Search Results for "squid-graph"
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Showing 16 open source projects for "squid-graph"
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Ship Agents FasterGemini Enterprise Agent Platform lets you rapidly build, scale, govern and optimize production-ready agents grounded in your organization's data. The platform enables developers to build custom or pre-built agents for virtually any use case. New customers get $300 in free credits.
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Stop Cyber Threats with VM-Series Next-Gen Firewall on AzureGain integrated visibility across all traffic in a single pass. Deploy Palo Alto Networks VM-Series to determine application identity and content while automating security policy updates via rich APIs.
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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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LangGraph.js
Framework to build resilient language agents as graphs
LangGraphJS is a JavaScript framework designed to build stateful AI applications and autonomous agents using graph-based execution models. Developed as part of the LangChain ecosystem, the framework allows developers to represent complex AI workflows as graphs where nodes represent tasks and edges define the flow of execution. This structure makes it easier to implement long-running agents, multi-step reasoning pipelines, and workflows that require persistent state. -
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Agent Framework
Framework for building, orchestrating, and deploying AI agents
...It includes tools and abstractions for constructing simple conversational agents as well as complex workflows where multiple agents collaborate to complete tasks. Microsoft Agent Framework supports graph-based orchestration that enables developers to connect agents, functions, and tools into structured workflows capable of handling multi-step processes. It also includes components such as agent sessions for managing state, context providers for maintaining memory, and middleware for intercepting and extending agent behavior. Developers can integrate external tools and services so that agents can execute actions beyond text generation. -
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agentmemory
#1 Persistent memory for AI coding agents
...The system automatically records what agents do, compresses that activity into searchable memory, and injects relevant context when a new coding session begins. It combines keyword search, vector search, confidence scoring, lifecycle management, and knowledge graph querying to retrieve useful memories without overloading the context window. The project also includes hooks, an API, import and export support, audit trails, and team sharing workflows. Overall, agentmemory is designed to make AI coding assistants more continuous, context-aware, and useful across long-running software projects. -
Your monitoring isn't a stack. It's a pile. Fix that.Replace Datadog, New Relic, and Sentry without adding three more dashboards.
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OpenMemory
Local long-term memory engine for AI apps with persistent storage
...It enables developers to give otherwise stateless models a structured memory layer that can store, retrieve, and manage contextual information over time. OpenMemory is built around a hierarchical memory architecture that organizes data into semantic sectors and connects them through a graph-based structure for efficient retrieval. It supports multiple embedding strategies, including synthetic and semantic embeddings, allowing developers to balance speed and accuracy depending on their use case. OpenMemory integrates with various AI tools and environments, offering SDKs and APIs that simplify adding memory capabilities to applications. ... -
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Mastra
The TypeScript AI agent framework
...Model routing lets you connect to dozens of providers (OpenAI, Anthropic, Gemini, and others) through a single standardized interface, while agents orchestrate LLM calls and tools to solve open-ended tasks with internal reasoning loops. When explicit control is needed, Mastra’s workflow engine uses a graph-style API (.then(), .branch(), .parallel()) to orchestrate multi-step processes. -
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Basic Memory
Persistent AI memory using local Markdown knowledge graphs
...It uses the Model Context Protocol (MCP) so compatible AI tools can access, update, and build on your notes across sessions. Basic Memory creates a semantic knowledge graph by linking related ideas, making it easier to retrieve, expand, and connect information over time. With a local-first design, your data stays private and portable, while optional cloud sync enables cross-device access. It combines simplicity with powerful indexing and search, giving you a flexible way to build long-term memory for projects, research, and workflows. -
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Lossless Claw
LCM (Lossless Context Management) plugin for OpenClaw
...Instead of relying on traditional sliding-window truncation or lossy summarization, it introduces a lossless architecture that preserves all historical messages while maintaining usable context within token limits. The system stores every interaction in a persistent database and incrementally summarizes older content into a hierarchical directed acyclic graph, allowing efficient compression without discarding information. This structure enables agents to dynamically reconstruct detailed context by expanding summaries when needed, effectively simulating perfect long-term memory. -
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Understand Anything
Turn any codebase into an interactive knowledge graph
Understand-Anything is an AI-driven tool designed to help users deeply understand any topic by generating structured explanations, summaries, and breakdowns. It focuses on transforming complex or unfamiliar subjects into clear, step-by-step explanations that are easier to grasp. The system leverages language models to provide layered insights, allowing users to explore topics at different levels of detail. It is particularly useful for learning, research, and quick comprehension of new... -
$300 Free Credits for Your Google Cloud ProjectsLaunch your next project with $300 in free Google Cloud credits—no strings attached. Test, build, and deploy without risk. Use your credits across the entire Google Cloud platform to find what works best for your needs. After your credits are used, continue with always-free tier services. Only pay when you're ready to scale. Sign up in minutes and start exploring.
