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TencentDB-Agent-Memory is a shared memory platform for large language model agents. It stores conversation facts, documents, code knowledge, and learned skills in one place, so later sessions can recall people, preferences, decisions, and context without users repeating themselves. Conversations are distilled asynchronously into layers: facts, scenario knowledge blocks, and long-term persona profiles.
The main features of tencentcloud/tencentdb-agent-memory are: Multi-Agent Memory Stores, Agent Memory Persistence, Shared Knowledge Stores, Versioned Skill Extraction, Cross-Session Conversation Memories, Layered Conversation Distillation, Code Knowledge Graphs, Agent Access Controls.
Projects with overlapping indexed features include: rohitg00/agentmemory — AgentMemory is a persistent knowledge store and memory server designed to provide AI coding agents with long-term… tigerless-labs/agent-memory — Agent Memory gives AI coding agents durable long-term memory that persists across sessions. Each memory is kept as a… mervinpraison/praisonai — PraisonAI is an autonomous AI agent platform that coordinates multiple LLM-powered agents for research, planning, and… prefecthq/marvin — an ambient intelligence library. plastic-labs/honcho — Honcho is an agent memory platform: a server and set of SDKs that store conversation history as messages and reason… vectifyai/openkb — OpenKB is a command-line tool that uses a large language model to compile raw documents into a linked wiki of summary,…
AgentMemory is a persistent knowledge store and memory server designed to provide AI coding agents with long-term memory. It functions as a knowledge graph engine and vector database store that saves and recalls project context, architectural decisions, and patterns across different sessions. The system distinguishes itself by using a tiered-memory consolidation pipeline that compresses raw observations into episodic, semantic, and procedural layers to optimize token usage. It employs a hybrid retrieval strategy combining keyword matching, vector embeddings, and graph traversal to surface rel
Agent Memory gives AI coding agents durable long-term memory that persists across sessions. Each memory is kept as a plain Markdown file with a metadata header in a single shared location, so the store stays human-readable, portable, and searchable with ordinary tools. Several agents, such as Claude Code and Codex, can read and write the same store through lifecycle hooks, a skill, a shell command line, or an optional tool server. No API key or account is required. Memory is captured automatically rather than left to the model's discretion. Session transcripts are distilled into structured me
PraisonAI is an autonomous AI agent platform that coordinates multiple LLM-powered agents for research, planning, and execution of complex workflows. It functions as a multi-agent orchestration framework, a workflow builder, and a Model Context Protocol server, while also providing retrieval-augmented generation through vector knowledge bases. Agents can interact via CLI, web, or standardized protocols with sandboxed code execution. The platform distinguishes itself with a rich set of agent communication protocols, including A2A, REST, WebSocket, voice and telephony integration, and MCP, allo
