Cognithor

Cognithor · Agent OS

A local-first, autonomous agent operating system for AI experimentation and personal automation.

Cognition + Thor — Intelligence with Power

cognithor.ai · Agent Packs · Docs · Blog

19 LLM Providers · 17 Channels · 6-Tier Memory · 4-Channel Search · Knowledge Vault · Security · Apache 2.0

> Pre-v1.0 Beta — Cognithor is under active development. APIs may change between releases. Not recommended for production customer-facing deployments. Bug reports and feedback welcome via [Issues](../../issues). > > While the test suite is extensive (17,000+ test functions, 89% coverage gate), the project has not been battle-tested in production environments. Expect rough edges, breaking changes between versions, and some German-language strings in system prompts and error messages. See [Status & Maturity](#status--maturity) for details. For non-technical users, wait until version 1.0.0 for stable long-term support.

[](https://clawdboard.ai/user/Alex8791-cyber)

Weekly Recap: Rank #1 | $1,644 spent vibe-engineering

> Vibe-Engineered, not vibe-coded. Cognithor is not a weekend hack held together by AI-generated spaghetti. Every module follows a deliberate architecture (PGE-Trinity, 6-phase gateway init, 3-layer security, TRUST-1..10 operational-trust stack), backed by 17,000+ test functions, structured plans, spec compliance reviews, and code quality gates. The AI writes the code — but a human engineers the system. There's a difference.

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Why Cognithor?

Most AI assistants send your data to the cloud. Cognithor runs entirely on your machine — with Ollama or LM Studio, no API keys required. Cloud providers are optional, not mandatory.

It replaces a patchwork of tools with one integrated system: 17 channels, 141 MCP tools across 30 modules, 6-tier memory with 4-channel hybrid search, knowledge vault, voice, browser automation, Computer Use, cross-platform social listening, signed audit receipts (TRUST-1..10), resumable batch workflows (CRWE), TUF-Light-signed pack registry, and more — all wired together from day one. The test suite is extensive (17,000+ test functions, 89% coverage gate). See [Status & Maturity](#status--maturity) for what that does and does not guarantee.

In plain terms: Cognithor is an AI assistant that lives entirely on your computer. You talk to it through your terminal, a web UI, Telegram, Discord, or any of 18 supported channels — and it talks back, remembers what you said last week, and acts on your behalf. It can search the web, write and edit files, run shell commands, control your browser, automate your desktop (clicking, typing, reading windows), manage your calendar, and learn new skills over time. Think of it as a local, private, self-improving Jarvis.

Unlike cloud-based assistants, Cognithor keeps all your data on your machine. Your conversations, memories, documents, and credentials never leave your hardware unless you explicitly configure a cloud LLM provider. It works fully offline with Ollama or LM Studio, and it encrypts everything at rest with SQLCipher (AES-256). If privacy matters to you — and it should — this is the architecture you want.

What makes it different from other local AI tools is that Cognithor is not just a chatbot. It is an agent operating system: it plans multi-step tasks, evaluates its own results, learns from mistakes, and improves autonomously. It can control your desktop through Computer Use (screenshots, clicks, keystrokes, window automation), compete in ARC-AGI-3 reasoning benchmarks, and manage a marketplace of community-contributed skills. It is built to grow with you.

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Status & Maturity

Cognithor is Beta / Experimental software. It is under rapid, active development.

