Ami vs Google Agent Development Kit: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Ami and Google Agent Development Kit — 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.
Google Agent Development Kit
Open-source, code-first toolkit for building, orchestrating, and deploying modular multi-agent systems across models and environments.
Key features
- Code-First Tooling: Provides Python and Java SDKs that let developers define agent behavior, tools, tests, and orchestration directly in code for robust versioning and debugging.
- Model-Agnostic Connectors: Optimized for Google Gemini but supports other LLMs (e.g., OpenAI, Anthropic, Meta) and local runtimes via adapters like LiteLLM, enabling flexible model selection.
- Built-in Orchestration & Multi-Agent Workflows: Native primitives for composing, coordinating, and scaling multi-agent workflows with session management and execution control.
- Context & Memory Management: Integrated context tracking and session memory to manage multi-turn conversations, long-running sessions, and state between agents.
- Tool Integration System: Simple mechanism to register arbitrary Python functions (API calls, data fetches, computations) as agent capabilities so agents can access external data and services.
- Developer Web UI (ADK Web): An integrated web-based developer interface for building, testing, debugging, and inspecting agents and workflows during development.
- Deployment Flexibility: Designed to deploy anywhere—from local machines to cloud environments—with compatibility for Google Cloud services and third-party deployment targets.
- Samples, Templates & Community Catalog: Official examples, sample agents, and a community-curated collection of production-ready agents and templates to accelerate development and learning.
- Code-first SDKs for Python and Java to define agent logic, tools, and orchestration in code
- Model-agnostic runtime: optimized for Google Gemini but supports other LLMs (OpenAI, Anthropic, Meta) via adapters like LiteLLM
- ADK Web: built-in developer web UI for development, inspection, debugging, and running agents
- Tool integration: plug any Python/Java function, external API call, OpenAPI spec, or existing tool as agent capabilities
- Multi-agent orchestration: compose and coordinate multiple specialized agents into workflows and hierarchies
- Context & memory management: built-in session memory, multi-turn conversation handling, and context tracking
- Deployment-agnostic: designed to run locally, on-prem, or integrated with Google Cloud services
- Rich samples and community-curated agents and templates for rapid prototyping and production-ready patterns
- Testability and versioning: encourages software-development practices (unit tests, version control) for agent behavior
- Extensible tool ecosystem and compatibility with existing frameworks and libraries
Best for
- Content Assistant: Build a terminal or web-based content-generation assistant that combines search, document retrieval, and LLM generation using ADK's tool integration and memory features.
- Automated Business Workflows: Orchestrate multi-agent workflows to automate multi-step business processes (e.g., data gathering, analysis, report generation) with stateful sessions and tool calls.
- Research & Experimentation: Rapidly prototype and compare agent behaviors across different LLM backends (Gemini, OpenAI, Anthropic) using ADK's model-agnostic connectors.
- Enterprise Service Integration: Create agents tightly integrated with Google Cloud services or internal APIs using the code-first Java and Python toolkits for production deployment.
- Education & Tutorials: Use official samples, tutorials, and the ADK Web UI to teach agent development, demonstrate multi-agent architectures, and run hands-on workshops or hackathons.
- Multi-Agent Coordination: Implement coordinator agents that delegate tasks to specialized worker agents and manage orchestration, retries, and aggregation of results.
- Debugging & Testing Pipelines: Define tests and evaluation harnesses in code to validate agent behavior, reproduce issues, and iterate quickly with the built-in developer UI.
- Interactive conversational assistants with long-running session memory and multi-turn context
- Composed multi-agent workflows for business process automation and orchestration
- Production-grade agent deployments integrated with Google Cloud services
- Rapid prototyping and developer debugging via ADK Web developer UI
- Research and experimentation with different LLMs and orchestration strategies
- Building domain-specific or specialized agents using pre-built templates and community examples