Release 1.1.0

Author: amikhail48

BUILD

@@ -1,25 +1,5 @@
 # Main library build recipe
 
-# C++ Requests
-cc_import(
-    name = "cpr",
-    shared_library = select({
-        "@platforms//os:macos": "lib/macos/libcpr.1.dylib",
-        "@platforms//os:linux": "lib/linux/libcpr.so.1",
-        "//conditions:default": None,
-    }),
-)
-
-# Curl
-cc_import(
-    name = "curl",
-    shared_library = select({
-        "@platforms//os:macos": "lib/macos/libcurl.4.dylib",
-        "@platforms//os:linux": "lib/linux/libcurl.so.4.11.0",
-        "//conditions:default": None,
-    }),
-)
-
 # Python for python execution tool
 cc_import(
     name = "python",
@@ -46,8 +26,7 @@ cc_import(
         "//conditions:default": None,
     }),
     deps = [
-        ":cpr",
-        ":curl",
+        "@nlohmann_json//:json",
         ":python"
     ],
     visibility = ["//visibility:public"]

MODULE.bazel

@@ -8,4 +8,7 @@ module(name = "agents", version = "0.1.0")
 bazel_dep(name = "platforms", version = "0.0.11")
 bazel_dep(name = "rules_cc", version = "0.1.4")
 
+# JSON library
+bazel_dep(name = "nlohmann_json", version = "3.11.3")
+
 # End of MODULE.bazel

MODULE.bazel.lock

@@ -9,26 +9,26 @@
 
 ## 🚀 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)  
+- ⚙️ **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] 
+  - 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).  
+- 🧠 **Extensible Tooling System** — Plug in your own tools or use built-in ones (Web Search, Wikipedia, Python Executor, etc).
 
 ## ⚙️ Requirements
 
@@ -37,8 +37,6 @@
 
 - Dependencies (already provided for convenience)
    - python3 (3.11+)
-   - libcpr (C++ Requests)
-   - libcurl
    - nlohmann/json
    - spdlog
 
@@ -105,7 +103,7 @@ The framework will check for API keys in the following order:
 Here's a simple example of creating and running an autonomous agent:
 
 ```cpp
-#include <agents-cpp/agent_context.h>
+#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>
@@ -117,7 +115,7 @@ int main() {
     auto llm = createLLM("anthropic", "<your_api_key_here>", "claude-3-5-sonnet-20240620");
 
     // Create agent context
-    auto context = std::make_shared<AgentContext>();
+    auto context = std::make_shared<Context>();
     context->setLLM(llm);
 
     // Register tools
@@ -238,7 +236,7 @@ bazel run examples:<simple_agent> -- your_api_key_here
 - `lib/`: Public library for SDK
 - `include/agents-cpp/`: Public headers
   - `types.h`: Common type definitions
-  - `agent_context.h`: Context for agent execution
+  - `context.h`: Context for agent execution
   - `llm_interface.h`: Interface for LLM providers
   - `tool.h`: Tool interface
   - `memory.h`: Agent memory interface
@@ -283,7 +281,7 @@ You can create custom workflows by extending the `Workflow` base class or combin
 ```cpp
 class CustomWorkflow : public Workflow {
 public:
-    CustomWorkflow(std::shared_ptr<AgentContext> context)
+    CustomWorkflow(std::shared_ptr<Context> context)
         : Workflow(context) {}
 
     JsonObject run(const String& input) override {

README.md

@@ -14,9 +14,6 @@
 #include <agents-cpp/workflows/parallelization_workflow.h>
 #include <agents-cpp/workflows/prompt_chaining_workflow.h>
 
-#include <chrono>
-#include <iostream>
-
 using namespace agents;
 using namespace agents::workflows;
 
@@ -122,7 +119,7 @@ int main(int argc, char* argv[]) {
 
     try {
         // Create LLM interface
-        auto llm = createLLM("google", api_key, "gemini-1.5-flash");
+        auto llm = createLLM("google", api_key, "gemini-2.0-flash");
 
