LiveKit Agents vs Bolna

Agent ops

1. ai-native userMy coding agent can provision a complete voice agent end to end — create the agent, attach a number, and place a call — through the API, CLI, or MCP without touching the dashboard

   weight 3 · round to Bolna
   LiveKit Agents~partialprobed5/10

   LiveKit provides a real CLI (`lk`), a documented telephony/SIP stack for provisioning inbound/outbound trunks and placing calls, and framework docs explicitly note it's 'built for coding agents like Claude Code, Cursor, and Codex,' suggesting API/CLI-driven workflows are possible without the dashboard. However, the MCP support documented is for the agent's own tool-calling (consuming MCP servers as tools), not an MCP server exposing agent-provisioning/number-attachment/call-placing actions to a coding agent, and no single evidenced workflow shows an agent programmatically creating an agent, attaching a number, and placing a call end-to-end via CLI/API in one flow — the Agent Builder path shown is dashboard/browser-based (no-code), which contradicts the 'without touching dashboard' framing. Missing for 10: an MCP server (or CLI/API recipe) that lets a coding agent itself create an agent, provision/attach a phone number, and place a call in one documented end-to-end sequence.

   • [github] “Works seamlessly with LiveKit's telephony stack, allowing your agent to make calls to or receive calls from phones.”
   • [claimed-docs] “LiveKit telephony lets you build AI-powered voice apps that handle inbound and outbound calls.”
   • [claimed-docs] “Outbound trunks are used to place outgoing calls.”
   • [probe] “official CLI documented at https://github.com/livekit/livekit-cli”
   • [probe] “PROBE runtime (recorded 2026-09-05): `lk --version` printed `lk version 2.18.6` after a plain `brew install livekit-cli` — the official CLI …”
   • [claimed-docs] “LiveKit is built for coding agents like Claude Code, Cursor, and Codex.”
   • [github] “MCP support: Native support for MCP. Integrate tools provided by MCP servers with one line of code.”
   • [claimed-docs] “LiveKit Agents has first-class support for Model Context Protocol (MCP) servers.”

   proof ↗⚑ flag

   Bolna✓fullprobed8/10

   Bolna offers all three surfaces needed for agent-native provisioning: a documented API quickstart to create an agent, attach a number, and place a call (bolna-docs-14, bolna-docs-15), a native Go CLI to manage agents, calls, and phone numbers from the terminal (bolna-docs-16, bolna-probe-4), and an official MCP server to connect Claude/Cursor/Windsurf clients (bolna-docs-17, bolna-probe-3), all without requiring dashboard interaction. missing for 10: independent/hands-on confirmation that MCP or CLI can complete the full create-attach-call flow end-to-end (only vendor docs), and no OpenAPI spec was resolvable (bolna-probe-2) to verify API completeness.

   • [claimed-docs] “Make your first Voice AI call with the Bolna API — verify your key, get an agent, place a call, and fetch the transcript in about five minut…”
   • [claimed-docs] “Click the Create a new API Key button to generate your unique authentication credentials.”
   • [claimed-docs] “The Bolna CLI (bolna) is a single Go binary that lets you manage agents, calls, phone numbers, and batches directly from your terminal”
   • [claimed-docs] “Connect Claude, Cursor, Windsurf, or any MCP client to your Bolna account with the Bolna MCP server.”
   • [probe] “official MCP server documented at https://www.bolna.ai/docs/build-with-ai/mcp”
   • [probe] “official CLI documented at https://www.bolna.ai/docs/cli/introduction”
   • [probe] “PROBE openapi: all candidate paths 404 (https://docs.bolna.ai/openapi.json, https://docs.bolna.ai/swagger.json, https://docs.bolna.ai/api/op…”

   proof ↗⚑ flag

2. ai-native userThe platform's own AI helps me author agents — generating or improving prompts, flows, and test cases from a description

   weight 1 · round drawn
   LiveKit Agents—none0/10

   Evidence shows a no-code 'Agent Builder' for browser prototyping and a testing framework, but nothing indicates the platform itself uses AI to generate or improve prompts, flows, or test cases from a natural-language description of the desired agent.

   • [claimed-docs] “LiveKit Agent Builder to prototype and deploy agents directly in your browser without writing code”
   • [claimed-docs] “LiveKit Agent Builder: Prototype and deploy voice agents directly in your browser, without writing any code.”
   • [claimed-docs] “Behavioral tests verify specific interactions and expected outcomes. They integrate with your existing test suite using pytest (Python) or V…”
   • [claimed-docs] “Behavioral tests verify specific interactions and expected outcomes... Agent simulations run end-to-end conversations between your agent and…”

   proof ↗⚑ flag

   Bolna—none0/10

   No evidence shows Bolna's own platform AI generating or improving prompts, flows, or test cases from a description; the closest feature (build-with-ai/mcp) is an MCP server that lets external AI clients like Claude or Cursor manage the account via API, not the platform itself authoring agents. Templates can be cloned, but that's static copying, not AI-generated authoring.

   • [claimed-docs] “Connect Claude, Cursor, Windsurf, or any MCP client to your Bolna account with the Bolna MCP server.”
   • [claimed-docs] “Clone any template and customize it for your business.”

   proof ↗⚑ flag

Build

1. developerBuild a working phone voice agent — prompt, voice, and phone number — and take my first live call within an hour

   weight 3 · round to Bolna
   LiveKit Agents✓fullprobed7/10

   Docs show a <10‑minute voice-assistant quickstart (prompt+voice) plus a dedicated telephony/SIP stack for inbound/outbound calls (trunks, phone number), and runtime probes confirm the framework and self-hosted server actually install and boot without extra keys. Together these cover prompt, voice, and phone number needed for a first live call within an hour. Missing for 10: a single unified, hands-on-verified tutorial that walks through phone-number provisioning and first live call end-to-end (currently voice-quickstart and telephony docs are separate), and independent (non-vendor) confirmation of the 'within an hour' timeline.

   • [claimed-docs] “Build and deploy a simple voice assistant in less than 10 minutes.”
   • [claimed-docs] “Build and deploy a simple voice assistant with Python or Node.js in less than 10 minutes.”
   • [claimed-docs] “LiveKit telephony lets you build AI-powered voice apps that handle inbound and outbound calls.”
   • [claimed-docs] “Enable your voice agent to make or take phone calls.”
   • [claimed-docs] “Outbound trunks are used to place outgoing calls.”
   • [github] “Works seamlessly with LiveKit's telephony stack, allowing your agent to make calls to or receive calls from phones.”
   • [probe] “PROBE runtime (recorded 2026-09-05): `lk --version` printed `lk version 2.18.6` after a plain `brew install livekit-cli` — the official CLI …”
   • [probe] “PROBE runtime (recorded 2026-09-05): `livekit-server --dev` (brew-installed, Apache-2.0 OSS) booted on this machine with NO account or key —…”
   • [probe] “PROBE runtime (recorded 2026-09-05): pypi livekit-agents 1.8.0 installs and `import livekit.agents` succeeds with no key (npm @livekit/agent…”

   proof ↗⚑ flag

   Bolna✓fullclaimed8/10

   Docs directly support the story: templates can be cloned and customized, build/deploy claimed under 5 minutes, voice/accent selection, phone number and telephony setup (BYOT/SIP), and a quickstart walking through key verification, agent creation, placing a call, and fetching a transcript in ~5 minutes. This covers prompt (agent config/templates), voice (voice/provider selection), and phone number (telephony/SIP) end-to-end for a first live call within an hour. Missing for 10: independent/hands-on confirmation of the 'under 5 minutes' or 'first call in an hour' claims beyond vendor docs, and no third-party success report.

   • [claimed-docs] “Build and deploy in under 5 minutes”
   • [claimed-docs] “Clone any template and customize it for your business.”
   • [claimed-docs] “You can choose different voices, accents, and speaking styles to match your brand.”
   • [claimed-docs] “Bring Your Own Telephony (BYOT) lets you connect any standards-compliant SIP trunk to the Bolna platform.”
   • [claimed-docs] “Make your first Voice AI call with the Bolna API — verify your key, get an agent, place a call, and fetch the transcript in about five minut…”
   • [claimed-docs] “Bolna supports Deepgram, Azure, ElevenLabs, and more.”

   proof ↗⚑ flag

2. developerRun conversations in multiple languages, including detecting and switching language mid-call

   weight 2 · round to Bolna
   LiveKit Agents—none0/10

   No evidence in the pack addresses multilingual conversation support, language detection, or dynamic language switching mid-call; the docs cover turn detection, interruption handling, tool use, MCP, and telephony but never mention STT/TTS language selection or switching logic. This is a fair axis for a voice-agent framework (many STT/TTS providers support multi-language), so absence of evidence means 'none' rather than 'na'.

   ⚑ flag

   Bolna~partialclaimed6/10

   Docs claim the agent "detects and switches mid-call on its own" and marketing mentions "multilingual intelligence" for thousands of calls, directly supporting the story, but there's no detail on supported languages, configuration options, or independent/hands-on verification of mid-call switching reliability. Missing for 10: language list/configuration docs, hands-on or third-party confirmation of switching accuracy, and edge-case handling details.

   • [claimed-docs] “the agent detects and switches mid-call on its own”
   • [claimed-docs] “Power thousands of inbound and outbound calls every minute with human-like, multilingual intelligence”

   proof ↗⚑ flag

3. founderDesign multi-step conversation flows in a visual builder with branching, states, and handoffs without writing code

   weight 2 · round to LiveKit Agents
   LiveKit Agents~partialclaimed3/10

   LiveKit mentions a browser-based 'Agent Builder' for no-code prototyping of voice agents, but the evidence never describes visual branching, explicit states, or handoff design — the core framework is fundamentally code-first (Python/Node.js) with tools, MCP, and turn-detection logic. missing for 10: documentation of branching/state UI, handoff modeling, or any screenshots/examples of the Agent Builder's flow-design capabilities beyond a vague no-code prototyping claim.

   • [claimed-docs] “Prototype and deploy voice agents directly in your browser, without writing any code.”
   • [claimed-docs] “LiveKit Agent Builder to prototype and deploy agents directly in your browser without writing code”
   • [claimed-docs] “LiveKit Agent Builder: Prototype and deploy voice agents directly in your browser, without writing any code.”

   proof ↗⚑ flag

   Bolna—none0/10

   Evidence shows Bolna is built around APIs, CLI, and MCP server for defining agents and multi-step outreach sequences (bolna-docs-12, bolna-docs-16, bolna-docs-17), but there is no mention of a visual/no-code builder with branching, states, or handoffs; workflows are described as 'driven entirely through the Bolna API.' Missing for 10: any visual flow-builder UI, drag-and-drop branching/state design, no-code handoff configuration.

   • [claimed-docs] “Build multi-step outreach sequences that combine voice agent calls, WhatsApp messages, API calls, waits and retries, driven entirely through…”
   • [claimed-docs] “The Bolna CLI (bolna) is a single Go binary that lets you manage agents, calls, phone numbers, and batches directly from your terminal”
   • [claimed-docs] “Connect Claude, Cursor, Windsurf, or any MCP client to your Bolna account with the Bolna MCP server.”
   • [github] “Bolna is the end-to-end open source production ready framework for quickly building LLM based voice driven conversational applications.”

   proof ↗⚑ flag

Personalization

1. developerInject dynamic variables and per-caller context at call time so each conversation is personalized

   weight 2 · round drawn
   LiveKit Agents—none0/10

   The evidence pack covers tool use, MCP, telephony, testing, and turn detection, but nothing addresses injecting dynamic variables or per-caller context (e.g., participant/room metadata, job context) into an agent's prompt or session at call start. Missing for 10: any documentation of job/participant metadata APIs, per-call context injection, or dynamic prompt personalization.

   ⚑ flag

   Bolna—none0/10

   The evidence pack covers agent creation, extraction categories, call summaries, workflows, and providers, but nothing documents passing per-caller dynamic variables or context payloads at call time to personalize a conversation.

   ⚑ flag

2. developerGround the agent on my documents with a built-in knowledge base or RAG so it answers from my content

   weight 2 · round to LiveKit Agents
   LiveKit Agents~partialclaimed4/10

   LiveKit Agents supports calling external APIs/tools that a developer can use to implement RAG ("Call external APIs or lookup data for RAG"), and has full LLM tool-use and MCP integration for wiring in retrieval systems, but there is no built-in knowledge base, document ingestion, or vector-store/RAG pipeline shipped by the framework itself — developers must bring their own RAG implementation via the tools API. Missing for 10: built-in document indexing/vector store, out-of-the-box knowledge-base feature, first-party RAG pipeline or example showing document grounding end-to-end.

