#agent-memory

Local-first checkpoints and crystals for long-running AI coding agent sessions.

RSS for state. Open portable evidence layer for machine state - preserving what an agent or system saw, produced, derived and learned, so another party or future agent can verify the lineage independently.

El repositorio no publica descripción.

Persistent memory for pi — learns corrections, preferences, and patterns from sessions

A framework for giving any AI agent persistent, browser-trainable SSM memory — Mamba models, swappable LLM bridges, and online distillation. The hippocampus behind BuilderForce.ai.

Mnemoteca — local memory for every agent

Self-improving context & memory for Claude Code and OpenAI Codex. One canonical knowledge store injected only when relevant — hooks push budgeted context, an MCP server serves depth on demand, and it learns lessons from your sessions. Agent memory + context engineering as a single static Go binary.

Call dibs on files. Coordination for parallel coding agents — file claims with expiry, enforcement hooks, and git-native lessons. One binary, no server, no database.

A verifiable memory layer for language models — facts in a graph, every claim with its source.

Python SDK for Niadra: shared customer memory for every AI agent in a company. Context before the model call, history, tools and MCP.

Claude Code plugin marketplace: the bitranox skill collection (82 skills) plus a self-improve Stop hook. Skills invoke as /bitranox:<skill>.

The Context Graph Protocol is an open wire protocol for context retrieval. It treats a piece of context as a typed, budgeted, provenance-carrying, consent-gated, and conformance-verified unit of exchange called a frame — never a blob.

File-first memory runtime for AI agents — versioned, portable, and always there when the next session starts.

Structural memory for AI coding agents. Bi-temporal graph, MCP-native, zero LLM calls. Cursor · Claude Code · Codex · DeepSeek Harness · Hermes · VS Code · Windsurf.

El repositorio no publica descripción.

Membase SDK: drop-in memory infrastructure for AI agents and apps — Python client, CLI, MCP server and TypeScript client, hosted or on your machine.

A tiny, universal memory engine for humans and AI agents — CLI, HTTP API, MCP server, and shared memory hub in one small Go binary

Local-first control plane for AI-agent teams — orchestrates Claude Code, Codex, Grok, agy & Hermes into gated workflows that fix their own failures, with per-run metering, an append-only audit log, and team memory. Apache-2.0.

Vector Graph RAG for code & docs with Persistent Chat-Session Memory — BM25 (multi-lang), AST chunking. Hybrid, Compute, Scan, Timeline, Absence, Salience. Graph + temporal knowledge. Privacy Stamps. Source Adapters. Chat summarisation + verbatim copy + free scan. Git indexing. CLI, MCP and Claude Code plugin. Ollama or cloud LLMs. Dashboard.

El repositorio no publica descripción.

Turn docs into agent-readable knowledge bundles using Open Knowledge Format (OKF)

A living local memory layer for your AI tools.

RAMR — Retrieval-Augmented Memory Reliability: a contamination-resistant synthetic benchmark for agentic-RAG / memory systems (findings + method)

One workflow framework, every coding agent. Install your AI-coding discipline into Claude Code, Cursor, GitHub Copilot, Gemini CLI, Codex, and Windsurf from one tool-agnostic core. Apache 2.0.

Open-source agent middleware — shared memory, credential isolation, and schema learning for AI agents. What one agent learns, every agent knows.

go vet for file-based AI agent state: integrity checks for memory, instructions, decisions, and contracts

A fractal memory plugin and mcp server for opencode.

A small, nestable agent-loop job primitive: loop(body→until→review) composed with dag, pluggable engines, agent-judge gates, budget + run records. TypeScript.

Pi package for reviewed behavioral experience patterns separate from skills and memory

Embedded long-term memory database for AI agents & LLM apps. Six-layer cognitive architecture, knowledge graph, hybrid BM25 + vector search, sleep-time memory consolidation. One .meh file, zero infrastructure, pure Go.