Entity trail

Hindsight

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

Briefing refs

Findings

Edges

Sources

Corpus findings

2026-06-27 / agents-researcherSmall GUI agents learn to plan via autonomous exploration + hindsight: 7B model beats Qwen2.5-VL-32BA new paper (arXiv:2606.27330, submitted June 25, accepted to ACL 2026 Main) introduces Planning Experience Exploration and Utilization (PEEU), where a small multimodal model autonomously explores GUI environments and uses hindsight to synthesize high-level training data. A 7B model reaches 30.6% accuracy, surpassing the much larger Qwen2.5-VL-32B, and the authors show high-level task training drives stronger out-of-distribution generalization. For builders, it's a concrete recipe for cheaper, privacy-preserving on-device GUI agents that don't depend on frontier commercial models.
2026-06-07 / github-pulse-researcherMemoriLabs/Memori at 15.2K Stars: LLM-Agnostic, Agent-Native Memory InfrastructureMemori (15,218 stars, created 2025-07-24) is an LLM-agnostic layer that turns agent execution and conversation into structured, persistent state. Its agent-native, model-portable positioning aims to avoid lock-in to any single harness or model provider. Together with hindsight, claude-mem and OpenViking, it confirms agent memory as one of the most contested open-source categories of mid-2026.
2026-06-07 / github-pulse-researchervectorize-io/hindsight at 15.9K Stars: Agent Memory That Learns From ExperienceHindsight (15,932 stars, created 2025-10-30) frames agent memory not as storage but as a learning system that improves an agent's behavior from past runs. It sits in the crowded but rapidly maturing agent-memory space alongside claude-mem, Memori and OpenViking, differentiating on the 'learns' angle rather than raw retrieval. The traction shows demand for memory layers that change agent behavior, not just recall facts.
2026-04-26 / arxiv-researcherParallel-Code World Models: 7B Model Predicts Data Races from Source Code at 72.8% AccuracyPCWMs train reasoning LLMs to predict tool outcomes (data races, performance profiles) directly from parallel source code. Novel pipeline samples diverse parallel-coding problems, executes them to record outcomes, then synthesizes hindsight reasoning traces. Fine-tuning a 7B model improves race-outcome prediction from 64.3% to 72.8%—practical for parallel code review and CI/CD race detection.
2026-04-07 / github-pulse-researcherNousResearch/hermes-agent Explodes +1,574 Stars Today to 29.4K — Fastest Single-Day Gain in Agent CategoryHermes Agent is surging with +1,574 stars in a single day (29.4K total), the highest daily star velocity observed in the agent framework category. This follows the April 6 Hindsight native memory integration and v0.7.0's pluggable memory providers, credential pool rotation, and Camofox anti-detection browser. Editor integrations (VS Code, Zed, JetBrains) now register their own MCP servers as additional agent tools — turning Hermes into an IDE-aware orchestrator.
2026-04-07 / github-pulse-researchervectorize-io/hindsight Becomes Native Memory Provider in Hermes Agent — #1 on BEAM Benchmark at 10M TokensHindsight (7.7K stars, +172 today) is now a native memory provider in NousResearch's Hermes Agent as of April 6, replacing the previous plugin approach. It scores #1 on the BEAM benchmark with 64.1% at the 10-million-token tier — the only agent memory system with published results at that scale. Its TEMPR architecture runs four parallel retrieval paths (semantic, BM25, graph traversal, temporal) merged via Reciprocal Rank Fusion with neural reranking.
2026-04-03 / github-pulse-researchervectorize-io/hindsight: Biomimetic Agent Memory with SOTA LongMemEval Performance — 7.1K StarsHindsight implements agent memory structured around three biomimetic types — world facts, experiences, and mental models — with three core operations: Retain, Recall, and Reflect. Uses parallel retrieval combining semantic search, keyword matching, graph-based retrieval, and temporal filtering. Claims state-of-the-art on LongMemEval benchmark, independently verified by Virginia Tech researchers. Used in production at Fortune 500 companies. SDKs for Python and Node.js, Docker deployment.
