31 Repos
Systems that store and retrieve structured information to provide context and persistent memory for AI agents.
Distinguishing note: Focuses on persistent storage of context for AI, distinct from general-purpose graph databases.
Explore 31 awesome GitHub repositories matching artificial intelligence & ml · Knowledge Graphs. Refine with filters or upvote what's useful.
Graphify is a knowledge retrieval system that transforms directories of source code and documentation into structured, queryable project maps. It utilizes a code-to-graph parser to extract technical metadata and system connectivity, converting a mix of code, SQL schemas, and documentation into a unified graph structure. The project distinguishes itself by integrating these knowledge graphs with AI coding assistants through a Model Context Protocol server and dedicated tool hooks. This allows AI agents to perform lookups and impact analysis on node neighbors and shortest paths to understand ho
Transforms folders of source code and documentation into queryable graphs to analyze system architecture and dependencies.
This project is a curated knowledge repository designed to support the professional development of software engineers. It functions as a comprehensive index of industry best practices, methodologies, and design principles, providing a structured roadmap for those seeking to improve their technical skills, architectural decision-making, and career trajectory. The repository distinguishes itself through a community-driven approach, relying on peer-reviewed contributions to maintain an up-to-date collection of resources. It organizes vast amounts of technical information into a hierarchical taxo
Connects technical concepts through internal anchors to create a web of related knowledge.
Codegraph is a local codebase indexer and static analysis graph database that serves as a context provider for AI agents. It parses multiple programming languages into a searchable knowledge graph of symbols and dependencies, exposing these relationships to AI tools through the Model Context Protocol. The project distinguishes itself by aggregating relevant code snippets and symbol flows to reduce token usage for large language models. It automates the configuration of server settings and steering instructions across various AI agent platforms and command line editors to enable automatic code
Analyzes call paths, imports, and inheritance to map symbol dependencies across multiple programming languages.
This project is an autonomous agent framework designed to integrate large language models with popular messaging platforms. It functions as a middleware platform that enables automated, multimodal interactions by decomposing complex user goals into sequential plans, executing them through external tools, and maintaining persistent context across sessions. The framework distinguishes itself through a modular skill architecture and a hybrid memory system. Users can extend system capabilities by installing custom logic modules from community hubs or generating them through natural language. The
Agent framework organizes information into a structured network by automatically extracting insights from conversations and building an interactive, cross-referenced graph of concepts.
This project is a comprehensive, community-driven knowledge repository designed to support software engineers in mastering distributed systems and architectural design. It functions as a structured compendium of engineering principles, providing a centralized index of patterns, trade-offs, and best practices required for building scalable and reliable software infrastructure. The repository distinguishes itself through a highly organized taxonomy that connects complex technical concepts into a cohesive learning path. It features a categorized collection of system design interview problems, ra
Connects disparate technical concepts and learning paths through a network of references.
This project is a comprehensive, community-driven directory that serves as a centralized discovery hub for the container ecosystem. It functions as a structured knowledge base, aggregating a wide array of software tools, educational materials, and technical resources designed to assist developers and operators in mastering containerization technologies. The repository distinguishes itself through a meticulously organized taxonomy that maps the entire container lifecycle, from initial development and image building to orchestration, security, and infrastructure operations. By curating disparat
Connects related technical resources through a web of references to map the ecosystem.
This project is a comprehensive, community-curated directory of resources and methodologies for open-source intelligence gathering. It serves as a centralized reference framework for researchers, providing a structured index of specialized tools, databases, and search techniques used to collect and analyze publicly available information from across the global internet. The directory distinguishes itself through a hierarchical taxonomy that organizes complex investigative domains, ranging from cyber threat intelligence and digital forensic investigation to geospatial analysis and operational s
Provides a community-curated collection of categorized links and resources for mapping investigative tools.
Graphiti is a backend framework and memory server designed to provide artificial intelligence agents with persistent, time-aware knowledge graph storage. It functions as a memory layer that enables agents to maintain context across long-term interactions by recording and evolving structured data over time. The system distinguishes itself through a specialized temporal graph database that tracks how entities and relationships change using validity windows. By combining semantic vector similarity, keyword matching, and graph topology traversal, the engine performs hybrid retrieval to locate rel
Maintains temporal knowledge graphs to provide structured, time-aware context and long-term memory for agents.
Forem is an open-source platform designed for building and managing technical communities. It functions as a social publishing engine that enables members to share long-form content, participate in threaded discussions, and engage through social interactions. The platform provides tools for organizations to maintain branded profiles, host community hackathons, and facilitate collaborative learning through structured educational tracks. Beyond its social features, Forem integrates advanced capabilities for AI agent workflow orchestration and codebase knowledge graphing. It allows developers to
Provides automated codebase mapping and dependency analysis to enable agents to navigate and query project architecture.
This project provides a framework for managing multi-agent systems, designed to automate complex software development, infrastructure, and business workflows. It functions as a multi-agent workflow orchestrator that routes tasks to domain-specific workers while maintaining state persistence and infrastructure automation. By leveraging large language models, the system decomposes high-level objectives into actionable plans, ensuring that complex operations are executed with consistency and reliability. The framework distinguishes itself through its hierarchical agent registry and policy-driven
Builds knowledge graphs to map entities and relationships across agent ecosystems.
