Add Thoth to competitive analysis; refine compute marketplace thesis

- Thoth: new Category 2 entry (Personal AI Assistants), LangGraph ReAct
  agent with knowledge graph, Developer/Designer studios, 151K LOC
- Compute marketplace: answer the structural question 'why buy compute
  if every user runs their own Passepartout?' — three structural reasons:
  specialized proof libraries, certification weight, bootstrap verification

ideas/competitive-analysis-2026-05.org

@@ -7,14 +7,14 @@
 
 * Overview
 
-Analyzed 8 competitor codebases alongside Passepartout. The competitive landscape
+Analyzed 9 competitor codebases alongside Passepartout. The competitive landscape
 divides into three categories:
 
 1. Coding agents (Aider, OpenCode, Codex CLI, Claude Code, Gemini CLI)
-2. Personal AI assistants (Hermes, OpenClaw)
+2. Personal AI assistants (Hermes, OpenClaw, Thoth)
 3. CI/check-based systems (Continue)
 
-None of the eight compete with Passepartout on all axes simultaneously. Passepartout's
+None of the nine compete with Passepartout on all axes simultaneously. Passepartout's
 strongest differentiators — Org-mode data model, deterministic gate stack, ACL2
 verification, Merkle-treed memory, and the triad architecture — are absent from
 every competitor.
@@ -263,6 +263,85 @@ no Org-mode, no verification, no neurosymbolic architecture. Differentiated by
 vastly broader channel support and mature plugin ecosystem. But architecturally
 conventional — LLM + tools + channels, no cognitive architecture innovation.
 
