Voker – Analytics for AI Agents

TL;DR

TL;DR: Voker transforms raw AI agent interactions into structured analytics so your whole team can monitor agent performance, catch regressions, and prove ROI — no more flying blind.

Source and Accuracy Notes

• Official site: voker.ai
• YC S24 batch: Launch HN thread

What Is Voker?

Voker is an analytics platform purpose-built for AI agents. It captures, structures, and visualizes everything your agents say and do — turning chaotic chat logs and API traces into dashboards that product managers, engineers, and business leads can all read.

The core problem it solves: AI agents are black boxes. When a customer reports “the agent gave a bad answer,” you have almost nothing to go on. Voker makes that tractable.

Built by the team at Voker (YC S24), the platform is already used by teams running multi-agent systems in production.

Setup Workflow

Getting Voker integrated with your AI agent takes about 15 minutes.

Step 1: Create a Voker Workspace

Sign up at voker.ai and create a new project for your agent. You’ll land in the onboarding flow which walks you through the integration options.

Step 2: Install the Voker SDK

npm install @voker/sdk
# or
pip install voker-sdk

Step 3: Initialize Voker in Your Agent

from voker import Voker

voker = Voker(api_key="voker_live_your_api_key")

# Wrap your agent's core execution
result = voker.trace(
    agent_id="support-agent-v2",
    input=user_message,
    output=agent_response,
    metadata={
        "model": "gpt-4o",
        "temperature": 0.7,
        "session_id": session_id,
    }
)

Step 4: Start Viewing Analytics

Once your agent is instrumented, open the Voker dashboard. You’ll see:

• Interaction volume over time
• Token spend per agent, per session
• Error rate and fallback triggers
• Session-level transcripts with search

Deeper Analysis

What Makes Voker Different

Most “AI analytics” tools are just LLM cost trackers. Voker goes further by structuring agent behavior into meaningful categories:

• Intent classification — What are users actually asking the agent to do?
• Response quality signals — Length, sentiment, and tool-use frequency
• Handoff detection — When does the agent give up and escalate?

This lets you build quantitative health scores for each agent without manually reviewing transcripts.

Self-Service Analytics

The core dashboard is designed for non-engineers. Product managers can filter by date range, agent version, or user cohort and get meaningful charts — not just raw log dumps.

Performance Intelligence

Voker tracks latency and cost per agent run and flags anomalies. If your GPT-4o-powered agent suddenly starts spending 3x more tokens per session, you’ll get an alert before it becomes a budget problem.

Integration Surface

Voker currently supports agents built with:

• OpenAI SDK (direct voker.trace wrapping)
• Anthropic SDK
• Custom LLM backends via the REST API

LangChain and LlamaIndex integrations are on the roadmap.

Practical Evaluation Checklist

Use this to assess whether Voker fits your setup:

• [ ] Running AI agents in production (chat, coding, support, or data agents)
• [ ] Team has multiple stakeholders who need visibility into agent behavior
• [ ] Current observability is limited to raw API logs or no logging at all
• [ ] Want to track cost per agent session or user cohort
• [ ] Need to catch regressions when you change agent prompts or model versions

If you checked 3+ boxes, Voker is worth a serious look.

Security Notes

• All data is encrypted in transit and at rest
• API keys are scoped to individual projects — no cross-project access
• Voker does not store your full prompt/response content by default; it stores aggregated analytics. Full session transcripts are opt-in -适合对数据主权有要求的团队 — SOC 2 Type II compliance is in progress

FAQ

Q: Does Voker work with agents that don’t use OpenAI or Anthropic?

A: Yes. The REST API integration works with any LLM-backed agent. You send structured events directly to Voker’s ingestion endpoint.

Q: How does Voker price its service?

A: Voker has a free tier covering up to 10K agent interactions/month. Paid plans start at $49/month for 100K interactions. Enterprise pricing includes SSO and audit logs.

Q: Can I export my data from Voker?

A: Yes. All analytics data can be exported as CSV or via the REST API. Full transcript export is available on paid plans.

Q: Does Voker support multi-agent workflows?

A: Yes. You can create agent hierarchies in Voker and track inter-agent calls as part of a parent session, which is useful for complex agentic pipelines.

Q: What’s the latency overhead of the Voker SDK?

A: The SDK adds approximately 5–15ms per agent call for the trace operation. Async/batch mode is available for high-throughput systems.

Conclusion

Voker fills a real gap in the AI agent stack: moving from gut-feel and guesswork to actual numbers on agent performance. If you’re shipping AI agents to real users, knowing what your agents are doing is not optional — and Voker makes that accessible without a DataDog-level setup.

Start at voker.ai, connect your first agent in 15 minutes, and you’ll have your first answer within an hour.

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