   • [claimed-docs] “Call external APIs or lookup data for RAG.”
   • [claimed-docs] “LiveKit Agents has full support for LLM tool use. This feature allows you to create a custom library of tools to extend your agent's context”
   • [claimed-docs] “LiveKit Agents has full support for LLM tool use.”
   • [github] “MCP support: Native support for MCP. Integrate tools provided by MCP servers with one line of code.”
   • [claimed-docs] “Wrap an MCP server in an \`MCPToolset\` and pass it to the agent's \`tools\` parameter”

   proof ↗⚑ flag

   Bolna—none0/10

   Bolna is a voice AI agent platform (telephony, TTS/STT, LLM providers, workflows) but the evidence pack contains no mention of a knowledge base, document ingestion, or RAG capability for grounding agent responses in custom content. This axis clearly applies to conversational agent-building platforms, so absence of evidence yields 'none'.

   ⚑ flag

Agent access

1. ai-native userPoint an agent at llms.txt or agent-oriented docs

   weight 2 · round to Bolna
   LiveKit Agents✓fullprobed8/10

   LiveKit publishes a working llms.txt (verified live at docs.livekit.io/llms.txt, HTTP 200) and agent-friendly markdown doc endpoints (e.g. /agents/.md), plus explicit docs noting the platform is 'built for coding agents like Claude Code, Cursor, and Codex,' confirming intentional support for AI-native doc consumption. missing for 10: no independent third-party confirmation that external agents successfully consume these endpoints in practice, and no dedicated documentation page explaining the llms.txt/agent-doc strategy itself.

   • [probe] “PROBE llms.txt: HTTP 200 at https://docs.livekit.io/llms.txt # LiveKit docs > LiveKit is an open-source platform for building voice, video,…”
   • [probe] “PROBE docs-md: HTTP 200 at https://docs.livekit.io/agents/.md LiveKit docs › Build Agents › Get Started › Introduction --- # Introduction …”
   • [claimed-docs] “LiveKit is built for coding agents like Claude Code, Cursor, and Codex.”

   proof ↗⚑ flag

   Bolna✓fullprobed9/10

   A probe confirms llms.txt is live and returns 200 with structured agent-oriented doc links (bolna-probe-1), and Bolna also documents an official MCP server for connecting AI clients like Claude/Cursor to its docs/account (bolna-docs-17, bolna-probe-3), directly enabling agent-native consumption of its documentation. Missing for 10: no independent third-party confirmation that agents successfully use llms.txt in practice.

   • [probe] “PROBE llms.txt: HTTP 200 at https://docs.bolna.ai/llms.txt # Bolna Docs > Quickly build and deploy conversational Voice AI agents - [Home]…”
   • [claimed-docs] “Connect Claude, Cursor, Windsurf, or any MCP client to your Bolna account with the Bolna MCP server.”
   • [probe] “official MCP server documented at https://www.bolna.ai/docs/build-with-ai/mcp”

   proof ↗⚑ flag

2. ai-native userRun the product headlessly / in CI for automation

   weight 2 · round to LiveKit Agents
   LiveKit Agents✓fullprobed8/10

   LiveKit Agents is a Python/Node.js SDK plus self-hostable server that runs as a headless programmatic participant with no UI dependency; it's pip/npm-installable, has a built-in test framework (pytest/Vitest, agent simulations) suitable for CI, and the server/CLI were verified runtime to work keylessly. missing for 10: no explicit documented CI pipeline example (e.g., GitHub Actions config) or headless-mode confirmation beyond inference from server/testing docs.

   • [claimed-docs] “A programmatic participant is any code that joins a LiveKit room as a participant — this includes AI agents, media processors, or custom log…”
   • [claimed-docs] “The Agents framework isn't limited to AI agents. You can use it to deploy any code that needs to process realtime media and data streams as …”
   • [claimed-docs] “Behavioral tests verify specific interactions and expected outcomes. They integrate with your existing test suite using pytest (Python) or V…”
   • [claimed-docs] “Agent simulations run end-to-end conversations between your agent and an LLM-driven user, then evaluate the results across the full interact…”
   • [github] “Builtin test framework: Write tests and use judges to ensure your agent is performing as expected.”
   • [probe] “PROBE runtime (recorded 2026-09-05): `livekit-server --dev` (brew-installed, Apache-2.0 OSS) booted on this machine with NO account or key —…”
   • [probe] “PROBE runtime (recorded 2026-09-05): pypi livekit-agents 1.8.0 installs and `import livekit.agents` succeeds with no key (npm @livekit/agent…”

   proof ↗⚑ flag

   Bolna~partialprobed6/10

   Bolna offers a CLI (single Go binary) for managing agents, calls, phone numbers, and batches from the terminal, plus a REST API for scripted call creation and transcript retrieval — both usable in CI/automation pipelines without a GUI. However, there's no explicit CI/pipeline example, headless-mode documentation, or exit-code/automation-specific guidance. missing for 10: explicit CI integration examples/guides, documented non-interactive/headless flags or exit codes, third-party confirmation of CI usage.

   • [claimed-docs] “The Bolna CLI (bolna) is a single Go binary that lets you manage agents, calls, phone numbers, and batches directly from your terminal”
   • [probe] “official CLI documented at https://www.bolna.ai/docs/cli/introduction”
   • [claimed-docs] “Make your first Voice AI call with the Bolna API — verify your key, get an agent, place a call, and fetch the transcript in about five minut…”
   • [claimed-docs] “Click the Create a new API Key button to generate your unique authentication credentials.”

   proof ↗⚑ flag

3. ai-native userPlug MCP servers into this product so it can use their tools

   weight 3 · round to LiveKit Agents
   LiveKit Agents✓fullclaimed8/10

   Docs and GitHub explicitly state 'first-class support for Model Context Protocol (MCP) servers' with a documented pattern to wrap an MCP server in an MCPToolset and pass it to the agent's tools parameter, described as one line of code integration. This is first-party documentation without independent hands-on verification of MCP-specific usage. Missing for 10: independent/community confirmation of MCP tool integration working end-to-end, and more detail on multi-server or auth configurations.

   • [claimed-docs] “Wrap an MCP server in an \`MCPToolset\` and pass it to the agent's \`tools\` parameter”
   • [claimed-docs] “Wrap an MCP server in an `MCPToolset` and pass it to the agent's `tools` parameter”
   • [claimed-docs] “LiveKit Agents has first-class support for Model Context Protocol (MCP) servers.”
   • [github] “MCP support: Native support for MCP. Integrate tools provided by MCP servers with one line of code.”
   • [github] “Native support for MCP. Integrate tools provided by MCP servers with one line of code.”

   proof ↗⚑ flag

   Bolna—none0/10

   Evidence shows Bolna ships an MCP *server* so external clients like Claude, Cursor, or Windsurf can control a Bolna account (bolna-docs-17, bolna-probe-3) — this is the reverse direction from the story, which asks whether Bolna's own agents can plug in external MCP servers to gain their tools. No evidence describes Bolna voice agents consuming or calling external MCP servers.

   • [claimed-docs] “Connect Claude, Cursor, Windsurf, or any MCP client to your Bolna account with the Bolna MCP server.”
   • [probe] “official MCP server documented at https://www.bolna.ai/docs/build-with-ai/mcp”

   proof ↗⚑ flag

4. ai-native userConnect an agent via an official MCP server

   weight 3 · round to Bolna
   LiveKit Agents—none0/10

   The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is "none", never "na". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)

   ⚑ flag

   Bolna✓fullprobed8/10

   Bolna documents an official MCP server that lets Claude, Cursor, Windsurf, or any MCP client connect to a Bolna account, confirmed by both docs and an independent probe hitting the live page. Missing for 10: independent hands-on validation or community confirmation of the MCP server working in practice beyond the docs/probe.

   • [claimed-docs] “Connect Claude, Cursor, Windsurf, or any MCP client to your Bolna account with the Bolna MCP server.”
   • [probe] “official MCP server documented at https://www.bolna.ai/docs/build-with-ai/mcp”

   proof ↗⚑ flag

5. ai-native userUse an official CLI

   weight 2 · round to LiveKit Agents
   LiveKit Agents✓fullprobed9/10

   LiveKit ships an official CLI (livekit-cli), documented on GitHub and verified hands-on to install and run (`lk --version` works via brew install), confirming it's a real, functional official CLI supporting the agent workflow. missing for 10: no deep documentation of full CLI command surface for agent-specific workflows within the evidence pack.

   • [probe] “official CLI documented at https://github.com/livekit/livekit-cli”
   • [probe] “PROBE runtime (recorded 2026-09-05): `lk --version` printed `lk version 2.18.6` after a plain `brew install livekit-cli` — the official CLI …”

   proof ↗⚑ flag

   Bolna✓fullprobed8/10

   Bolna documents an official CLI (single Go binary) letting users manage agents, calls, phone numbers, and batches from the terminal, corroborated by a live docs probe confirming the page exists. missing for 10: no independent/hands-on community usage reports or version/release history confirming real-world adoption beyond first-party docs.

   • [claimed-docs] “The Bolna CLI (bolna) is a single Go binary that lets you manage agents, calls, phone numbers, and batches directly from your terminal”
   • [probe] “official CLI documented at https://www.bolna.ai/docs/cli/introduction”

   proof ↗⚑ flag

6. ai-native userDrive the product through a documented public API

   weight 3 · round drawn
   LiveKit Agents✓fullprobed8/10

   LiveKit Agents ships a well-documented, pip/npm-installable Python and Node.js SDK with extensive public API surface (tools, MCP, turn detection, telephony, testing), plus a llms.txt and hands-on verified installs/runtime confirming the API is real and usable by AI-native developers. Missing for 10: independent third-party API reference/versioning audit and deeper evidence of API stability guarantees beyond docs and probes.

   • [claimed-docs] “The Agents framework lets you add any Python or Node.js program to LiveKit rooms as full realtime participants.”
   • [claimed-docs] “A programmatic participant is any code that joins a LiveKit room as a participant — this includes AI agents, media processors, or custom log…”
   • [claimed-docs] “The Agents framework isn't limited to AI agents. You can use it to deploy any code that needs to process realtime media and data streams as …”
   • [github] “MCP support: Native support for MCP. Integrate tools provided by MCP servers with one line of code.”
   • [claimed-docs] “LiveKit Agents has full support for LLM tool use. This feature allows you to create a custom library of tools to extend your agent's context”
   • [probe] “PROBE llms.txt: HTTP 200 at https://docs.livekit.io/llms.txt # LiveKit docs > LiveKit is an open-source platform for building voice, video,…”
   • [probe] “PROBE docs-md: HTTP 200 at https://docs.livekit.io/agents/.md LiveKit docs › Build Agents › Get Started › Introduction --- # Introduction …”
   • [probe] “PROBE runtime (recorded 2026-09-05): pypi livekit-agents 1.8.0 installs and `import livekit.agents` succeeds with no key (npm @livekit/agent…”

   proof ↗⚑ flag

   Bolna✓fullprobed8/10

   Bolna provides a documented public API with quickstart, authentication, agent/call/extraction endpoints, and workflow automation entirely through the API, plus a CLI and MCP server built on top of it. Missing for 10: a discoverable OpenAPI/Swagger spec (probe found all standard OpenAPI paths 404) and independent third-party corroboration of API robustness.

   • [claimed-docs] “Make your first Voice AI call with the Bolna API — verify your key, get an agent, place a call, and fetch the transcript in about five minut…”
   • [claimed-docs] “Click the Create a new API Key button to generate your unique authentication credentials.”
   • [claimed-docs] “Build multi-step outreach sequences that combine voice agent calls, WhatsApp messages, API calls, waits and retries, driven entirely through…”
   • [claimed-docs] “The Bolna CLI (bolna) is a single Go binary that lets you manage agents, calls, phone numbers, and batches directly from your terminal”
   • [probe] “PROBE openapi: all candidate paths 404 (https://docs.bolna.ai/openapi.json, https://docs.bolna.ai/swagger.json, https://docs.bolna.ai/api/op…”
   • [probe] “official CLI documented at https://www.bolna.ai/docs/cli/introduction”

   proof ↗⚑ flag

7. ai-native userIssue scoped/least-privilege API credentials for an agent

   weight 2 · round drawn
   LiveKit Agents—none0/10

   The evidence pack covers voice/agent features, tool-calling, MCP, testing, and telephony, but contains no mention of scoped or least-privilege API key/token issuance for agents (e.g., LiveKit's grant-based access tokens or credential scoping). Since LiveKit is a platform with API keys and would plausibly support such scoping, this is an applicable but undocumented axis in the given evidence.

