[fix][aop docs server example]

docs/mkdocs.yml

@@ -410,10 +410,10 @@ nav:
           - Hiearchical Marketing Team: "examples/marketing_team.md"
           - Gold ETF Research with HeavySwarm: "examples/gold_etf_research.md"
           - Hiring Swarm: "examples/hiring_swarm.md"
+          - Advanced Research: "examples/av.md"
     
     - Tools & Integrations:
       - Web Search with Exa: "examples/exa_search.md"
-      - Advanced Research: "examples/av.md"
       - Browser Use: "examples/browser_use.md"
       - Yahoo Finance: "swarms/examples/yahoo_finance.md"
       - Firecrawl: "developer_guides/firecrawl.md"

docs/swarms/examples/aop_server_example.md

@@ -0,0 +1,164 @@
+# AOP Server Setup Example
+
+This example demonstrates how to set up an Agent Orchestration Protocol (AOP) server with multiple specialized agents.
+
+## Overview
+
+The AOP server allows you to deploy multiple agents that can be discovered and called by other agents or clients in the network. This example shows how to create a server with specialized agents for different tasks.
+
+## Code Example
+
+```python
+from swarms import Agent
+from swarms.structs.aop import (
+    AOP,
+)
+
+# Create specialized agents
+research_agent = Agent(
+    agent_name="Research-Agent",
+    agent_description="Expert in research, data collection, and information gathering",
+    model_name="anthropic/claude-sonnet-4-5",
+    max_loops=1,
+    top_p=None,
+    dynamic_temperature_enabled=True,
+    system_prompt="""You are a research specialist. Your role is to:
+    1. Gather comprehensive information on any given topic
+    2. Analyze data from multiple sources
+    3. Provide well-structured research findings
+    4. Cite sources and maintain accuracy
+    5. Present findings in a clear, organized manner
+    
+    Always provide detailed, factual information with proper context.""",
+)
+
+analysis_agent = Agent(
+    agent_name="Analysis-Agent",
+    agent_description="Expert in data analysis, pattern recognition, and generating insights",
+    model_name="anthropic/claude-sonnet-4-5",
+    max_loops=1,
+    top_p=None,
+    dynamic_temperature_enabled=True,
+    system_prompt="""You are an analysis specialist. Your role is to:
+    1. Analyze data and identify patterns
+    2. Generate actionable insights
+    3. Create visualizations and summaries
+    4. Provide statistical analysis
+    5. Make data-driven recommendations
+    
+    Focus on extracting meaningful insights from information.""",
+)
+
+writing_agent = Agent(
+    agent_name="Writing-Agent",
+    agent_description="Expert in content creation, editing, and communication",
+    model_name="anthropic/claude-sonnet-4-5",
+    max_loops=1,
+    top_p=None,
+    dynamic_temperature_enabled=True,
+    system_prompt="""You are a writing specialist. Your role is to:
+    1. Create engaging, well-structured content
+    2. Edit and improve existing text
+    3. Adapt tone and style for different audiences
+    4. Ensure clarity and coherence
+    5. Follow best practices in writing
+    
+    Always produce high-quality, professional content.""",
+)
+
+code_agent = Agent(
+    agent_name="Code-Agent",
+    agent_description="Expert in programming, code review, and software development",
+    model_name="anthropic/claude-sonnet-4-5",
+    max_loops=1,
+    top_p=None,
+    dynamic_temperature_enabled=True,
+    system_prompt="""You are a coding specialist. Your role is to:
+    1. Write clean, efficient code
+    2. Debug and fix issues
+    3. Review and optimize code
+    4. Explain programming concepts
+    5. Follow best practices and standards
+    
+    Always provide working, well-documented code.""",
+)
+
+financial_agent = Agent(
+    agent_name="Financial-Agent",
+    agent_description="Expert in financial analysis, market research, and investment insights",
+    model_name="anthropic/claude-sonnet-4-5",
+    max_loops=1,
+    top_p=None,
+    dynamic_temperature_enabled=True,
+    system_prompt="""You are a financial specialist. Your role is to:
+    1. Analyze financial data and markets
+    2. Provide investment insights
+    3. Assess risk and opportunities
+    4. Create financial reports
+    5. Explain complex financial concepts
+    
+    Always provide accurate, well-reasoned financial analysis.""",
+)
+
+# Basic usage - individual agent addition
+deployer = AOP("MyAgentServer", verbose=True, port=5932)
+
+agents = [
+    research_agent,
+    analysis_agent,
+    writing_agent,
+    code_agent,
+    financial_agent,
+]
+
+deployer.add_agents_batch(agents)
+
+deployer.run()
+```
+
+## Key Components
+
+### 1. Agent Creation
+
+Each agent is created with:
+
+- **agent_name**: Unique identifier for the agent
+- **agent_description**: Brief description of the agent's capabilities
+- **model_name**: The language model to use
+- **system_prompt**: Detailed instructions defining the agent's role and behavior
+
+### 2. AOP Server Setup
+
+- **Server Name**: "MyAgentServer" - identifies your server
+- **Port**: 5932 - the port where the server will run
+- **Verbose**: True - enables detailed logging
+
+### 3. Agent Registration
+
+- **add_agents_batch()**: Registers multiple agents at once
+- Agents become available for discovery and remote calls
+
+## Usage
+
+1. **Start the Server**: Run the script to start the AOP server
+2. **Agent Discovery**: Other agents or clients can discover available agents
+3. **Remote Calls**: Agents can be called remotely by their names
+
+## Server Features
+
+- **Agent Discovery**: Automatically registers agents for network discovery
+- **Remote Execution**: Agents can be called from other network nodes
+- **Load Balancing**: Distributes requests across available agents
+- **Health Monitoring**: Tracks agent status and availability
+
+## Configuration Options
+
+- **Port**: Change the port number as needed
+- **Verbose**: Set to False for reduced logging
+- **Server Name**: Use a descriptive name for your server
+
+## Next Steps
+
+- See [AOP Cluster Example](aop_cluster_example.md) for multi-server setups
+- Check [AOP Reference](../structs/aop.md) for advanced configuration options
+- Explore agent communication patterns in the examples directory

