AIエージェント
memvid
A serverless, single-file memory layer for AI agents - replace complex RAG pipelines
★ 16.6k スターApache-2.0
ガイドを見る →m_flowは、FlowElement-xinliuyuansuが公開しているオープンソースのAIエージェントプロジェクトです（GitHub: FlowElement-xinliuyuansu/m_flow）。GitHubスターは4,514、フォークは258で、Apache-2.0ライセンスの下で公開されています。GitHubでの最終更新は2026年9月1日です。
M-flow is a bio-inspired cognitive memory engine for AI agents — a graph alternative to flat RAG
Gather the conversations, documents, or files you want the memory engine to learn from.
Install from PyPI with pip or uv.
Point M-flow at an LLM (for example an OpenAI-style API) used for ingestion and query.
Add your data, then ask a question and compare episodic versus triplet-completion retrieval.
Episodes decompose into facets, facet points, and entities, so retrieval can start at the right level of abstraction.
Answers are ranked by the strongest chain of weighted evidence, not just embedding similarity.
Pronouns and references are resolved at write time, so related facts join the same chains.
Episodic, Procedural, Triplet Completion, Lexical, and Cypher modes for different question types.
Remembers how tasks were done, not only facts that were stated.
50+ file formats supported, plus MCP integration for agent clients.
Clone the project and enter its directory for a source install.
git clone https://github.com/FlowElement-xinliuyuansu/m_flow.git
cd m_flowInstall the published package (or `pip install -e .` from the clone).
pip install mflow-aiUse the Python API documented in the README to add data and run your first query.
# see README for the exact ingest/query API callsFlowElement (flowelement.ai) develops M-flow as an open-source project.
Yes, Apache-2.0 licensed. You provide the LLM used for ingestion and querying.
It needs an LLM for ingestion/query — typically your own API key for an OpenAI-style provider, as configured per the README.
Vector stores retrieve similar chunks; GraphRAG builds entity graphs. M-flow's cone graph adds graded abstraction layers and scores answers by evidence-path strength with coreference resolved at ingestion.
Run `pip install mflow-ai`, configure your LLM, ingest a small dataset, and try the Episodic and Triplet Completion modes from the README examples.
A serverless, single-file memory layer for AI agents - replace complex RAG pipelines
The open-source memory layer for autonomous agents — knowledge-graph memory that emulates human memory
Self-evolving memory OS for LLMs and agents: persistent graph memory, hybrid retrieval, skill reuse.
Multi-agent court inspired by ancient China: specialized agents debate and decide.
A memory management kit for agents — remember me, refine me.
Make your agents learn from experience with a dedicated context engine.
LibreAIカタログに掲載されたAIエージェントプロジェクトの中で最もスターの多いもの — m_flowが合わなかった場合に検討したい、現実的な候補リストです。
A personal AI that really does things — on any OS, with your data staying yours.
An agentic skills framework & software development methodology that works.
Agent harness optimization system with skills, memory and security for Claude Code, Codex and more
The agent that grows with you — a Python framework for adaptive AI agents.
DeepSeek's plugin-based agent harness where everything is a plugin.
カタログの中でm_flowとスター数が近いAIエージェントプロジェクト — 前後の棚もそのまま見て回れます。
LongMemory: local-first memory for AI agents with temporal truth, provenance, and hybrid recall
Pydantic Logfire: Python and AI observability on OpenTelemetry — SQL-queryable traces, metrics, and logs
Convene 18 legendary thinkers as an AI council that debates your hardest questions — inside Claude Code
Agent framework for the JVM. Pronounced Em-BAY-bel /ɛmˈbeɪbəl/
Microsoft's open framework for red-teaming generative AI systems.
Memory-LanceDB-Pro gives OpenClaw agents hybrid search, smart extraction, and intelligent forgetting