Merge remote-tracking branch 'fork/feat/graphworkflow-conditional-edges' into feat/graphworkflow-retry-policy

# Conflicts:
#	swarms/structs/graph_workflow.py

Author: ayaangazali

CLAUDE.md

@@ -507,10 +507,8 @@ result = router.run("Write a blog post about transformer architectures.")
 | `"HeavySwarm"` | Intensive multi-loop deep analysis |
 | `"RoundRobin"` | Round-robin task distribution |
 | `"PlannerWorkerSwarm"` | Planner + worker delegation |
-| `"BatchedGridWorkflow"` | Grid-based batch execution |
 | `"LLMCouncil"` | LLM-based council decisions |
 | `"AutoSwarmBuilder"` | Auto-configures everything |
-| `"auto"` | Router selects swarm_type automatically |
 
 ---
 
@@ -938,7 +936,7 @@ agent = Agent(
 | High-stakes ruling with deliberation | `CouncilAsAJudge` |
 | Structured adversarial debate | `DebateWithJudge` |
 | Deep research, many loops | `HeavySwarm` |
-| Don't know yet / rapid prototyping | `AutoSwarmBuilder` or `SwarmRouter(swarm_type="auto")` |
+| Don't know yet / rapid prototyping | `AutoSwarmBuilder` |
 | Need to switch architectures easily | `SwarmRouter` |
 
 ---
@@ -1086,8 +1084,6 @@ from swarms import Agent
 
 **Don't instantiate heavyweight structures inside tight loops** — create agents and workflows once, reuse them across calls.
 
-**Don't pass `tools=[]` (empty list)** — pass `tools=None` instead. An empty list can confuse schema generation.
-
 **Don't use `streaming_on=True` and `streaming_callback` together on the same agent** — `streaming_on` streams to stdout; `streaming_callback` streams to your function. Pick one.
 
 **Don't set `context_compression=False` on very long autonomous sessions** — without compression the agent will eventually hit the context limit and raise an error.

README.md

@@ -729,69 +729,6 @@ Perfect for implementing complex multi-agent workflows, collaborative problem-so
 
 ---
 
-### Agent Orchestration Protocol (AOP)
-
-The **Agent Orchestration Protocol (AOP)** is a powerful framework for deploying and managing agents as distributed services. AOP enables agents to be discovered, managed, and executed through a standardized protocol, making it perfect for building scalable multi-agent systems. [Learn more about AOP](https://docs.swarms.world/api/aop)
-
-```python
-from swarms import Agent, AOP
-
-# Create specialized agents
-research_agent = Agent(
-    agent_name="Research-Agent",
-    agent_description="Expert in research and data collection",
-    model_name="anthropic/claude-sonnet-4-5",
-    max_loops=1,
-    tags=["research", "data-collection", "analysis"],
-    capabilities=["web-search", "data-gathering", "report-generation"],
-    role="researcher"
-)
-
-analysis_agent = Agent(
-    agent_name="Analysis-Agent", 
-    agent_description="Expert in data analysis and insights",
-    model_name="anthropic/claude-sonnet-4-5",
-    max_loops=1,
-    tags=["analysis", "data-processing", "insights"],
-    capabilities=["statistical-analysis", "pattern-recognition", "visualization"],
-    role="analyst"
-)
-
-# Create AOP server
-deployer = AOP(
-    server_name="ResearchCluster",
-    port=8000,
-    verbose=True
-)
-
-# Add agents to the server
-deployer.add_agent(
-    agent=research_agent,
-    tool_name="research_tool",
-    tool_description="Research and data collection tool",
-    timeout=30,
-    max_retries=3
-)
-
-deployer.add_agent(
-    agent=analysis_agent,
-    tool_name="analysis_tool", 
-    tool_description="Data analysis and insights tool",
-    timeout=30,
-    max_retries=3
-)
-
-# List all registered agents
-print("Registered agents:", deployer.list_agents())
-
-# Start the AOP server
-deployer.run()
-```
-
-Perfect for deploying large scale multi-agent systems. [Read the complete AOP documentation](https://docs.swarms.world/api/aop)
-
----
-
 ## Documentation
 
