final deliverables must be in `/mnt/user-data/outputs`
- Clarity: Be direct and helpful, avoid unnecessary meta-commentary
@@ -176,9 +302,7 @@ def _get_memory_context() -> str:
return ""
memory_data = get_memory_data()
memory_content = format_memory_for_injection(
memory_data, max_tokens=config.max_injection_tokens
)
memory_content = format_memory_for_injection(memory_data, max_tokens=config.max_injection_tokens)
if not memory_content.strip():
return ""
@@ -192,29 +316,24 @@ def _get_memory_context() -> str:
return ""
def apply_prompt_template() -> str:
def apply_prompt_template(subagent_enabled: bool = False) -> str:
# Load only enabled skills
skills = load_skills(enabled_only=True)
# Get skills container path from config
# Get config
try:
from src.config import get_app_config
config = get_app_config()
container_base_path = config.skills.container_path
except Exception:
# Fallback to default if config fails
# Fallback to defaults if config fails
container_base_path = "/mnt/skills"
# Generate skills list XML with paths (path points to SKILL.md file)
if skills:
skill_items = "\n".join(
f" <skill>\n"
f" <name>{skill.name}</name>\n"
f" <description>{skill.description}</description>\n"
f" <location>{skill.get_container_file_path(container_base_path)}</location>\n"
f" </skill>"
for skill in skills
f" <skill>\n <name>{skill.name}</name>\n <description>{skill.description}</description>\n <location>{skill.get_container_file_path(container_base_path)}</location>\n </skill>" for skill in skills
)
skills_list = f"<available_skills>\n{skill_items}\n</available_skills>"
else:
@@ -223,11 +342,31 @@ def apply_prompt_template() -> str:
# Get memory context
memory_context = _get_memory_context()
# Include subagent section only if enabled (from runtime parameter)
subagent_section = SUBAGENT_SECTION if subagent_enabled else ""
# Add subagent reminder to critical_reminders if enabled
subagent_reminder = (
"- **Orchestrator Mode**: You are a task orchestrator - decompose complex tasks into parallel sub-tasks and launch multiple subagents simultaneously. Synthesize results, don't execute directly.\n"
if subagent_enabled
else ""
)
# Add subagent thinking guidance if enabled
subagent_thinking = (
"- **DECOMPOSITION CHECK: Can this task be broken into 2+ parallel sub-tasks? If YES, decompose and launch multiple subagents in parallel. Your role is orchestrator, not executor.**\n"
if subagent_enabled
else ""
)
# Format the prompt with dynamic skills and memory
prompt = SYSTEM_PROMPT_TEMPLATE.format(
skills_list=skills_list,
skills_base_path=container_base_path,
memory_context=memory_context,
subagent_section=subagent_section,
subagent_reminder=subagent_reminder,
subagent_thinking=subagent_thinking,
)
return prompt + f"\n<current_date>{datetime.now().strftime('%Y-%m-%d, %A')}</current_date>"