   ⚑ flag

   Bolna—none0/10

   Bolna documents API key creation but there is no evidence of scoped or least-privilege credential controls (e.g., role-based permissions, scoped tokens, restricted access levels) — only a single account-wide key generation flow is described.

   • [claimed-docs] “Click the Create a new API Key button to generate your unique authentication credentials.”

   proof ↗⚑ flag

8. ai-native userBuild against official SDKs

   weight 2 · round to LiveKit Agents
   LiveKit Agents✓fullprobed9/10

   LiveKit provides official Python and Node.js SDKs (livekit-agents), verified hands-on to pip-install and import with no key, plus documented plugin ecosystem, tool use, MCP support, testing frameworks, and telephony integration—clearly an official SDK ecosystem for AI-native building. Missing for 10: independent third-party benchmarking of SDK API stability/versioning beyond community latency complaints unrelated to SDK build story.

   • [claimed-docs] “The Agents framework lets you add any Python or Node.js program to LiveKit rooms as full realtime participants.”
   • [claimed-docs] “LiveKit Agents includes a large ecosystem of open source plugins for a variety of AI providers.”
   • [claimed-docs] “LiveKit Agents has full support for LLM tool use. This feature allows you to create a custom library of tools to extend your agent's context”
   • [github] “MCP support: Native support for MCP. Integrate tools provided by MCP servers with one line of code.”
   • [probe] “PROBE runtime (recorded 2026-09-05): pypi livekit-agents 1.8.0 installs and `import livekit.agents` succeeds with no key (npm @livekit/agent…”
   • [claimed-docs] “Behavioral tests verify specific interactions and expected outcomes. They integrate with your existing test suite using pytest (Python) or V…”

   proof ↗⚑ flag

   Bolna~partialprobed5/10

   Bolna documents an official Web Call SDK (bolna-docs-18) under a 'developer-resources/sdks' path, plus a CLI and MCP server, giving AI-native builders some official tooling to build against, but there is no evidence of broader language SDKs (e.g., Python, Node, Go client libraries) beyond the browser-based Web Call SDK. missing for 10: multi-language server-side SDKs, independent/hands-on verification of SDK usability, breadth beyond the single web-call SDK.

   • [claimed-docs] “The Web Call SDK connects a browser tab directly to a Bolna voice agent for a live, two-way conversation.”
   • [claimed-docs] “The Bolna CLI (bolna) is a single Go binary that lets you manage agents, calls, phone numbers, and batches directly from your terminal”
   • [probe] “official CLI documented at https://www.bolna.ai/docs/cli/introduction”
   • [claimed-docs] “Connect Claude, Cursor, Windsurf, or any MCP client to your Bolna account with the Bolna MCP server.”

   proof ↗⚑ flag

9. ai-native userSubscribe to events via webhooks

   weight 2 · round drawn
   LiveKit Agents—none0/10

   No evidence pack items mention webhooks or an event-subscription mechanism for LiveKit Agents; the docs cover MCP tool integration, telephony, testing, and turn detection, but nothing about webhook-based event notifications.

   ⚑ flag

   Bolna—none0/10

   No evidence in the pack mentions webhooks or event subscription for Bolna; the docs cover agents, calls, CLI, MCP, SIP, pricing, and security but nothing about outbound event notifications. Missing for 10: any webhook registration/config API, event payload documentation, or third-party confirmation of webhook delivery.

   ⚑ flag

Agentic features

1. ai-native userGet AI-generated insights and suggestions from my data inside the product

   weight 2 · round to Bolna
   LiveKit Agents—none0/10

   The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is "none", never "na". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)

   ⚑ flag

   Bolna~partialclaimed5/10

   Bolna offers agent-level call_summary_enabled to auto-generate AI summaries and an extraction-categories API to pull structured insights out of call data, both of which are AI-generated insights derived from user data (calls). However, there's no evidence of broader cross-call analytics, trend insights, or proactive suggestions surfaced in a dashboard beyond per-call outputs. Missing for 10: aggregated/dashboard-level insights across multiple calls, proactive recommendation surfacing, and independent confirmation of quality of generated summaries/extractions.

   • [claimed-docs] “POST /agent/{agent_id}/extraction-categories — create a category (name + model) and attach it to an agent”
   • [claimed-docs] “Call summaries are an agent-level toggle: call_summary_enabled (boolean, off by default) on the agent create and update APIs”

   proof ↗⚑ flag

2. ai-native userSet up automations that run autonomously in the background

   weight 2 · round to LiveKit Agents
   LiveKit Agents✓fullprobed7/10

   LiveKit Agents workers run as persistent 'programmatic participants' deployed to LiveKit Cloud or self-hosted infrastructure, processing realtime streams and telephony calls autonomously in the background without human intervention (docs-8, docs-20, docs-30, docs-26, gh-2), and this is verified hands-on as a real pip-installable OSS framework with a working self-hosted backbone (probe-rt-2, probe-rt-3). Missing for 10: explicit scheduling/cron-trigger mechanisms, independent third-party reports of long-running unattended stability (the one community mention is about observability tooling gaps, not autonomy failure).

   • [claimed-docs] “A programmatic participant is any code that joins a LiveKit room as a participant — this includes AI agents, media processors, or custom log…”
   • [claimed-docs] “The Agents framework isn't limited to AI agents. You can use it to deploy any code that needs to process realtime media and data streams as …”
   • [claimed-docs] “Run your agent on LiveKit Cloud's global infrastructure.”
   • [claimed-docs] “LiveKit telephony lets you build AI-powered voice apps that handle inbound and outbound calls.”
   • [github] “Works seamlessly with LiveKit's telephony stack, allowing your agent to make calls to or receive calls from phones.”
   • [probe] “PROBE runtime (recorded 2026-09-05): `livekit-server --dev` (brew-installed, Apache-2.0 OSS) booted on this machine with NO account or key —…”
   • [probe] “PROBE runtime (recorded 2026-09-05): pypi livekit-agents 1.8.0 installs and `import livekit.agents` succeeds with no key (npm @livekit/agent…”

   proof ↗⚑ flag

   Bolna~partialprobed6/10

   Bolna's workflows feature lets users build multi-step outreach sequences (voice calls, WhatsApp, API calls, waits, retries) driven entirely through the API, which supports background automation, and the CLI can manage batches of calls autonomously. However there's no documented evidence of scheduling/triggers, monitoring/alerting on autonomous runs, or a dashboard for managing long-running background jobs beyond the API-driven workflow primitives. missing for 10: scheduling/trigger mechanisms, autonomous-run monitoring/alerting, independent hands-on confirmation of unattended long-running workflows.

   • [claimed-docs] “Build multi-step outreach sequences that combine voice agent calls, WhatsApp messages, API calls, waits and retries, driven entirely through…”
   • [claimed-docs] “The Bolna CLI (bolna) is a single Go binary that lets you manage agents, calls, phone numbers, and batches directly from your terminal”
   • [probe] “official CLI documented at https://www.bolna.ai/docs/cli/introduction”

   proof ↗⚑ flag

3. ai-native userDelegate tasks to a built-in AI assistant inside the product

   weight 3 · round drawn
   LiveKit Agents—none0/10

   The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is "none", never "na". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)

   ⚑ flag

   Bolna—none0/10

   The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is "none", never "na". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)

   ⚑ flag

4. ai-native userOperate the product with natural-language commands

   weight 2 · round to Bolna
   LiveKit Agents~partialprobed4/10

   LiveKit ships an 'Agent Builder' that lets users prototype/deploy voice agents in-browser without code, and docs explicitly note the framework is 'built for coding agents like Claude Code, Cursor, and Codex' plus agent-readable llms.txt docs — suggesting some AI-native/no-code operability. However there is no evidence of a native natural-language command interface for operating LiveKit itself (e.g., NL-driven CLI, chatops control plane); the MCP support described is for agents calling external tools, not for controlling LiveKit via NL. Missing for 10: a documented NL-driven interface/CLI for configuring or operating the platform itself, and independent confirmation of the Agent Builder's no-code UX actually being NL-based rather than GUI-based.

   • [claimed-docs] “LiveKit is built for coding agents like Claude Code, Cursor, and Codex.”
   • [claimed-docs] “Prototype and deploy voice agents directly in your browser, without writing any code.”
   • [claimed-docs] “LiveKit Agent Builder to prototype and deploy agents directly in your browser without writing code”
   • [probe] “PROBE llms.txt: HTTP 200 at https://docs.livekit.io/llms.txt # LiveKit docs > LiveKit is an open-source platform for building voice, video,…”
   • [probe] “PROBE docs-md: HTTP 200 at https://docs.livekit.io/agents/.md LiveKit docs › Build Agents › Get Started › Introduction --- # Introduction …”

   proof ↗⚑ flag

   Bolna✓fullprobed7/10

   Bolna ships an official MCP server that lets AI-native tools like Claude, Cursor, or Windsurf connect to a Bolna account and operate it (manage agents, calls, etc.) via natural-language commands, confirmed both in docs and via a live probe. Missing for 10: independent/hands-on verification that the MCP integration works as described, and no additional natural-language control surfaces (e.g., chat-based dashboard) beyond MCP.

   • [claimed-docs] “Connect Claude, Cursor, Windsurf, or any MCP client to your Bolna account with the Bolna MCP server.”
   • [probe] “official MCP server documented at https://www.bolna.ai/docs/build-with-ai/mcp”

   proof ↗⚑ flag

Api quality

1. ai-native userExplore an interactive API reference with runnable examples

   weight 2 · round drawn
   LiveKit Agents—none0/10

   Evidence shows extensive quickstart guides, markdown-based docs (llms.txt), and a no-code 'Agent Builder' prototyping tool, but nothing describes an interactive API reference (e.g., embedded code sandbox, live runnable examples, or Swagger-like explorer) for exploring the SDK/API itself.

   ⚑ flag

   Bolna—none0/10

   Bolna has an API reference doc listing basics like API key creation, but there is no evidence of an interactive reference with runnable/try-it examples, and a probe for a machine-readable OpenAPI spec (which typically powers such interactive docs) returned 404 on all checked paths.

   • [claimed-docs] “Click the Create a new API Key button to generate your unique authentication credentials.”
   • [probe] “PROBE openapi: all candidate paths 404 (https://docs.bolna.ai/openapi.json, https://docs.bolna.ai/swagger.json, https://docs.bolna.ai/api/op…”

   proof ↗⚑ flag

2. ai-native userDownload a machine-readable API spec (OpenAPI or equivalent)

   weight 2 · round drawn
   LiveKit Agents—none0/10

   The evidence pack documents LiveKit's SDKs, CLI, docs (llms.txt), and framework capabilities but contains no mention of a downloadable OpenAPI/Swagger spec or other machine-readable API description for LiveKit's server or Agents APIs. Since LiveKit exposes a real API surface (server API, cloud API), this axis applies but is unevidenced.

   • [probe] “official CLI documented at https://github.com/livekit/livekit-cli”
   • [probe] “PROBE llms.txt: HTTP 200 at https://docs.livekit.io/llms.txt # LiveKit docs > LiveKit is an open-source platform for building voice, video,…”
   • [probe] “PROBE docs-md: HTTP 200 at https://docs.livekit.io/agents/.md LiveKit docs › Build Agents › Get Started › Introduction --- # Introduction …”

   proof ↗⚑ flag

   Bolna—none0/10

   A direct probe for OpenAPI/Swagger spec files at all standard locations returned 404s, and no evidence pack item shows a downloadable machine-readable API spec despite having an API reference page.

   • [probe] “PROBE openapi: all candidate paths 404 (https://docs.bolna.ai/openapi.json, https://docs.bolna.ai/swagger.json, https://docs.bolna.ai/api/op…”
   • [claimed-docs] “Click the Create a new API Key button to generate your unique authentication credentials.”

   proof ↗⚑ flag

3. ai-native userTest against a sandbox environment without touching production data

   weight 1 · round to LiveKit Agents
   LiveKit Agents~partialprobed6/10

   LiveKit provides a self-hostable dev server (`livekit-server --dev`) that boots locally with placeholder keys, separate from any production deployment, plus a built-in testing framework with behavioral tests and LLM-driven agent simulations that evaluate agent behavior without needing real production data. However, there is no explicitly branded 'sandbox environment' or staging/production data-isolation feature documented — the sandbox-like capability is inferred from dev-mode self-hosting and test simulations rather than a dedicated sandbox product feature. Missing for 10: an explicit sandbox/staging environment offering with documented separation from production data, and independent confirmation that test simulations never touch production data stores.

