Search Results for "call graph visualization"
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Showing 23 open source projects for "call graph visualization"
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codebase-memory-mcp
High-performance code intelligence MCP server
...It ships as a single static binary for macOS, Linux, and Windows with no shared library dependencies. It also includes semantic search, BM25 search, graph visualization, auto-indexing, and cross-repository intelligence. -
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Code-Graph-RAG
The ultimate RAG for your monorepo
...This structured approach enables more accurate and context-aware querying compared to traditional text-based search methods, allowing users to ask natural language questions about code structure and functionality. The system integrates with graph databases such as Memgraph to store and manage relationships, enabling efficient querying and visualization of complex dependencies. It also supports AI-driven query translation, converting natural language into graph queries for deeper analysis and interaction. -
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CodeGraph
Pre-indexed code knowledge graph for Claude Code, Codex, Cursor
CodeGraph is a local-first code intelligence tool that gives AI coding agents a pre-indexed understanding of a repository. Instead of forcing agents to repeatedly scan files with grep, glob, and read commands, it builds a searchable knowledge graph of symbols, relationships, call graphs, and code structure. The project uses deterministic parsing rather than LLM summaries, which makes its indexed data more predictable and grounded in the actual source tree. It is designed for tools such as Claude Code, Codex, Cursor, and OpenCode, with MCP support for agent access. CodeGraph stores project data locally, which helps reduce token usage and repeated file exploration. ... -
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Graphify
AI coding assistant skill (Claude Code, Codex, OpenCode, OpenClaw)
Graphify is a data visualization and transformation tool designed to convert structured or semi-structured data into graph-based representations, enabling better understanding of relationships and dependencies. It focuses on building visual models such as nodes and edges that represent entities and their connections, making complex datasets easier to interpret. -
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grepai
Semantic Search & Call Graphs for AI Agents
...It builds a semantic index of a project using vector embeddings, enabling natural language queries like “authentication logic” to return contextually relevant functions and modules even when naming differs dramatically, making code exploration far more intuitive. In addition to semantic search, grepai offers call graph tracing so developers can understand which functions call or are called by others, aiding impact analysis and confident refactoring. Because it runs 100 % locally, your codebase never leaves your machine, preserving privacy and security while supporting AI agents and custom integrations. -
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LangGraph Studio
Desktop app for prototyping and debugging LangGraph applications
LangGraph Studio offers a new way to develop LLM applications by providing a specialized agent IDE that enables visualization, interaction, and debugging of complex agentic applications. With visual graphs and the ability to edit state, you can better understand agent workflows and iterate faster. LangGraph Studio integrates with LangSmith so you can collaborate with teammates to debug failure modes. While in Beta, LangGraph Studio is available for free to all LangSmith users on any plan... -
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Persona
Create agentic front-end experiences for the web in VanillaJS
...The widget is backend-agnostic and connects to any SSE-capable agent or model backend through the Persona streaming protocol. It supports streaming chat, multimodal attachments, voice input and output, WebMCP page tools, local client tools, approval gates, tool call visualization, artifact rendering, and safe markdown or HTML output. It also includes configurable layouts such as floating, docked, fullscreen, and inline experiences. Overall, it is useful for teams that want a customizable web assistant UI without committing to a specific framework. -
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SynaBun
Persistent vector memory for AI assistants
...The system integrates tightly with developer workflows by running alongside tools like Claude Code, enabling automatic memory capture, retrieval, and contextual augmentation through lifecycle hooks and commands. One of its defining characteristics is its Neural Interface, a browser-based 3D visualization that represents stored memories as nodes in an interactive graph, allowing users to explore relationships, edit entries, and manage knowledge visually. -
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Materials Discovery: GNoME
AI discovers 520000 stable inorganic crystal structures for research
Materials Discovery (GNoME) is a large-scale research initiative by Google DeepMind focused on applying graph neural networks to accelerate the discovery of stable inorganic crystal materials. The project centers on Graph Networks for Materials Exploration (GNoME), a message-passing neural network architecture trained on density functional theory (DFT) data to predict material stability and energy formation. Using GNoME, DeepMind identified 381,000 new stable materials, later expanding the... -
Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
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MathCode
A Frontier Mathematical Coding Agent
...It supports an agentic proving workflow where the system behaves more like an interactive mathematical engineer than a one-shot text generator. MathCode also includes visualization-oriented tooling such as theorem graph generation for Obsidian knowledge workflows. Its main value is bridging natural-language mathematics with formal verification systems in a way that is more automated, inspectable, and iterative than traditional theorem-proving pipelines. -
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claude-devtools
A desktop app that reconstructs exactly what Claude Code did
