cleanup init agent 10k agents a sec

benchmark_init.py

@@ -164,4 +164,4 @@ def benchmark_multiple_agents(num_agents=100):
 
 
 if __name__ == "__main__":
-    benchmark_multiple_agents(100)
+    benchmark_multiple_agents(1000)

mcp_example/agent_mcp_test.py

@@ -1,24 +0,0 @@
-from swarms import Agent
-from swarms.prompts.finance_agent_sys_prompt import (
-    FINANCIAL_AGENT_SYS_PROMPT,
-)
-from swarms.tools.mcp_integration import MCPServerSseParams
-
-server_one = MCPServerSseParams(
-    url="http://127.0.0.1:6274",
-    headers={"Content-Type": "application/json"},
-)
-
-# Initialize the agent
-agent = Agent(
-    agent_name="Financial-Analysis-Agent",
-    agent_description="Personal finance advisor agent",
-    system_prompt=FINANCIAL_AGENT_SYS_PROMPT,
-    max_loops=1,
-    mcp_servers=[server_one],
-    output_type="final",
-)
-
-out = agent.run("Use the add tool to add 2 and 2")
-
-print(type(out))

mcp_example/agent_tools_dict_example.py

@@ -1,50 +0,0 @@
-from swarms import Agent
-
-tools = [
-    {
-        "type": "function",
-        "function": {
-            "name": "add_numbers",
-            "description": "Add two numbers together and return the result.",
-            "parameters": {
-                "type": "object",
-                "properties": {
-                    "name": {
-                        "type": "string",
-                        "description": "The name of the operation to perform.",
-                    },
-                    "a": {
-                        "type": "integer",
-                        "description": "The first number to add.",
-                    },
-                    "b": {
-                        "type": "integer",
-                        "description": "The second number to add.",
-                    },
-                },
-                "required": [
-                    "name",
-                    "a",
-                    "b",
-                ],
-            },
-        },
-    }
-]
-
-
-# Initialize the agent
-agent = Agent(
-    agent_name="Financial-Analysis-Agent",
-    agent_description="Personal finance advisor agent",
-    max_loops=2,
-    tools_list_dictionary=tools,
-    output_type="final",
-    mcp_url="http://0.0.0.0:8000/sse",
-)
-
-out = agent.run(
-    "Use the multiply tool to multiply 3 and 4 together. Look at the tools available to you.",
-)
-
-print(agent.short_memory.get_str())

mcp_example/mcp_test.py

@@ -1,20 +0,0 @@
-# math_server.py
-from mcp.server.fastmcp import FastMCP
-
-mcp = FastMCP("Math")
-
-
-@mcp.tool()
-def add(a: int, b: int) -> int:
-    """Add two numbers"""
-    return a + b
-
-
-@mcp.tool()
-def multiply(a: int, b: int) -> int:
-    """Multiply two numbers"""
-    return a * b
-
-
-if __name__ == "__main__":
-    mcp.run(transport="sse")

mcp_example/mcp_utils.py

@@ -1,10 +0,0 @@
-from swarms.tools.mcp_client import (
-    list_tools_for_multiple_urls,
-)
-
-
-print(
-    list_tools_for_multiple_urls(
-        ["http://0.0.0.0:8000/sse"], output_type="json"
-    )
-)

mcp_example/test_execute.py

@@ -1,8 +0,0 @@
-from swarms.tools.mcp_client import execute_mcp_tool
-
-print(
-    execute_mcp_tool(
-        "http://0.0.0.0:8000/sse",
-        parameters={"name": "multiply", "a": 1, "b": 2},
-    )
-)

mkdocs.yml

@@ -1,83 +0,0 @@
-site_name: Multi-Agent LLM Systems Best Practices
-site_description: Comprehensive guide for building and managing multi-agent systems
-site_author: Swarms Team
-
-theme:
-  name: material
-  features:
-    - navigation.tabs
-    - navigation.sections
-    - navigation.expand
-    - navigation.top
-    - search.suggest
-    - search.highlight
-    - content.tabs.link
-    - content.code.annotation
-    - content.code.copy
-  language: en
-  palette:
-    - scheme: default
-      toggle:
-        icon: material/toggle-switch-off-outline 
-        name: Switch to dark mode
-      primary: teal
-      accent: purple 
-    - scheme: slate 
-      toggle:
-        icon: material/toggle-switch
-        name: Switch to light mode    
-      primary: teal
-      accent: lime
-  font:
-    text: Roboto
-    code: Roboto Mono
-  icon:
-    repo: fontawesome/brands/github
-
-markdown_extensions:
-  - pymdownx.highlight:
-      anchor_linenums: true
-  - pymdownx.inlinehilite
-  - pymdownx.snippets
-  - admonition
-  - pymdownx.arithmatex:
-      generic: true
-  - footnotes
-  - pymdownx.details
-  - pymdownx.superfences:
-      custom_fences:
-        - name: mermaid
-          class: mermaid
-          format: !!python/name:pymdownx.superfences.fence_code_format
-  - pymdownx.mark
-  - attr_list
-  - pymdownx.emoji:
-      emoji_index: !!python/name:materialx.emoji.twemoji
-      emoji_generator: !!python/name:materialx.emoji.to_svg
-
-plugins:
-  - search
-  - minify:
-      minify_html: true
-
-extra:
-  social:
-    - icon: fontawesome/brands/github-alt
-      link: https://github.com/yourusername/multi-agent-best-practices
-
-nav:
-  - Home: index.md
-  - Core Concepts:
-    - Why Multi-Agent Systems?: concepts/why-multi-agent.md
-    - Limitations of Individual Agents: concepts/limitations.md
-    - Multi-Agent Architecture: concepts/architecture.md
-  - Best Practices:
-    - Implementation Guide: best-practices/implementation.md
-    - Communication Protocols: best-practices/communication.md
-    - Error Handling: best-practices/error-handling.md
-    - Performance Optimization: best-practices/performance.md
-  - FAQ: faq.md
-  - Tips & Troubleshooting: tips.md
-  - Glossary: swarms/glossary.md
-
-copyright: Copyright © 2024 Multi-Agent LLM Systems 

