Search Results for "context"
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373 projects for "context" with 2 filters applied:
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Context Engineering
A frontier, first-principles handbook
Context Engineering is a comprehensive, open-source project serving as a first-principles handbook for the emerging discipline of context design and optimization in AI. Moving beyond traditional prompt engineering, this repository defines and explores how to craft and provide complete context payloads — not just single prompts — to large language models so they can perform tasks more reliably and intelligently. -
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Context Hub
Makes coding agents get smarter with every task
...It also supports local annotations, allowing an agent to remember project-specific notes, pitfalls, or workarounds across future sessions. Feedback can be sent back to maintainers so shared documentation improves over time. Context Hub is especially useful for teams that use AI coding assistants and want more reliable API usage, fewer hallucinated calls, and a transparent source of agent-readable context. -
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Sec-Context
AI Code Security Anti-Patterns distilled from 150+ sources
Sec-Context is a curated security research project that distills common code anti-patterns and vulnerabilities that generative AI tends to produce, presenting them as a comprehensive set of examples and secure alternatives that can be used to train or guide AI assistants and reviewers toward safer code generation. It compiles insights from over 150 industry and academic sources into structured reference documents that outline real-world security problems such as hardcoded secrets, SQL injection, cross-site scripting, command injection, weak password storage, and other frequent issues that occur when code is auto-generated without context of best practices. ... -
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Context Engineering Template
Context engineering is the new vibe coding
Context Engineering Template is a comprehensive template and workflow repository designed to teach and implement context engineering, a structured approach to preparing and organizing the information necessary for AI coding assistants to complete complex tasks reliably. Instead of relying solely on short prompts, this project encourages developers to create rich, structured context files that include project rules, examples, and validation criteria so that AI systems can act more like informed collaborators and less like general-purpose generators. ... -
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Prompt in-context learning
Resources for in-context learning and prompt engineering
Prompt-In-Context-Learning is an open-source repository that serves as a comprehensive engineering guide and curated resource collection for understanding and applying in-context learning and prompt engineering with large language models. The project gathers research papers, tutorials, prompt examples, and practical guides that help developers and researchers learn how to design effective prompts for models such as GPT-3, ChatGPT, and other foundation models. -
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Agentic Context Engine
Make your agents learn from experience
Agentic Context Engine (ACE) is an open-source framework designed to help AI agents improve their performance by learning from their own execution history. Instead of relying solely on model training or fine-tuning, the framework focuses on structured context engineering, allowing agents to accumulate knowledge from past successes and failures during task execution. -
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Model Context Protocol (MCP)
Specification and documentation for the Model Context Protocol
Model Context Protocol is an open protocol that standardizes how LLM applications connect to external data sources, tools, and runtime context. It separates the concern of providing context from the model interaction itself, allowing AI applications to access resources through a common interface. The project includes the specification, documentation, SDKs, maintained servers, and community infrastructure around the protocol. -
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Lossless Claw
LCM (Lossless Context Management) plugin for OpenClaw
Lossless Claw is an advanced context management plugin for the OpenClaw agent ecosystem that redefines how conversational memory is handled in large language model systems. 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. -
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LlamaIndex
Central interface to connect your LLM's with external data
...It provides the following tools in an easy-to-use fashion. Provides indices over your unstructured and structured data for use with LLM's. These indices help to abstract away common boilerplate and pain points for in-context learning. Dealing with prompt limitations (e.g. 4096 tokens for Davinci) when the context is too big. Offers you a comprehensive toolset, trading off cost and performance. -
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GLM-5
From Vibe Coding to Agentic Engineering
...Building on earlier GLM series models, GLM-5 dramatically scales the parameter count (to roughly 744 billion) and expands pre-training data to significantly improve performance on complex tasks such as multi-step reasoning, software engineering workflows, and agent orchestration compared to its predecessors like GLM-4.5. It incorporates innovations like DeepSeek Sparse Attention (DSA) to preserve massive context windows while reducing deployment costs and supporting long context processing, which is crucial for detailed plans and agent tasks. -
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Claude Cognitive
Persistent context and multi-instance coordination
Claude Cognitive is an advanced memory and context-management extension designed to address the stateless limitations of Claude Code by giving the model a form of persistent “working memory” and multi-instance coordination. It introduces an attention-based context router that prioritizes files and content relevant to the current development discussion — tagging them as HOT, WARM, or COLD based on recency and keyword activation — so Claude Code doesn’t waste token budget rereading irrelevant code. ... -
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Claude Code
Claude Code is an agentic coding tool that lives in your terminal
...It helps developers code faster by executing routine tasks, explaining complex code snippets, and managing git workflows—all via natural language commands. Claude Code integrates seamlessly into your terminal, IDE, or GitHub by tagging @claude to interact with your code context. The tool is designed to simplify development by automating repetitive work and providing instant clarifications on code behavior. User feedback and usage data are collected responsibly, with strict privacy safeguards and limited retention, ensuring no feedback is used to train generative models. Claude Code is open and actively maintained with community-driven bug reporting and feature requests. ... -
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VoxCPM
TTS for Context-Aware Speech Generation and True-to-Life Voice Cloning
...Trained on a large 1.8-million-hour bilingual corpus, VoxCPM can infer appropriate speaking style from context, dynamically adjusting intonation, rhythm, and emotional tone. It supports zero-shot voice cloning from a short reference audio clip, capturing timbre, accent, and pacing to closely mimic a target speaker without per-speaker fine-tuning. -