+** Thoth (Python, ~151K lines, Apache 2.0)
+
+https://github.com/siddsachar/Thoth — Personal AI Sovereignty. Local-first
+desktop AI assistant with knowledge graph, tools, voice, vision, shell,
+browser automation, workflow engine, and messaging channels.
+
+Architecture: LangGraph create_react_agent (prebuilt ReAct pattern). Dual-mode
+streaming via agent.stream(). NiceGUI web UI served by Python app.py with
+desktop launcher (tray icon, Ollama auto-start, browser/OS window). Context
+trimming via tiktoken to ~85% of model window, base64 data redaction, stale
+browser snapshot compression (keeps last 8), MD5 tool result dedup, old tool
+result summarization. 50-step recursion limit (chat), 100 (tasks), 120 (Developer
+Studio). Agent graph cached by tool set + model override. Checkpoints via
+LangGraph's SQLite-backed checkpointer. 30+ tool modules.
+
+Safety model: Shell command classification (tools/shell_tool.py) with 17 blocked
+patterns (rm -rf /, mkfs, dd of=/dev/, shutdown, fork bombs, pipe-to-bash, etc.),
+30+ safe auto-execute prefixes (ls, cat, grep, git status, etc.), needs-approval
+for compound commands (;, &&, ||, |, $(), backticks). Interactive interrupt() for
+non-safe shell — LangGraph human-in-the-loop pauses the graph. Per-workflow safety
+modes: block (default, refuse non-safe), approve (pause), allow_all.
+Prompt-injection defense: scans tool outputs and user inputs for 5 categories
+(role overrides, instruction hijacking, data exfiltration, invisible unicode,
+hidden HTML directives) — detection-only, no stripping. Filesystem workspace
+boundary (~/Documents/Thoth). Opt-in Docker Sandbox for Developer Studio.
+Destructive ops (file delete, moderate shell, Gmail send, calendar delete,
+memory/task/tracker delete) require confirmation. MCP servers disabled until
+tested. Custom Tools reviewed and promoted. No sandboxing of agent runtime
+itself — agent runs in-process. No response-level guardrails.
+
+Data model: SQLite (WAL mode) at ~/.thoth/memory.db — shared between knowledge
+graph and legacy memory. Knowledge graph: SQLite (durable) + NetworkX MultiDiGraph
+(in-memory, rebuilt on startup) + FAISS vector index (semantic recall, rebuilt on
+every entity write). 11 entity types (person, preference, fact, event, place,
+project, organisation, concept, skill, media, self_knowledge). 67+ typed relations
+with 30+ LLM-produced aliases mapped to canonical forms. Dream Cycle refinement
+pipeline for entity dedup/merge/stale-confidence decay. Config: JSON files
+(skills_config.json, api_keys.json, providers.json, channels_config.json). Keys in
+OS credential store (Windows Credential Manager, macOS Keychain, Linux Secret
+Service/KWallet). Memory extraction background daemon scanning past conversations
+every ~2 hours.
+
+Self-modification: Agent CAN create/update/delete skills via dedicated tools
+(thoth_create_skill, thoth_patch_skill, thoth_delete_skill). SKILL.md files with
+YAML frontmatter at ~/.thoth/skills/. Bundled skills (read-only) at app root;
+user skills override by name. Skill patching requires user confirmation + auto
+backup. Maximum 1 patch proposal per conversation. Tool guides cannot be patched.
+Self-knowledge block injected into system prompt. No tool to modify agent.py,
+prompts.py, or system prompt directly. Developer Studio provides code editing
+through approval-gated tools (tool-assisted human workflow, not agent self-mod).
+
+Verification: None formal. Update signature verification (updater.py).
+Comprehensive test suite at tests/test_suite.py. No tool-call verification beyond
+LangGraph schema validation. No output verification or fact-checking.
+
+Key differentiators vs other assistants: LangGraph ReAct agent with structured
+streaming event model. Personal knowledge graph (11 entity types, 67 relations,
+NetworkX + FAISS). Developer Studio (Docker sandbox, code threads, Git operations,
+approval modes). Designer Studio (decks, documents, landing pages, sandboxed
+interactive runtime). 5 messaging channels (Telegram, Discord, Slack, WhatsApp,
+SMS) with streaming, reactions, media processing. Background workflow engine
+(schedules, webhooks, step pipelines, conditions, approvals, concurrency groups).
+30+ tool modules including browser automation, shell, Gmail, Calendar, X, image/
+video generation. 39 curated Ollama tool-calling models. 10 LLM providers (Ollama,
+OpenAI, Anthropic, Google AI/Gemini, xAI/Grok, MiniMax, OpenRouter, Ollama Cloud,
+ChatGPT/Codex subscription, custom endpoints). MCP client (stdio, Streamable HTTP,
+SSE) with namespaced tools, approval gates. No accounts, no telemetry, no hosted
+server. Local-first with OS credential store.
+
+Key gap vs Passepartout: No deterministic gate stack — shell safety is pattern
+list (17 blocked, 30 safe), not typed gates. No sandboxed agent runtime. No
+proof system. No output guardrails. No neurosymbolic architecture. No Org-mode.
+No Merkle-tree memory. Knowledge graph (SQLite+FAISS) is richer than Hermes but
+is LLM-driven entity extraction — no structural integrity guarantees. Thoth's
+differentiation from Hermes/OpenClaw is the knowledge graph + Developer/Designer
+studios + embedded LangGraph framework — a broader product scope, but still
+architecturally conventional (LLM + tools + channels + KG), not a new cognitive
+architecture.
+
 * Category 3: CI/Check Systems
 
 ** Continue (TypeScript, ~328K lines, Apache 2.0)
@@ -379,5 +458,6 @@ Repository dumps and analysis artifacts at /tmp/:
 - /tmp/claude-code-leaked-source/ — Claude Code leaked (TypeScript/Bun)
 - /tmp/gemini-cli/ — Google Gemini CLI (TypeScript)
 - /tmp/openclaw/ — OpenClaw source (TypeScript)
+- /tmp/thoth/ — Thoth source (Python)
 - /tmp/continue/ — Continue source (TypeScript)
 - /usr/local/lib/hermes-agent/ — Hermes Agent (Python)