🌊 The original agent harness. Deploy intelligent multi-player swarms, coordinate autonomous workflows, and build conversational AI systems. Features adaptive memory, self-learning intelligence, federation, vector RAG integration, and native Claude Code / Codex / Hermes and many more Integrated

Search that learns from feedback, over text and images: embeds locally, sends DNA only vectors.

Local deterministic memory and safety kernel for AI agents. Replaces vector RAG with STRATA, a cryptographically signed, append-only ledger, so every agent state carries proof. Uses causal_walk engine to trace execution failures back to their root cause, and Operator Lite to block destructive terminal terminal commands before they ever run

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Context engineering for AI agents. ~80% fewer tokens. Fix tool overload. Skills and memory with in-process BM25 and semantic retrieval. Progressive Disclosure. No vector DB.

The open-source context layer for AI agents. PipesHub turns your company's knowledge (Slack, Drive, Jira, GitHub, Microsoft 365 and 40+ connectors) into a permission-aware workspace that agents can search, grep, navigate and cite. MCP, SDKs and built-in agents. Self-hosted.

A lightweight, rollbackable, and visual Long-Term Memory Server for MCP Agents. Say goodbye to Vector RAG and amnesia. Empower your AI with persistent, graph-like structured memory across any model, session, or tool. Drop-in replacement for OpenClaw.

Stores what AI agents learn — thoughts, decisions, session events and repo facts — in PostgreSQL with pgvector and serves it back through versioned MCP tools with hybrid vector-plus-full-text retrieval and budgeted context packs. Enables multiple agents to share one scoped, role-isolated long-term memory, with deliberate promotion of distilled candidates into durable memories.

Mounts a headless, low-memory browser for agents with a text-first tool interface, enabling navigation, semantic tree/markdown extraction, structured-data scraping, screenshots, form interaction, and session capture. Reads and interactions are returned as semantic trees, markdown, or CSS-selector JSON rather than pixels, which is cheaper for a model to reason over.

Enables agents to search local Chinese text memories and fetch individual records through stdio tools, using bigram FTS5 recall with optional vector rank fusion.

⚡ Cut Claude token usage by 90%+ — free, open-source, local-first context compression for Claude Code. Hybrid RAG (BM25 + ONNX vectors), AST chunking, reranking. No API needed.

A self-hosted MCP memory server that gives every MCP-compatible agent a shared, persistent memory layer, so preferences, project conventions, and past decisions carry across sessions and across different agents and vendors. It combines hybrid keyword-plus-vector retrieval with automatic context injection, zero-config sync, and selective injection of memories by agent and priority.

Enables Claude to search and read an organization's internal documents (Word, PDF, HTML, Markdown) through hybrid BM25 + vector retrieval, returning answers with exact citations while enforcing server-fixed role permissions, hiding confidential and superseded content, and logging the token cost of every request.

Enables any agent to search skill packs with bounded results, fetch one full validated skill on demand, and act without copying directories into prompts.

Enables any MCP-compatible client to persist long-term memories for AI agents, with LLM-based fact extraction, semantic vector search, update/delete/list operations, and queue status checks across sessions.

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