Godmode

Breaks down user-provided goals into discrete subtasks and executes them sequentially with minimal human intervention, using an agentic loop pattern similar to AutoGPT/BabyAGI. The system maintains task state, evaluates completion criteria, and routes subtasks to appropriate tools or LLM calls based on task type and available integrations.

Routes subtasks to appropriate external tools (web search, code execution, file operations, API calls) based on task semantics and available integrations. Uses a schema-based tool registry pattern where each tool exposes input/output contracts, and the agent selects tools via LLM reasoning or predefined rules.

Displays task decomposition trees, subtask execution status, and intermediate results in a web UI with live updates as the agent progresses. Uses WebSocket or server-sent events to stream execution logs and state changes to the client, enabling users to monitor and potentially intervene in running workflows.

Accepts high-level user goals and uses LLM reasoning to clarify ambiguities, ask clarifying questions, and refine the goal into a concrete, executable task specification before decomposition begins. May iterate with the user to gather missing context or constraints.

Abstracts away provider-specific API differences (OpenAI, Anthropic, local models, etc.) behind a unified interface, allowing users to switch providers or configure fallback chains without changing the agent logic. Handles provider-specific features like function calling, streaming, and token limits transparently.

Integrates web search capabilities (via search APIs or embedded search) into the agentic loop, allowing subtasks to retrieve current information from the internet. The agent can decide when to search, formulate queries, and incorporate search results into reasoning.

Allows the agent to generate and execute code (Python, JavaScript, etc.) in isolated sandbox environments, capturing output and errors. Supports both code generation (agent writes code to solve a subtask) and code execution (agent runs pre-written code). Sandboxing prevents malicious or buggy code from affecting the host system.

Captures task execution results, intermediate outputs, and generated artifacts, storing them persistently (in database, file storage, or user-accessible format) and enabling export in multiple formats (JSON, CSV, Markdown, etc.). Users can retrieve past results and share them with collaborators.

Monitors API costs across LLM calls and tool integrations, providing per-task cost breakdowns and recommendations for optimization (e.g., using cheaper models for routing, batching requests). Enables users to set cost budgets and receive alerts if spending exceeds thresholds.

Enables users to design task templates, save them, and share with team members or the community. Supports parameterization (variables, conditional logic) so templates can be reused across different inputs. May include a template marketplace or gallery for discovering pre-built workflows.