 The full documentation lives at **[docs.swarms.world](https://docs.swarms.world)**. Below are the resources most useful when building with Swarms — both for humans and for AI coding assistants.
@@ -837,7 +774,6 @@ Swarms seamlessly integrates with industry-standard protocols and open specifica
 |----------|-------------|---------------|
 | **[MCP (Model Context Protocol)](https://docs.swarms.world/integrations/mcp)** | Standardized protocol for AI agents to interact with external tools and services through MCP servers. Enables dynamic tool discovery and execution. | [MCP Integration Guide](https://docs.swarms.world/integrations/mcp) |
 | **[X402](https://docs.swarms.world/examples/integrations/x402-payment)** | Cryptocurrency payment protocol for API endpoints. Enables monetization of agents with pay-per-use models. | [X402 Quickstart](https://docs.swarms.world/examples/integrations/x402-payment) |
-| **[AOP (Agent Orchestration Protocol)](https://docs.swarms.world/examples/multi-agent/aop-medical)** | Framework for deploying and managing agents as distributed services. Enables agent discovery, management, and execution through standardized protocols. | [AOP Reference](https://docs.swarms.world/api/aop) |
 | **[Swarms Marketplace](https://swarms.world)** | Platform for discovering and sharing production-ready prompts, agents, and tools. Enables automatic prompt loading from the marketplace and publishing your own prompts directly from code. | [Marketplace Tutorial](https://docs.swarms.world/integrations/marketplace) |
 | **[Open Responses](https://www.openresponses.org/)** | Open-source specification and ecosystem for multi-provider, interoperable LLM interfaces based on the OpenAI Responses API. Provides a unified schema and tooling for calling language models, streaming results, and composing agentic workflows—independent of provider. | [Open Responses Website](https://www.openresponses.org/) |
 | **[Agent Skills](https://docs.swarms.world/agents/agent-skills)** | Lightweight, markdown-based format for defining modular, reusable agent capabilities introduced by Anthropic. Enables specialization of agents without modifying code by loading skill definitions from simple SKILL.md files. | [Agent Skills Documentation](https://docs.swarms.world/agents/agent-skills) |

examples/README.md

@@ -151,35 +151,6 @@ This directory contains comprehensive examples demonstrating various capabilitie
 | [05_auth_and_config.py](mcp/client/05_auth_and_config.py) | API keys, bearer tokens, headers, env secrets, OAuth |
 | [06_remote_agents.py](mcp/client/06_remote_agents.py) | Spawn and run agents on a remote MCP server |
 
-#### Agents over Protocol (AOP)
-
-| Example | Description |
-|---------|-------------|
-| [aop_examples/](aop_examples/) | Agents over Protocol (AOP) examples demonstrating MCP server setup, agent discovery, client interactions, queue-based task submission, medical AOP implementations, and utility functions |
-| [README.md](aop_examples/README.md) | AOP examples documentation |
-| [server.py](aop_examples/server.py) | AOP server implementation |
-| [client/](aop_examples/client/) | AOP client examples and agent discovery including cluster, queue, raw client, and task examples |
-| [aop_cluster_example.py](aop_examples/client/aop_cluster_example.py) | AOP cluster example |
-| [aop_queue_example.py](aop_examples/client/aop_queue_example.py) | AOP queue-based task submission |
-| [aop_raw_client_code.py](aop_examples/client/aop_raw_client_code.py) | Raw AOP client implementation |
-| [aop_raw_task_example.py](aop_examples/client/aop_raw_task_example.py) | Raw AOP task example |
-| [example_new_agent_tools.py](aop_examples/client/example_new_agent_tools.py) | New agent tools example |
-| [get_all_agents.py](aop_examples/client/get_all_agents.py) | Get all available agents |
-| [list_agents_and_call_them.py](aop_examples/client/list_agents_and_call_them.py) | List and call agents example |
-| [discovery/](aop_examples/discovery/) | Agent discovery examples including communication and discovery testing |
-| [example_agent_communication.py](aop_examples/discovery/example_agent_communication.py) | Agent communication example |
-| [simple_discovery_example.py](aop_examples/discovery/simple_discovery_example.py) | Simple discovery example |
-| [example_aop_discovery.py](aop_examples/discovery/example_aop_discovery.py) | AOP discovery example |
-| [medical_aop/](aop_examples/medical_aop/) | Medical AOP implementations |
-| [server.py](aop_examples/medical_aop/server.py) | Medical AOP server |
-| [client.py](aop_examples/medical_aop/client.py) | Medical AOP client |
-| [utils/](aop_examples/utils/) | AOP utility functions |
-| [network_management_example.py](aop_examples/utils/network_management_example.py) | Network management example |
-| [comprehensive_aop_example.py](aop_examples/utils/comprehensive_aop_example.py) | Comprehensive AOP example |
-| [persistence_management_example.py](aop_examples/utils/persistence_management_example.py) | Persistence management example |
-| [network_error_example.py](aop_examples/utils/network_error_example.py) | Network error handling example |
-| [persistence_example.py](aop_examples/utils/persistence_example.py) | Persistence example |
-
 ### Advanced Capabilities
 
