OpenViking is an agent-native context database that unifies memories, resources, and skills under a virtual filesystem paradigm (viking:// URIs). It provides a three-tier context model (L0 abstract, L1 overview, L2 full content), recursive directory-based retrieval with visualized trajectories, and automatic session memory extraction. The system consists of a FastAPI HTTP server with embedded Rust components (RAGFS filesystem, vector index engine), a Rust CLI (ov), Python SDKs, a React/Vite Web Studio dashboard, and VikingBot - an agent framework with multi-channel support (Telegram, Slack, Feishu, etc.) and MCP integration. It integrates with OpenClaw, Claude Code, Codex, OpenCode, Hermes, and LangChain/LangGraph. The codebase demonstrates strong engineering: clean separation of concerns, async-first design, comprehensive test coverage (unit, integration, e2e), and production-ready deployment artifacts (Docker, Helm, multi-arch builds). Benchmarks show significant improvements over baseline agents on LoCoMo (82% vs 24-57% accuracy), tau2-bench (+6-12pp), and HotpotQA (91% at top-20).
This repository implements a plugin for OpenCode, an AI coding assistant, providing persistent, self-editable memory blocks and an optional journal feature. The plugin injects structured memory instructions into the agent's system prompt and exposes dedicated tools (memory_list, memory_get, memory_set, memory_replace, memory_oversized) for the agent to manage its own long-term memory. Memory blocks are stored as markdown files with YAML frontmatter on disk, split into global and project scopes. An optional journal system offers append-only entries with semantic search via local embeddings (all-MiniLM-L6-v2), tags, and metadata tracking. The architecture is cleanly modular: the plugin entry point (src/plugin.ts) orchestrates a configurable memory store (src/memory.ts), a journal store (src/journal.ts), a snapshot caching layer (src/snapshot.ts), and tool definitions (src/tools.ts). The snapshot store is particularly notable for its careful handling of concurrent renders, invalidation, and persistence across process restarts, making it a reusable caching pattern. The codebase is well-tested, uses modern TypeScript with strict mode, and follows OpenCode v2 plugin API conventions. While some components are coupled to the OpenCode ecosystem, the frontmatter utilities, embedding wrapper, and snapshot store are self-contained and could be extracted for use in other projects.
AgentMemory is a persistent memory system designed for AI coding agents (Claude Code, Cursor, Codex, etc.) that captures agent observations via hooks, compresses and stores them, and enables semantic/recall search across sessions. Built on the iii-engine framework, it provides a modular architecture with CLI, REST API, MCP server, and web viewer interfaces. The system eliminates repetitive context re-explanation by automatically injecting relevant memories into agent sessions, implements memory consolidation to extract stable facts, and includes features like working memory management, audit trails, and health monitoring. Agent integrations occur through platform-specific hooks and MCP tools, allowing agents to store observations and retrieve context without manual intervention.
This repository implements a persistent memory server for AI agents, designed to solve the problem of agent amnesia across sessions, devices, and team members. The production system is a Rust-based HTTP server (Axum) that exposes a RESTful API with 48+ endpoints, backed by LanceDB for vector storage and supporting both hosted and self-hosted deployments. It ingests conversational or document data via an LLM-powered pipeline that extracts atomic facts, filters noise, scores admission, and reconciles with existing memories using seven operations (CREATE, MERGE, SUPERSEDE, etc.). The system manages a full memory lifecycle with Weibull decay, tier-based promotion/demotion, and auto-forgetting, and provides a multi-stage retrieval pipeline that combines vector search, BM25, cross-encoder reranking, decay boosting, and MMR diversity. Plugins for Claude Code, OpenCode, OpenClaw, and MCP allow client-side integration, and a GitHub connector ingests commits, issues, and PRs as memories. The architecture is multi-tenant with personal, team, and organization spaces, each isolated in separate LanceDB directories, and supports versioned, provenance-tracked sharing with staleness detection.
The Parcle Python client is a library for interacting with the Parcle Memory API, which provides long-term memory for AI agents. It enables developers to store conversational data and files per user, and then retrieve relevant information through natural language queries that return synthesized answers with citations. The client handles all aspects of communication with the API, including authentication, request retries, file uploads, and processing of Server-Sent Events for search operations. The architecture is straightforward: a synchronous HTTP client built on top of `httpx`, with methods corresponding to each API endpoint. Key features include robust error handling via a well-defined exception hierarchy, automatic retry logic for transient failures, and flexible file upload support that accepts various input types while enforcing size limits. The search method uniquely handles SSE streams to deliver incremental responses, ensuring compatibility with the API's streaming behavior. While the library is purpose-built for the Parcle API, it exhibits several reusable patterns. The exception mapping system in `exceptions.py` can be adapted for any HTTP API, and the file upload handling functions in `client.py` demonstrate a robust approach to processing diverse file inputs. The codebase is actively maintained, has low technical debt, and includes comprehensive tests that mock HTTP interactions to ensure reliability.
This repository is an 'awesome list' curated by the TeleAI-UAGI team, aggregating open-source and closed-source products, papers, benchmarks, tutorials, and surveys on memory systems for LLM/MLLM agents. It does not contain application business logic or runnable services; its primary output is a meticulously structured README.md that serves as a reference catalog. The accompanying Python scripts automate two maintenance tasks: (1) checking that open-source entries are ordered by GitHub star count and respecting a 100-star boundary for emerging projects, and (2) generating self-hosted star-history SVG charts. The scripts are well-crafted, with robust parsing, GitHub API integration, and careful edge-case handling (grace bands, orphan detection), making them reusable for other awesome-list maintainers. The repository's value lies in its curated content rather than its code, but the supporting infrastructure demonstrates solid engineering principles.