| Aspect | Status | |--------|--------| | Core agent loop (PGE) | Stable — well-tested and functional | | Memory system | Stable — 6-tier architecture works reliably | | CLI channel | Stable — primary development interface | | Flutter Command Center | Beta — Sci-Fi aesthetic, cross-platform, GEPA pipeline visualization, Robot Office pathfinding, 20+ config pages, chat, voice, learning dashboard | | Messaging channels (Telegram, Discord, etc.) | Beta — basic flows work, edge cases may break | | Voice mode / TTS | Alpha — experimental, hardware-dependent | | Browser automation | Stable — Playwright-based, CAPTCHA solving, stealth mode | | Computer Use | Stable — 6 phases (Vision, Agent Loop, Planner Intelligence, Security, Robustness, UI Automation) | | ARC-AGI-3 Benchmark | Beta — 13/25 games solved (24 levels), 4 solver families incl. SmartExplorer | | Skill Marketplace | Stable — GitHub registry, 5-check validation, publisher verification | | Windows UI Automation | Beta — pywinauto UIA for exact element coordinates | | Deployment (Docker, bare-metal) | Beta — tested on limited configurations | | SSH Remote Execution | Beta — tested against Docker containers, key-based auth | | Evolution Engine | Stable — all 6 phases complete, autonomous deep learning with quality self-examination, GDPR-compliant | | Autonomous Task Framework | Beta — task decomposition, self-evaluation, recurring scheduling | | Background Process Manager | Beta — 6 MCP tools, 5-method ProcessMonitor, SQLite persistence | | Multi-Agent System | Beta — 5 specialized agents with model/temperature/topp overrides | | Audit & Compliance | Beta — HMAC + Ed25519 signatures, RFC 3161 TSA, GDPR Art. 15/33, WORM-ready, hash-chained prev_hash over canonical NFC-JSON, dedicated AuditCategory.REFLECTION channel for autonomous learning (Compliance-Spring v0.98.0) | | Resilient Workflow Engine (CRWE) | Stable — cognithor task with JSONL streaming, atomic checkpoint, file-lock, SIGINT/SIGTERM emergency-checkpoint between tasks, manifest-tamper detection on --resume, audit-chain integration. Crash-recovery integration test passes on Windows under SIGKILL (v0.99.0). | | Pack Registry Signing | Beta — TUF-Light Ed25519 (offline Root + online Targets) + SHA-256 verifier; marketplace dormant by default until owner mints Root keypair | | Video Composition & Rendering | Beta — cognithor.video package with pluggable RendererABC; default HyperFrames backend (Apache-2.0); 5 MCP tools (video_compose GREEN, video_render ORANGE, raw HTML RED at Gatekeeper); render-receipt linked to TRUST-1 via run_id; composer prompts + skill templates ship in cognithor.video.skills | | Enterprise features (GDPR, A2A, Governance) | Stable — GDPR 100% user rights, consent management, SQLCipher encryption, audit trail | | Encryption at Rest | Stable — SQLCipher (AES-256) for all databases, Fernet for files, OS Keyring key management | | Cross-Platform Social Listening | Beta — Reddit + Hacker News + Discord scanning, LLM-scored leads, unified MCP tools | | Hierarchical Document Reasoning | Beta — Tree-based retrieval for PDF/DOCX/HTML/Markdown, LLM-navigated section selection | | CAG Layer (Cache-Augmented Generation) | Beta — Deterministic prefix generation for LLM KV-cache reuse, prefix + native builders | | CLI Config TUI | Stable — Interactive terminal config editor (rich + prompttoolkit), model discovery | | AST-Based Security | Stable — Python AST + bashlex shell analysis replacing regex-based guards | | OSINT / HIM Module | Beta — person/project/org investigation with trust scoring | | Observer Audit Layer | Stable — post-response LLM quality check across 4 dimensions (hallucinations, sycophancy, laziness, tool-ignorance); triggers regeneration or full PGE re-loop; fails open |

What the test suite covers: Unit tests, integration tests, property-based tests (Hypothesis), audit-completeness burn-ins (nightly CI), real-life scenario tests, and live Ollama tests for all modules. The 17,000+ test functions verify code correctness in controlled environments.

What the test suite does NOT cover: Real-world deployment scenarios, network edge cases, long-running stability, multi-user load, hardware-specific voice/GPU issues, or actual LLM response quality.