   • [claimed-docs] “Behavioral tests verify specific interactions and expected outcomes. They integrate with your existing test suite using pytest (Python) or V…”
   • [claimed-docs] “Agent simulations run end-to-end conversations between your agent and an LLM-driven user, then evaluate the results across the full interact…”
   • [claimed-docs] “Behavioral tests verify specific interactions and expected outcomes... Agent simulations run end-to-end conversations between your agent and…”
   • [github] “Builtin test framework: Write tests and use judges to ensure your agent is performing as expected.”
   • [probe] “PROBE runtime (recorded 2026-09-05): `livekit-server --dev` (brew-installed, Apache-2.0 OSS) booted on this machine with NO account or key —…”

   proof ↗⚑ flag

   Bolna—none0/10

   No evidence pack item mentions a sandbox environment, test mode, staging account, or separation from production data for Bolna; the closest concepts (self-hosting, BYOK, data residency) are about deployment/privacy, not a distinct sandbox/test mode. missing for 10: any mention of a sandbox/test environment, test API keys, or safe testing workflow that avoids production data.

   ⚑ flag

4. ai-native userRely on versioned APIs with a documented deprecation policy

   weight 2 · round drawn
   LiveKit Agents—none0/10

   No evidence in the pack mentions API versioning scheme, version compatibility guarantees, or a documented deprecation policy for LiveKit Agents' SDKs or APIs.

   ⚑ flag

   Bolna—none0/10

   There is a changelog page referenced and an API reference, but no evidence of API versioning scheme or a documented deprecation policy anywhere in the pack; OpenAPI spec probes even returned 404s. Missing for 10: versioned API endpoints/headers, explicit deprecation policy documentation, migration/versioning changelog entries.

   • [probe] “PROBE openapi: all candidate paths 404 (https://docs.bolna.ai/openapi.json, https://docs.bolna.ai/swagger.json, https://docs.bolna.ai/api/op…”
   • [claimed-docs] “Click the Create a new API Key button to generate your unique authentication credentials.”

   proof ↗⚑ flag

1. ai-native userPerform bulk operations across many items at once

   weight 2 · round to Bolna
   LiveKit Agents—none0/10

   The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is "none", never "na". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)

   ⚑ flag

   Bolna~partialclaimed5/10

   The CLI explicitly manages 'batches' of calls alongside agents and phone numbers (bolna-docs-16), and the platform claims to power 'thousands of inbound and outbound calls every minute' (bolna-docs-22), implying built-in bulk calling capability. However, there is no documentation of a specific bulk API endpoint, CSV/list upload, or bulk agent/category management workflow. Missing for 10: explicit bulk-operation API docs (e.g., batch call creation, bulk agent updates), a documented interface for uploading many items at once, and independent confirmation of bulk throughput.

   • [claimed-docs] “The Bolna CLI (bolna) is a single Go binary that lets you manage agents, calls, phone numbers, and batches directly from your terminal”
   • [claimed-docs] “Power thousands of inbound and outbound calls every minute with human-like, multilingual intelligence”
   • [claimed-docs] “Build multi-step outreach sequences that combine voice agent calls, WhatsApp messages, API calls, waits and retries, driven entirely through…”

   proof ↗⚑ flag

2. ai-native userDefine rules that trigger actions automatically on events

   weight 3 · round to LiveKit Agents
   LiveKit Agents~partialclaimed5/10

   LiveKit Agents supports event-driven automatic behaviors — tool-calling on LLM decisions, turn-detector triggering response timing, and adaptive interruption handling triggering barge-in logic — all of which are 'events auto-trigger actions' patterns built into the agent runtime. However, the evidence shows these as built-in framework behaviors and developer-coded event handlers rather than a user-facing declarative rule engine (e.g., no evidence of a 'when X happens do Y' config surface, webhook/rule subscription API, or no-code rule builder). Missing for 10: explicit rule-definition/webhook-trigger API exposed to non-developer users, documentation of a generalized event-subscription system beyond tool calls and turn logic, and independent confirmation of custom rule automation in production use.

   • [claimed-docs] “Call external APIs or lookup data for RAG.”
   • [claimed-docs] “LiveKit Agents has full support for LLM tool use. This feature allows you to create a custom library of tools to extend your agent's context”
   • [claimed-docs] “LiveKit Agents has full support for LLM tool use.”
   • [claimed-docs] “LiveKit's `TurnDetector` is an audio model that encodes user audio directly, capturing both _what_ is said and _how_ it's said.”
   • [claimed-docs] “A turn detector model can predict that they have more to say and wait for them to finish before responding.”
   • [claimed-docs] “Adaptive interruption handling allows an agent to respond naturally when users speak mid-response... to identify intentional interruptions (…”
   • [claimed-docs] “the model analyzes the acoustic signals to identify intentional interruptions (barge-ins) from conversational backchanneling”
   • [claimed-docs] “Adaptive interruption handling allows an agent to respond naturally when users speak mid-response.”

   proof ↗⚑ flag

   Bolna~partialclaimed4/10

   Bolna's workflow feature lets users chain voice calls, WhatsApp messages, API calls, waits and retries via the API, which is a form of automation, but the docs never describe defining conditional 'if event X happens then do Y' rules or an event/trigger system — it's presented as sequential outreach flows rather than rule-based event triggers. Missing for 10: explicit event-trigger/rule definition syntax, conditional branching logic, and any webhook-or-event-driven automation examples beyond the sequential workflow description.

   • [claimed-docs] “Build multi-step outreach sequences that combine voice agent calls, WhatsApp messages, API calls, waits and retries, driven entirely through…”

   proof ↗⚑ flag

3. ai-native userSchedule recurring jobs or workflows

   weight 2 · round drawn
   LiveKit Agents—none0/10

   The evidence pack covers LiveKit Agents' realtime voice/video agent capabilities, tool use, MCP integration, telephony, and testing, but contains no mention of any scheduling, cron-like recurring job, or workflow-automation trigger mechanism. This is a fair capability to ask about for an automation-focused agent framework, but no evidence supports it.

   ⚑ flag

   Bolna—none0/10

   Bolna's workflow docs describe multi-step outreach sequences with waits and retries (bolna-docs-12), but there is no evidence of a scheduler for recurring/cron-based jobs or workflows that re-trigger automatically over time. missing for 10: recurring/scheduled trigger mechanism, cron-like job definitions, evidence of periodic re-execution.

   • [claimed-docs] “Build multi-step outreach sequences that combine voice agent calls, WhatsApp messages, API calls, waits and retries, driven entirely through…”

   proof ↗⚑ flag

4. ai-native userVersion, review, and roll back my automations

   weight 1 · round to LiveKit Agents
   LiveKit Agents~partialclaimed4/10

   The pricing page explicitly claims 'Instant rollback to a previous agent deployment,' and the testing/simulation framework (behavioral tests, LLM-judge simulations) supports a review step before deployment, but there's no documented versioning system, changelog, or diff/history UI for automations. Missing for 10: explicit version history/diffing of agent configs, a documented review/approval workflow beyond test suites, and independent confirmation that rollback works in practice.

   • [claimed-docs] “Instant rollback to a previous agent deployment”
   • [claimed-docs] “Behavioral tests verify specific interactions and expected outcomes. They integrate with your existing test suite using pytest (Python) or V…”
   • [claimed-docs] “Agent simulations run end-to-end conversations between your agent and an LLM-driven user, then evaluate the results across the full interact…”
   • [github] “Builtin test framework: Write tests and use judges to ensure your agent is performing as expected.”

   proof ↗⚑ flag

   Bolna—none0/10

   Bolna is a voice AI agent platform (agent creation, calls, workflows, telephony) but the evidence pack shows no version control, review workflow, or rollback mechanism for automations/agents — no mention of agent versioning, change history, approval flows, or revert capability.

   ⚑ flag

Compliance

1. founderMeet call-recording consent and disclosure obligations with per-call recording controls and configurable data retention

   weight 2 · round drawn
   LiveKit Agents—none0/10

   No evidence in the pack mentions call recording, consent disclosure, or data retention controls anywhere in LiveKit Agents docs, GitHub, or community sources; the pack covers telephony, tool use, MCP, testing, and turn detection but nothing about recording/retention compliance features. missing for 10: recording consent/disclosure mechanisms, per-call recording toggles, configurable data retention policies, and any documentation or hands-on evidence of these compliance controls.

   ⚑ flag

   Bolna—none0/10

   The evidence pack shows security/data-residency features and call-summary toggles, but nothing about call-recording consent, per-call recording enable/disable controls, disclosure/announcement features, or configurable retention periods for recordings/transcripts. No mention of recording consent mechanisms or retention settings anywhere in the docs pack.

   ⚑ flag

2. platform-engineerRun regulated workloads with HIPAA/BAA support, SOC 2, and data-residency options

   weight 2 · round to Bolna
   LiveKit Agents—none0/10

   The evidence pack contains no mention of HIPAA/BAA agreements, SOC 2 certification, or data-residency controls anywhere in LiveKit's docs, GitHub repo, or probes; self-hosting and TURN/security items only cover TLS/SSL and self-hosted deployment, not compliance attestations. As a platform serving enterprise/regulated workloads, this axis clearly applies, so lack of evidence yields 'none'.

   • [claimed-docs] “The good news is LiveKit includes an embedded TURN server. It's a secure TURN implementation that has integrated authentication with the res…”
   • [claimed-docs] “In order to have a secure LiveKit deployment, you will need a domain as well as a SSL certificate for that domain.”

   proof ↗⚑ flag

   Bolna~partialclaimed3/10

   Bolna documents Indian data residency and self-hosting via Docker/Kubernetes for private enterprise deployments, but there is no mention of HIPAA/BAA support or SOC 2 certification anywhere in the evidence pack. missing for 10: HIPAA/BAA attestation, SOC 2 report or certification, broader multi-region data-residency options beyond India.

   • [claimed-docs] “Indian data residency is available for deployments where data must remain in India.”
   • [claimed-docs] “Learn how to self-host Bolna Voice AI on your own servers using Docker or Kubernetes for secure, private, and scalable enterprise deployment…”

   proof ↗⚑ flag

Scale

1. platform-engineerSee documented concurrency limits and scale to many simultaneous calls without manual capacity begging

   weight 2 · round drawn
   LiveKit Agents~partialcommunity3/10

   Docs mention running agents on LiveKit Cloud's global infrastructure and self-hosting with an embedded TURN server, implying scale-out capability, but no evidence pack item cites concrete documented concurrency limits, per-instance call caps, or autoscaling guarantees; a community report even flags lack of visibility into per-call cost/latency at scale. missing for 10: published concurrency/capacity numbers, autoscaling documentation, load-testing benchmarks, and confirmation that scaling requires no manual quota requests.

   • [claimed-docs] “Run your agent on LiveKit Cloud's global infrastructure.”
   • [claimed-docs] “The good news is LiveKit includes an embedded TURN server. It's a secure TURN implementation that has integrated authentication with the res…”
   • [community] “We've been running multiple voice AI agents on LiveKit and kept running into visibility issues — no way to measure TTFT, latency across STT …”

   proof ↗⚑ flag

   Bolna~partialclaimed3/10

   Bolna's marketing page claims it can 'power thousands of inbound and outbound calls every minute' and docs mention self-hosting via Docker/Kubernetes for 'scalable enterprise deployments,' but there are no documented concurrency limits, rate-limit tables, or self-serve capacity-scaling mechanisms (e.g., no API rate-limit docs, no autoscaling guide, no numeric SLA). missing for 10: documented numeric concurrency/rate limits, self-serve scale-up process, autoscaling architecture details, independent verification of claimed call volume.

