GitHub - XAIHT/Tlamatini: Tlamatini is a self-hosted AI developer assistant for local or cloud LLMs: hybrid RAG (FAISS+BM25) over your codebase, a 100+ tool Multi-Turn engine, an 86-agent visual workflow designer, 28 SKILL.md skills, a universal MCP client, and ACPX, a runtime for external coding agents like Claude Code, Codex, Cursor and Gemini CLI.

The local-first AI developer assistant with a visual workflow designer — and the reach to touch hardware, 3D engines, and any external tool.
"one who knows" — she doesn't just edit code. She flashes your board, drives your engine, and orchestrates whole agent workflows on a canvas. On your machine.

⚠️ Ollama Pro or higher is required for the complete Tlamatini experience.
Tlamatini is free and open-source; an Ollama subscription is purchased directly from Ollama. We are not sponsored by, affiliated with, or paid by Ollama. Tlamatini was engineered around Ollama's cloud-model interface, capacity, and tool-calling characteristics, so at least the Pro plan is part of the intended system requirements—not a promotion.

🌐 Website · ▶️ One-minute teaser · 📖 Full docs · 💬 Discord

💬 Join the Tlamatini community on Discord — get help, show what you build, report bugs, and shape the roadmap.

Table of contents

1. What is Tlamatini
2. How it works
3. Get started in five steps
4. Current release
5. The full capability list
6. Enable Tlamatini as a Blue-hat agent
7. Installation
8. Tech stack
9. Contributing
10. License

What is Tlamatini

Tlamatini is a local-first AI development and automation environment for Windows. It combines a conversational assistant, whole-project code operations, a visual workflow designer, 88 specialized agent types, 108 built-in Multi-Turn tools, hardware and firmware control, Unreal Engine and Blender automation, security tooling, and a universal External-MCP client in one application.

“Local-first” describes where control lives: the application, workflow canvas, database, configuration, credentials, agent code, project access, and small embedding model run under your control on your machine. It does not mean that Tlamatini was designed as a local-model-only assistant. Its complete reasoning and orchestration experience was designed and coded primarily around the larger cloud models exposed through Ollama.

How it works

Tlamatini has three cooperating layers:

1. Local control plane — the Django application, chat interface, SQLite database, visual workflow designer, agent programs, permissions, files, tools, and hardware connections remain on your computer.
2. Local retrieval — nomic-embed-text builds embeddings locally so Tlamatini can retrieve relevant project context efficiently.
3. Ollama cloud reasoning — configured :cloud models perform the demanding chat, tool-calling, coding, long-context, vision, planning, and Multi-Turn reasoning that coordinates Tlamatini's agents. Ollama keeps the same local API experience while offloading these larger models to its cloud service.

Ollama currently advertises Pro at $20/month or $200/year, with greater cloud usage and concurrency than the free account; Max also satisfies Tlamatini's requirement. Pricing and limits belong to Ollama and may change, so verify them on Ollama's official site before subscribing. Ollama's official cloud documentation explains that cloud models are offloaded to Ollama's service while remaining accessible through the familiar local Ollama API: Ollama cloud-model documentation.

🚀 Get Started — 5 steps to a cloud-powered Tlamatini

Tlamatini itself is free, but a complete installation assumes Ollama Pro or higher as an operating requirement. Subscribe directly with Ollama, sign in through the Ollama CLI, and Tlamatini can coordinate 88 agent types and 108 built-in Multi-Turn tools from your machine using the cloud-model capacity around which it was designed. XAIHT receives no payment or benefit from that subscription.

1 · Install Tlamatini

Pick one of two paths. Tlamatini itself is free — you never pay us; the only cost is Ollama (Step 3).

🟢 Option A — Release installer (recommended · no Python needed)

Best for most people. The installer bundles its own Python 3.12.10 and every dependency, so you install nothing else.

1. Open the Releases page and download the latest installer (.exe).
2. Run it and follow the wizard.
3. Launch Tlamatini from the Start-menu shortcut.
4. Your browser opens at http://127.0.0.1:8000/ — log in with user / changeme. (8000 is the default port; if it's taken or Windows has reserved it, set django_port in config.json — see the port note below.)

🔄 Updating later is one click: About ▸ Check for updates inside the app — it keeps your config, database, keys, templates, external-MCP catalog, and existing Uninstaller.exe.

🔵 Option B — From source (for developers)

Best if you want to read, modify, or contribute to the code. Requires Python 3.12.10 and git already installed.

git clone https://github.com/XAIHT/Tlamatini.git
cd Tlamatini
python -m venv venv && venv\Scripts\activate
pip install -r requirements.txt
python Tlamatini/manage.py migrate
python Tlamatini/manage.py runserver --noreload
# then open http://127.0.0.1:8000/   (default login: user / changeme)

--noreload is optional (since 2026-07-11): plain python Tlamatini/manage.py runserver now boots clean and auto-reloads on code edits. It used to double-start the MCP helper ports :8765 / :50051 and crash with WinError 10048; fixed by a reloader-aware gate in agent/apps.py.

netsh interface ipv4 show excludedportrange protocol=tcp

2 · Install Ollama

Install Ollama for Windows. Ollama is the engine that serves every model to Tlamatini — the local embedding model and the cloud chat models.

3 · Activate Ollama Pro or higher — required for complete operation

Go to ollama.com, sign in, and activate at least Ollama Pro. Ollama currently lists Pro at $20/month or $200/year and describes it as providing substantially more cloud usage plus concurrent cloud-model capacity; Ollama Max also satisfies this requirement. Check Ollama's site for current pricing and limits.