Important notes for users:

• This project is developed by a solo developer with AI assistance. Code is human-reviewed, but the pace is fast.
• Breaking changes may occur between minor versions. Pin your version if stability matters.
• The default language is German, switchable to English via the Flutter Command Center or config.yaml. See [Language & Internationalization](#language--internationalization).
• For production use, thorough testing in your specific environment is strongly recommended.
• Bug reports and contributions are welcome — see Issues.

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> Cognithor is a fully local, Ollama/LM Studio-powered, autonomous agent operating system that acts as your personal AI assistant. All data stays on your machine — no cloud, no mandatory API keys, full GDPR compliance. It supports tasks ranging from research, project management, and knowledge organization to file management and automated workflows. Optional cloud LLM providers (OpenAI, Anthropic, Gemini, and 12 more) can be enabled with a single API key. Users can add custom skills and rules to tailor the agent to their needs.

Table of Contents

• [Why Cognithor?](#why-cognithor)
• [Status & Maturity](#status--maturity)
• [Highlights](#highlights)
• [Architecture](#architecture)
• [LLM Providers](#llm-providers)
• [Channels](#channels)
• [Quick Start](#quick-start) (under 5 minutes)
• [Configuration](#configuration)
• [Security](#security)
• [Operational Trust (TRUST-1..10)](#operational-trust-trust-110)
• [MCP Tools](#mcp-tools)
• [Tests](#tests)
• [Deployment](#deployment)
• [Language & Internationalization](#language--internationalization)
• [License](#license)
• [What's New](#whats-new)

