Community resources docs

3 weeks ago · 92c9db48e1
parent 7b0510bcd5
commit 92c9db48e1
9 changed files with 84 additions and 31 deletions
--- a/docs/examples/community_resources.md
+++ b/docs/examples/community_resources.md
@ -0,0 +1,36 @@
+# Community Resources
+
+Welcome to the Community Resources page! Here you'll find a curated collection of articles, tutorials, and guides created by the Swarms community and core contributors. 
+
+These resources cover a wide range of topics, including building your first agent, advanced multi-agent architectures, API integrations, and using Swarms with both Python and Rust. Whether you're a beginner or an experienced developer, these links will help you deepen your understanding and accelerate your development with the Swarms framework.
+
+
+## Python
+
+| Title | Description | Link |
+|-------|-------------|------|
+| **Build Your First Swarms Agent in Under 10 Minutes** | Step-by-step beginner guide to creating your first Swarms agent quickly. | [Read Article](https://medium.com/@devangvashistha/build-your-first-swarms-agent-in-under-10-minutes-ddff23b6c703) |
+| **Building Multi-Agent Systems with GPT-5 and The Swarms Framework** | Learn how to leverage GPT-5 with Swarms for advanced multi-agent system design. | [Read Article](https://medium.com/@kyeg/building-multi-agent-systems-with-gpt-5-and-the-swarms-framework-e52ffaf0fa4f) |
+| **Learn How to Build Production-Grade Agents with OpenAI’s Latest Model: GPT-OSS Locally and in the Cloud** | Guide to building robust agents using OpenAI’s GPT-OSS, both locally and in cloud environments. | [Read Article](https://medium.com/@kyeg/learn-how-to-build-production-grade-agents-with-openais-latest-model-gpt-oss-locally-and-in-the-c5826c7cca7c) |
+| **Building Gemini 2.5 Agents with Swarms Framework** | Tutorial on integrating Gemini 2.5 models into Swarms agents for enhanced capabilities. | [Read Article](https://medium.com/@kyeg/building-gemini-2-5-agents-with-swarms-framework-20abdcf82cac) |
+| **Enterprise Developer Guide: Leveraging OpenAI’s o3 and o4-mini Models with The Swarms Framework** | Enterprise-focused guide to using OpenAI’s o3 and o4-mini models within Swarms. | [Read Article](https://medium.com/@kyeg/enterprise-developer-guide-leveraging-openais-o3-and-o4-mini-models-with-the-swarms-framework-89490c57820a) |
+| **Enneagram of Thoughts Using the Swarms Framework: A Multi-Agent Approach to Holistic Problem Solving** | Explores using Swarms for holistic, multi-perspective problem solving via the Enneagram model. | [Read Article](https://medium.com/@kyeg/enneagram-of-thoughts-using-the-swarms-framework-a-multi-agent-approach-to-holistic-problem-c26c7df5e7eb) |
+| **Building Production-Grade Financial Agents with tickr-agent: An Enterprise Solution for Comprehensive Stock Analysis** | How to build advanced financial analysis agents using tickr-agent and Swarms. | [Read Article](https://medium.com/@kyeg/building-production-grade-financial-agents-with-tickr-agent-an-enterprise-solution-for-db867ec93193) |
+| **Automating Your Startup’s Financial Analysis Using AI Agents: A Comprehensive Guide** | Comprehensive guide to automating your startup’s financial analysis with AI agents using Swarms. | [Read Article](https://medium.com/@kyeg/automating-your-startups-financial-analysis-using-ai-agents-a-comprehensive-guide-b2fa0e2c09d5) |
