Ami vs Gradient Bang: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Ami and Gradient Bang — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Ami
AiSDR
AI GTM agent that picks the audience, writes and launches outbound campaigns, reads the results and fixes what stops working.
Key features
- Autonomous Campaign Loop: Ami picks the target audience, builds and launches the campaign, reads what comes back and changes what is not working, so each campaign sharpens the next without a human restarting the cycle.
- Baked-In GTM Experience: Arrives with 27 industry playbooks and the lessons of 17,150 prior AiSDR campaigns and 19,501 meetings, so a first campaign launches with patterns other teams paid to learn.
- Signal-Triggered Outreach: Watches hiring, funding and job-change signals and acts at the moment they happen rather than months later.
- Performance Triage: When response rates slip, Ami digs into audience, message and sequence to pinpoint what is breaking and proposes fixes before the budget is spent — flagging, for example, a positive response rate under 1% after 21+ days.
- Omnichannel Sequences: Configurable sequences combining email via Gmail or Outlook, LinkedIn connection requests, DMs and InMail, and AI call steps through the Aircall dialer with scripts and automated follow-ups.
- Deep Per-Lead Personalization: Researches the top three most relevant data points per lead and personalizes from ICP data, activity, LinkedIn data and HubSpot properties.
- Native CRM Sync: Two-way HubSpot sync on every plan and two-way Salesforce sync on higher tiers, with AI research and monitoring running over that CRM data.
- Review Mode: Campaigns and Ami's proposed corrections stay drafts until approved, so the agent's autonomy is opt-in rather than assumed.
Best for
- Founder-Led Outbound: A solo founder builds pipeline without hiring an SDR, starting self-serve with no sales call required.
- Rescuing Stalled Campaigns: A revenue team catches a dying sequence early when Ami flags a collapsing positive-response rate and rewrites the audience or message.
- Replacing Outbound Agencies: A company that has paid outside firms without results brings the motion in-house under one agent.
- Warm-Signal Prospecting: A sales team reaches buyers right after a funding round, a relevant hire or a job change instead of cold-listing an industry.
- CRM-Grounded Targeting: A HubSpot or Salesforce team has outreach built from and logged back into existing CRM data rather than a disconnected tool.
- Multichannel Follow-Up: A team runs email, LinkedIn and dialer touches in a single sequence with replies handled in 5-10 minutes or in co-pilot mode.
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