claude-devtools is an open-source desktop observability tool designed to provide deep visibility into Claude Code sessions by reconstructing execution activity directly from local session logs. Rather than acting as a wrapper or modifying Claude Code behavior, the application passively reads the logs stored in the user’s environment and rebuilds a structured, searchable timeline of what actually occurred during each session. The tool was created to address the loss of detail in the standard... -
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Apache Hamilton
Helps data scientists define testable self-documenting dataflows
Apache Hamilton is an open-source Python framework designed to simplify the creation and management of dataflows used in analytics, machine learning pipelines, and data engineering workflows. The framework enables developers to define data transformations as simple Python functions, where each function represents a node in a dataflow graph and its parameters define dependencies on other nodes. Hamilton automatically analyzes these functions and constructs a directed acyclic graph... -
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Aix-DB
Based on the LangChain/LangGraph framework
Aix-DB is an open-source intelligent data analysis platform that combines large language models with database technologies to enable conversational data exploration. The system is designed as a ChatBI solution that allows users to query datasets using natural language and receive structured insights, charts, and visualizations automatically. Built on frameworks such as LangChain and LangGraph, Aix-DB integrates retrieval-augmented generation and Text-to-SQL capabilities to convert user... -
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MLDatasets.jl
Utility package for accessing common Machine Learning datasets
This package represents a community effort to provide a common interface for accessing common Machine Learning (ML) datasets. In contrast to other data-related Julia packages, the focus of MLDatasets.jl is specifically on downloading, unpacking, and accessing benchmark datasets. Functionality for the purpose of data processing or visualization is only provided to a degree that is special to some datasets. -
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Agentic Data Scientist
An end-to-end Data Scientist
...Unlike traditional scripted pipelines, this project lets AI agents break down high-level research goals into sub-tasks such as data acquisition, cleaning, modeling, evaluation, and reporting, with minimal human direction. Each agent is designed to independently call functions, interact with data sources, and adapt to uncertainties during processing, enabling iterative refinement of models without manual coordination. The framework supports interoperability with existing data tools and libraries, letting the agents leverage libraries like pandas, scikit-learn, and visualization frameworks to perform real computations rather than mock demonstrations. -
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Knowledge + Chat
Knowledge is a tool for saving, searching, accessing, and chatting
Knowledge is an open-source desktop application designed for organizing, exploring, and interacting with information gathered from websites, documents, and other digital sources. The platform allows users to collect knowledge from multiple formats including web pages, PDF files, videos, and other documents and organize them into structured projects and subprojects. These sources can then be visualized and explored using several views such as graph, grid, table, and calendar interfaces,... -
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Pattern
Web mining module for Python, with tools for scraping
Pattern is an open-source Python library that provides tools for web mining, natural language processing, machine learning, and network analysis. The project integrates multiple capabilities into a single framework that allows developers to collect, process, and analyze textual data from the web. It includes modules for web scraping and crawling that can retrieve information from sources such as social media platforms, search engines, and online knowledge bases. In addition to data mining... -
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TFLearn
Deep learning library featuring a higher-level API for TensorFlow
...Full transparency over Tensorflow. All functions are built over tensors and can be used independently of TFLearn. Powerful helper functions to train any TensorFlow graph, with support of multiple inputs, outputs, and optimizers. Easy and beautiful graph visualization, with details about weights, gradients, activations, and more. Effortless device placement for using multiple CPU/GPU. The high-level API currently supports the most of the recent deep learning models, such as Convolutions, LSTM, BiRNN, BatchNorm, etc. -
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StellarGraph
Machine Learning on Graphs
StellarGraph is a Python library for machine learning on graphs and networks. The StellarGraph library offers state-of-the-art algorithms for graph machine learning, making it easy to discover patterns and answer questions about graph-structured data. It can solve many machine learning tasks. Graph-structured data represent entities as nodes (or vertices) and relationships between them as edges (or links), and can include data associated with either as attributes. For example, a graph can... -
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Agent.GUI
The Project moved to github https://github.com/EnFlexIT/AgentWorkbench
The project has moved to github https://github.com/EnFlexIT/AgentWorkbench Agent.GUI is a simulation framework and toolkit based on the JADE framework. It provides functionalities for time aspects, agent-environment interaction, visualization and load balancing, Furthermore, the included application focuses the usability for end users. -
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Java Universal Network/Graph Framework
Java graph/network library
JUNG provides a common and extendible language for the modeling, analysis, and visualization of data that can be represented as a graph or network. New version now available on GitHub: https://github.com/jrtom/jung/releases/tag/jung-2.1 -
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RectaGraphTrace aims to assist in step-by-step visualization of graph search algorithms by displaying graphs and automating the creation and correction of graph traces such as those that a student would produce while manually executing a search.
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