Shilo vs Agenta
Detailed side-by-side comparison to help you choose the right tool
Shilo
Business AI Solutions
AI assistant built for real estate teams that listens, coaches, and guides agents in real time to help them close deals with confidence.
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CustomAgenta
🟡Low CodeBusiness AI Solutions
All-in-one LLM development platform. Manage prompts, run evaluations, and monitor AI apps in production. Open-source with team collaboration features.
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FreeFeature Comparison
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Shilo - Pros & Cons
Pros
- ✓Specialized focus on live real estate sales conversations rather than trying to be an all-in-one platform, filling a gap that general CRMs leave open
- ✓Real-time AI coaching during calls provides agents with contextual suggestions and objection-handling prompts without leaving the conversation
- ✓Manager dashboard provides granular visibility into team performance, coaching adherence metrics, and training opportunity identification
- ✓Integrates with real estate CRMs and existing telephony stacks rather than requiring agents to switch platforms
- ✓AI suggestions improve over time by learning from a team's own successful calls, tailoring to specific markets and property types
- ✓Objection library covers over 200 common real estate objections with AI-generated rebuttals tuned to property-specific vocabulary
Cons
- ✗Pricing is not publicly listed and requires contacting sales, making quick budget comparisons difficult—refer to comparable platforms like Gong ($100–$150/user/month) for general market context
- ✗Relatively new entrant in the market with limited long-term performance data across diverse economic conditions
- ✗Vendor-published performance claims have not been independently verified by third-party audits as of early 2026
- ✗Focused narrowly on call coaching, so teams still need separate tools for lead generation, marketing automation, and transaction management
- ✗Effectiveness may vary significantly across different real estate markets, property types, and buyer demographics, requiring a pilot period to validate
Agenta - Pros & Cons
Pros
- ✓Open-source foundation with MIT licensing providing complete control and avoiding vendor lock-in
- ✓Unified platform combining prompt management, evaluation, and observability in integrated workflows
- ✓Enterprise-grade security with SOC2 Type I certification and comprehensive data protection
- ✓Collaborative features enabling cross-functional teams to work together effectively on LLM projects
- ✓Self-hosting options available for organizations requiring maximum data privacy and control
- ✓Comprehensive evaluation framework with both automated and human evaluation capabilities
- ✓Active open-source community with regular updates and community-driven improvements
- ✓Full API/UI parity enabling seamless integration into existing development workflows
Cons
- ✗Self-hosted deployments require meaningful DevOps effort to run, scale, and maintain compared to pure SaaS alternatives
- ✗Ecosystem and community are smaller than established competitors like Langfuse or Weights & Biases, so third-party tutorials are limited
- ✗Pro-to-Business pricing jump ($49 to $399/month) is steep for mid-sized teams that outgrow the hobby limits
- ✗LLM-as-a-judge and automated evaluators still require careful calibration to produce reliable signals on domain-specific tasks
- ✗Deep integrations with niche agent frameworks or custom orchestration may require manual SDK instrumentation
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