Agents

🧠 Agents-SDK - A High Performance C++ Framework for AI Agents

Agents-SDK is a portable, high-performance C++ framework for building on-device, agentic AI systems — think LangChain for the edge. This SDK is purpose-built for developers who want to create local-first AI agents that can reason, plan, and act without relying on the cloud.

🚀 Features

• ⚙️ Modular Architecture — Compose agents from interchangeable components.
• 🧩 Multi-LLM Support — Connect to multiple providers seamlessly:
  • OpenAI (GPT-4o, GPT-4, GPT-3.5 Turbo)
  • Anthropic (Claude 3 family models (Opus, Sonnet, Haiku)
  • Google (Gemini family models (Pro, Flash)
  • Ollama/llama-cpp (local models like Llama, Mistral, etc.)
• ⚡ Optimized for Speed and Memory — Built in C++ with focus on performance.
• 🔁 Built-In Workflow Patterns
  • Prompt Chaining
  • Routing
  • Parallelization
  • Orchestrator-Workers
  • Evaluator-Optimizer
• 🤖 Autonomous Agents — Supports modern reasoning strategies:
  • ReAct (Reason + Act)
  • CoT (Chain-of-Thought) [In Development]
  • Plan and Execute
  • Zero-Shot [In Development]
  • Reflexion [In Development]
• 🧠 Extensible Tooling System — Plug in your own tools or use built-in ones (Web Search, Wikipedia, Python Executor, etc).

⚙️ Requirements

• C++20 compatible compiler (GCC 14+, Clang 17+, MSVC 2022+)
• Bazel 8.3.1+
• Dependencies (already provided for convenience)
  • python3 (3.11+)
  • nlohmann/json
  • spdlog

🧭 Quick Start

Installation

1. Clone the repository:

   git clone https://github.com/RunEdgeAI/agents-sdk.git

2. Navigate to SDK:

   cd agents-sdk

3. Obtain API keys:
   • For OpenAI models: Get an API key from OpenAI's platform
   • For Anthropic models: Get an API key from Anthropic's console
   • For Google models: Get an API key from Google AI Studio
   • For Websearch tool: Get an API key from serpapi

Building

Build everything in this space:

bazel build ...

Configuration

You can configure API keys and other settings in three ways:

1. Using a .env file:

   # Copy the template
   cp .env.template .env
    
   # Edit the file with your API keys
   vi .env  # or use any editor

2. Using environment variables:

   export OPENAI_API_KEY=your_api_key_here
   export ANTHROPIC_API_KEY=your_api_key_here
   export GEMINI_API_KEY=your_api_key_here
   export WEBSEARCH_API_KEY=your_api_key_here

3. Passing API keys as command-line arguments (not recommended for production):

   bazel run examples:simple_agent -- your_api_key_here

The framework will check for API keys in the following order:

1. .env file
2. Environment variables
3. Command-line arguments

Python Tool Setup

In order to use the Python Code Execution Tool, ensure your Python environment is correctly configured so that the SDK can locate your Python runtime and libraries.

export PYTHONHOME=$(python3 -c "import sys; print(sys.prefix)")
export PYTHONPATH=$(python3 -c "import sysconfig; print(sysconfig.get_path('stdlib'))")

Usage

Here's a simple example of creating and running an autonomous agent:

#include <agents-cpp/context.h>
#include <agents-cpp/agents/autonomous_agent.h>
#include <agents-cpp/llm_interface.h>
#include <agents-cpp/tools/tool_registry.h>
 
using namespace agents;
 
int main() {
    // Create LLM
    auto llm = createLLM("anthropic", "<your_api_key_here>", "claude-3-5-sonnet-20240620");
 
    // Create agent context
    auto context = std::make_shared<Context>();
    context->setLLM(llm);
 
    // Register tools
    context->registerTool(tools::createWebSearchTool(llm));
 
    // Create the agent
    AutonomousAgent agent(context);
    agent.setPlanningStrategy(AutonomousAgent::PlanningStrategy::REACT);
 
    // Run the agent
    JsonObject result = agent.run("Research the latest developments in quantum computing");
 
    // Access the result
    std::cout << result["answer"].get<std::string>() << std::endl;
 
    return 0;
}

Running Your First Example

The simplest way to start is with the simple_agent example, which creates a basic autonomous agent that can use tools to answer questions:

1. Navigate to the release directory:

   cd agents-sdk

1. From the release directory, run the example:

   bazel run examples:simple_agent -- your_api_key_here

   Alternatively, you can set your API key as an environment variable:

   export OPENAI_API_KEY=your_api_key_here
   bazel run examples:simple_agent your_api_key_here

2. Once running, you'll be prompted to enter a question or task. For example:

   Enter a question or task for the agent (or 'exit' to quit):
   > What's the current status of quantum computing research?