 | Example | Description |
@@ -225,7 +196,7 @@ This directory contains comprehensive examples demonstrating various capabilitie
 | [README.md](guides/README.md) | Guides documentation |
 | [hiearchical_marketing_team.py](guides/hiearchical_marketing_team.py) | Hierarchical marketing team example |
 | [840_update/](guides/840_update/) | Update examples from version 8.4.0 including agent rearrange, auto swarm builder, and fallback examples |
-| [850_workshop/](guides/850_workshop/) | Workshop examples from version 8.5.0 including AOP, MOA, peer review, and concurrent examples |
+| [850_workshop/](guides/850_workshop/) | Workshop examples from version 8.5.0 including MOA, peer review, and concurrent examples |
 | [880_update_changelog_examples/](guides/880_update_changelog_examples/) | Changelog examples showcasing new features including marketplace integration, multi-agent structures, workflow orchestration, voice agents, evaluation & debate, routing, and autosaving |
 | [changelog_890/](guides/changelog_890/) | Changelog examples from January 2026 release including dynamic skills loader, autonomous agent loop, agent handoffs, API key validation, max loops parameter, multi-tool agent tutorial, hierarchical voice agent, and agent rearrange patterns |
 | [mem0/](guides/mem0/) | Mem0 integration examples for memory management with Swarms |
@@ -265,7 +236,7 @@ This directory contains comprehensive examples demonstrating various capabilitie
 | [fetch_prompt.py](utils/fetch_prompt.py) | Prompt fetching utilities |
 | [litellm_connect_issue.py](utils/litellm_connect_issue.py) | LiteLLM connection issue examples |
 | [litellm_network_error_handling.py](utils/litellm_network_error_handling.py) | LiteLLM network error handling |
-| [misc/](utils/misc/) | Miscellaneous utility functions including AOP, conversation, CSV agents, and visualization |
+| [misc/](utils/misc/) | Miscellaneous utility functions including conversation, CSV agents, and visualization |
 | [agent_map_test.py](utils/misc/agent_map_test.py) | Agent map test |
 | [conversation_simple.py](utils/misc/conversation_simple.py) | Simple conversation example |
 | [conversation_test_truncate.py](utils/misc/conversation_test_truncate.py) | Conversation truncate test |
@@ -275,8 +246,6 @@ This directory contains comprehensive examples demonstrating various capabilitie
 | [swarm_matcher_example.py](utils/misc/swarm_matcher_example.py) | Swarm matcher example |
 | [test_load_conversation.py](utils/misc/test_load_conversation.py) | Load conversation test |
 | [visualizer_test.py](utils/misc/visualizer_test.py) | Visualizer test |
-| [aop/](utils/misc/aop/) | AOP utility examples |
-| [client.py](utils/misc/aop/client.py) | AOP client utility |
 | [telemetry/](utils/telemetry/) | Telemetry and monitoring utilities |
 