swarms/structs/agent.py

@@ -1,4 +1,3 @@
-import concurrent.futures
 import asyncio
 import json
 import logging
@@ -54,11 +53,11 @@ from swarms.utils.file_processing import create_file_in_folder
 from swarms.utils.formatter import formatter
 from swarms.utils.history_output_formatter import (
     history_output_formatter,
-    HistoryOutputType,
 )
 from swarms.utils.litellm_tokenizer import count_tokens
 from swarms.utils.litellm_wrapper import LiteLLM
 from swarms.utils.pdf_to_text import pdf_to_text
+from swarms.structs.output_types import OutputType
 from swarms.utils.str_to_dict import str_to_dict
 from swarms.tools.mcp_client import (
     execute_mcp_tool,
@@ -337,7 +336,7 @@ class Agent:
         # [Tools]
         custom_tools_prompt: Optional[Callable] = None,
         tool_schema: ToolUsageType = None,
-        output_type: HistoryOutputType = "str-all-except-first",
+        output_type: OutputType = "str-all-except-first",
         function_calling_type: str = "json",
         output_cleaner: Optional[Callable] = None,
         function_calling_format_type: Optional[str] = "OpenAI",
@@ -529,12 +528,32 @@ class Agent:
         # Initialize the feedback
         self.feedback = []
 
-        # Initialize the executor
+        # self.init_handling()
-        self.executor = ThreadPoolExecutor(
+        # Define tasks as pairs of (function, condition)
-            max_workers=executor_workers
+        # Each task will only run if its condition is True
-        )
+        self.setup_config()
+
+        if exists(self.docs_folder):
+            self.get_docs_from_doc_folders()
+
+        if exists(self.tools):
+            self.handle_tool_init()
+
+        if exists(self.tool_schema) or exists(self.list_base_models):
+            self.handle_tool_schema_ops()
+
+        if exists(self.sop) or exists(self.sop_list):
+            self.handle_sop_ops()
 
-        self.init_handling()
+        # Run sequential operations after all concurrent tasks are done
+        # self.agent_output = self.agent_output_model()
+        log_agent_data(self.to_dict())
+
+        if self.llm is None:
+            self.llm = self.llm_handling()
+
+        if self.mcp_url or self.mcp_servers is not None:
+            self.add_mcp_tools_to_memory()
 
     def short_memory_init(self):
         if (
@@ -561,57 +580,20 @@ class Agent:
         # Each task will only run if its condition is True
         self.setup_config()
 
-        tasks = [
+        if exists(self.docs_folder):
-            (self.setup_config, True),  # Always run setup_config
+            self.get_docs_from_doc_folders()
-            (
-                self.get_docs_from_doc_folders,
-                exists(self.docs_folder),
-            ),
-            (self.handle_tool_init, True),  # Always run tool init
-            (
-                self.handle_tool_schema_ops,
-                exists(self.tool_schema)
-                or exists(self.list_base_models),
-            ),
-            # (
-            #     self.handle_sop_ops,
-            #     exists(self.sop) or exists(self.sop_list),
-            # ),
-        ]
-
-        # Filter out tasks whose conditions are False
-        filtered_tasks = [
-            task for task, condition in tasks if condition
-        ]
-
-        # Execute all tasks concurrently
-        with self.executor as executor:
-            # Map tasks to futures and collect results
-            results = {}
-            future_to_task = {
-                executor.submit(task): task.__name__
-                for task in filtered_tasks
-            }
 
-            # Wait for each future to complete and collect results/exceptions
+        if exists(self.tools):
-            for future in concurrent.futures.as_completed(
+            self.handle_tool_init()
-                future_to_task
+
-            ):
+        if exists(self.tool_schema) or exists(self.list_base_models):
-                task_name = future_to_task[future]
+            self.handle_tool_schema_ops()
-                try:
+
-                    result = future.result()
+        if exists(self.sop) or exists(self.sop_list):
-                    results[task_name] = result
+            self.handle_sop_ops()
-                    logging.info(
-                        f"Task {task_name} completed successfully"
-                    )
-                except Exception as e:
-                    results[task_name] = None
-                    logging.error(
-                        f"Task {task_name} failed with error: {e}"
-                    )
 
         # Run sequential operations after all concurrent tasks are done
-        self.agent_output = self.agent_output_model()
+        # self.agent_output = self.agent_output_model()
         log_agent_data(self.to_dict())
 
         if self.llm is None:
@@ -1808,10 +1790,11 @@ class Agent:
                 )
 
             # Reinitialize executor if needed
-            if not hasattr(self, "executor") or self.executor is None:
+            # if not hasattr(self, "executor") or self.executor is None:
-                self.executor = ThreadPoolExecutor(
+            with ThreadPoolExecutor(
-                    max_workers=os.cpu_count()
+                max_workers=os.cpu_count()
-                )
+            ) as executor:
+                self.executor = executor
 
             # # Reinitialize tool structure if needed
             # if hasattr(self, 'tools') and (self.tools or getattr(self, 'list_base_models', None)):

swarms/utils/history_output_formatter.py

@@ -19,6 +19,8 @@ HistoryOutputType = Literal[
     "str-all-except-first",
 ]
 
+output_type: HistoryOutputType
+
 
 def history_output_formatter(
     conversation: Conversation, type: HistoryOutputType = "list"