   • [claimed-docs] “Power thousands of inbound and outbound calls every minute with human-like, multilingual intelligence”
   • [claimed-docs] “Learn how to self-host Bolna Voice AI on your own servers using Docker or Kubernetes for secure, private, and scalable enterprise deployment…”

   proof ↗⚑ flag

Self host

1. platform-engineerSelf-host the voice agent runtime from open-source code on my own infrastructure

   weight 3 · round to LiveKit Agents
   LiveKit Agents✓fullprobed9/10

   LiveKit Agents is open source, pip/npm installable and runs the agent runtime independent of any vendor account, and the underlying WebRTC/SIP server (livekit-server) is Apache-2.0 and self-hostable, confirmed via hands-on probes (booting `livekit-server --dev` locally with no keys, and installing livekit-agents/CLI keylessly) plus official self-hosting deployment docs covering TURN, TLS/domain setup. Missing for 10: no first-party production-scale self-hosting case study or independent report of large-scale self-hosted deployment beyond dev-mode probe.

   • [probe] “PROBE runtime (recorded 2026-09-05): `lk --version` printed `lk version 2.18.6` after a plain `brew install livekit-cli` — the official CLI …”
   • [probe] “PROBE runtime (recorded 2026-09-05): `livekit-server --dev` (brew-installed, Apache-2.0 OSS) booted on this machine with NO account or key —…”
   • [probe] “PROBE runtime (recorded 2026-09-05): pypi livekit-agents 1.8.0 installs and `import livekit.agents` succeeds with no key (npm @livekit/agent…”
   • [claimed-docs] “The good news is LiveKit includes an embedded TURN server. It's a secure TURN implementation that has integrated authentication with the res…”
   • [claimed-docs] “In order to have a secure LiveKit deployment, you will need a domain as well as a SSL certificate for that domain.”
   • [claimed-docs] “A programmatic participant is any code that joins a LiveKit room as a participant — this includes AI agents, media processors, or custom log…”

   proof ↗⚑ flag

   Bolna✓fullclaimed8/10

   Bolna is explicitly an open-source framework on GitHub, and docs include dedicated enterprise instructions for self-hosting on Docker or Kubernetes for private/enterprise deployments. Missing for 10: independent hands-on reports confirming successful self-host deployment and more detail on infrastructure requirements/limitations of the self-hosted version vs SaaS.

   • [github] “Bolna is the end-to-end open source production ready framework for quickly building LLM based voice driven conversational applications.”
   • [claimed-docs] “Learn how to self-host Bolna Voice AI on your own servers using Docker or Kubernetes for secure, private, and scalable enterprise deployment…”

   proof ↗⚑ flag

Latency

1. platform-engineerSee documented end-to-end voice latency numbers or tuning guidance backing the platform's speed claims

   weight 3 · round drawn
   LiveKit Agents—none0/10

   The evidence pack contains no documented end-to-end latency benchmarks or explicit tuning guidance for reducing turn-taking latency — only feature descriptions (turn detector, adaptive interruption handling) without numbers or configuration guidance. A community report explicitly states users could not measure TTFT or STT→LLM→TTS latency, reinforcing that this visibility/documentation is absent.

   • [claimed-docs] “Adaptive interruption handling allows an agent to respond naturally when users speak mid-response... to identify intentional interruptions (…”
   • [claimed-docs] “LiveKit's `TurnDetector` is an audio model that encodes user audio directly, capturing both _what_ is said and _how_ it's said.”
   • [claimed-docs] “A turn detector model can predict that they have more to say and wait for them to finish before responding.”
   • [claimed-docs] “the model analyzes the acoustic signals to identify intentional interruptions (barge-ins) from conversational backchanneling”
   • [community] “We've been running multiple voice AI agents on LiveKit and kept running into visibility issues — no way to measure TTFT, latency across STT …”

   proof ↗⚑ flag

   Bolna—none0/10

   No evidence pack item provides documented latency numbers, benchmarks, or tuning guidance for end-to-end voice latency; only vague marketing claims like 'build in under 5 minutes' or 'power thousands of calls' appear, with no actual latency figures or turn-taking tuning documentation.

   • [claimed-docs] “Build and deploy in under 5 minutes”
   • [claimed-docs] “Power thousands of inbound and outbound calls every minute with human-like, multilingual intelligence”

   proof ↗⚑ flag

Turn taking

1. developerRely on the agent to handle interruptions (barge-in) gracefully — stopping speech, updating context, and recovering the turn

   weight 3 · round to LiveKit Agents
   LiveKit Agents✓fullprobed8/10

   LiveKit Agents has well-documented, purpose-built features for barge-in: adaptive interruption handling distinguishes intentional interruptions from backchanneling, and the TurnDetector model analyzes acoustic and semantic signals to manage turn-taking recovery. These are first-party docs with specific technical detail on the mechanism (stopping speech, updating context, recovering turn), and the runtime probes confirm the framework is real and installable. Missing for 10: independent/hands-on evidence specifically testing barge-in behavior in production (only community evidence is about latency observability, not interruption handling itself) and no third-party benchmark of interruption accuracy.

   • [claimed-docs] “Adaptive interruption handling allows an agent to respond naturally when users speak mid-response... to identify intentional interruptions (…”
   • [claimed-docs] “the model analyzes the acoustic signals to identify intentional interruptions (barge-ins) from conversational backchanneling”
   • [claimed-docs] “LiveKit's `TurnDetector` is an audio model that encodes user audio directly, capturing both _what_ is said and _how_ it's said.”
   • [claimed-docs] “A turn detector model can predict that they have more to say and wait for them to finish before responding.”
   • [claimed-docs] “Adaptive interruption handling allows an agent to respond naturally when users speak mid-response.”
   • [claimed-docs] “LiveKit's TurnDetector is an audio model that encodes user audio directly, capturing both what is said and how it's said.”
   • [probe] “PROBE runtime (recorded 2026-09-05): pypi livekit-agents 1.8.0 installs and `import livekit.agents` succeeds with no key (npm @livekit/agent…”

   proof ↗⚑ flag

   Bolna—none0/10

   No evidence pack item mentions barge-in, interruption handling, speech-stop, or turn recovery mechanics; the docs cover extraction categories, summaries, providers, CLI, MCP, pricing, etc., but nothing about turn-taking or interruption behavior. missing for 10: any documentation of barge-in/interruption detection, mid-speech stop mechanics, context update on interruption, turn recovery logic.

   ⚑ flag

2. developerEnable noise suppression or audio filtering so the agent stays coherent on noisy real-world calls

   weight 1 · round drawn
   LiveKit Agents—none0/10

   The evidence pack covers turn detection, interruption handling, telephony, MCP tools, and testing, but contains no mention of noise suppression or audio filtering plugins/features anywhere. Missing for 10: any docs or plugin reference to noise cancellation (e.g. Krisp/BVC), background noise filtering, or audio pre-processing configuration.

   ⚑ flag

   Bolna—none0/10

   No evidence pack item mentions noise suppression, background noise handling, or audio filtering settings for calls; the docs cover extraction categories, summaries, telephony, SDKs, and pricing but not audio quality/noise robustness features. Missing for 10: any mention of noise suppression/audio filtering configuration, VAD tuning for noisy environments, or documented handling of real-world call noise.

   ⚑ flag

3. developerUse model-based end-of-turn detection beyond simple VAD silence timeouts so the agent doesn't talk over slow speakers

   weight 2 · round to LiveKit Agents
   LiveKit Agents✓fullclaimed9/10

   LiveKit's TurnDetector is a dedicated audio+semantic model (not just VAD silence) that predicts whether a user has finished speaking or has more to say, waiting accordingly, and adaptive interruption handling further distinguishes real barge-ins from backchanneling — directly addressing not talking over slow speakers. This is documented in detail across multiple first-party doc pages plus GitHub feature lists. missing for 10: independent/hands-on benchmark or community validation of the turn-detector's real-world latency/accuracy impact.

   • [claimed-docs] “LiveKit's `TurnDetector` is an audio model that encodes user audio directly, capturing both _what_ is said and _how_ it's said.”
   • [claimed-docs] “A turn detector model can predict that they have more to say and wait for them to finish before responding.”
   • [claimed-docs] “LiveKit's TurnDetector is an audio model that encodes user audio directly, capturing both what is said and how it's said.”
   • [claimed-docs] “Adaptive interruption handling allows an agent to respond naturally when users speak mid-response... to identify intentional interruptions (…”
   • [claimed-docs] “the model analyzes the acoustic signals to identify intentional interruptions (barge-ins) from conversational backchanneling”
   • [claimed-docs] “Adaptive interruption handling allows an agent to respond naturally when users speak mid-response.”

   proof ↗⚑ flag

   Bolna—none0/10

   No evidence describes model-based end-of-turn/turn-taking detection distinct from VAD silence timeouts; evidence only covers mid-call model switching, extraction categories, and unrelated features. Missing for 10: any mention of endpointing model, semantic turn detection, or configurable end-of-turn logic beyond silence thresholds.

   ⚑ flag

1. ai-native userDo everything through the API that I can do in the UI

   weight 2 · round to Bolna
   LiveKit Agents~partialprobed6/10

   LiveKit Agents is API/SDK-first (Python/Node.js) with a full programmatic surface for building agents, tools, telephony, and testing, and there is also a no-code 'Agent Builder' UI for prototyping. However, the evidence doesn't confirm that everything achievable in that browser-based Agent Builder UI (or LiveKit Cloud dashboard features like deployment management, rollback) is equally exposed via API/CLI, so parity between UI and API is not fully demonstrated. missing for 10: explicit evidence that Agent Builder's no-code UI actions (and Cloud dashboard deployment/rollback controls) are all reachable via API/CLI, and confirmation of full CLI/API parity with dashboard features.

   • [claimed-docs] “Prototype and deploy voice agents directly in your browser, without writing any code.”
   • [claimed-docs] “LiveKit Agent Builder to prototype and deploy agents directly in your browser without writing code”
   • [claimed-docs] “LiveKit Agent Builder: Prototype and deploy voice agents directly in your browser, without writing any code.”
   • [claimed-docs] “Instant rollback to a previous agent deployment”
   • [probe] “official CLI documented at https://github.com/livekit/livekit-cli”
   • [probe] “PROBE runtime (recorded 2026-09-05): `lk --version` printed `lk version 2.18.6` after a plain `brew install livekit-cli` — the official CLI …”
   • [claimed-docs] “A programmatic participant is any code that joins a LiveKit room as a participant — this includes AI agents, media processors, or custom log…”

   proof ↗⚑ flag

   Bolna~partialprobed7/10

   Docs show the platform is API-first — agent creation, extraction categories, call summaries, workflows, and telephony are all described as configurable via the API, and a CLI and MCP server both wrap that same API, implying broad parity. However, there is no explicit documentation stating full UI/API feature parity, and probes found no discoverable OpenAPI/swagger spec (404s), leaving some doubt about completeness of coverage for every UI action. Missing for 10: explicit UI-API parity statement, published OpenAPI/swagger spec, independent confirmation that all UI actions have API equivalents.

   • [claimed-docs] “POST /agent/{agent_id}/extraction-categories — create a category (name + model) and attach it to an agent”
   • [claimed-docs] “Call summaries are an agent-level toggle: call_summary_enabled (boolean, off by default) on the agent create and update APIs”
   • [claimed-docs] “Build multi-step outreach sequences that combine voice agent calls, WhatsApp messages, API calls, waits and retries, driven entirely through…”
   • [claimed-docs] “The Bolna CLI (bolna) is a single Go binary that lets you manage agents, calls, phone numbers, and batches directly from your terminal”
   • [claimed-docs] “Connect Claude, Cursor, Windsurf, or any MCP client to your Bolna account with the Bolna MCP server.”
   • [probe] “PROBE openapi: all candidate paths 404 (https://docs.bolna.ai/openapi.json, https://docs.bolna.ai/swagger.json, https://docs.bolna.ai/api/op…”
   • [probe] “official CLI documented at https://www.bolna.ai/docs/cli/introduction”

   proof ↗⚑ flag

2. ai-native userExport all of my data in open formats and leave

   weight 3 · round to Bolna
   LiveKit Agents—none0/10

   The evidence pack documents LiveKit Agents' open-source, self-hostable nature (Apache-2.0 server, pip/npm installable framework) but contains no explicit mention of data export tooling, open data formats for conversation/session logs, or a documented exit/migration path for user data. Self-hosting mitigates lock-in in principle, but that is not the same as a documented 'export all data' capability.