 ### User Interface
@@ -317,7 +286,6 @@ This directory contains comprehensive examples demonstrating various capabilitie
 | Want to use the CLI? | Check out [cli/](cli/) for all CLI command examples |
 | Want multi-agent workflows? | Check out [multi_agent/duo_agent.py](multi_agent/duo_agent.py) |
 | Need tool integration? | Explore [tools/agent_as_tools.py](tools/agent_as_tools.py) |
-| Interested in AOP? | Try [aop_examples/client/example_new_agent_tools.py](aop_examples/client/example_new_agent_tools.py) for agent discovery |
 | Want to see social algorithms? | Check out [multi_agent/social_algorithms_examples/](multi_agent/social_algorithms_examples/) |
 | Looking for guides? | Visit [guides/](guides/) for comprehensive tutorials |
 | Need RAG? | Try [single_agent/rag/](single_agent/capabilities/rag/) for RAG examples |
@@ -476,11 +444,9 @@ This directory contains comprehensive examples demonstrating various capabilitie
 | **Multi-Agent Architecture** | Agents as Tools | Using agents as tools in workflows | [Agents as Tools](https://docs.swarms.world/architectures/overview) |
 | **Multi-Agent Architecture** | Aggregate Responses | Combining multiple agent outputs | [Aggregate Examples](https://docs.swarms.world/architectures/mixture-of-agents) |
 | **Multi-Agent Architecture** | Interactive GroupChat | Real-time agent interactions | [Interactive GroupChat](https://docs.swarms.world/examples/group-chat-example) |
-| **Deployment Solutions** | Agent Orchestration Protocol (AOP) | Deploy agents as distributed services with discovery and management | [AOP Reference](https://docs.swarms.world/api/aop) |
 | **Applications** | Advanced Research System | Multi-agent research system inspired by Anthropic's research methodology | [AdvancedResearch](https://github.com/The-Swarm-Corporation/AdvancedResearch) |
 | **Applications** | Hospital Simulation | Healthcare simulation system using multi-agent architecture | [HospitalSim](https://github.com/The-Swarm-Corporation/HospitalSim) |
 | **Applications** | Browser Agents | Web automation with agents | [Browser Agents](https://docs.swarms.world/examples/integrations/browser-use) |
-| **Applications** | Medical Analysis | Healthcare applications | [Medical Examples](https://docs.swarms.world/examples/multi-agent/aop-medical) |
 | **Applications** | Finance Analysis | Financial applications | [Finance Examples](https://docs.swarms.world/examples/use-cases/financial-analysis) |
 | **Cookbook & Templates** | Examples Overview | Complete examples directory | [Examples Index](https://docs.swarms.world/examples/) |
 | **Cookbook & Templates** | Cookbook Index | Curated example collection | [Cookbook](https://docs.swarms.world/examples/overviews/cookbook) |

examples/changelogs/v14-zena/08_computer_use_tools.py

@@ -24,7 +24,6 @@ A few examples reference other providers (`claude-sonnet-4-6` in the hierarchica
 | [05_auction_swarm.py](05_auction_swarm.py) | AuctionSwarm | Agents bidding on a task; default and custom scoring |
 | [06_groupchat_turn_based.py](06_groupchat_turn_based.py) | Turn-Based GroupChat | Single-speaker bidding with a recency penalty |
 | [07_hierarchical_swarm_recovery.py](07_hierarchical_swarm_recovery.py) | HierarchicalSwarm | Worker retry, task reassignment, planning, judge, director overrides |
-| [08_computer_use_tools.py](08_computer_use_tools.py) | Computer-Use Tools | Full toolset, and a read-only subset |
 | [09_class_to_pydantic.py](09_class_to_pydantic.py) | Pydantic Schemas | Schema from a constructor, and the round-trip back to an `Agent` |
 | [10_concurrent_workflow_on_error.py](10_concurrent_workflow_on_error.py) | ConcurrentWorkflow | `on_error` failure policy |
 | [11_graph_workflow.py](11_graph_workflow.py) | Performance | Fan-out/fan-in DAG on native rustworkx |