+| **Managing Thousands of Agent Outputs at Scale with The Spreadsheet Swarm: All-New Multi-Agent Architecture** | Learn how to manage and scale thousands of agent outputs efficiently using the Spreadsheet Swarm architecture. | [Read Article](https://medium.com/@kyeg/managing-thousands-of-agent-outputs-at-scale-with-the-spreadsheet-swarm-all-new-multi-agent-f16f5f40fd5a) |
+| **Introducing GPT-4o Mini: The Future of Cost-Efficient AI Intelligence** | Discover the capabilities and advantages of GPT-4o Mini for building cost-effective, intelligent agents. | [Read Article](https://medium.com/@kyeg/introducing-gpt-4o-mini-the-future-of-cost-efficient-ai-intelligence-a3e3fe78d939) |
+
+
+### API
+
+| Title | Description | Link |
+|-------|-------------|------|
+| **Specialized Healthcare Agents with Swarms Agent Completions API** | Guide to building healthcare-focused agents using the Swarms API. | [Read Article](https://medium.com/@kyeg/specialized-healthcare-agents-with-swarms-agent-completions-api-b56d067e3b11) |
+| **Building Multi-Agent Systems for Finance & Accounting with the Swarms API: A Technical Guide** | Technical walkthrough for creating finance and accounting multi-agent systems with the Swarms API. | [Read Article](https://medium.com/@kyeg/building-multi-agent-systems-for-finance-accounting-with-the-swarms-api-a-technical-guide-bf6f7005b708) |
+
+### Swarms Rust
+
+| Title | Description | Link |
+|-------|-------------|------|
+| **Building Medical Multi-Agent Systems with Swarms Rust: A Comprehensive Tutorial** | Comprehensive tutorial for developing medical multi-agent systems using Swarms Rust. | [Read Article](https://medium.com/@kyeg/building-medical-multi-agent-systems-with-swarms-rust-a-comprehensive-tutorial-1e8e060601f9) |
+| **Building Production-Grade Agentic Applications with Swarms Rust: A Comprehensive Tutorial** | Learn to build robust, production-ready agentic applications with Swarms Rust. | [Read Article](https://medium.com/@kyeg/building-production-grade-agentic-applications-with-swarms-rust-a-comprehensive-tutorial-bb567c02340f) |
--- a/docs/mkdocs.yml
+++ b/docs/mkdocs.yml
@ -354,6 +354,7 @@ nav:
    - CookBook Index: "examples/cookbook_index.md"
    - Paper Implementations: "examples/paper_implementations.md"
    - Templates & Applications: "examples/templates.md"
+    - Community Resources: "examples/community_resources.md"
    - Basic Examples:
      - Individual Agents:
        - Basic Agent: "swarms/examples/basic_agent.md"
--- a/examples/single_agent/llms/mistral_example.py
+++ b/examples/single_agent/llms/mistral_example.py
@ -0,0 +1,20 @@
+from swarms import Agent
+
+# Initialize the agent
+agent = Agent(
+    agent_name="Quantitative-Trading-Agent",
+    agent_description="Quantitative trading and analysis agent",
+    system_prompt="You are an expert quantitative trading agent. Answer concisely and accurately using your knowledge of trading strategies, risk management, and financial markets.",
+    model_name="mistral/mistral-tiny",
+    dynamic_temperature_enabled=True,
+    output_type="str-all-except-first",
+    max_loops="auto",
+    interactive=True,
+    no_reasoning_prompt=True,
+    streaming_on=True,
+)
+
+out = agent.run(
+    task="What are the best top 3 etfs for gold coverage?"
+)
+print(out)
--- a/swarms/sims/senator_assembly.py
+++ b/swarms/sims/senator_assembly.py
@ -6,13 +6,14 @@ each with detailed backgrounds, political positions, and comprehensive system pr
 that reflect their real-world characteristics, voting patterns, and policy priorities.
 """

