Tools tagged with "Knowledge graphs"

Graphiti is a self-hosted Python framework for developers building AI agents that need to remember changing facts. It builds knowledge graphs from conversations, structured records and unstructured text, so an agent can query current information or recover what was true earlier. It's open source under Apache 2.0.

LightRAG combines knowledge graphs with vector search to answer questions across a document collection. It's a self-hosted Python framework for developers building document assistants, particularly where answers depend on relationships between facts in different files, such as legal or financial material.

MCP Reference Servers is a collection of locally run examples for developers building connections between AI applications and external tools or data. Maintained by the MCP steering group, the servers demonstrate the protocol and its SDKs. They're educational implementations, so developers should assess security requirements before using them in production.

Zep Community Edition v1.0.2 is a deprecated, unsupported self-hosted memory service for developers building AI agents and conversational assistants. This legacy edition uses the Apache 2.0 license. It turns chat history into a knowledge graph that records how facts change over time, so an assistant can distinguish a user's current preferences from earlier ones.

R2R is a self-hosted AI retrieval system for developers building applications that answer questions using their own documents. It combines search, retrieval-augmented generation (RAG) and a reasoning agent behind a REST API. The project is open source under the MIT license and runs as a Python service or in Docker.

SurrealDB is a self-hosted database for developers building AI agents, knowledge graphs and applications that need several kinds of data together. It stores documents, relationships, vectors and time-series data in one engine, so an application's records and its AI retrieval layer can share the same database.

CAMEL is an open-source Python framework for developers and researchers building systems where AI agents work together. Its focus is on agent roles, communication, and behavior across extended tasks, with applications in synthetic training data, task automation, and simulated societies. It uses the Apache 2.0 license.

txtai is a Python framework for developers building search applications, chat with their data, and AI agents on their own hardware or servers. Its embeddings database combines sparse and dense vector search with graphs and relational data, so the same system can find related content and supply context to language models. It's open source under Apache 2.0.

GraphRAG builds a knowledge graph from text so an LLM can answer questions that depend on connections across documents or themes across a whole collection. It's for developers and researchers working with private datasets, such as business documents, proprietary research, or communications.

Cognee gives AI agents persistent memory across sessions, connecting documents, code, and conversations in a searchable knowledge graph. It's for developers who want agents to retain project context and teams whose knowledge sits across tickets, discussions, and repositories. The Python package is open source under Apache 2.0.