3. The agent will:
   • Break down the task into steps
   • Use tools (like web search) to gather information
   • Ask for your approval before proceeding with certain steps (if human-in-the-loop is enabled)
   • Provide a comprehensive answer
4. Example output:

   Step: Planning how to approach the question
   Status: Completed
   Result: {
     "plan": "1. Search for recent quantum computing research developments..."
   }
   --------------------------------------
   Step: Searching for information on quantum computing research
   Status: Waiting for approval
   Context: {"search_query": "current status quantum computing research 2024"}
   Approve this step? (y/n): y
   ...

Configuring the Example

You can modify examples/simple_agent.cpp to explore different configurations:

• Change the LLM provider:

  // For Anthropic Claude
  auto llm = createLLM("anthropic", api_key, "claude-3-5-sonnet-20240620");
   
  // For Google Gemini
  auto llm = createLLM("google", api_key, "gemini-pro");

• Add different tools:

  // Add more built-in tools
  context->registerTool(tools::createCalculatorTool());
  context->registerTool(tools::createPythonCodeExecutionTool());

• Change the planning strategy:

  // Use ReAct planning (reasoning + acting)
  agent.setPlanningStrategy(AutonomousAgent::PlanningStrategy::REACT);
   
  // Or use CoT planning (chain-of-thought)
  agent.setPlanningStrategy(AutonomousAgent::PlanningStrategy::COT);

🧪 Included Examples

The repository includes several examples demonstrating different workflow patterns:

Example	Description
simple_agent	Basic autonomous agent
prompt_chain_example	Prompt chaining workflow
routing_example	Multi-agent routing
parallel_example	Parallel task execution
orchestrator_example	Orchestrator–worker pattern
evaluator_optimizer_example	Evaluator–optimizer feedback loop
multimodal_example	Support for voice, audio, image, docs
autonomous_agent_example	Full-featured autonomous agent

Run examples available:

bazel run examples:<simple_agent> -- your_api_key_here

📂 Project Structure

• lib/: Public library for SDK
• include/agents-cpp/: Public headers
  • types.h: Common type definitions
  • context.h: Context for agent execution
  • llm_interface.h: Interface for LLM providers
  • tool.h: Tool interface
  • memory.h: Agent memory interface
  • workflow.h: Base workflow interface
  • agent.h: Base agent interface
  • workflows/: Workflow pattern implementations
  • agents/: Agent implementations
  • tools/: Tool implementations
  • llms/: LLM provider implementations
• bin/examples/: Example applications

🛠️ Extending the SDK

Adding Custom Tools

auto custom_tool = createTool(
    "calculator",
    "Evaluates mathematical expressions",
    {
        {"expression", "The expression to evaluate", "string", true}
    },
    [](const JsonObject& params) -> ToolResult {
        std::string expr = params["expression"];
        // Implement calculation logic here
        double result = evaluate(expr);
        return ToolResult{
            true,
            "Result: " + std::to_string(result),
            {{"result", result}}
        };
    }
);
 
context->registerTool(custom_tool);

Creating Custom Workflows

You can create custom workflows by extending the Workflow base class or combining existing workflows:

class CustomWorkflow : public Workflow {
public:
    CustomWorkflow(std::shared_ptr<Context> context)
        : Workflow(context) {}
 
    JsonObject run(const std::string& input) override {
        // Implement your custom workflow logic here
    }
};

🆘 Support

• Email: suppo.nosp@m.rt@r.nosp@m.unedg.nosp@m.e.ai
• Discord: https://discord.gg/B7SgdBmN

📚 Acknowledgements

This implementation is inspired by Anthropic's article "Building effective agents" and re-engineered in C++ for real-time, low overhead usage on edge devices.

⚖️ License

This project is licensed under a proprietary License - see the LICENSE file for details.

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