A local AI agent harness in Go that runs 24/7 with auto-built tools, MCP, and Ollama

Run AI apps via inference.sh CLI - image generation, video creation, LLMs, search, 3D, Twitter automation. Models: FLUX, Veo, Gemini, Grok, Claude, Seedance, OmniHuman, Tavily, Exa, OpenRouter, and many more. Use when running AI apps, generating images/videos, calling LLMs, web search, or automating

Autonomous AI agent memory system with CLAUDE.md protocol enforcement

Run AI apps via inference.sh CLI - image generation, video creation, LLMs, search, 3D, Twitter automation. Models: FLUX, Veo, Gemini, Grok, Claude, Seedance, OmniHuman, Tavily, Exa, OpenRouter, and many more. Use when running AI apps, generating images/videos, calling LLMs, web search, or automating

Run AI apps via inference.sh CLI - image generation, video creation, LLMs, search, 3D, Twitter automation. Models: FLUX, Veo, Gemini, Grok, Claude, Seedance, OmniHuman, Tavily, Exa, OpenRouter, and many more. Use when running AI apps, generating images/videos, calling LLMs, web search, or automating

Run AI apps via inference.sh CLI - image generation, video creation, LLMs, search, 3D, Twitter automation. Models: FLUX, Veo, Gemini, Grok, Claude, Seedance, OmniHuman, Tavily, Exa, OpenRouter, and many more. Use when running AI apps, generating images/videos, calling LLMs, web search, or automating

Merge autonomy for AI-authored code. Turn your repo's standards into evidence-backed readiness reports the agent reads back.

Meet Mitsu, your chibi red-team operator. Connect your AI provider, set the scope, and she can take an authorized pentest from recon and attack-surface mapping through vulnerability discovery, > exploitation, and post-exploitation, armored with 165+ MCP-connected tools and live session intel. Guide every step or let her pwn autonomously. ( ◠‿◠)

A local AI agent harness in Go that runs 24/7 with auto-built tools, MCP, and Ollama

Self-hosted project management • Customizable boards • Cross-project workload and flow analytics • Calendar/Agenda • Sticky-note wall • Imports/Backups • Email notifications • MCP/Agent automation

Automated harness evolution for AI agents. A Claude Code plugin that iteratively optimizes system prompts, routing, retrieval, and orchestration code using full-trace counterfactual diagnosis. Based on Meta-Harness (Lee et al., 2026).

Identity, trust, and A2A orchestration for autonomous AI agents. Official A2A partner.

The memory your AI should have had from the start. Automatic capture, automatic recall, 100% local. One SQLite file, zero cloud. Works with Claude Code, Claude CLI, Cursor, Codex CLI, Gemini CLI.

Strategic BDI reasoning engine for autonomous AI agents — goal decomposition, utility scoring, risk evaluation, and reactive replanning over PuterVision memory and world models.

Use GPT, DeepSeek, GLM, Gemini, Kimi and 400+ models in Claude Desktop's own model picker and in Claude Code, while staying signed in to your Claude subscription (1P, no gateway mode). Auto reasoning effort per prompt, subagents routed by difficulty, cache and limit warnings. Claude inside Codex too.

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.

Exposes the full Plexim PLECS Standalone JSON-RPC interface as MCP tools, letting agents auto-start and connect to PLECS to load and edit models, run simulations and analyses, manipulate scopes and webserver data, and perform code generation or FMU export with structured, size-aware results. Includes write guardrails, concurrency handling for long simulations, and batch/raw passthrough escape hatches.

Enables MCP-capable AI agents to control Windows desktops through UI Automation element trees, native screenshots, app launching, batched action sequences, smart waits, and safety gates using normalized coordinates.

A zero-dependency MCP suite exposing computational tools across metabolic, cardio-renal, chronobiology, neuro-recovery, and biomechanics domains. It enables clients to derive longevity and physiology metrics such as autophagy index, glucose-ketone ratio, glycemic variability, peripheral resistance, nocturnal BP dipping, DLMO clock time, melanopic lux, caffeine clearance, EEG fatigue entropy, and grip vitality age from user-supplied measurements.

Exposes MCP tools that compute longevity and metabolic metrics (autophagy index, GKI, MAGE), cardio-renal biomarkers (BUN/creatinine ratio, cardiac output and TPR, nocturnal BP dipping), chronobiology estimates (DLMO, melanopic lux, caffeine clearance), plus neuro-recovery and biomechanics scores. Runs as a zero-dependency Python JSON-RPC 2.0 stdio server so any MCP-compatible client can query these physiological domain calculators.

Enables AI agents to store decisions as a versioned history, linking each new decision to the one it replaces so that recall always resolves to the decision currently in force instead of obsolete ones. It runs fully locally on a single SQLite file with BM25 search, and can optionally detect replacements automatically via an LLM or heuristic linker.

Enables agents to perform autonomous multi-angle specular glare healing and planar foil defect metrology by ranking candidate views, fusing glare-free views, and finalizing inspection or escalating unresolved cases through MCP tools.

Enables agents to use ACT-R spreading activation memory, semantic association graphs, and automated memory consolidation via the Model Context Protocol.

Enables multiple autonomous agents to safely modify shared workspaces and documents by performing three-way reconciliation, detecting collisions, and returning structured conflict data for automated re-arbitration.