Duvi vs Gradient Bang: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Duvi and Gradient Bang — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Duvi
Duvi DigiIQ, Inc.
Build voice and chat support agents by describing them in conversation; one configuration answers on your website, WhatsApp and phone line.
Key features
- Conversational Agent Builder: Creating an agent opens a conversation with a builder that writes the system prompt, picks a model and ingests the websites the agent should answer from, so setup is a dialogue rather than a configuration form.
- Unified Omnichannel Configuration: One agent configuration serves website chat, a WhatsApp number and a phone line, with the same knowledge behind every channel so context is not lost when a customer switches.
- Live Knowledge Lookups: The agent checks your connected store as it answers, so stock and catalogue responses reflect what is actually available at that moment rather than a stale snapshot.
- Website Actions: With the customer's instruction the agent operates the on-page controls you allow, completing the task in front of them instead of handing them a link and instructions.
- Grounded Answering: The agent answers from the pages you point it at and says so when the information is not there, rather than guessing.
- Preview Before Launch: Agents are tested in Preview and only go live once the domain is allowed and a snippet is pasted on your site.
- Broad Connector Library: Sign-in level integrations for Shopify, WooCommerce, Wix, Salesforce Commerce Cloud, Stripe, PayPal, Notion, Airtable, Webflow, Linear, monday.com, Sentry, Supabase, Cloudflare and Zapier.
- Team Workspace: Staff query the day's conversations and orders through their own authorized connection, so the answer reflects the order a customer placed moments ago.
Best for
- Ecommerce Support Deflection: A Shopify store answers stock, shipping and returns questions automatically, with the agent reading live catalogue data instead of a static FAQ.
- Lead Capture With Context: An agent takes a caller's email or number and routes it to the team with the whole conversation attached, so nobody asks the customer to repeat themselves.
- Phone Line Replacement: A small team replaces a recorded phone menu with an agent that answers real questions using the same knowledge base as the website chat.
- WhatsApp Commerce: A brand serving customers primarily on WhatsApp runs the same support agent there without maintaining a separate bot.
- Startup Support Coverage: An early-stage team keeps answering customers around the clock while engineers focus on building the product.
- Enterprise Support Augmentation: An established support operation adds agents to an existing stack via connectors rather than replacing its tooling.
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