+from functools import lru_cache
 from typing import Dict, List, Optional

-from swarms import Agent
-from swarms.structs.multi_agent_exec import run_agents_concurrently
-from functools import lru_cache
 from loguru import logger
+
+from swarms.structs.agent import Agent
 from swarms.structs.conversation import Conversation
+from swarms.structs.multi_agent_exec import run_agents_concurrently


@lru_cache(maxsize=1)
--- a/swarms/structs/init.py
+++ b/swarms/structs/init.py
@ -96,7 +96,6 @@ from swarms.structs.swarming_architectures import (
    star_swarm,
 )

-
 __all__ = [
    "Agent",
    "BaseStructure",
--- a/swarms/structs/agent.py
+++ b/swarms/structs/agent.py
@ -27,9 +27,12 @@ from pydantic import BaseModel
 from swarms.agents.ape_agent import auto_generate_prompt
 from swarms.artifacts.main_artifact import Artifact
 from swarms.prompts.agent_system_prompts import AGENT_SYSTEM_PROMPT_3
+from swarms.prompts.max_loop_prompt import generate_reasoning_prompt
 from swarms.prompts.multi_modal_autonomous_instruction_prompt import (
    MULTI_MODAL_AUTO_AGENT_SYSTEM_PROMPT_1,
 )
+from swarms.prompts.react_base_prompt import REACT_SYS_PROMPT
+from swarms.prompts.safety_prompt import SAFETY_PROMPT
 from swarms.prompts.tools import tool_sop_prompt
 from swarms.schemas.agent_mcp_errors import (
    AgentMCPConnectionError,
@ -41,19 +44,30 @@ from swarms.schemas.base_schemas import (
    ChatCompletionResponseChoice,
    ChatMessageResponse,
 )
+from swarms.schemas.conversation_schema import ConversationSchema
 from swarms.schemas.llm_agent_schema import ModelConfigOrigin
+from swarms.schemas.mcp_schemas import (
+    MCPConnection,
+)
 from swarms.structs.agent_rag_handler import (
-    RAGConfig,
    AgentRAGHandler,
+    RAGConfig,
 )
 from swarms.structs.agent_roles import agent_roles
 from swarms.structs.conversation import Conversation
+from swarms.structs.ma_utils import set_random_models_for_agents
 from swarms.structs.safe_loading import (
    SafeLoaderUtils,
    SafeStateManager,
 )
 from swarms.telemetry.main import log_agent_data
 from swarms.tools.base_tool import BaseTool
+from swarms.tools.mcp_client_call import (
+    execute_multiple_tools_on_multiple_mcp_servers_sync,
+    execute_tool_call_simple,
+    get_mcp_tools_sync,
+    get_tools_for_multiple_mcp_servers,
+)
 from swarms.tools.py_func_to_openai_func_str import (
    convert_multiple_functions_to_openai_function_schema,
 )
@ -64,28 +78,14 @@ from swarms.utils.generate_keys import generate_api_key
 from swarms.utils.history_output_formatter import (
    history_output_formatter,
 )
-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.prompts.react_base_prompt import REACT_SYS_PROMPT
-from swarms.prompts.max_loop_prompt import generate_reasoning_prompt
-from swarms.prompts.safety_prompt import SAFETY_PROMPT
-from swarms.structs.ma_utils import set_random_models_for_agents
-from swarms.tools.mcp_client_call import (
-    execute_multiple_tools_on_multiple_mcp_servers_sync,
-    execute_tool_call_simple,
-    get_mcp_tools_sync,
-    get_tools_for_multiple_mcp_servers,
-)
-from swarms.schemas.mcp_schemas import (
-    MCPConnection,
-)
 from swarms.utils.index import (
    exists,
    format_data_structure,
 )
-from swarms.schemas.conversation_schema import ConversationSchema
+from swarms.utils.litellm_tokenizer import count_tokens
+from swarms.utils.litellm_wrapper import LiteLLM
 from swarms.utils.output_types import OutputType
+from swarms.utils.pdf_to_text import pdf_to_text


 def stop_when_repeats(response: str) -> bool:
@ -899,9 +899,9 @@ class Agent:
            bool: True if model supports vision and image is provided, False otherwise.
        """
        from litellm.utils import (
-            supports_vision,
            supports_function_calling,
            supports_parallel_function_calling,
+            supports_vision,
        )

        # Only check vision support if an image is provided
@ -1549,11 +1549,11 @@ class Agent:
            raise

    def reliability_check(self):
+        from litellm import model_list
        from litellm.utils import (
-            supports_function_calling,
            get_max_tokens,
+            supports_function_calling,
        )
-        from litellm import model_list

        if self.system_prompt is None:
            logger.warning(
--- a/swarms/structs/concurrent_workflow.py
+++ b/swarms/structs/concurrent_workflow.py
@ -5,12 +5,12 @@ from typing import Callable, List, Optional, Union
 from swarms.structs.agent import Agent
 from swarms.structs.base_swarm import BaseSwarm
 from swarms.structs.conversation import Conversation
+from swarms.utils.formatter import formatter
 from swarms.utils.get_cpu_cores import get_cpu_cores
 from swarms.utils.history_output_formatter import (
    history_output_formatter,
 )
 from swarms.utils.loguru_logger import initialize_logger
-from swarms.utils.formatter import formatter

 logger = initialize_logger(log_folder="concurrent_workflow")

--- a/swarms/structs/deep_research_swarm.py
+++ b/swarms/structs/deep_research_swarm.py
@ -23,10 +23,6 @@ MAX_WORKERS = (
    os.cpu_count() * 2
 )  # Optimal number of workers based on CPU cores

-###############################################################################
-# 1. System Prompts for Each Scientist Agent
-###############################################################################
-

 def exa_search(query: str, **kwargs: Any) -> str:
    """Performs web search using Exa.ai API and returns formatted results."""
--- a/swarms/tools/py_func_to_openai_func_str.py
+++ b/swarms/tools/py_func_to_openai_func_str.py
@ -1,8 +1,8 @@
-import os
 import concurrent.futures
 import functools
 import inspect
 import json
+import os
 from logging import getLogger
 from typing import (
    Any,