Highlights

• 19 LLM Providers — Ollama (local), LM Studio (local), vLLM (local), llama-cpp-python (local), OpenAI, Anthropic, Google Gemini, Groq, DeepSeek, Mistral, Together AI, OpenRouter, xAI (Grok), Cerebras, GitHub Models, AWS Bedrock, Hugging Face, Moonshot/Kimi, Claude Code — plus any custom OpenAI-compatible endpoint
• 17 Communication Channels — CLI, Web UI, REST API, Telegram, Discord, Slack, WhatsApp, Signal, iMessage, Microsoft Teams, Matrix, Google Chat, Mattermost, Feishu/Lark, IRC, Twitch, Voice (STT/TTS)
• 6-Tier Cognitive Memory — Core identity, episodic logs, semantic knowledge graph, procedural skills, working memory, tactical memory
• 4-Channel Hybrid Search — BM25 full-text + vector embeddings + knowledge graph traversal + hierarchical document reasoning with score fusion
• PGE Architecture — Planner (LLM) -> Gatekeeper (deterministic policy engine) -> Executor (sandboxed)
• Crew-Layer (v0.93.0) — high-level Multi-Agent API (from cognithor import Crew, CrewAgent, CrewTask) on top of PGE-Trinity. Async kickoff with parallel fan-out, idempotent replay, guardrails, 5 templates (research, customer-support, data-analyst, content, versicherungs-vergleich).
• 8-page Quickstart — [docs/quickstart/](docs/quickstart/) — from pip install to your first Crew in under 10 minutes, bilingual (DE primary + EN). Includes runnable examples for first-crew, first-tool, first-skill, guardrails, and PKV report.
• Security — Platform-adaptive sandbox (bubblewrap on Linux, subprocess+timeout fallback), AST-based Python/Shell code analysis (Python ast.NodeVisitor + bashlex parser), SHA-256 audit chain, credential vault, runtime token encryption (Fernet AES-256), Gatekeeper policy engine with GREEN/YELLOW/ORANGE/RED risk classification (not independently audited — see [Status & Maturity](#status--maturity))
• Knowledge Vault — Obsidian-compatible Markdown vault with YAML frontmatter, tags, [[backlinks]], full-text search
• Document Analysis — LLM-powered structured analysis of PDF/DOCX/HTML (summary, risks, action items, decisions)
• Video Input — Attach local videos (.mp4 / .webm / .mov / .mkv / .avi) or paste direct video URLs; Qwen3.6-27B (or any video-capable VLM) analyzes them end-to-end via vLLM's native video_url content type. Adaptive frame sampling (fps=3 for short clips, num_frames=32 for long) via ffprobe. Single video per turn, served from a 127.0.0.1-only HTTP file server, 24h auto-cleanup. Requires vLLM backend — Windows installer bundles LGPL ffmpeg. See [docs/vllm-user-guide.md](docs/vllm-user-guide.md).
• VLM Router — Three-tier profile system (fast / balanced / premium) for video and image understanding. Heuristic classifier inspects the user's prompt and clip metadata, picks the right VLM (Qwen3-VL-8B-Instruct → Qwen3-VL-8B-Thinking → Qwen3.6-27B-NVFP4), exposes the routing decision via TRUST-2 (rule_id + matched_pattern) so a receipt reviewer can replay why a particular model was chosen. Override via with router.quality_scope("premium"): or config.vllm.quality_default. ContextVar-isolated for async safety. See [src/cognithor/core/vlm_router.py](src/cognithor/core/vlm_router.py).
• Video Composition & Rendering — Cognithor doesn't only read videos, it can make them. The cognithor.video package ships a thin RendererABC abstraction with HyperFrames (Apache-2.0) as the default backend; future renderers (Remotion, cloud) can be swapped without touching the MCP-tool layer. Five MCP tools cover the workflow: video_compose (GREEN — pure-function, builds a self-contained HTML composition from a structured spec, no subprocess, no FS write), video_compose_explainer (16:9 title-card + body + CTA preset), video_compose_social_cut (vertical 9:16 hook + fast-cut beats + outro), video_caption_overlay (parallel caption track), and video_render (ORANGE — renders composition HTML to MP4 / MOV / WebM under ~/.cognithor/render//). Raw user-supplied HTML is RED at the Gatekeeper — only structured specs reach the renderer. Render output is linked to the agent run via the same run_id the streaming EventEmitter and TRUST-1 receipt API use, so every frame is provenance-attributable end-to-end. Composer prompts + reusable templates live in cognithor.video.skills (HF-5). VLM video-read can drive composition directly — see VLM-4 smoke at [tests/test_video/](tests/test_video/).
• Model Context Protocol (MCP) — 141 tools across 30 modules (filesystem, shell, memory, web, browser, media, vault, synthesis, code, skills, documents, social, kanban, identity, evolution, computer-use, sevDesk, A2A and more) — counts auto-generated into [docs/integrations/catalog.json](docs/integrations/catalog.json) on every release
• Computer Use — Complete desktop automation: screenshots, clicking, typing, scrolling, dragging, Windows UI Automation via pywinauto for exact element coordinates, 3-layer security, adaptive wait
• ARC-AGI-3 Benchmark Agent — Compete in ARC Prize 2026: 13/25 games solved (24 levels), 4 solver families (ClusterClick, SequenceClick+SimA*, KeyboardDFS, SmartExplorer), persistent game profiles, multimodal vision (qwen3-vl)
• Distributed Locking — Redis-backed (with file-based fallback) locks for multi-instance deployments
• Durable Message Queue — SQLite-backed persistent queue with priorities, DLQ, and automatic retry
• Prometheus Metrics — /metrics endpoint with Grafana dashboard for production observability
• Skill Marketplace — SQLite-persisted skill marketplace with ratings, search, and REST API
• Community Skill Marketplace — GitHub-hosted registry with publisher verification (4 trust levels), 5-check validation pipeline, ToolEnforcer runtime sandboxing, async install/search/report
• Telegram Webhook — Polling + webhook mode with sub-100ms latency
• **Auto-Depend

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Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: Alex8791-cyber
• Source: Alex8791-cyber/cognithor
• License: Apache-2.0
• Homepage: https://cognithor.ai

Install and usage instructions live in the source repository linked above.