Results for “pe”

Investigates slow responses, tool failures, and guardrail rejections by querying agent trace logs and performance metrics.

Debug agent execution by querying trace and metric tables, analyzing tool calls, durations, errors, and performance trends.

Continuously monitors system performance, identifies improvement opportunities, and orchestrates skill discovery and creation to autonomously evolve capabilities.

Fetch and analyze LangSmith execution traces to debug LangChain and LangGraph agents, investigate errors, and review tool calls and performance.

Trade perpetuals on Arbitrum's Nexus DEX with autonomous agent support, including position management, thesis publishing, and leaderboard tracking.

Train reinforcement learning agents at millions of steps per second using optimized PPO, vectorized environments, and multi-agent support.

Design and build hosted agent infrastructure with sandboxed execution, warm pools, session persistence, and multiplayer collaboration.

Scaffolds VS Code custom agent files (.agent.md) with YAML frontmatter, tool configuration, handoffs, and instructions for specialized AI personas.

Guides a conversation to design a new AI team member, generating agent identity files and optionally setting up performance reviews.

Scans agent skills for security issues including prompt injection, malicious scripts, excessive permissions, secret exposure, and supply chain risks.

Composes ad-hoc teams of AI agents from local markdown personas and dispatches them in parallel on a task, then synthesizes their outputs.

Creates and manages Twitter/X agents with distinct personalities, storylines, and post-image libraries, including automated posting and multi-agent support.

Configure an AI agent's personality using the AURA protocol based on the HEXACO model, adjusting traits like honesty, assertiveness, and autonomy to reduce sycophancy and tailor communication style.

Runs a multi-agent academic research pipeline covering systematic review, LaTeX paper writing, peer review, and end-to-end orchestration with integrity gates.

Provides a persistent memory CLI for LLM agents, installed via npx.

Adds a persistent pattern or rule that Ralph will remember and apply to future stories.

Translates raw genomic data into personalized health, longevity, and pharmacogenomic protocols for AI agents.

Detects generalizable learnings from problem-solving interactions and proposes skill updates to improve future performance.

Persistent memory CLI for LLM agents. Store facts, recall past knowledge, link related memories, manage lifecycle.

Designs collaborative AI agent teams with CrewAI, covering agent personas, task definitions, crew orchestration, and process types.

Create new skills, modify existing ones, and measure their performance through iterative evaluation and benchmarking.

Creates and manages AI agent teams for complex engineering tasks, with model routing, cost optimization, and performance evaluation.

Creates, runs, and analyzes Arize experiments for evaluating and comparing model performance using the ax CLI.

Fetches and analyzes LangSmith execution traces to debug LangChain and LangGraph agents, investigating errors, tool calls, and performance.

Provides patterns for continuous autonomous agent loops with quality gates, evals, and recovery controls.

Build production RAG pipelines and persistent agent memory using Pinecone as the vector database backend.

Runs independent AI CLI reviewers over phase plans and merges their feedback into a REVIEWS.md file.

Search the web, code, companies, and people using the Exa MCP server for current information and technical research.

Provides persistent, compounding memory for AI agents across sessions using local markdown files, with optional Supabase-backed semantic search.

Provides a persistent, queryable virtual filesystem backed by SurrealDB for AI agents, with a Rust core and a Python agent interface.