         // Set up options
         LLMOptions options;
@@ -150,13 +147,13 @@ int main(int argc, char* argv[]) {
         );
 
         // Create agent context
-        auto context = std::make_shared<AgentContext>();
+        auto context = std::make_shared<Context>();
         context->setLLM(llm);
         context->registerTool(calculator);
         context->registerTool(weather);
 
         // Example 1: Using the prompt chaining workflow
-        std::cout << "\n=== Example 1: Prompt Chaining Workflow ===\n\n";
+        Logger::info("\n=== Example 1: Prompt Chaining Workflow ===\n\n");
 
         auto chaining_workflow = std::make_shared<PromptChainingWorkflow>(context);
 
@@ -179,10 +176,10 @@ int main(int argc, char* argv[]) {
         // Initialize and execute the workflow
         auto result = chaining_workflow->run();
 
-        std::cout << "Prompt chaining result: " << result.dump(2) << "\n\n";
+        Logger::info("Prompt chaining result: {}", result.dump(2));
 
         // Example 2: Using the parallelization workflow
-        std::cout << "\n=== Example 2: Parallelization Workflow (Sectioning) ===\n\n";
+        Logger::info("\n=== Example 2: Parallelization Workflow (Sectioning) ===\n\n");
 
         auto parallel_workflow = std::make_shared<ParallelizationWorkflow>(
             context, ParallelizationWorkflow::Strategy::SECTIONING
@@ -208,10 +205,10 @@ int main(int argc, char* argv[]) {
         parallel_workflow->init();
         result = parallel_workflow->run();
 
-        std::cout << "Parallelization result: " << result.dump(2) << "\n\n";
+        Logger::info("Parallelization result: {}", result.dump(2));
 
         // Example 3: Using the actor agent
-        std::cout << "\n=== Example 3: Actor Agent with Tools ===\n\n";
+        Logger::info("\n=== Example 3: Actor Agent with Tools ===\n\n");
 
         auto agent = std::make_shared<ActorAgent>(context);
 
@@ -229,7 +226,7 @@ int main(int argc, char* argv[]) {
 
         // Register status callback
         agent->setStatusCallback([](const String& status) {
-            std::cout << "Agent status: " << status << "\n";
+            Logger::info("Agent status: {}", status);
         });
 
         // Initialize and run the agent
@@ -243,17 +240,17 @@ int main(int argc, char* argv[]) {
         };
 
         for (const auto& task : tasks) {
-            std::cout << "\nTask: " << task << "\n";
+            Logger::info("Task: {}", task);
             result = blockingWait(agent->run(task));
-            std::cout << "Result: " << result.dump(2) << "\n";
+            Logger::info("Result: {}", result.dump(2));
 
             // Small delay between tasks
             std::this_thread::sleep_for(std::chrono::seconds(1));
         }
 
         return EXIT_SUCCESS;
     } catch (const std::exception& e) {
-        std::cerr << "Error: " << e.what() << "\n";
+        Logger::error("Error: {}", e.what());
         return EXIT_FAILURE;
     }
-}
+}

examples/actor_agent_example.cpp

@@ -116,7 +116,7 @@ int main() {
     llm->setOptions(options);
 
     // Create agent context
-    auto context = std::make_shared<AgentContext>();
+    auto context = std::make_shared<Context>();
     context->setLLM(llm);
 
     // Set system prompt for the context
@@ -146,7 +146,7 @@ int main() {
             int max_length = params.contains("max_length") ? params["max_length"].get<int>() : 100;
 
