Voker Launches Agent Analytics Platform with Intent/Correction/Resolution Primitives
Voker.ai, a YC S24 startup, has launched an analytics platform purpose-built for AI agents. The core product is a lightweight SDK (Python & TypeScript) that wraps LLM calls to OpenAI, Anthropic, and Gemini, automatically collecting conversation data and annotating three primitives: Intents, Corrections, and Resolutions.
What It Does
Voker processes LLM calls by automatically classifying user goals (intents), detecting when users correct the agent (corrections), and measuring when the agent resolves the intent (resolutions). It then uses hierarchical text classification (not LLMs for data engineering) to aggregate these into dynamic categories, giving product teams self-service insights without reading individual traces.
Key Details from the Launch
- SDK Integration: Two lines to install:
pip install vokerand wrapping the LLM provider (e.g.,from voker.ai.provider_openai import OpenAI). - LLM Stack Agnostic: Works with OpenAI, Anthropic, Gemini, Langchain, CrewAI, and Vercel AI SDK.
- Pricing: Free tier — 2,000 events/month (email signup required). Paid plans start at $80/month with a 30-day free trial.
- Data Engineering Philosophy: Voker explicitly avoids using LLMs for core data processing to ensure consistent, reproducible, and accurate statistics. Co-founders note that uploading logs to ChatGPT often yields overfitted or inconsistent insights.
Why It Exists
According to a survey of YC founders, 90%+ said the only way they know agents are failing is via customer complaints. Existing tools fall short: observability (e.g., Langfuse, Langsmith) is good for trace debugging but not accessible to non-engineers; evals test known issues but miss unexpected trends; traditional analytics (PostHog, Mixpanel) aren't built for unstructured conversational data.
Who It's For
Teams running high-volume conversational agents (1k+ chat sessions per month) with complex multi-turn interactions, needing insights that cross-functional teams (PMs, engineers, analysts) can self-serve.
📖 Read the full source: HN AI Agents
👀 See Also
ReasonDB: Open-Source Document Database Using LLM-Guided Tree Navigation Instead of Vector Search
ReasonDB is an open-source document database that preserves document structure as a hierarchy and uses LLM-guided tree traversal for retrieval instead of vector search. It uses BM25 for initial search, tree-grep for structure filtering, and beam-search traversal where the LLM visits ~25 nodes out of millions.
ProofShot: CLI for AI Agents to Verify UI Code with Browser Recording
ProofShot is a CLI tool that lets AI coding agents open a browser, interact with pages, record sessions, and collect errors, then bundles everything into a self-contained HTML file for review. It works with any AI agent via shell commands and is packaged as a skill.
Graphify: A Claude Code Skill That Built a Knowledge Graph of Your Repo — 450k Downloads, 40k Stars in 26 Days
Graphify is a Claude Code skill that reads every file in your repo, builds a knowledge graph with Leiden community detection, and queries it at 71x fewer tokens than raw files. 450k+ PyPI downloads, ~40k GitHub stars, #2 global rank in first week.
Flavian: A WordPress Development Framework with 24 Specialized Claude Code Agents
Flavian is an open-source WordPress development framework built around Claude Code, featuring 24 specialized agents for tasks like frontend development, security audits, and Figma-to-WordPress conversion. The creator found domain-specific agents significantly outperform general-purpose ones for WordPress development.