ARC-AGI-2 solver: 95.7% public eval at $3.12/task — lowest cost above 95%. Full inference traces included.
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Updated
May 12, 2026 - Python
ARC-AGI-2 solver: 95.7% public eval at $3.12/task — lowest cost above 95%. Full inference traces included.
Agent emotional continuity with PAD state, trust, appraisal, and compact emotion logs.
The first open evaluation framework for AI continuity. 250 narrative tests, 1835 verification questions, 10 checkpoints. Benchmark for AI memory systems, stateful agents, and long-term context persistence. No LLM in the evaluation loop.
LangGraph is a powerful framework built on LangChain that enables the creation of stateful, multi-step, and agentic workflows using directed graphs. It simplifies complex LLM orchestration by allowing conditional branching, memory, and tool integrations in a visual and modular way.
Agent state infrastructure: state-trace Python working memory plus @razroo/parallel-mcp durable MCP orchestration.
A cognitive runtime that gives LLM agents persistent state, identity, and learning across turns. Memory, beliefs, drives, self-evolution, skills, and affective state — engineered scaffolding outside the model.
Structured memory and snapshot history system for AI agents (OpenCode / Claude Code)
Durable state-machine agents for long-running mission flows
LangGraph stateful agent — directed graph with intent classification, tool routing, execution, and response synthesis nodes. FAISS semantic memory + streaming chat UI.
A hands-on roadmap to mastering Agentic AI using Google ADK, featuring modules on multi-agent delegation, parallel execution, and persistent memory.
A controlled, auditable implementation of agent memory that separates ephemeral state from persisted memory and exposes how policies govern state across runs.
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