This project is a static code analysis tool and local-first code indexer that builds a persistent dependency graph of functions, classes, and imports. It functions as an AI context optimizer and codebase dependency graph, designed to reduce token usage by providing AI assistants with only the most relevant code fragments and impact analysis for a given change. The system implements a Model Context Protocol server that exposes code intelligence and architectural graph queries to external AI coding tools. It distinguishes itself by computing the change blast radius and risk scores of modificati
Creates a searchable structural map of functions and dependencies to visualize and understand architectural relationships.
DeepResearch is an autonomous research agent framework designed to orchestrate multi-step information gathering and complex reasoning tasks. The platform functions as an agent orchestration system that manages the entire lifecycle of autonomous research, from initial planning and web navigation to the synthesis of evidence-backed reports. The framework distinguishes itself through a specialized training pipeline that supports the development and fine-tuning of autonomous models using reinforcement learning and structured knowledge graph synthesis. By employing parallel agent coordination, the
Transforms retrieved web information into structured knowledge graphs to support complex reasoning.
Cognee is an agentic memory management platform designed to provide autonomous agents with long-term semantic recall and structured knowledge. It functions as a framework for building persistent memory systems that connect large language models to graph-based knowledge and vector storage, enabling agents to maintain context across complex tasks and multiple sessions. The platform distinguishes itself through a hybrid approach that combines semantic similarity search with structural graph traversal, allowing for context-aware information retrieval. It features a modular architecture that orche
Connects persistent knowledge graphs to AI assistants to provide long-term context and semantic understanding.
Pentagi is an autonomous security testing framework and agent orchestrator designed to plan and execute end-to-end security assessments. It utilizes a coordination engine to decompose complex goals into actionable subtasks, performing automated penetration testing and vulnerability research within isolated container environments. The system distinguishes itself through a temporal knowledge graph that tracks semantic relationships between entities and vulnerabilities to reuse intelligence across projects. It includes a web intelligence reconnaissance tool for automated data gathering and agent
Implements a temporal knowledge graph to track semantic relationships between entities and vulnerabilities for AI-driven analysis.
Beads is a versioned, dependency-aware graph database designed for distributed issue tracking and project management. It functions as an agentic workflow orchestrator, providing a structured environment where tasks, dependencies, and project metadata are linked through relational hierarchies. By maintaining a persistent, version-controlled record of project state, the system enables teams to manage complex work items across multiple repositories and environments. The platform distinguishes itself through its deep integration with automated coding agents, acting as a Model Context Protocol ser
Links tasks through relational hierarchies to maintain structured project context for automated agents.
Neo4j is a native graph database management system designed to store and query highly connected data using a property-graph model. It provides an ACID-compliant transaction engine that ensures data integrity, supported by a distributed cluster architecture that maintains causal consistency across nodes. Users interact with the system through a declarative query language, which allows for complex pattern matching and path traversal without requiring manual traversal logic. The platform distinguishes itself through its hybrid approach to data retrieval, combining traditional graph-based queries
Store, search, and update entities, observations, and relationships to maintain structured memory or knowledge bases within a graph.
Gitdiagram is a software architecture visualization tool that generates interactive diagrams from repository file hierarchies. By performing automated static code analysis, the system maps file structures and component dependencies to provide a visual representation of how different modules relate within a codebase. The platform functions as a searchable documentation catalog, allowing users to discover and explore architectural visualizations of public repositories. It combines server-side rendering for initial delivery with a client-side engine that enables users to dynamically manipulate a
Parses source code to generate interactive visual maps of file structures and dependencies.
Planning with files is an enterprise knowledge graph platform designed to transform unstructured organizational data into a searchable, interconnected network. By utilizing a graph-based retrieval-augmented generation engine, the system grounds language model outputs in verified internal data, ensuring that responses are explainable, traceable, and free from hallucinations. The platform distinguishes itself through a focus on data sovereignty and secure, private infrastructure deployment. It enables organizations to maintain full control over sensitive information by processing data locally o
Utilizes knowledge graphs to provide context and persistent memory for AI agents, grounding generated content in verified internal data.
This project is a multi-model database system designed to store and manage information as documents, graphs, and key-value pairs within a single engine. It functions as a graph database and knowledge graph platform, providing the infrastructure to build, query, and visualize structured data models. By integrating vector search capabilities, the system serves as a vector database that supports retrieval-augmented generation for artificial intelligence applications. The platform distinguishes itself through a unified query language that allows users to perform document lookups, graph traversals
Provides a data management environment for building and querying structured knowledge bases for AI context.
Spring-analysis is a diagnostic utility designed to visualize the internal architecture and execution logic of Java applications built on the Spring Framework. It functions as a static analysis tool that parses source code to map structural relationships and component interactions without requiring the program to execute. The tool distinguishes itself by automatically extracting configuration and annotation data to identify beans and service definitions, which it then translates into visual representations of the system. By reconstructing method call hierarchies and event propagation paths, i
Maps complex software architectures and component interactions to improve understanding of enterprise Java codebases.