Shilo vs AgentOps

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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Starting Price

Custom

AgentOps

🔴Developer

Business AI Solutions

Developer platform for AI agent observability, debugging, and cost tracking with two-line SDK integration.

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Starting Price

Free

Feature Comparison

Scroll horizontally to compare details.

FeatureShiloAgentOps
CategoryBusiness AI SolutionsBusiness AI Solutions
Pricing Plans4 tiers8 tiers
Starting PriceFree
Key Features
  • Real-time speech-to-text transcription for real estate conversations
  • Real-time call guidance and coaching prompts for agents
  • Real-time call grading and conversation analysis
  • Two-line SDK integration
  • Time travel debugging
  • Session replay analytics

Shilo - Pros & Cons

Pros

  • Purpose-built for real estate teams rather than generic sales teams, with website messaging focused on agents, team leaders, appointments, contracts, CRMs, transaction management, and lead sources.
  • Combines real-time call analysis with coaching feedback, so the product is designed for in-the-moment improvement rather than only after-the-fact reporting.
  • Includes training workflows such as role play based on previous calls, prompts, and feedback around objection handling, question frequency, and talk-to-listen ratio.
  • Can automatically enrich CRM records after calls with AI-powered insights and actionable next steps, reducing reliance on agents manually writing complete notes.
  • Designed to fit into an existing real estate workflow instead of replacing the team's CRM, transaction management platform, or lead sources.
  • Public case study and testimonials cite operational outcomes including increased outbound call volume, improved conversation quality, reduced manual call review, and higher conversion rates for featured customers.

Cons

  • Pricing is not published on the website; teams must book a demo to understand cost, packaging, contract terms, and minimum seat requirements.
  • The product appears optimized for real estate teams with meaningful call volume, so solo agents or very small teams may not get the same value from coaching analytics and team-level reporting.
  • Shilo depends on integration with existing workflows such as CRM, transaction management, and lead sources, which means setup quality and available integrations may materially affect results.
  • The website highlights customer outcomes but does not provide a self-serve trial, detailed implementation timeline, or public technical documentation for buyers to evaluate before contacting sales.
  • Teams that want an all-in-one real estate CRM, website, lead generation system, or marketing automation platform will still need other tools because Shilo is positioned as an enhancement layer, not a full stack replacement.

AgentOps - Pros & Cons

Pros

  • Two-line integration makes adoption nearly frictionless for existing agent projects
  • Framework-agnostic design works with CrewAI, AutoGen, LangChain, OpenAI Agents SDK, and custom setups
  • Time travel debugging is a genuinely differentiated capability for diagnosing non-deterministic agent failures
  • Fully open source under MIT license with self-hosting option gives teams full control
  • Real-time cost tracking across 400+ LLM models enables granular spend optimization
  • Multi-agent visualization untangles complex inter-agent communication patterns
  • Generous free tier of 5,000 events per month supports individual developers and prototyping
  • Both Python and TypeScript SDK support covers the primary AI development ecosystems

Cons

  • Purpose-built for agent workflows, so less useful for general LLM application monitoring
  • Public pricing details beyond the free tier require contacting sales for Enterprise plans
  • Value depends on using supported frameworks or investing in custom SDK instrumentation
  • Adds an external dependency and network calls that may impact latency-sensitive applications
  • As a relatively young platform the ecosystem and community are still maturing compared to established APM tools

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