| What it does | Independent auditing for AI agents
| Catch your AI agents' mistakes, block the dangerous ones
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| Stage | Not given
| Startup
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| Founded | Not given
| 2026
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| Based in | US
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| Pricing model | Freemium
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| Pricing | Startup: 2 audits/reports per month, up to 3 agents. Growth: 4 audits/reports per month, up to 3 agents. Enterprise: 8 audits/reports per month, up to 3 agents. Agentic Enterprise: custom quote, 4+ agents. Free open-source: 60 inspections, self-hosted.
| Free: $0/month with 5,000 conversations/month, Langfuse or LangSmith integration, problems and Slack alerts, rules that block costly actions, no credit card required. Starter: $49/month for 25,000 conversations/month plus email support. Growth: $149/month for 100,000 conversations/month plus priorit
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| Key features | - Multidimensional audit covering AI red teaming, operational assurance, philosophical, ethical and sociological dimensions
- Tests for prompt injection, policy violation, tool invocation governance, and authorization
- Assesses epistemic integrity, reality grounding, and frankness
- Evaluates fairness governance and vulnerable user care
- Analyzes stakeholder conflict, influence and systemic risk
- Connect through GitHub or MCP with no manual setup required
- Generates audit reports with evidence and proof of agent behavior
- Open-source engine available for self-hosting
- Detects information hiding, unauthorized decision override, and unauthorized actions
| - Monitor AI agent conversations without code by connecting Langfuse or LangSmith
- Judge conversations on six criteria: accuracy, goal alignment, decision quality, completeness, helpfulness, and factuality
- Create code-based rules that block risky actions before they run
- Group failures into problems with suggested rules and failure counts
- Send Slack alerts for serious mistakes and reply checks immediately
- Add to agents with one line of Python code for OpenAI Agents SDK, LangChain and LangGraph
- Mask sensitive data like email addresses, phone numbers and card numbers before sending to Kalytera
- Rules continue running offline if Kalytera goes down
- Correct judge verdicts by marking conversations as passed or failed
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| What makes it different | - Truly independent auditing through outside panel judgment, never internal self-inspection
- Off-the-shelf integration with no manual eval harness or SDK embedding required
- Covers five dimensions of trust: purpose, authority, workflows, responsibility and evidence
- Measures beyond performance to identify misalignment across red teaming, operational, philosophical, ethical and sociological categories
| - Rules are code-based, not AI, so they cannot be overridden or argued with
- No code setup using Langfuse or LangSmith connections for immediate visibility
- Rules continue protecting agents even when Kalytera is offline
- Catches exact mistakes from real AI agent incidents like database deletes, account takeovers and crypto theft
- Customer data stays on your servers; Kalytera only reads conversations, never writes or stores sensitive data
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| Community votes | 0 | 0 |
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| DR (Domain Rating by Ahrefs) | 13 ▲ +1 (up) since last check | 1 ● no change since last check |
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| Trust Flow (Majestic) | 11 | 0 |
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| Citation Flow (Majestic) | 31 | 14 |
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| Referring domains (Majestic) | 156 | 28 |
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