Duvi vs PandaProbe: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Duvi and PandaProbe — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Duvi
Duvi DigiIQ, Inc.
Build voice and chat support agents by describing them in conversation; one configuration answers on your website, WhatsApp and phone line.
Key features
- Conversational Agent Builder: Creating an agent opens a conversation with a builder that writes the system prompt, picks a model and ingests the websites the agent should answer from, so setup is a dialogue rather than a configuration form.
- Unified Omnichannel Configuration: One agent configuration serves website chat, a WhatsApp number and a phone line, with the same knowledge behind every channel so context is not lost when a customer switches.
- Live Knowledge Lookups: The agent checks your connected store as it answers, so stock and catalogue responses reflect what is actually available at that moment rather than a stale snapshot.
- Website Actions: With the customer's instruction the agent operates the on-page controls you allow, completing the task in front of them instead of handing them a link and instructions.
- Grounded Answering: The agent answers from the pages you point it at and says so when the information is not there, rather than guessing.
- Preview Before Launch: Agents are tested in Preview and only go live once the domain is allowed and a snippet is pasted on your site.
- Broad Connector Library: Sign-in level integrations for Shopify, WooCommerce, Wix, Salesforce Commerce Cloud, Stripe, PayPal, Notion, Airtable, Webflow, Linear, monday.com, Sentry, Supabase, Cloudflare and Zapier.
- Team Workspace: Staff query the day's conversations and orders through their own authorized connection, so the answer reflects the order a customer placed moments ago.
Best for
- Ecommerce Support Deflection: A Shopify store answers stock, shipping and returns questions automatically, with the agent reading live catalogue data instead of a static FAQ.
- Lead Capture With Context: An agent takes a caller's email or number and routes it to the team with the whole conversation attached, so nobody asks the customer to repeat themselves.
- Phone Line Replacement: A small team replaces a recorded phone menu with an agent that answers real questions using the same knowledge base as the website chat.
- WhatsApp Commerce: A brand serving customers primarily on WhatsApp runs the same support agent there without maintaining a separate bot.
- Startup Support Coverage: An early-stage team keeps answering customers around the clock while engineers focus on building the product.
- Enterprise Support Augmentation: An established support operation adds agents to an existing stack via connectors rather than replacing its tooling.
PandaProbe
PandaProbe
Open-source, self-hostable agent engineering platform that provides traces, evaluations, and metrics to debug and improve AI agents.
Key features
- Distributed Tracing: Captures step-by-step execution traces of agent workflows, including prompts, model responses, tool calls, and intermediate state to help engineers pinpoint failure modes and reasoning paths.
- Evaluation Pipelines: Runs automated, configurable evals (scenario-based tests, rubric scoring, and behaviour checks) against agents to measure correctness, safety, and task performance over time.
- Metrics & Dashboards: Exposes aggregated metrics, time-series performance data, and customizable dashboards to monitor agent latency, success rates, error patterns, and regressions in production.
- Self-Hostable Architecture: Provides a deployable stack that teams can host on their infrastructure to preserve data privacy and compliance, with components designed to scale for multi-agent environments.
- Instrumentation SDKs & Integrations: Offers SDKs and integration hooks to instrument popular agent frameworks and LLM runtimes so traces and metrics can be captured with minimal code changes.
- Trace Visualization & Search: Interactive trace viewer and searchable trace logs that allow engineers to filter by run, agent, prompt, or error to accelerate debugging and root-cause analysis.
- Versioning & Comparison: Tracks agent versions, evaluation histories, and metric baselines to compare changes across prompt tweaks, model updates, or policy changes and identify regressions.
- Alerting & Export: Supports exportable metrics and alerting hooks (webhooks/metrics endpoints) so teams can connect PandaProbe monitoring to incident workflows and observability stacks.
- Execution tracing of AI agent workflows to inspect step-by-step behavior
- Evaluation tooling for systematically measuring agent performance and behaviors
- Metrics collection and dashboards for monitoring agent health and reliability
- Self-hostable deployment model for on-premises or private cloud use
- Architected for scale to support production and large-scale experimentation
- Open-source codebase enabling customization and integration
- Support for debugging and improving agent policies and pipelines
Best for
- Root-Cause Debugging of Agent Failures: Use step-level traces to identify where an agent’s reasoning or tool call chain diverged, enabling faster bug fixes and prompt adjustments.
- Continuous Evaluation of Agent Behavior: Automate scenario-based tests and rubric scoring to detect regressions after model updates or prompt changes and gate releases based on eval results.
- Production Monitoring at Scale: Monitor latency, success rate, and error distributions across many deployed agents to prioritize fixes and capacity planning.
- Privacy-Preserving Self-Hosting: Deploy PandaProbe on private infrastructure to keep sensitive conversation data in-house while still gaining observability into agent behavior.
- Benchmarking and Model Comparison: Compare metrics and eval outcomes across different LLMs, prompts, or tool integrations to select the best configuration for a task.
- Regression Testing for Prompt Engineering: Track performance changes tied to prompt revisions, enabling safe iterative prompt engineering and reproducible experiments.
- Debugging and tracing multi-step agent executions to find failure points
- Evaluating different agent versions or policies with automated evals
- Monitoring agent performance and operational metrics in production
- Running reproducible experiments and benchmarks for agent research
- Self-hosted deployments for teams requiring data locality or compliance