Agentes de IA
memvid
A serverless, single-file memory layer for AI agents - replace complex RAG pipelines
★ 16,6 mil estrelasApache-2.0
Ver o guia →Memary é um projeto de agentes de IA de código aberto de kingjulio8238 (kingjulio8238/Memary no GitHub). Tem 2.655 estrelas no GitHub e 205 forks, é publicado sob a licença MIT e foi atualizado pela última vez no GitHub em 22 de outubro de 2024.
The open-source memory layer for autonomous agents — knowledge-graph memory that emulates human memory
Decide whether you are adding memory to your own agent, or evaluating Memary through its bundled personal-agent demo.
pip install memary (Python <= 3.11.9), or clone the repo and install requirements for the full demo.
Create a .env with your graph database credentials (free FalkorDB or Neo4j instance) and any API keys, choose local Ollama models or OpenAI models, and edit the user/system persona files.
Launch the Streamlit app, chat with the agent, and watch its knowledge graph and memory modules update in the dashboard.
Facts are stored in a graph database (FalkorDB or Neo4j) via LlamaIndex, with recursive retrieval and multi-hop reasoning over entity subgraphs.
After initialization, memory updates automatically as the agent interacts, and agent responses are written back into the graph.
Captures every entity inserted into the graph with timestamps, enabling timeline and theme analysis of a user's interests.
Tracks frequency and recency per entity to rank what the user knows deeply versus superficially.
A demo ChatAgent with default tools (search, vision via LLaVA, location via Google Maps, stocks), plus add_tool/remove_tool for custom tools.
With FalkorDB, multiple graphs keyed by user/agent ID let different agents keep separate memory contexts.
Clone Memary for the full demo; the library alone installs from PyPI in step 2.
git clone https://github.com/kingjulio8238/Memary.git
cd MemaryInstall Memary (Python <= 3.11.9 per the README) and, for a local checkout, its requirements.
pip install memary
# local checkout: pip install -r requirements.txtConfigure .env and the persona files first, then start the Streamlit app.
cd streamlit_app
streamlit run app.pykingjulio8238 created and maintains it; documentation is at kingjulio8238.github.io.
Yes, MIT-licensed. Its graph databases (FalkorDB/Neo4j) have free tiers, and models can run locally via Ollama.
Not necessarily — it defaults to local Ollama models. Hosted options (OpenAI, Perplexity, Google Maps, Alpha Vantage) use your own keys if you enable those tools.
Memobase focuses on user profiles for chatbots as a service. Memary is a research-oriented memory layer centered on a knowledge graph plus memory-stream/entity-store modules you embed yourself.
pip install memary, set up a free FalkorDB or Neo4j instance in .env, then run the Streamlit demo from the repo. Note the repo's last push was in October 2024.
A serverless, single-file memory layer for AI agents - replace complex RAG pipelines
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