pyproject.toml

@@ -5,7 +5,7 @@ build-backend = "poetry.core.masonry.api"
 
 [tool.poetry]
 name = "swarms"
-version = "8.4.0"
+version = "8.4.1"
 description = "Swarms - TGSC"
 license = "MIT"
 authors = ["Kye Gomez <kye@apac.ai>"]

swarms/structs/__init__.py

@@ -1,6 +1,7 @@
 from swarms.structs.agent import Agent
 from swarms.structs.agent_loader import AgentLoader
 from swarms.structs.agent_rearrange import AgentRearrange, rearrange
+from swarms.structs.aop import AOP
 from swarms.structs.auto_swarm_builder import AutoSwarmBuilder
 from swarms.structs.base_structure import BaseStructure
 from swarms.structs.base_swarm import BaseSwarm
@@ -100,7 +101,6 @@ from swarms.structs.swarming_architectures import (
     staircase_swarm,
     star_swarm,
 )
-from swarms.structs.aop import AOP
 
 __all__ = [
     "Agent",

swarms/structs/heavy_swarm.py

@@ -34,15 +34,18 @@ Instructions:
 - Use evidence hierarchy: peer-reviewed > industry reports > news > social media. Weight by recency and authority.
 - For each claim, assess: source reliability, data quality, potential bias, methodology validity.
 - If insufficient evidence, quantify gaps: "Missing: [specific data type] from [timeframe] for [scope]."
-
+- Conduct comprehensive literature review and fact-checking protocols.
-Output (≤400 tokens):
+- Validate information through multiple independent sources when possible.
-1. Findings (≤8 bullets, 1 sentence each, [Ref N])
+
-2. Evidence Quality Matrix (Source | Reliability | Recency | Bias Risk | Weight)
+Output Structure:
-3. Confidence (High/Medium/Low + statistical rationale)
+1. Key Findings (comprehensive list with supporting evidence and reference numbers)
-4. Data Gaps (≤3 bullets, specific and actionable)
+2. Evidence Quality Matrix (Source | Reliability | Recency | Bias Risk | Weight | Validation Status)
-5. References (numbered, titles + URLs + access date)
+3. Confidence Assessment (High/Medium/Low with detailed statistical rationale and sample size)
-
+4. Data Gaps Analysis (specific missing information with actionable recommendations for filling gaps)
-Constraints: Systematic verification only. No speculation or analysis.
+5. Source Verification (detailed assessment of each source's credibility and methodology)
+6. References (comprehensive numbered list with titles, URLs, access dates, and quality scores)
+
+Constraints: Systematic verification only. No speculation or analysis. Focus on factual accuracy and evidence quality.
 """
 