   • [probe] “PROBE runtime (recorded 2026-09-05): `livekit-server --dev` (brew-installed, Apache-2.0 OSS) booted on this machine with NO account or key —…”
   • [probe] “PROBE runtime (recorded 2026-09-05): pypi livekit-agents 1.8.0 installs and `import livekit.agents` succeeds with no key (npm @livekit/agent…”
   • [claimed-docs] “The good news is LiveKit includes an embedded TURN server. It's a secure TURN implementation that has integrated authentication with the res…”

   proof ↗⚑ flag

   Bolna~partialclaimed3/10

   Bolna's API lets you fetch call transcripts (bolna-docs-14) and the platform can be self-hosted via Docker/Kubernetes (bolna-docs-9), with an open-source core framework (bolna-gh-1), which together provide some data portability, but there is no documented bulk 'export all data' feature or guarantee of open, standard export formats covering call recordings, agent configs, analytics, etc. Missing for 10: an explicit full-data export tool/endpoint, documented open format specification for exported data, and independent confirmation that a user can fully migrate away with all their data.

   • [claimed-docs] “Make your first Voice AI call with the Bolna API — verify your key, get an agent, place a call, and fetch the transcript in about five minut…”
   • [claimed-docs] “Learn how to self-host Bolna Voice AI on your own servers using Docker or Kubernetes for secure, private, and scalable enterprise deployment…”
   • [github] “Bolna is the end-to-end open source production ready framework for quickly building LLM based voice driven conversational applications.”

   proof ↗⚑ flag

3. ai-native userRead the product's source under an open license

   weight 2 · round to LiveKit Agents
   LiveKit Agents✓fullprobed8/10

   LiveKit Agents is hosted at github.com/livekit/agents and documented as an open-source framework, with hands-on verification that the packages install and run without any account or key (pip/npm) and that the underlying server is Apache-2.0 OSS. The llms.txt docs also explicitly describe LiveKit as 'an open-source platform.' Missing for 10: an explicit license file/badge citation specifically for the livekit/agents repo (only inferred via probe evidence and sibling repo license).

   • [github] “MCP support: Native support for MCP. Integrate tools provided by MCP servers with one line of code.”
   • [probe] “PROBE llms.txt: HTTP 200 at https://docs.livekit.io/llms.txt # LiveKit docs > LiveKit is an open-source platform for building voice, video,…”
   • [probe] “PROBE runtime (recorded 2026-09-05): `livekit-server --dev` (brew-installed, Apache-2.0 OSS) booted on this machine with NO account or key —…”
   • [probe] “PROBE runtime (recorded 2026-09-05): pypi livekit-agents 1.8.0 installs and `import livekit.agents` succeeds with no key (npm @livekit/agent…”
   • [probe] “official CLI documented at https://github.com/livekit/livekit-cli”

   proof ↗⚑ flag

   Bolna~partialclaimed5/10

   The GitHub repo description explicitly calls Bolna 'the end-to-end open source production ready framework,' supporting that its source is publicly available under an open-license framing, but no specific license (e.g., MIT/Apache) is named or verified, and no independent corroboration exists. Missing for 10: explicit license identification (e.g., LICENSE file contents), confirmation the entire product (not just the framework) is open-sourced, and independent verification.

   • [github] “Bolna is the end-to-end open source production ready framework for quickly building LLM based voice driven conversational applications.”

   proof ↗⚑ flag

4. ai-native userSelf-host the core product

   weight 3 · round to LiveKit Agents
   LiveKit Agents✓fullprobed9/10

   LiveKit provides a documented, Apache-2.0 open-source self-hosting deployment guide including embedded TURN server and SSL setup, and this was hands-on verified: `livekit-server --dev` boots with no account or key, and the open-source livekit-agents framework installs and imports keylessly via pip/npm. This directly demonstrates the core product (server + agents framework) can be self-hosted. Missing for 10: independent third-party production self-hosting case study beyond the probe verification.

   • [claimed-docs] “The good news is LiveKit includes an embedded TURN server. It's a secure TURN implementation that has integrated authentication with the res…”
   • [claimed-docs] “In order to have a secure LiveKit deployment, you will need a domain as well as a SSL certificate for that domain.”
   • [probe] “PROBE runtime (recorded 2026-09-05): `livekit-server --dev` (brew-installed, Apache-2.0 OSS) booted on this machine with NO account or key —…”
   • [probe] “PROBE runtime (recorded 2026-09-05): pypi livekit-agents 1.8.0 installs and `import livekit.agents` succeeds with no key (npm @livekit/agent…”
   • [probe] “PROBE runtime (recorded 2026-09-05): `lk --version` printed `lk version 2.18.6` after a plain `brew install livekit-cli` — the official CLI …”

   proof ↗⚑ flag

   Bolna✓fullclaimed8/10

   Bolna is explicitly open source (GitHub repo) and documentation provides dedicated on-premise instructions for self-hosting via Docker or Kubernetes for enterprise deployments, directly satisfying the self-host story. Missing for 10: independent/hands-on confirmation of a successful self-hosted deployment and more detail on feature parity between self-hosted and cloud versions.

   • [github] “Bolna is the end-to-end open source production ready framework for quickly building LLM based voice driven conversational applications.”
   • [claimed-docs] “Learn how to self-host Bolna Voice AI on your own servers using Docker or Kubernetes for secure, private, and scalable enterprise deployment…”

   proof ↗⚑ flag

Pricing

1. founderSee published per-minute or usage pricing and estimate cost per call before committing

   weight 2 · round to Bolna
   LiveKit Agents—none0/10

   The pack references a pricing URL (livekit.io/pricing) only for an unrelated 'rollback' feature and never quotes actual per-minute or usage rates; a community comment even highlights that operators struggled with 'cost per call' visibility, but no evidence shows published pricing figures a founder could use to estimate costs before committing.

   • [claimed-docs] “Instant rollback to a previous agent deployment”
   • [community] “We've been running multiple voice AI agents on LiveKit and kept running into visibility issues — no way to measure TTFT, latency across STT …”

   proof ↗⚑ flag

   Bolna~partialclaimed4/10

   There is a dedicated call-pricing doc page and mentions that BYOK/connected providers aren't charged (bolna-docs-19, bolna-docs-10), indicating some usage-based pricing structure exists, but no evidence pack item actually quotes a per-minute rate, tiered pricing table, or a cost calculator a founder could use to estimate cost per call. Missing for 10: explicit per-minute/usage rate figures, a pricing calculator or worked cost example, and independent confirmation that published rates are accurate.

   • [claimed-docs] “When you bring your own keys (BYOK), Bolna does not charge for those components.”
   • [claimed-docs] “We don't charge for any usage for providers that you have connected to Bolna.”

   proof ↗⚑ flag

1. ai-native userChoose where my data is stored (region/residency)

   weight 2 · round to Bolna
   LiveKit Agents~partialprobed5/10

   LiveKit Agents/server is open-source and self-hostable (embedded TURN server, deployment docs, hands-on verified self-host boot), which lets an operator choose their own hosting region/data location, but there is no explicit documented region-selection or data-residency feature for LiveKit Cloud itself. missing for 10: explicit LiveKit Cloud region-selection/data-residency controls, compliance certifications (e.g. GDPR/SOC2 region guarantees), and any customer-facing UI for choosing storage region.

   • [claimed-docs] “The good news is LiveKit includes an embedded TURN server. It's a secure TURN implementation that has integrated authentication with the res…”
   • [claimed-docs] “In order to have a secure LiveKit deployment, you will need a domain as well as a SSL certificate for that domain.”
   • [probe] “PROBE runtime (recorded 2026-09-05): `livekit-server --dev` (brew-installed, Apache-2.0 OSS) booted on this machine with NO account or key —…”

   proof ↗⚑ flag

   Bolna~partialclaimed6/10

   Docs explicitly mention Indian data residency as an available option, and self-hosting via Docker/Kubernetes lets enterprises control where data lives entirely. However, there's no broader multi-region selection (e.g., EU, US) documented beyond India, and no detail on how residency is configured or enforced. Missing for 10: documented multi-region choice beyond India, configuration/UI for selecting region, and independent confirmation of residency enforcement.

   • [claimed-docs] “Indian data residency is available for deployments where data must remain in India.”
   • [claimed-docs] “Learn how to self-host Bolna Voice AI on your own servers using Docker or Kubernetes for secure, private, and scalable enterprise deployment…”

   proof ↗⚑ flag

2. ai-native userPrevent my data from being used to train AI models

   weight 3 · round drawn
   LiveKit Agents—none0/10

   No evidence in the pack addresses data usage for AI model training, opt-out policies, or any privacy commitments regarding training data; the evidence pack is entirely about agent framework features (tools, telephony, testing, MCP support). This is a plausible axis for a platform handling user voice/media data, but absent any documentation on training-data usage or opt-out, it cannot be credited as delivered.

   ⚑ flag

   Bolna—none0/10

   No evidence pack item addresses a training-data opt-out or policy on AI model training use; security docs cover data residency and IP whitelisting only, not training-data usage controls. missing for 10: any explicit statement on AI training data usage/opt-out policy, data processing agreement terms, or contractual guarantee against training use.

   ⚑ flag

3. ai-native userControl data retention and deletion

   weight 2 · round drawn
   LiveKit Agents—none0/10

   The evidence pack contains no mention of data retention policies, recording/session deletion controls, or configurable retention windows for LiveKit Agents. While self-hosting is documented (implying infrastructure control), no explicit retention/deletion feature or API is evidenced.

   ⚑ flag

   Bolna—none0/10

   No evidence of data retention controls, deletion APIs, or user-facing privacy/data-lifecycle settings; only tangential mentions of security (IP whitelisting, India data residency) which do not address retention/deletion. Self-hosting option exists but that's deployment control, not a retention/deletion feature.

   • [claimed-docs] “Indian data residency is available for deployments where data must remain in India.”
   • [claimed-docs] “Whitelist all three IPs on your server or firewall”
   • [claimed-docs] “Learn how to self-host Bolna Voice AI on your own servers using Docker or Kubernetes for secure, private, and scalable enterprise deployment…”

   proof ↗⚑ flag

4. ai-native userOpt out of telemetry and usage tracking

   weight 2 · round drawn
   LiveKit Agents—none0/10

   No evidence pack item discusses telemetry, usage tracking, analytics collection, or an opt-out mechanism for LiveKit Agents; the framework being open-source and self-hostable is not sufficient evidence of an explicit telemetry opt-out control.

   ⚑ flag

   Bolna—none0/10

   No evidence pack item addresses telemetry, analytics opt-out, or usage-tracking controls for the platform itself; existing security/data-residency docs don't cover this. Missing for 10: any documentation of telemetry collection or an opt-out/disable mechanism, privacy policy statement on usage tracking, self-host confirmation of no phone-home behavior.

   ⚑ flag

Call control

1. developerEscalate a live call to a human with warm or blind transfer, passing context along

   weight 2 · round drawn
   LiveKit Agents—none0/10

   LiveKit Agents documents inbound/outbound telephony (SIP trunks, phone calls) but the evidence pack contains no mention of call transfer (warm or blind) or passing conversational context to a human agent during a live call — this specific escalation capability is never described.

   • [claimed-docs] “LiveKit telephony lets you build AI-powered voice apps that handle inbound and outbound calls.”
   • [claimed-docs] “Enable your voice agent to make or take phone calls.”
   • [claimed-docs] “Outbound trunks are used to place outgoing calls.”
   • [github] “Works seamlessly with LiveKit's telephony stack, allowing your agent to make calls to or receive calls from phones.”

   proof ↗⚑ flag

   Bolna—none0/10

   No evidence in the pack describes call transfer functionality (warm or blind) to a human agent, or context handoff during a live call; the closest items relate to model switching, summaries, and telephony/SIP trunking but not transfer escalation.

   ⚑ flag

2. developerMy agent can send DTMF keypresses, navigate IVR menus, and detect or leave voicemail

   weight 1 · round drawn
   LiveKit Agents—none0/10

   Evidence confirms LiveKit's telephony/SIP stack supports inbound/outbound calls (docs-26, docs-31, docs-32, gh-2), but nothing in the pack mentions DTMF keypress sending, IVR menu navigation, or voicemail detection/leaving capabilities specifically. Missing for 10: any documentation or example of DTMF tone generation, automated IVR menu traversal, or voicemail detection/answering-machine-detection logic.

   • [claimed-docs] “LiveKit telephony lets you build AI-powered voice apps that handle inbound and outbound calls.”
   • [claimed-docs] “Enable your voice agent to make or take phone calls.”
   • [claimed-docs] “Outbound trunks are used to place outgoing calls.”
   • [github] “Works seamlessly with LiveKit's telephony stack, allowing your agent to make calls to or receive calls from phones.”

   proof ↗⚑ flag

   Bolna—none0/10

   No evidence in the pack mentions DTMF keypress sending, IVR menu navigation, or voicemail detection/leaving; only unrelated telephony features like SIP trunking, call summaries, and workflows are documented.

