billion-context

Context Mode is an MCP server and plugin for AI coding agents that reduces context-window usage by routing large tool outputs through sandboxed subprocesses. Its execution tools run code in supported languages, process files, fetch and analyze URLs, and return selected stdout or search results instead of exposing raw logs, snapshots, API responses, or file contents to the conversation. The project reports up to 98% context reduction in its benchmarks. It also provides session continuity across supported agents. Hooks capture tool calls, edits, prompts, decisions, errors, and other session events in SQLite; before compaction, the system builds a prioritized resume snapshot, and after compaction or session resumption it retrieves relevant events through SQLite FTS5 search with BM25 ranking. Content indexing uses heading-aware chunking, stemming, trigram matching, reciprocal-rank fusion, proximity reranking, fuzzy correction, and smart snippets. The MCP interface includes execution, batching, indexing, search, fetching, statistics, diagnostics, upgrade, and purge tools. Context Mode supports multiple coding-agent platforms through MCP servers, native plugins, and platform-specific hooks, with automatic routing enforcement where hooks are available and instruction files for platforms without them. The repository states that processing and SQLite storage are local, with no account or telemetry requirement. It is licensed under the Elastic License 2.0, which permits use, modification, and distribution but restricts offering the software as a hosted or managed service and removing licensing notices.

Paritok is an open-source compression gateway for AI coding agents. It runs as a drop-in proxy between an agent and an upstream LLM API, stripping irrelevant tool schemas, compressing tool results and file reads, and summarizing stale conversation history before forwarding requests. Compressed content remains recoverable on demand through the gateway, while responses pass back to the agent unchanged. It supports agents such as Claude Code, Cursor, Codex, and OpenHands, as well as agents that support BASE_URL configuration, and is powered by an open-source code-focused 4B compression model.

Context Engine Simulator is an open-source tool for creating context bundles locally and A/B testing agent tasks with and without pre-gathered context. It is presented as a way to test whether additional context from sources such as pull requests, Slack threads, and incident history improves coding-agent tasks and reduces token use.

Context Ontology Accelerator is an open-source semantic context layer for AWS and AI agents. It combines knowledge graphs, formal ontologies, rule-based systems, and modern AI in a Scan → Model → Serve workflow: it connects data sources, discovers schemas, enriches metadata, and ingests documents; induces and manages ontologies, defines metrics, and builds a unified semantic graph; then serves context through SPARQL federation using a Virtual Knowledge Graph, knowledge-graph traversal, queries, and MCP tools. The repository includes source-ingestion services for databases and documents, ontology induction and reasoning with HermiT and ELK, an Ontop-based Virtual Knowledge Graph, a context manager for query orchestration, and a React frontend. Access is controlled through namespace isolation and role-based authorization, with namespace-scoped roles and platform-level roles. The implementation uses Python and TypeScript, AWS CDK, Smithy-generated API contracts, and an Nx-managed monorepo; the project is licensed under Apache License 2.0.

Paritok-4B-v1 is an open-source 4B model for compressing coding-agent context, including tool outputs, file reads, and older conversation history. Used through a local proxy, it replaces compressed blocks with reference IDs so an agent can request the exact original content when needed. The repository says the model was trained on 45K teacher-distilled agent trajectories using a Qwen3-4B backbone.