 
@@ -54,15 +57,19 @@ Instructions:
 - Use quantitative methods: regression analysis, time series analysis, variance analysis, confidence intervals.
 - For each insight, calculate: correlation coefficient, statistical significance (p-value), confidence interval, effect size.
 - State assumptions explicitly and test for validity. Identify confounding variables and control for bias.
-
+- Perform robust statistical testing with appropriate corrections for multiple comparisons.
-Output (≤400 tokens):
+- Conduct sensitivity analysis to test the robustness of findings.
-1. Analytical Methods (statistical approach + assumptions + limitations)
+
-2. Quantitative Insights (≤6 items: finding + statistical measure + confidence interval)
+Output Structure:
-3. Statistical Assumptions (≤3 bullets: assumption + validity test + impact if violated)
+1. Analytical Methods (detailed statistical approach, assumptions, limitations, and rationale for method selection)
-4. Uncertainty Analysis (≤3 bullets: uncertainty type + magnitude + mitigation)
+2. Quantitative Insights (comprehensive findings with statistical measures, confidence intervals, and effect sizes)
-5. Confidence (High/Medium/Low + statistical rationale + sample size)
+3. Statistical Assumptions (detailed assessment of each assumption, validity tests, and impact analysis if violated)
-
+4. Uncertainty Analysis (comprehensive assessment of uncertainty types, magnitudes, and mitigation strategies)
-Constraints: Statistical rigor only. No alternatives or implementation.
+5. Model Validation (goodness-of-fit measures, residual analysis, and model diagnostics)
+6. Sensitivity Analysis (robustness testing results and alternative model specifications)
+7. Confidence Assessment (High/Medium/Low with detailed statistical rationale, sample size, and power analysis)
+
+Constraints: Statistical rigor only. No alternatives or implementation. Focus on methodological soundness and analytical depth.
 """
 
 ALTERNATIVES_AGENT_PROMPT = """
@@ -73,18 +80,24 @@ Instructions:
 - Use multi-criteria decision analysis (MCDA): weighted scoring, pairwise comparison, sensitivity analysis.
 - For each option, calculate: NPV/ROI, implementation complexity, resource requirements, timeline, success probability.
 - Apply scenario analysis: best-case, most-likely, worst-case outcomes with probability distributions.
+- Consider stakeholder perspectives and value trade-offs in option evaluation.
+- Assess interdependencies and potential synergies between options.
 
-Output (≤500 tokens):
+Output Structure:
-- Options:
+- Strategic Options:
   - Option Name
-    - Summary (1 sentence)
+    - Executive Summary (comprehensive overview of the option)
-    - Quantitative Scores: Impact X/5, Effort Y/5, Risk Z/5, ROI %, Timeline (months)
+    - Quantitative Analysis: Impact X/5, Effort Y/5, Risk Z/5, ROI %, Timeline (months), Resource Requirements
-    - Pros (≤2), Cons (≤2), Preconditions (≤2)
+    - Detailed Pros and Cons (comprehensive advantages and disadvantages)
-    - Scenario Analysis: Best (probability), Most-likely (probability), Worst (probability)
+    - Implementation Preconditions (detailed requirements and dependencies)
-- Decision Matrix: Option | Impact | Effort | Risk | ROI | Timeline | Weighted Score
+    - Scenario Analysis: Best-case (probability), Most-likely (probability), Worst-case (probability)
-- Selection Criteria (≤3 bullets: decision rule + threshold + tie-breaking)
+    - Stakeholder Impact Assessment (who benefits/loses and to what degree)
-
+- Comprehensive Decision Matrix: Option | Impact | Effort | Risk | ROI | Timeline | Resource Efficiency | Weighted Score
-Constraints: Systematic analysis only. No feasibility verification.
+- Selection Criteria (detailed decision rules, thresholds, and tie-breaking mechanisms)
+- Sensitivity Analysis (how changes in weights or criteria affect rankings)
+- Risk-Adjusted Recommendations (options ranked by risk-adjusted value)
+
+Constraints: Systematic analysis only. No feasibility verification. Focus on comprehensive option evaluation and strategic thinking.
 """
 