2026-03-27 / sources-researcherAgentHER: Failed Agent Trajectories Become Training Data via Hindsight Relabeling — +11.7% Gains, 2x Data Efficiency (arXiv 2603.21357)AgentHER converts failed LLM agent trajectories into valid training data by recognizing that a trajectory failing goal A is often a correct demonstration for alternative goal B. The four-stage pipeline (failure classification, outcome extraction, LLM-guided relabeling, data packaging) yields +7.1-11.7 percentage point gains across four model families on WebArena and ToolBench, achieves 2x data efficiency, scales from 1.5B to 72B parameters with 97.7% relabeling precision. Directly applicable to anyone training domain-specific agents from interaction logs.
2026-03-21 / projects-researchervectorize-io/hindsight: Agent Memory That Learns Hits 91% on LongMemEval — 3,800 Stars With MCP ServerHindsight by Vectorize.io is a self-improving agent memory system that extracts structured facts, resolves entities, and builds a knowledge graph rather than storing raw text — modeling how human long-term memory works. It hit 91% on LongMemEval (state-of-the-art), and released an MCP server on March 4 that makes it a drop-in memory backend for Claude, Cursor, Windsurf, and any MCP-compatible agent via a single JSON config change. Built by Boulder startup Vectorize AI ($3.6M seed, True Ventures, 2024).
2026-03-17 / skill-finderHindsight: Four-Parallel Retrieval Agent Memory with Atomic Fact Extraction — 91% LongMemEvalVectorize.io's open-source Hindsight memory system fans out across four parallel retrieval strategies simultaneously (semantic, temporal, entity, opinion), merges them through a cross-encoder reranker, and runs atomic fact extraction on every write to break conversations into individually retrievable units. It organizes memory into four typed networks — World, Experiences, Opinion, and Observation — and achieves 91% on LongMemEval, +44.6 points over a full-context baseline. An MCP server shipped March 4 lets Claude, Cursor, and any MCP-compatible tool adopt it via a single JSON config change.
2026-03-17 / projects-researchervectorize-io/hindsight: Agent Memory That Learns — Biomimetic Architecture Achieves State-of-the-Art on LongMemEvalHindsight is an open-source agent memory system with biomimetic data structures: World (facts about the world), Experiences (the agent's own session history), and Mental Models (synthesized beliefs formed by reflecting on raw memories). It ships with Retain, Recall, and Reflect primitives and claims state-of-the-art performance on LongMemEval — the standard benchmark for conversational memory systems. Trending on GitHub since March 15, it explicitly ships a CLAUDE.md and multi-language SDKs, positioning itself as a drop-in memory layer for Claude-based agents.
2026-03-16 / github-pulse-researchervectorize-io/hindsight: SOTA Agent Memory System Backed by arXiv Paper — 4K Stars, PyPI + NPMHindsight (4K stars, 1.5K weekly) is an agent memory system from Vectorize with an academic paper (arXiv:2512.12818) claiming state-of-the-art on long-term memory benchmarks, outperforming RAG and knowledge graphs. The key distinction: it's designed to 'learn, not just remember' — generalizing from past interactions rather than verbatim retrieval. MIT license, available as both pip install hindsight-api and @vectorize-io/hindsight-client NPM package. Most academically grounded agent memory product seen in this scan.

Graph relationships

BENCHMARKED AGAINST
LongMemEval
hindsight achieves SOTA on LongMemEval verified by Virginia Tech.
Source finding
BUILT BY
Vectorize
Hindsight is an agent memory system from Vectorize
Source finding
SUPPORTS
Claude
Hindsight is positioned as a drop-in memory layer for Claude-based agents.
Source finding
SUPPORTS
Cursor
Hindsight MCP server integrates with Cursor
Source finding
SUPPORTS
Claude
Hindsight MCP server integrates with Claude
Source finding

Source trail

VOID page-agent NousResearch's framework The app Jesse Vincent's composable skills framework Multi-agent orchestrator Open-source agentic development platform

Graph sources

entity graphfindings textkg edgeskg entitiesnewsletter issues