             // Create a specific context for summarization
-            auto summary_context = std::make_shared<AgentContext>(*context);
+            auto summary_context = std::make_shared<Context>(*context);
             summary_context->setSystemPrompt(
                 "You are a summarization assistant. Your task is to create concise, accurate summaries "
                 "that capture the main points of the provided text."
@@ -274,4 +274,4 @@ int main() {
     }
 
     return EXIT_SUCCESS;
-}
+}

examples/autonomous_agent_example.cpp

@@ -42,7 +42,7 @@ bool humanApproval(const String& message, const JsonObject& context, String& mod
 }
 
 // Example coroutine that performs a multi-step task using tools
-Task<JsonObject> performResearchTask(std::shared_ptr<AgentContext> context, const String& topic) {
+Task<JsonObject> performResearchTask(std::shared_ptr<Context> context, const String& topic) {
     Logger::info("Starting research on topic: {}", topic);
 
     // Perform a search to get initial information
@@ -80,7 +80,7 @@ Task<JsonObject> performResearchTask(std::shared_ptr<AgentContext> context, cons
 }
 
 // Example coroutine that generates content in parallel
-Task<JsonObject> generateContentInParallel(std::shared_ptr<AgentContext> context, const String& topic) {
+Task<JsonObject> generateContentInParallel(std::shared_ptr<Context> context, const String& topic) {
     Logger::info("Generating content for topic: {}", topic);
 
     // Create a prompt for the introduction
@@ -123,7 +123,7 @@ Task<JsonObject> generateContentInParallel(std::shared_ptr<AgentContext> context
 }
 
 // Example showing streaming text with coroutines
-Task<void> streamText(std::shared_ptr<AgentContext> context, const String& prompt) {
+Task<void> streamText(std::shared_ptr<Context> context, const String& prompt) {
     Logger::info("Streaming response for prompt: {}", prompt);
 
     // Get a streaming generator
@@ -136,8 +136,8 @@ Task<void> streamText(std::shared_ptr<AgentContext> context, const String& promp
         String chunk = *item;
         std::cout << chunk << std::flush;
     }
-
     std::cout << std::endl;
+
     Logger::info("Streaming complete!");
     co_return;
 }
@@ -177,36 +177,36 @@ int main(int argc, char* argv[]) {
     llm->setOptions(options);
 
     // Create agent context
-    auto context = std::make_shared<AgentContext>();
+    auto context = std::make_shared<Context>();
     context->setLLM(llm);
 
     // Register tools
     context->registerTool(tools::createWebSearchTool());
     context->registerTool(tools::createWikipediaTool());
-    context->registerTool(tools::createSummarizationTool(context));
+    context->registerTool(tools::createSummarizationTool(llm));
 
     // Menu-driven example to demonstrate various coroutines
     while (true) {
-        std::cout << "\n========== COROUTINE EXAMPLES ==========\n";
-        std::cout << "1. Run autonomous agent with coroutines\n";
-        std::cout << "2. Perform research with parallel tool use\n";
-        std::cout << "3. Generate content in parallel\n";
-        std::cout << "4. Stream text example\n";
-        std::cout << "5. Exit\n";
-        std::cout << "Enter your choice: ";
+        Logger::info("\n========== COROUTINE EXAMPLES ==========");
+        Logger::info("1. Run autonomous agent with coroutines");
+        Logger::info("2. Perform research with parallel tool use");
+        Logger::info("3. Generate content in parallel");
+        Logger::info("4. Stream text example");
+        Logger::info("5. Exit");
+        Logger::info("Enter your choice:");
 
         int choice;
         std::cin >> choice;
         std::cin.ignore(); // Clear the newline
 
-        if (choice == 5) {
+        if (choice >= 5 || choice < 1) {
             break;
         }
 
         // Get topic from user
         std::string topic;
         if (choice >= 1 && choice <= 4) {
-            std::cout << "Enter a topic: ";
+            Logger::info("Enter a topic: ");
             std::getline(std::cin, topic);
         }
 
@@ -273,4 +273,4 @@ int main(int argc, char* argv[]) {
     }
 
     return EXIT_SUCCESS;
-}
+}