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Cody
Type less, code more: Cody is an AI code assistant
...Run Cody's one-click prompts or create your own custom prompts to execute AI workflows. Cody generates single lines, or whole functions, in any programming language, configuration file, or documentation. Cody uses your code graph plus Code Search to autocomplete, explain, and edit your code with additional context. Cody supports the latest LLMs including Claude 3.5, GPT-4o, Gemini 1.5, and Mixtral-8x7B. You can also bring your own LLM key with Amazon Bedrock and Azure OpenAI. -
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Spring AI Alibaba Examples
Spring AI Alibaba examples for building and testing AI apps
...It is designed to help developers understand core concepts, explore practical implementations, and follow best practices when building AI-powered systems using the Spring ecosystem. Each module focuses on a specific use case such as chat, image processing, audio handling, graph workflows, and retrieval-augmented generation. The examples highlight how to integrate AI models, manage prompts, handle memory, and build multi-model or multi-agent workflows. Developers can explore individual project folders for detailed instructions and implementation guidance. Spring AI Alibaba Examples also supports experimentation through playground modules and encourages contributions to expand real-world AI use cases and improve development practices. -
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AI Maestro
Give AI Agents superpowers: memory search, code graph queries
AI Maestro is a framework designed to orchestrate AI workflows and coordinate multiple components into cohesive systems. It focuses on enabling structured interaction between different AI modules, allowing them to collaborate on complex tasks. The system emphasizes modular design, enabling developers to build workflows by combining independent components. It supports automation, allowing tasks to be executed with minimal manual intervention. The framework is designed to be flexible,... -
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Rowboat
Open-source AI coworker, with memory
Rowboat is an open-source, local-first AI automation and multi-agent development platform designed to help developers and knowledge workers create, orchestrate, and manage intelligent workflows with minimal boilerplate and maximum flexibility. It functions as both an AI-powered IDE and a CLI that lets you build multi-agent systems using natural language prompts, connect to MCP and agent tool servers, and integrate automations into everyday work tasks like summarizing emails or generating... -
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Mantic.sh
A structural code search engine for Al agents
...Unlike traditional text-based search tools that mainly match keywords, Mantic.sh understands code structure and meaning by combining syntactic heuristics with neural semantic reranking to produce results that reflect conceptual relevance, which helps find functions, definitions, and patterns that literal search might miss. It uses local embeddings and code graph awareness so that queries like “authentication flow” return not just superficially matching text but contextually related code across multiple repositories. The tool supports navigational commands such as go-to-definition and find-references, simplifying large-scale refactoring, comprehension, and exploration tasks for teams. -
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Rivet
Visual AI IDE for building agents with prompt chains and graphs
Rivet is an open source visual AI programming environment designed to help developers build complex AI agents using a node-based interface and prompt chaining workflows. It provides a desktop application that allows users to visually construct and debug AI logic as interconnected graphs, making it easier to manage sophisticated interactions between language models and external tools. Rivet also includes a TypeScript library that enables these visual graphs to be executed and integrated... -
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Knowledge + Chat
Knowledge is a tool for saving, searching, accessing, and chatting
...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, making it easier to understand relationships between pieces of information. The application also includes a conversational chat interface that allows users to ask questions about stored sources and receive AI-generated explanations or summaries. A built-in Chromium-based browser enables users to capture and analyze web content directly within the application environment.
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