N6agent -

| Layer | Name | Function | |-------|------|----------| | 1 | | Parses raw input (text, images, JSON) into structured intent vectors. | | 2 | Reasoning | Applies chain-of-thought (CoT) and tree-of-thought (ToT) to break the goal into sub-tasks. | | 3 | Planning | Generates a dynamic execution graph (not a fixed DAG). Edges can be rewired mid-task. | | 4 | Tool Selection | Queries a vector DB of available tools (APIs, code functions, web search) and selects the optimal set. | | 5 | Execution | Runs selected tools in parallel or serially with error handling and timeout management. | | 6 | Reflection | Evaluates outcomes against the original goal. If criteria aren’t met, loops back to Layer 2 with new context. |

from n6agent import N6Agent, Tool tools = [ Tool(name="web_search", func=google_search), Tool(name="send_email", func=email_sender) ] n6agent

In the rapidly expanding ecosystem of AI agents, most systems fall into two categories: simple chatbot wrappers or complex, brittle automation scripts. N6Agent emerges as a hybrid architecture designed to bridge the gap between conversational AI and autonomous, goal-driven execution. | Layer | Name | Function | |-------|------|----------|

agent = N6Agent( llm="gpt-4o", tools=tools, memory_type="long_term", max_reflections=3 ) Edges can be rewired mid-task