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Agentic coding platform (Cosmos) that runs software-dev agents at org scale, using a codebase context engine to cut token cost.
AI coding platform and IDE that orchestrates multiple agent sessions and lets teams plug in their own AI subscriptions.
Prompt engineering, management, and LLM observability platform.
No-code platform for building and running AI agents that automate work across data, sales and support tasks.
Spreadsheet-style Python data tool inside Jupyter that generates code via AI so analysts and data scientists can skip hand-coding.
Free trial available
No public pricing
No public pricing
No public pricing
- ✦Context Engine for codebase understanding
- ✦Agents across the full SDLC
- ✦Model routing / bring-your-own-keys
- ✦Automated code review and test coverage
- ✦CLI, MCP and native tool integrations
- ✦Enterprise security (SOC 2, ISO 42001, SSO)
- ✦AI coding IDE with agent orchestration
- ✦Run and manage multiple agent sessions
- ✦Task, artifact and collaboration tools
- ✦Bring-your-own AI subscription or API keys
- ✦Cloud-scale agent execution
- ✦Prompt management
- ✦Prompt evaluations
- ✦LLM observability
- ✦Team collaboration
- ✦Version control for prompts
- ✦A/B testing of prompts
- ✦Prompt Registry
- ✦Historical backtests
- ✦Regression tests
- ✦Usage monitoring
- ✦Visual canvas to orchestrate multi-agent workflows
- ✦Prebuilt specialized agents (data, support, CRM, sales)
- ✦Access to many AI models with no vendor lock-in
- ✦Slack, Teams and email agent interaction
- ✦Recurring/scheduled tasks and triggers
- ✦Enterprise security: RBAC, VPC, audit logs, spend controls
- ✦Spreadsheet interface inside Jupyter notebooks
- ✦AI-generated Python code from spreadsheet actions
- ✦Runs on customer infrastructure (no data sent to Mito)
- ✦Bring-your-own LLM API keys
- ✦Excel-to-Python conversion
- ✦Compatible with existing Jupyter extensions
- →Automating PR code review
- →Raising test coverage
- →Incident investigation and remediation
- →Large-scale migrations and onboarding
- →Shipping code faster with AI agents
- →Coordinating agent work across a team
- →Managing tasks and artifacts in one place
- →Running many parallel agent sessions
- →Scaling customer support automation with LLMs
- →Empowering non-technical teams with prompt engineering
- →Building personalized AI interactions
- →Debugging LLM agents
- →Improving content creation processes
- →Managing and monitoring prompts with a team
- →Automate data analysis and reporting
- →Triage support tickets and spot patterns
- →Keep a CRM updated and research prospects
- →Deploy AI agents across a team's tools
- →Analysts automating Excel-style reports without hand-writing code
- →Data scientists speeding up exploratory data analysis
- →ML engineers iterating on feature engineering in notebooks
- →Enterprises needing private, self-hosted AI coding assistance