   ⚑ flag

Campaigns

1. founderRun batch outbound call campaigns with scheduling and throughput controls

   weight 2 · round to Bolna
   LiveKit Agents—none0/10

   Evidence shows LiveKit Agents can place individual outbound calls via SIP outbound trunks (docs-26, docs-32, docs-31), but there is no evidence of any batch campaign management, call scheduling, or throughput/rate-limiting controls for running outbound calling campaigns at scale — these features are simply not documented anywhere in the pack.

   • [claimed-docs] “LiveKit telephony lets you build AI-powered voice apps that handle inbound and outbound calls.”
   • [claimed-docs] “Outbound trunks are used to place outgoing calls.”
   • [claimed-docs] “Enable your voice agent to make or take phone calls.”
   • [github] “Works seamlessly with LiveKit's telephony stack, allowing your agent to make calls to or receive calls from phones.”

   proof ↗⚑ flag

   Bolna~partialprobed5/10

   The CLI explicitly manages 'batches' of calls alongside agents/phone numbers, and marketing copy claims support for 'thousands of inbound and outbound calls every minute,' plus a workflows API for multi-step outreach with waits/retries — together implying batch campaign and throughput capability. However, there's no explicit documentation of scheduling controls (e.g., time windows, campaign start/stop) or throughput/rate-limiting configuration for outbound campaigns specifically. Missing for 10: explicit scheduling/calendar controls for campaigns, documented throughput/rate-limit configuration knobs, and any hands-on/independent evidence of batch campaign execution.

   • [claimed-docs] “The Bolna CLI (bolna) is a single Go binary that lets you manage agents, calls, phone numbers, and batches directly from your terminal”
   • [claimed-docs] “Power thousands of inbound and outbound calls every minute with human-like, multilingual intelligence”
   • [claimed-docs] “Build multi-step outreach sequences that combine voice agent calls, WhatsApp messages, API calls, waits and retries, driven entirely through…”
   • [probe] “official CLI documented at https://www.bolna.ai/docs/cli/introduction”

   proof ↗⚑ flag

Numbers

1. developerProvision phone numbers and run both inbound and outbound calls through the platform's API

   weight 3 · round drawn
   LiveKit Agents~partialclaimed6/10

   LiveKit's SIP/telephony stack clearly supports inbound and outbound calling ('LiveKit telephony lets you build AI-powered voice apps that handle inbound and outbound calls', 'Outbound trunks are used to place outgoing calls', 'Enable your voice agent to make or take phone calls') and integrates with the agents runtime via SIP trunks. However, the evidence never shows LiveKit itself provisioning phone numbers through its own API — telephony typically requires configuring an external SIP trunk provider, and no docs here describe a native number-provisioning endpoint. Missing for 10: explicit documentation/evidence of a LiveKit API call that provisions/purchases phone numbers directly (rather than just configuring trunks against externally-acquired numbers), and independent/hands-on confirmation of the inbound+outbound call flow.

   • [claimed-docs] “LiveKit telephony lets you build AI-powered voice apps that handle inbound and outbound calls.”
   • [claimed-docs] “Enable your voice agent to make or take phone calls.”
   • [claimed-docs] “Outbound trunks are used to place outgoing calls.”
   • [github] “Works seamlessly with LiveKit's telephony stack, allowing your agent to make calls to or receive calls from phones.”

   proof ↗⚑ flag

   Bolna~partialprobed6/10

   Bolna's API/CLI clearly support placing outbound calls and receiving inbound calls (quickstart shows placing a call and fetching transcript via API, CLI manages calls and phone numbers, marketing claims thousands of inbound/outbound calls per minute), and BYOT lets you attach SIP trunks/numbers. However there's no explicit documented endpoint for provisioning/purchasing new phone numbers through the API itself — only CLI mention of managing 'phone numbers' and bringing your own trunk. Missing for 10: explicit phone-number provisioning API endpoint/documentation, independent confirmation of end-to-end inbound+outbound flow.

   • [claimed-docs] “Make your first Voice AI call with the Bolna API — verify your key, get an agent, place a call, and fetch the transcript in about five minut…”
   • [claimed-docs] “The Bolna CLI (bolna) is a single Go binary that lets you manage agents, calls, phone numbers, and batches directly from your terminal”
   • [claimed-docs] “Bring Your Own Telephony (BYOT) lets you connect any standards-compliant SIP trunk to the Bolna platform.”
   • [claimed-docs] “Power thousands of inbound and outbound calls every minute with human-like, multilingual intelligence”
   • [probe] “official CLI documented at https://www.bolna.ai/docs/cli/introduction”

   proof ↗⚑ flag

Sip

1. platform-engineerConnect my own carrier or PBX via SIP trunking (or import Twilio/Telnyx numbers) instead of being locked to bundled telephony

   weight 2 · round to Bolna
   LiveKit Agents✓fullclaimed7/10

   LiveKit's SIP-based telephony stack lets agents make/receive calls via inbound and outbound SIP trunks, which is the standard mechanism for bringing your own carrier or PBX (docs-26, docs-32, gh-2, docs-31). This is architecture-agnostic SIP, not a bundled/proprietary telephony lock-in, satisfying the platform-engineer's need to connect external trunks. Missing for 10: explicit documented walkthroughs or examples of importing Twilio/Telnyx numbers specifically, and no independent/hands-on confirmation of a real carrier trunk connection succeeding.

   • [claimed-docs] “LiveKit telephony lets you build AI-powered voice apps that handle inbound and outbound calls.”
   • [claimed-docs] “Outbound trunks are used to place outgoing calls.”
   • [github] “Works seamlessly with LiveKit's telephony stack, allowing your agent to make calls to or receive calls from phones.”
   • [claimed-docs] “Enable your voice agent to make or take phone calls.”

   proof ↗⚑ flag

   Bolna✓fullclaimed8/10

   Bolna explicitly documents 'Bring Your Own Telephony (BYOT)' allowing any standards-compliant SIP trunk to connect to the platform, plus IP whitelisting requirements for the connection, directly addressing carrier/PBX independence from bundled telephony. missing for 10: explicit step-by-step Twilio/Telnyx number import walkthrough and independent/hands-on verification of SIP trunk setup working end-to-end.

   • [claimed-docs] “Bring Your Own Telephony (BYOT) lets you connect any standards-compliant SIP trunk to the Bolna platform.”
   • [claimed-docs] “Whitelist all three IPs on your server or firewall”

   proof ↗⚑ flag

Analytics

1. ai-native userThe platform's AI reviews my calls for me — scoring quality, flagging failures, and analyzing resolution automatically

   weight 2 · round to Bolna
   LiveKit Agents!disputedcontradicted3/10

   LiveKit ships a dev-time testing framework with 'judges' and agent simulations to evaluate scripted interactions (docs-9/10/25, gh-4), but this is pre-deployment test tooling, not automatic scoring/flagging/resolution-analysis of live production calls. A hands-on community report explicitly says operators running real agents on LiveKit have no built-in way to measure latency or cost per call, let alone automated quality/resolution review, contradicting the idea that the platform reviews calls for you. Missing for 10: production call-level QA scoring, automatic failure flagging on real traffic, resolution/outcome analysis dashboards.

   • [claimed-docs] “Behavioral tests verify specific interactions and expected outcomes. They integrate with your existing test suite using pytest (Python) or V…”
   • [claimed-docs] “Agent simulations run end-to-end conversations between your agent and an LLM-driven user, then evaluate the results across the full interact…”
   • [github] “Builtin test framework: Write tests and use judges to ensure your agent is performing as expected.”
   • [community] “We've been running multiple voice AI agents on LiveKit and kept running into visibility issues — no way to measure TTFT, latency across STT …”

   proof ↗⚑ flag

   Bolna~partialclaimed3/10

   Bolna offers post-call analysis primitives — configurable extraction-categories (bolna-docs-1) and toggleable call summaries (bolna-docs-2) — which could be used to build custom scoring/flagging, but there's no documented automated QA/quality-scoring, failure-flagging, or resolution-analysis feature out of the box. Missing for 10: dedicated call-scoring/QA dashboard, automatic failure detection, resolution-analysis reporting, and independent evidence these extraction fields are used for review purposes.

   • [claimed-docs] “POST /agent/{agent_id}/extraction-categories — create a category (name + model) and attach it to an agent”
   • [claimed-docs] “Call summaries are an agent-level toggle: call_summary_enabled (boolean, off by default) on the agent create and update APIs”

   proof ↗⚑ flag

2. founderSee call analytics — success rates, durations, outcomes, sentiment — in dashboards without building my own

   weight 2 · round drawn
   LiveKit Agents—none0/10

   The evidence pack shows testing/simulation tools (pytest/Vitest, LLM-driven simulations) but no built-in production analytics dashboard for success rates, durations, outcomes, or sentiment. A community report explicitly confirms this gap — users running LiveKit voice agents said they had 'no way to measure TTFT, latency across STT→LLM→TTS, or even cost per call' and found debugging painful, indicating no such dashboard exists out of the box.

   • [community] “We've been running multiple voice AI agents on LiveKit and kept running into visibility issues — no way to measure TTFT, latency across STT …”
   • [claimed-docs] “Behavioral tests verify specific interactions and expected outcomes. They integrate with your existing test suite using pytest (Python) or V…”
   • [claimed-docs] “Agent simulations run end-to-end conversations between your agent and an LLM-driven user, then evaluate the results across the full interact…”
   • [claimed-docs] “Behavioral tests verify specific interactions and expected outcomes... Agent simulations run end-to-end conversations between your agent and…”

   proof ↗⚑ flag

   Bolna—none0/10

   Evidence only shows API-level features like call summaries (call_summary_enabled) and extraction categories, but there is no mention of a dashboard UI presenting success rates, durrations, outcomes, or sentiment analytics — missing for 10: any dashboard/UI screenshot or docs describing aggregated call analytics views.

   • [claimed-docs] “POST /agent/{agent_id}/extraction-categories — create a category (name + model) and attach it to an agent”
   • [claimed-docs] “Call summaries are an agent-level toggle: call_summary_enabled (boolean, off by default) on the agent create and update APIs”

   proof ↗⚑ flag

Monitoring

1. platform-engineerMonitor live calls in production and get alerts when agents misbehave or error rates spike

   weight 1 · round drawn
   LiveKit Agents—none0/10

   No evidence pack items describe production monitoring dashboards, error-rate alerting, or call-quality observability tooling for LiveKit Agents; the only related evidence is a community report explicitly describing the lack of visibility into TTFT, per-call latency, and cost — a gap, not a delivered capability.

   • [community] “We've been running multiple voice AI agents on LiveKit and kept running into visibility issues — no way to measure TTFT, latency across STT …”

   proof ↗⚑ flag

   Bolna—none0/10

   Evidence covers call summaries, extraction categories, transcripts, CLI, MCP, and SIP/telephony features, but nothing describes live-call monitoring dashboards, real-time observability, or alerting on agent misbehavior or error-rate spikes. No mention of metrics, alert thresholds, webhooks for anomaly detection, or a monitoring console for platform engineers. Missing for 10: real-time monitoring dashboard, alerting/notification system for errors or misbehavior, error-rate tracking, on-call/integration with observability tools.

   ⚑ flag

Testing

1. developerTest agents with simulated conversations or evals before putting them on real phone calls

   weight 2 · round to LiveKit Agents
   LiveKit Agents✓fullclaimed8/10

   Docs explicitly describe both behavioral tests (pytest/Vitest) and agent simulations that run end-to-end conversations with an LLM-driven user, evaluating results before deployment, and GitHub notes a 'builtin test framework' with judges — directly matching the testing-before-real-calls story, complemented by separate telephony/SIP support for real calls. Missing for 10: independent/hands-on validation of the eval/simulation framework and explicit documentation tying test simulations to pre-phone-call validation workflows.

