Gradient Bang vs Proto-Mind: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Gradient Bang and Proto-Mind — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Gradient Bang
Pipecat AI
A multiplayer space-trading game universe where every entity (ships, NPCs, systems) is driven by LLM-powered AI agents.
Key features
- LLM-Driven Agents: Core gameplay entities (ships, NPCs, systems) are implemented as language-model agents that make decisions, communicate, and act autonomously in the game world.
- Multiplayer Universe Orchestration: A networked environment combining player actions and agent behaviors with server-side orchestration (Supabase, edge functions, and environment configuration) for persistent multiplayer interactions.
- Asset Pipeline (/newspaper): A scriptable content generator that drafts copy and renders visual assets (e.g., 2048×1024 news banners, front pages) via specialized rendering scripts with outputs stored under artifacts/ for community and in-game use.
- Multi-LLM Support & Config: Pluggable LLM provider configuration (examples reference Gradium, Cartesia, Claude, Gemini, etc.) with environment-driven keys and settings to swap or benchmark different models.
- Developer Tooling & Local Dev: Local development scripts and instructions (Supabase local start, edge function serving, env templates) to run the bot, seed data, and iterate on game logic, plus automated unit and integration test scripts.
- Context Inspection & Debugging: Companion tools (gb-context-viewer) to upload and inspect LLM context dumps produced by the game, aiding debugging and analysis of agent decisions and prompts.
- Benchmarking Suite: A separate benchmarks repo (gb-benchmarks) providing structured multi-agent tasks, metrics, and comparisons across different LLMs for performance and orchestration evaluation.
- Configurable Gameplay Mechanics: Exposed runtime environment variables (combat ticks, shield regen, move delays, spawn distances, credits, etc.) for fine-grained tuning of game balance and simulation parameters.
- LLM-driven agents for all in-game entities (player ships, NPCs, subagents)
- Newspaper asset pipeline: single /newspaper entrypoint that renders banners and front pages to artifacts/ (PNG output, e.g. 2048×1024; supports --size override)
- Client-side GB Context Viewer (web app) to upload/paste and inspect LLM context JSON dumps (built with Vite/TypeScript)
- Benchmark repository (gb-benchmarks) for multi-agent task evaluation and model comparisons
- Configurable runtime via .env files and env.example templates (.env.bot, env.supabase.example, etc.)
- Persistence and auth integrations: Supabase (SUPABASE_URL, SUPABASE_ANON_KEY, SUPABASE_SERVICE_ROLE_KEY) and Postgres (POSTGRES_POOLER_URL)
- Edge API runtime and token-based orchestration (EDGE_API_TOKEN, X-Edge-Auth, app_runtime_config.edge_api_token)
- Pluggable LLM and TTS providers (examples: gradium, cartesia) with provider selection via env variables (TTS_PROVIDER etc.)
- Server/game tuning parameters exposed as env keys (MOVE_DELAY_SCALE, MOVE_DELAY_SECONDS_PER_TURN, COMBAT_TICK_BATCH_SIZE, COMBAT_ROUND_TIMEOUT, SHIELD_REGEN_PER_ROUND, SALVAGE_TTL_SECONDS, CHARACTER_STARTING_CREDITS, CORPORATION_SHIP_OWNER_CAP)
- CI automation and tests using GitHub Actions (workflows included in repository)
- Polyglot codebase: Node/Vite/TypeScript front-end, Python components (pyproject.*), and nix development shells (shell.nix)
Best for
- Emergent Multiplayer Gameplay: Running a public or private server where human players explore, trade, form corporations, and battle while interacting with autonomous LLM-controlled ships and NPCs.
- LLM Behavior Research: Using the project's multi-agent benchmarks and context dumps to evaluate and compare different language models on coordination, task completion, and in-world decision making.
- Automated Asset Production: Generating community-facing visual and textual assets (news banners, front pages, prompt experiments) for announcements, lore, or marketing using the `/newspaper` pipeline.
- Development & Modding Sandbox: Self-hosting the codebase locally to modify game mechanics, tune environment variables, add new agent scripts, and run automated tests for iterative development.
- Debugging Agent Interactions: Uploading LLM context dumps into the gb-context-viewer to inspect prompts, agent state, and message history to diagnose unexpected behaviors or improve system prompts.
- Benchmarking Orchestration Designs: Running the gb-benchmarks scenarios to measure orchestration latency, success rates, and cost/performance trade-offs across LLM providers for multi-agent systems.
- Play and experiment in a multiplayer universe powered by LLM-controlled agents (explore/trade/battle/collaborate)
- Generate retro-digital visual assets (news banners, front pages) programmatically for community or in-game content
- Inspect and debug LLM context and agent state using the GB Context Viewer
- Research and benchmark multi-agent orchestration and LLM behavior across providers
- Develop and extend the game server or agents using provided env templates, CI workflows, and open-source code
Proto-Mind
VIRENCORE
A native macOS floating workspace that keeps AI conversations, project memory, files and live voice together on your Mac.
Key features
- Floating Cube Workspace: Hover the cube to reveal the workspace and click to pin it, or move away to hide it while tasks keep running in the background.
- Per-Conversation Model Routing: Each chat picks its own model and account — ChatGPT with Codex access, supported model APIs, or a local Ollama model.
- Editable Project Memory: Notes, decisions and preferences stay attached to a project and carry into later conversations, and you can review, change or remove any of them.
- Live Voice Control: Speak to open a project, steer a running task or send new work, and add a correction while the task is still going.
- Detachable Companion Windows: Pull out and resize a browser, a file or a second conversation so reference material sits beside the work.
- Explicit Mac Access: Codex can work with files and run commands only after you turn Mac access on; screen control additionally requires Codex Desktop's signed Computer Use helper.
- Local Data Storage: Conversation history and saved memory live on your Mac, and cloud processing happens only when you choose a cloud model or voice.
- Open Source Beta: The macOS installer and the Apache 2.0 source are both published, so the workspace can be inspected and built from source.
Best for
- Long-Running Project Work: Keep a website or client project's decisions in project memory so each session resumes instead of re-explaining the brief.
- Brief to Deliverable: Have the agent read a client brief and save a proposal document, then open it in a companion window next to the conversation.
- Parallel Task Execution: Start several tasks across different models at once and check back on them without blocking the conversation you are in.
- Hands-Free Steering: Dictate a correction or open a project by voice while your hands are busy elsewhere on the Mac.
- Privacy-Sensitive Drafting: Run a local Ollama model so conversation content never leaves the machine.
- Model Comparison: Put the same question to a Codex route and a local model in adjacent windows to compare the answers side by side.