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Kimi-K3
Open Frontier Intelligence
...Its design combines Kimi Delta Attention, gated multihead latent attention, Attention Residuals, and Stable LatentMoE routing. The model can process text and images and supports a context window of more than one million tokens. It is built for long engineering sessions, large repository navigation, tool orchestration, research, visualization, and other extended tasks. The repository publishes the model overview, technical report, evaluation results, architecture specifications, and open weights under the Kimi K3 License. -
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DeepSeek Coder V2
DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models
...The repository provides updated model weights, evaluation results on benchmarks (e.g. HumanEval, MultiPL-E, APPS), and new inference/serving scripts. Compared to the original, DeepSeek-Coder-V2 likely incorporates improved context management, caching strategies, or enhanced infilling capabilities. The project aims to provide a more performant and reliable open-source alternative to closed-source code models, optimized for practical usage in code completion, infilling, and code understanding across English and Chinese codebases. -
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Codex MCP Server
MCP server wrapper for OpenAI Codex CLI
Codex MCP Server is an open-source integration tool that allows AI development environments to access the capabilities of the OpenAI Codex command-line interface through the Model Context Protocol. The project acts as a bridge between AI assistants such as Claude Code and the Codex CLI, enabling those assistants to perform advanced coding operations using Codex as a backend engine. Through this architecture, developers can request tasks such as code explanation, refactoring, or analysis directly from their AI assistant while the server forwards the request to Codex. ... -
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DeepSeek R1
Open-source, high-performance AI model with advanced reasoning
...DeepSeek R1 offers unrestricted access for both commercial and academic use. The model employs a Mixture of Experts (MoE) architecture, comprising 671 billion total parameters with 37 billion active parameters per token, and supports a context length of up to 128,000 tokens. DeepSeek-R1's training regimen uniquely integrates large-scale reinforcement learning (RL) without relying on supervised fine-tuning, enabling the model to develop advanced reasoning capabilities. This approach has resulted in performance comparable to leading models like OpenAI's o1, while maintaining cost-efficiency. ... -
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Kanwas
Shared context board for teams and agents
Kanwas is an open-source shared context board built for teams and AI agents working together in the same workspace. It gives people and agents a common canvas where documents, evidence, decisions, notes, tasks, embeds, and outputs can live side by side. Instead of scattering context across chats, documents, and disconnected tools, Kanwas turns messy collaborative work into a shared visual environment that both humans and agents can read and update. -
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MoBA
MoBA: Mixture of Block Attention for Long-Context LLMs
...The architecture adapts ideas from Mixture-of-Experts networks and applies them directly to the attention mechanism of transformer models. Instead of forcing each token to attend to every other token in the sequence, MoBA divides the context into blocks and dynamically routes queries to only the most relevant segments of information. This routing strategy reduces the computational cost associated with traditional attention while preserving performance on reasoning and long-context tasks. The approach allows language models to scale to significantly longer input contexts without the quadratic computational cost normally associated with transformer attention mechanisms. -
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GLM-4.6
Agentic, Reasoning, and Coding (ARC) foundation models
GLM-4.6 is the latest iteration of Zhipu AI’s foundation model, delivering significant advancements over GLM-4.5. It introduces an extended 200K token context window, enabling more sophisticated long-context reasoning and agentic workflows. The model achieves superior coding performance, excelling in benchmarks and practical coding assistants such as Claude Code, Cline, Roo Code, and Kilo Code. Its reasoning capabilities have been strengthened, including improved tool usage during inference and more effective integration within agent frameworks. ... -
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CAG
Cache-Augmented Generation: A Simple, Efficient Alternative to RAG
...The framework is particularly effective when the knowledge base is limited enough to fit within the extended context window of modern language models. -
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RAPTOR
The official implementation of RAPTOR
RAPTOR is a retrieval architecture designed to improve retrieval-augmented generation systems by organizing documents into hierarchical structures that enable more effective context retrieval. Traditional RAG systems typically retrieve small text chunks independently, which can limit a model’s ability to understand broader document context. RAPTOR addresses this limitation by recursively embedding, clustering, and summarizing documents to create a tree-structured hierarchy of information. Each level of the tree represents summaries at different levels of abstraction, allowing retrieval to operate at both detailed and high-level conceptual layers. ... -
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FastAPI-MCP
Expose your FastAPI endpoints as Model Context Protocol (MCP) tools
fastapi_mcp lets you expose existing FastAPI endpoints as Model Context Protocol (MCP) tools with minimal setup, so AI agents can call your app as first-class tools. Rather than acting as a thin converter, it’s built as a native FastAPI extension that understands dependency injection, so you can reuse Depends() for authentication and authorization across your MCP tools. The server speaks directly to your app over its ASGI interface, avoiding extra HTTP hops between the MCP layer and your API, which reduces latency and simplifies deployment. ... -
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Harness Engineering
Field guide, and agent context bundle for harness engineering
Harness Engineering is a retrieval-optimized anthology, field guide, and agent context bundle for improving AI coding-agent performance. It treats the model and agent as fixed while strengthening the surrounding context, tools, constraints, and proof mechanisms. The repository organizes developed arguments, practical cases, source evidence, evaluations, and reusable playbooks into distinct layers. Its agent guide routes each task to the smallest relevant set of materials instead of loading the entire corpus. ... -
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MemMachine
Universal memory layer for AI Agents
MemMachine is a universal memory layer designed for AI agents that provides persistent, rich memory storage and retrieval capabilities so autonomous agent systems can recall context, personal preferences, and long-term interaction history across sessions, models, and use cases. Unlike ephemeral LLM prompt state, MemMachine supports distinct memory types—short-term conversational context, long-term persistent knowledge, and profile memory for personalized facts—persisted in optimized stores (e.g., graph databases for episodic lines of reasoning and SQL for user facts) to support robust, context-aware intelligence in agents. ...
