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📄 .clang-format Initial commit: Agent Smith C++ Framework 7 days ago
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📄 CMakeLists.txt Refactor: Split build system into a library and an executable CLI target 7 days ago
📄 README.md Docs: Update PRD and Design docs, and create README 7 days ago
📄 design.md Initial commit: Agent Smith C++ Framework 7 days ago
📄 prd.md Initial commit: Agent Smith C++ Framework 7 days ago

Agent Smith 🕶️

Agent Smith is a high-performance, modular C++23 framework for building LLM-powered agents.

Designed with composability and safety in mind, it provides a clean, dependency-injectable architecture for orchestrating LLM interactions, managing conversation history, and natively executing tools/functions.

Key Features

Integration

Agent Smith is designed to be consumed as a CMake library. The easiest way to add it to your project is via FetchContent.

CMakeLists.txt

include(FetchContent)

FetchContent_Declare(
  agent_smith
  GIT_REPOSITORY https://github.com/YourOrg/agent-smith.git
  # GIT_TAG main
)

FetchContent_MakeAvailable(agent_smith)

add_executable(my_agent src/main.cpp)
target_link_libraries(my_agent PRIVATE agent_smith)

Quick Start Example

Here is a minimal example of how to configure an OpenAI-compatible client, define a custom tool, and run an agent loop.

#include <iostream>
#include <memory>
#include <agent.hpp>
#include <llm_client.hpp>
#include <memory.hpp>
#include <tool.hpp>

// 1. Define a custom tool
class MyCalculator : public Tool {
public:
    std::string name() const override { return "calculator"; }
    std::string description() const override { return "Performs basic addition."; }

    nlohmann::json parametersSchema() const override {
        return R"({
            "type": "object",
            "properties": {
                "a": {"type": "number"},
                "b": {"type": "number"}
            },
            "required": ["a", "b"]
        })"_json;
    }

    Task<mw::E<std::string>> execute(const nlohmann::json& args) override {
        double a = args["a"];
        double b = args["b"];
        co_return std::to_string(a + b);
    }
};

// Simple blocking runner for coroutines
void runTask(Task<mw::E<std::string>> task) {
    auto res = task.get();
    if(res.has_value()) std::cout << "Agent: " << res.value() << "
";
    else std::cout << "Error: " << mw::errorMsg(res.error()) << "
";
}

int main() {
    // 2. Setup the infrastructure
    auto client = std::make_unique<OpenAiClient>("your-api-key");
    auto memory = std::make_unique<InMemoryMemory>();
    ToolRegistry registry;

    registry.registerTool(std::make_unique<MyCalculator>());

    // 3. Instantiate the agent
    Agent agent(std::move(client), std::move(memory), registry);
    agent.allowTool("calculator");

    Skill math_skill{
        "math_bot", 
        "You are a helpful math bot. Use your tools to answer questions.", 
        {"calculator"}
    };
    agent.activateSkill(math_skill);

    // 4. Run the conversational loop
    runTask(agent.run("What is 42 + 8?"));

    return 0;
}

Built-in CLI

The repository also builds an interactive CLI executable (agent_smith_cli) out of the box, which serves as a great reference implementation.

# Build the CLI and tests
cmake -S . -B build
cmake --build build -j

# Run the CLI using a custom local endpoint
./build/agent_smith_cli --api-key "dummy" --endpoint "http://localhost:8080/v1" --model "local-model"

Dependencies