AI Agents
memvid
A serverless, single-file memory layer for AI agents - replace complex RAG pipelines
★ 16.6k starsApache-2.0
View guide →M-flow is a bio-inspired cognitive memory engine for AI agents — a graph alternative to flat RAG
m_flow is an open-source ai agents project by FlowElement-xinliuyuansu (FlowElement-xinliuyuansu/m_flow on GitHub). It has 4,514 GitHub stars and 258 forks, is released under the Apache-2.0 license, and was last updated on GitHub on 1 September 2026.
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.
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