标签 · Ai-Memory — Glean

Recent picks

10picks · chronological

08-23

Long-term memory for coding agents with cross-vendor handoffs

ai-memory is a Rust-based long-term memory service for coding agents, addressing context loss when switching between tools like Claude Code, Codex, or Command Code. It uses MCP configuration and lifecycle hooks to automatically capture prompts, tool calls, and session boundaries, then compiles a concise summary and handoff at session end. The wiki is stored as plain Markdown in a git repo—grep-able, Obsidian-friendly, and backup-friendly—with no vector database to maintain. Retrieval blends FTS5, entity-match RRF, and graph-neighbor ranking, with optional embeddings and an optional LLM for consolidation. Best for engineers juggling multiple AI coding agents who want project memory to persist across sessions and vendors.

github.com · 62 min · Agent Engineering · Ai-Memory · CLI

08-16

Your Agent Remembers Everything and Understands Nothing

The article argues that agent memory systems today are stuck at the storage-and-recall stage: they return the right facts but miss the structural patterns spanning multiple conversations. Using a project-management assistant as a running example, it shows three engineers filing blockers that are individually accurate yet actually point to one shared root cause — a delayed auth-service refactor. Better retrieval, rerankers, or wider search scopes don’t surface that chain because the insight was never stored as a discrete piece of content. The proposed mechanism is Zep’s Observations: a deterministic algorithm first reduces every fact to a signature of two entities plus the relationship type, then builds an episode graph where conversations are nodes and shared signatures are edges, producing connected components that expose transitive dependency chains. An LLM then writes a name and summary, but grouping is never left to the model. No embeddings, no semantic similarity — pure graph topology. Relevant for engineers working on agent memory, knowledge graphs, and context engineering. Note: this is a sponsored, technically dense explainer for Zep.

x.com · 13 min · Ai-Memory · Context Engineering · Knowledge Graph

07-18

Persistent Memory Compression System for Coding Agents Across Sessions

Claude-Mem is a persistent memory plugin for coding agents like Claude Code. It solves the problem of context amnesia between AI coding sessions by automatically capturing all tool-use observations, generating AI-compressed semantic summaries, and injecting relevant context into future sessions. It uses a progressive disclosure retrieval pattern that saves tokens by loading memory details in layers. Featuring an SQLite backend, a Chroma vector database for hybrid search, a real-time web viewer, and MCP-based natural language search tools, it helps developers maintain project continuity across reboots without manual intervention.

github.com · 11 min · Ai-Memory · Claude Code · Memory

07-08

12-step guide to persistent memory for Claude agents

A practical 12-step walkthrough for giving Claude agents persistent memory across sessions. Covers four layers: built-in Chat Memory, Project instructions, a lean memory file (CLAUDE.md), and Dreaming – a scheduled background process that consolidates and reorganized memory. Includes setup steps, API calls, and advice on filtering what to remember. Harvey reported ~6x task-completion rate improvement with Dreaming. Ideal for engineers building long-running agents.

x.com · 12 min · Agent Engineering · Ai-Memory · Claude

06-22

How to Build an AI Second Brain With Claude and Obsidian That Gets Smarter Every Day (Full Guide)

A step-by-step guide to building a persistent 'second brain' using Claude and Obsidian, based on Andrej Karpathy's LLM Wiki pattern. Obsidian stores all notes as local plain text files, while Claude (via MCP protocol) reads, organizes, and links the entire vault. Key steps: install Claude Desktop (paid plan), install Obsidian with Local REST API plugin, connect via MCP, create a CLAUDE.md profile via interview, structure projects with Inputs/Process/Outputs/Feedback folders, build reusable skills, wire in live data (calendar, email), and set up autopilot scheduling. The author stresses ownership (plain text, vendor-independent) and security ('keys, not prompts'). Aimed at engineers and knowledge workers tired of context loss.

x.com · 11 min · Ai-Memory · Claude Code · Context Engineering

06-20

Skillify: turn every agent failure into a permanent structural fix

Garry Tan presents 'Skillify': a methodology that turns every AI agent failure into a permanent structural fix instead of relying on prompt tweaks or apologies. Using two real failures—an agent bypassing a local script for calendar search and doing mental timezone math—he walks through a 10-step verification checklist: SKILL.md contract, deterministic script, unit tests, integration tests, LLM evals, resolver trigger, resolver eval, reachability audit, smoke test, and brain filing rules. This workflow is built into GBrain, an open-source knowledge engine that ensures agent judgment improves permanently and verifiably. Targeted at developers frustrated by recurring agent mistakes.

x.com · 22 min · Agent Architecture · Agents · Ai-Memory

06-19

Resolvers: The Routing Table for Intelligence

Garry Tan argues that resolvers—lightweight context routers—are the missing governance layer in agent systems, more crucial than models or skills. Using a mis-filed article as a trigger, he demonstrates how a 200-line resolver replaced 20,000 lines of crammed context, fixing model attention degradation and knowledge base drift. He details a production audit revealing that 10 of 13 skills ignored the resolver, and how he built trigger evals, a "check-resolvable" meta-skill to detect dark capabilities, and a self-healing loop against context rot. The piece reframes resolvers as the organizational chart and management of an agent system, and announces the open-sourcing of his personal architecture (GBrain/GStack) that embodies these patterns. Key evidence: real agent managing 25,000 files and 200 daily inputs, with concrete metrics on skill reachability defects.

x.com · 18 min · Agent Architecture · Agents · Ai-Memory

06-18

Stop Giving Every Agent Its Own Skull

Pejman argues that we are replicating a core human limitation—knowledge siloed in individual brains—inside agent systems. Using OpenClaw, Codex, and Claude Code, each agent retains isolated context about him and his projects. The critical gap is not in the repo's artifacts but in the session itself: the debates, dead ends, and pruned idea branches that markdown cannot capture. With literal physical separation across machines, this fragmentation intensifies. The missing layer is a shared, user-owned memory substrate that transcends agent boundaries. He highlights GBrain and CASS as early signals tackling parts of this problem. The piece resonates with engineers building or deeply integrating multi-agent workflows.

x.com · 7 min · Agent Architecture · Agents · Ai-Memory

06-17

Persistent Memory Engine for AI: Auto-Extract, Update, and Forget Intelligently

Supermemory is a memory and context layer for AI. It automatically extracts facts from conversations, builds and maintains user profiles, resolves contradictions, and intelligently forgets expired information. Combining hybrid search (RAG + memory), document processing, and live connectors (Google Drive, GitHub, etc.) into one API, it gives AI agents instant, personalized context. With plugins for Claude Code, Cursor, and more, it targets both developers integrating memory into apps and users wanting persistent AI memory across tools.

github.com · 14 min · Agent-Memory · Ai-Memory · Cloudflare

06-16

The Context Compression Layer for AI Agents: 60–95% Fewer Tokens, Zero Accuracy Loss

Headroom is a local-first context compression layer for AI agents that slashes token usage from tool outputs, logs, files, and RAG chunks by 60–95% before they reach the LLM, with preserved accuracy. It offers library, proxy, MCP server, and agent wrapper modes, using a content router to select the best compressor for JSON, code, or prose. Reversible compression ensures originals are retrievable on demand. With cross-agent memory and `headroom learn` for mining failed sessions, it is ideal for engineers running coding agents daily and anyone seeking to slash LLM costs without changing their workflow.

github.com · 18 min · Agent Architecture · Ai-Memory · Context Engineering