   • [claimed-docs] “Behavioral tests verify specific interactions and expected outcomes. They integrate with your existing test suite using pytest (Python) or V…”
   • [claimed-docs] “Agent simulations run end-to-end conversations between your agent and an LLM-driven user, then evaluate the results across the full interact…”
   • [claimed-docs] “Behavioral tests verify specific interactions and expected outcomes... Agent simulations run end-to-end conversations between your agent and…”
   • [claimed-docs] “Behavioral tests verify specific interactions and expected outcomes... using pytest (Python) or Vitest (Node.js)”
   • [github] “Builtin test framework: Write tests and use judges to ensure your agent is performing as expected.”
   • [github] “Works seamlessly with LiveKit's telephony stack, allowing your agent to make calls to or receive calls from phones.”
   • [claimed-docs] “LiveKit telephony lets you build AI-powered voice apps that handle inbound and outbound calls.”

   proof ↗⚑ flag

   Bolna—none0/10

   No evidence of any simulated conversation testing, evals, or pre-production testing framework for agents; the evidence pack only covers deployment, telephony, providers, pricing, and SDKs. Missing for 10: any mention of test/eval tooling, simulated call testing, or conversation quality scoring before live calls.

   ⚑ flag

Post call

1. developerExtract structured data from every call — outcomes, entities, dispositions — delivered via API or webhook after the call

   weight 2 · round to Bolna
   LiveKit Agents—none0/10

   LiveKit Agents provides tool-calling, MCP integration, and telephony hooks, but nothing in the evidence describes a built-in mechanism for automatically extracting structured call outcomes/entities/dispositions and delivering them via API or webhook after a call ends — that would have to be custom-built by the developer using the tool-calling primitives. Since no such capability is documented, this applicable axis is unmet.

   • [claimed-docs] “Call external APIs or lookup data for RAG.”
   • [claimed-docs] “LiveKit Agents has full support for LLM tool use. This feature allows you to create a custom library of tools to extend your agent's context”
   • [claimed-docs] “LiveKit telephony lets you build AI-powered voice apps that handle inbound and outbound calls.”
   • [claimed-docs] “Outbound trunks are used to place outgoing calls.”

   proof ↗⚑ flag

   Bolna~partialclaimed6/10

   Bolna provides extraction-categories API to attach custom entity/data extraction to agents and a call_summary_enabled toggle for summaries, plus transcript fetch via API — covering entities, summaries, and outcomes. However, there's no explicit documentation of a unified structured 'disposition' field, no clear webhook delivery mechanism for post-call structured payloads (only API fetch is shown), and no example of the combined extraction+summary+disposition JSON schema. missing for 10: webhook-based delivery of extraction results, documented disposition/outcome field, end-to-end example combining all three data types in one payload.

   • [claimed-docs] “POST /agent/{agent_id}/extraction-categories — create a category (name + model) and attach it to an agent”
   • [claimed-docs] “Call summaries are an agent-level toggle: call_summary_enabled (boolean, off by default) on the agent create and update APIs”
   • [claimed-docs] “Make your first Voice AI call with the Bolna API — verify your key, get an agent, place a call, and fetch the transcript in about five minut…”

   proof ↗⚑ flag

Tools

1. ai-native userMy voice agent can plug in MCP servers as tool sources so one integration grants it whole toolsets mid-call

   weight 2 · round to LiveKit Agents
   LiveKit Agents✓fullclaimed9/10

   LiveKit Agents has first-class, documented MCP support ("first-class support for Model Context Protocol (MCP) servers", "Integrate tools provided by MCP servers with one line of code") via `MCPToolset`, which wraps an MCP server and passes it directly to the agent's tools parameter — enabling mid-call toolset access for voice agents. This is corroborated across both docs and GitHub README. Missing for 10: independent hands-on validation of actual mid-call MCP toolset usage beyond vendor docs.

   • [claimed-docs] “Wrap an MCP server in an \`MCPToolset\` and pass it to the agent's \`tools\` parameter”
   • [claimed-docs] “Wrap an MCP server in an `MCPToolset` and pass it to the agent's `tools` parameter”
   • [claimed-docs] “LiveKit Agents has first-class support for Model Context Protocol (MCP) servers.”
   • [github] “MCP support: Native support for MCP. Integrate tools provided by MCP servers with one line of code.”
   • [github] “Native support for MCP. Integrate tools provided by MCP servers with one line of code.”

   proof ↗⚑ flag

   Bolna—none0/10

   Evidence only shows Bolna exposing its OWN MCP server so external clients (Claude, Cursor, Windsurf) can manage a Bolna account (bolna-docs-17, bolna-probe-3) — the reverse direction of the story, which asks whether a Bolna voice agent can consume external MCP servers as tool sources mid-call. No documentation shows Bolna agents acting as an MCP client or importing toolsets from third-party MCP servers during a call. missing for 10: any evidence of Bolna voice agents connecting to external MCP servers as tool sources, mid-call toolset loading via MCP.

   • [claimed-docs] “Connect Claude, Cursor, Windsurf, or any MCP client to your Bolna account with the Bolna MCP server.”
   • [probe] “official MCP server documented at https://www.bolna.ai/docs/build-with-ai/mcp”

   proof ↗⚑ flag

2. developerMy agent can call external APIs and custom functions mid-conversation and speak the result without awkward dead air

   weight 3 · round to LiveKit Agents
   LiveKit Agents✓fullprobed9/10

   LiveKit Agents has full documented support for LLM tool/function calling (custom Python/Node functions calling external APIs mid-conversation) plus MCP server tool integration, combined with adaptive interruption handling and turn-detection to avoid awkward dead air while tools execute. Runtime probes confirm the framework is real and installable, corroborating the docs claims. missing for 10: independent/hands-on evidence specifically showing tool-call latency handled gracefully in a live conversation (only vendor docs cover this exact combination).

   • [claimed-docs] “Call external APIs or lookup data for RAG.”
   • [claimed-docs] “LiveKit Agents has full support for LLM tool use. This feature allows you to create a custom library of tools to extend your agent's context”
   • [claimed-docs] “LiveKit Agents has full support for LLM tool use.”
   • [claimed-docs] “LiveKit Agents has first-class support for Model Context Protocol (MCP) servers.”
   • [claimed-docs] “Wrap an MCP server in an \`MCPToolset\` and pass it to the agent's \`tools\` parameter”
   • [claimed-docs] “Adaptive interruption handling allows an agent to respond naturally when users speak mid-response... to identify intentional interruptions (…”
   • [claimed-docs] “the model analyzes the acoustic signals to identify intentional interruptions (barge-ins) from conversational backchanneling”
   • [probe] “PROBE runtime (recorded 2026-09-05): pypi livekit-agents 1.8.0 installs and `import livekit.agents` succeeds with no key (npm @livekit/agent…”

   proof ↗⚑ flag

   Bolna—none0/10

   The evidence pack covers extraction categories, call summaries, workflows, and custom LLM providers, but nothing describes an agent invoking external APIs or custom functions mid-call and voicing the result seamlessly. Missing for 10: documentation of a function-calling/tool-use API, examples of mid-call API invocation, and any mention of latency-masking/filler techniques to avoid dead air.

   ⚑ flag

Recording

1. platform-engineerRetrieve full call recordings and transcripts programmatically for every call

   weight 2 · round to Bolna
   LiveKit Agents—none0/10

   The evidence pack covers agent building, tool use, MCP, telephony, and testing but contains no mention of recording APIs, transcript storage, or programmatic retrieval of call recordings/transcripts for platform engineers.

   ⚑ flag

   Bolna~partialclaimed5/10

   Docs confirm fetching call transcripts via API (quickstart mentions 'fetch the transcript') and call summaries can be toggled, but there is no explicit documentation of a dedicated recordings-retrieval endpoint or bulk/programmatic export for all calls. missing for 10: explicit call-recording download/API endpoint, confirmation that recordings are retained and accessible for every call, and independent verification of transcript completeness/format.

   • [claimed-docs] “Make your first Voice AI call with the Bolna API — verify your key, get an agent, place a call, and fetch the transcript in about five minut…”
   • [claimed-docs] “Call summaries are an agent-level toggle: call_summary_enabled (boolean, off by default) on the agent create and update APIs”
   • [claimed-docs] “POST /agent/{agent_id}/extraction-categories — create a category (name + model) and attach it to an agent”

   proof ↗⚑ flag

Transcription

1. developerGet accurate real-time transcription with control over the STT provider, language models, or key terms

   weight 2 · round to LiveKit Agents
   LiveKit Agents~partialclaimed6/10

   LiveKit Agents supports swapping STT/LLM providers via its open-source plugin ecosystem and lets you override API key/base URL for any provider (docs-12, docs-24, docs-35), giving real control over transcription and language-model choice. However there is no evidence of support for custom vocabulary/key-term or hotword boosting in STT, and no independent benchmark of transcription accuracy. Missing for 10: key-term/hotword/phrase-hint configuration support, accuracy benchmarks or independent corroboration of transcription quality.

   • [claimed-docs] “LiveKit Agents includes a large ecosystem of open source plugins for a variety of AI providers.”
   • [claimed-docs] “You can use LiveKit Inference to access many of these models directly through LiveKit Cloud, or you can use the open source plugins to conne…”
   • [claimed-docs] “For any provider not included, you can override the API key and base URL at initialization for the LLM, STT, and TTS interfaces in the plugi…”

   proof ↗⚑ flag

   Bolna~partialclaimed5/10

   Docs confirm STT provider choice (Deepgram, Azure, ElevenLabs) and full LLM control including custom OpenAI-compatible endpoints, plus transcript retrieval via API, giving developers real provider/model flexibility. However there is no evidence of key-term/vocabulary boosting or accuracy tuning controls, nor any real-time transcription accuracy benchmarks. Missing for 10: key-term/keyword-boost configuration, accuracy/latency benchmarks for real-time transcription, and independent validation of transcription quality.

   • [claimed-docs] “Bolna supports Deepgram, Azure, ElevenLabs, and more.”
   • [claimed-docs] “For custom llm simply keep provider in the llm_agent key as custom and add a openai compatible base_url”
   • [claimed-docs] “Make your first Voice AI call with the Bolna API — verify your key, get an agent, place a call, and fetch the transcript in about five minut…”

   proof ↗⚑ flag

Voices

1. founderClone a custom brand voice and use it for my agents, with a documented consent process

   weight 2 · round drawn
   LiveKit Agents—none0/10

   The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is "none", never "na". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)

   ⚑ flag

   Bolna—none0/10

   Evidence shows Bolna lets founders choose among third-party voices/accents/styles (bolna-docs-8) and integrates providers like ElevenLabs (bolna-docs-6), but there is no mention of a voice-cloning feature for a custom brand voice, nor any documented consent process for cloning. Missing for 10: voice cloning capability, consent/verification workflow documentation, any policy on brand voice usage.

   • [claimed-docs] “You can choose different voices, accents, and speaking styles to match your brand.”
   • [claimed-docs] “Bolna supports Deepgram, Azure, ElevenLabs, and more.”

   proof ↗⚑ flag

2. developerChoose from a broad voice library or plug in multiple TTS providers to get the voice I want

   weight 2 · round drawn
   LiveKit Agents~partialclaimed6/10

   Docs confirm a large open-source plugin ecosystem for TTS/STT/LLM providers, plus LiveKit Inference for provider access, and an override mechanism for API key/base URL for providers not natively included, supporting multi-provider TTS flexibility. However, there's no explicit mention of a 'voice library' (e.g., curated voice catalog/selection UI) or enumeration of specific TTS providers/voices, so the 'broad voice library' half of the story is unevidenced. Missing for 10: explicit voice catalog/list of supported TTS providers and voices, evidence of ease of switching between TTS voices, independent confirmation of provider breadth.

   • [claimed-docs] “LiveKit Agents includes a large ecosystem of open source plugins for a variety of AI providers.”
   • [claimed-docs] “You can use LiveKit Inference to access many of these models directly through LiveKit Cloud, or you can use the open source plugins to conne…”
   • [claimed-docs] “For any provider not included, you can override the API key and base URL at initialization for the LLM, STT, and TTS interfaces in the plugi…”

   proof ↗⚑ flag

   Bolna~partialclaimed6/10

   Docs confirm multiple TTS providers (Deepgram, Azure, ElevenLabs, and more) plus voice/accent/style customization, satisfying the 'plug in providers' part of the story. However, there's no evidence of a broad pre-built voice library or catalog to browse/select from, only provider integration. Missing for 10: evidence of a large built-in voice library/catalog, provider-count breadth beyond three named examples, independent/hands-on confirmation of voice quality or selection UX.

   • [claimed-docs] “Bolna supports Deepgram, Azure, ElevenLabs, and more.”
   • [claimed-docs] “You can choose different voices, accents, and speaking styles to match your brand.”

   proof ↗⚑ flag