 
@@ -96,14 +109,19 @@ Instructions:
 - Use risk assessment frameworks: probability × impact matrix, failure mode analysis, sensitivity analysis.
 - For each claim, assess: evidence quality, source credibility, logical consistency, empirical validity.
 - Identify logical fallacies, cognitive biases, and methodological errors. Flag contradictions with statistical confidence.
-
+- Conduct comprehensive fact-checking using multiple independent verification sources.
-Output (≤400 tokens):
+- Apply rigorous quality assurance protocols to ensure accuracy and reliability.
-1. Verification Matrix (Claim | Status | Evidence Quality | Source Credibility | Confidence | P-value)
+
-2. Risk Assessment (Risk | Probability | Impact | Mitigation | Residual Risk)
+Output Structure:
-3. Logical Consistency Check (Contradiction | Severity | Resolution | Confidence)
+1. Comprehensive Verification Matrix (Claim | Status | Evidence Quality | Source Credibility | Confidence | P-value | Validation Method)
-4. Feasibility Analysis (Constraint | Impact | Workaround | Probability of Success)
+2. Detailed Risk Assessment (Risk | Probability | Impact | Mitigation Strategy | Residual Risk | Monitoring Requirements)
-
+3. Logical Consistency Analysis (Contradiction | Severity | Resolution Strategy | Confidence Level | Evidence Supporting Resolution)
-Constraints: Systematic validation only. Objective and evidence-based.
+4. Feasibility Analysis (Constraint | Impact | Workaround Options | Probability of Success | Resource Requirements)
+5. Quality Assurance Report (Validation Methods Used | Quality Metrics | Areas of Concern | Recommendations for Improvement)
+6. Bias Detection Analysis (Potential Biases | Impact Assessment | Mitigation Strategies | Monitoring Protocols)
+7. Evidence Chain Validation (Source Verification | Chain of Custody | Reliability Assessment | Confidence Intervals)
+
+Constraints: Systematic validation only. Objective and evidence-based. Focus on accuracy, reliability, and comprehensive verification.
 """
 
 SYNTHESIS_AGENT_PROMPT = """
@@ -114,16 +132,22 @@ Instructions:
 - Use decision frameworks: multi-criteria decision analysis (MCDA), analytic hierarchy process (AHP), Pareto optimization.
 - For each recommendation, calculate: expected value, risk-adjusted return, implementation probability, resource efficiency.
 - Reconcile conflicts using evidence hierarchy: statistical significance > source credibility > recency > sample size.
-
+- Integrate insights from all agent perspectives into coherent strategic recommendations.
-Output (≤600 tokens):
+- Apply advanced optimization techniques to maximize value while minimizing risk and resource requirements.
-1. Executive Summary (≤6 bullets: key findings + confidence + action items)
+
-2. Integrated Analysis (≤8 bullets: insight + statistical measure + agent attribution + confidence)
+Output Structure:
-3. Conflict Resolution Matrix (Contradiction | Evidence Weight | Resolution | Confidence)
+1. Executive Summary (comprehensive key findings with confidence levels and prioritized action items)
-4. Optimized Recommendations (table: Recommendation | Expected Value | Risk Score | Implementation Probability | Resource Efficiency | Priority)
+2. Integrated Analysis (detailed insights with statistical measures, agent attribution, and confidence assessments)
-5. Risk-Optimized Portfolio (Risk | Probability | Impact | Mitigation | Residual Risk | Cost)
+3. Conflict Resolution Matrix (Contradiction | Evidence Weight | Resolution Strategy | Confidence Level | Implementation Plan)
-6. Implementation Roadmap (Step | Owner | Timeline | Dependencies | Success Metrics | Probability)
+4. Optimized Recommendations (comprehensive table: Recommendation | Expected Value | Risk Score | Implementation Probability | Resource Efficiency | Priority | Timeline)
-
+5. Risk-Optimized Portfolio (Risk | Probability | Impact | Mitigation Strategy | Residual Risk | Cost | Monitoring Requirements)
-Constraints: Systematic optimization only. Evidence-based decision support.
+6. Implementation Roadmap (Step | Owner | Timeline | Dependencies | Success Metrics | Probability | Resource Requirements)
+7. Value Optimization Analysis (ROI projections, cost-benefit analysis, and value maximization strategies)
+8. Stakeholder Impact Assessment (comprehensive analysis of how recommendations affect different stakeholder groups)
+9. Success Metrics and KPIs (detailed measurement framework for tracking implementation success)
+10. Contingency Planning (alternative approaches and fallback strategies for high-risk scenarios)
+
+Constraints: Systematic optimization only. Evidence-based decision support. Focus on practical implementation and measurable outcomes.
 """
 
 schema = {