Ollama may provide limited cloud access with a free account, but Tlamatini does not treat the free allowance as a fully functional production configuration. Its Multi-Turn loops can make many model calls during one task, and its vision and orchestration paths may use multiple cloud models in parallel. Pro is therefore the minimum intended capacity tier, not an optional donation or affiliate offer.

Independence disclosure: Tlamatini/XAIHT is not sponsored by, affiliated with, endorsed by, or compensated by Ollama. There is no referral relationship. You pay Ollama directly; this README names Pro solely because Tlamatini was engineered around Ollama's cloud-model behavior and capacity.

After subscribing, connect the Ollama installation on your machine to your Ollama account:

4 · Download the models

Pull the small local embedding model, plus the cloud chat models Tlamatini will use:

# Local embedding model (small, runs on your own GPU/CPU)
ollama pull nomic-embed-text

# Cloud models — use a signed-in Pro-or-higher account for full Tlamatini operation
ollama pull glm-5.2:cloud
ollama pull qwen3.5:cloud

Any cloud model works — these two are the current recommended pair (older screenshots below may still show earlier model names).

5 · Point Tlamatini at the models

In the Tlamatini navbar, open the Config menu:

a) Config ▸ Models — set the Ollama model for each subsystem (each one must already exist in your Ollama catalog), then click Save:

b) Config ▸ Access Keys Wizard — whether you need an Ollama token depends on where Ollama runs:

• 🖥️ Ollama on your own machine (localhost)? Leave the token blank — a local Ollama needs no auth.
• ☁️ Ollama on a remote server (e.g. Vast.ai)? Paste the Ollama token so Tlamatini can reach it.

Add any cloud-CLI keys here too — plus the messaging keys, the Kali server URL, and the OPTIONAL ProjectDiscovery Cloud (PDCP) key under "Security Recon (ProjectDiscovery)". Blank fields keep what's already configured; click Save:

Done — tick Multi-Turn in the chat toolbar and put Tlamatini to work.

Current release — v1.50.6

v1.50.6 is the newest annotated release. The tag, local HEAD, and origin/main all resolve to commit 6e4ffa73; runtime identity still comes from Git/build metadata rather than this prose. The newest public-release path no longer requires a machine-local .private_targets.json on a pristine clone: a target-independent privacy preflight distinguishes a genuinely clean tree from a maintainer tree that still contains private evidence, fails toward refusal when probes are unreadable or private material is detected, keeps the tracked private_targets.example.json template inert, and never introduces a runtime dependency on either targets-file spelling. The 26-test agent.test_public_release_targets suite pins fresh-clone builds, runtime independence, structural-only verification, secret restoration, and PII-safe refusal behavior.

v1.50.5 fixes the Windows Ctrl+C shutdown deadlock. The signal handler now performs only a re-entry-guarded Event set; a boot-created daemon worker performs best-effort cleanup, a watchdog guarantees exit after the grace period, and a second Ctrl+C hard-exits immediately. Source-contract tests and a visible end-to-end console test prove that the real Django/Daphne process terminates instead of stacking nested signal handlers. The same release adds PromptDesigner.jpg as a tracked design-reference capture; it is repository artwork, not a claim that a separate Prompt Designer runtime is already wired into Tlamatini.

The carried v1.50.4 runtime fix keeps optional Transformers and its transitively imported Torch stack out of the frozen Django process while preserving Torch in the carried Python used by Talker. v1.50.3 corrects Bing redirect decoding and Mojeek self-result filtering in Googler. The v1.50.0 security-harnessing arsenal remains fully carried: the administrator-operated security/ toolkit supplies ten host-monitor families, detect-only/armed/watch/aggressive modes, path-aware process protection, bounded IP/process response, persistent enablement, and a non-destructive visible regression harness. The v1.49.1 foundation remains the active surface: NetSpeed-Calculator as workflow agent 88 and wrapped launcher 66; WAL-safe database copy/hot-swap; structured and resilient Googler discovery; guarded External-MCP onboarding; Deep Internet Research prompt 118; Ollama Pro-or-higher guidance; and private-contact synchronization that keeps public builds empty of contact PII.

NetSpeed-Calculator measures download, upload, latency, jitter, packet loss, and bufferbloat against several keyless providers. It discards TCP slow start, samples throughput as d(bytes)/dt, rejects outliers, publishes Student-t confidence intervals, and fuses providers with fixed- or random-effects meta-analysis plus Cochran's Q/I². Its full, download, and upload actions consume real bandwidth; validate only checks provider reachability, while providers lists the catalog. A full run commonly transfers about 100-200 MB, so the wrapped tool is in Ask-Execs tier D and must not be repeated casually.

Database copies no longer treat a bare WAL-mode db.sqlite3 file as complete. agent/sqlite_copy.py::consistent_copy() reads through the live WAL with SQLite's online backup API, converts the destination to a self-contained DELETE-journal database, and requires PRAGMA quick_check before success. Backup DB and Set DB both use that path; pre-Django hot-swap archives the outgoing database with its -wal/-shm/-journal sidecars, clears stale destination sidecars, and only then promotes the staged copy.

Googler's visual/pool agent now compiles structured search fields into valid Google syntax instead of making operators a fragile hand-written exercise. Presets cover books, public/open books, papers, manuals, documents, slides, sheets, and directory listings; aliases expand classes such as ebook, docs, code, and data; multiple sites or file types become parenthesized uppercase-OR groups; and exact phrases, exclusions, proximity, ranges, dates, title/URL/text/anchor filters, and discovery operators are normalized mechanically. Explicit fields always override preset defaults. For PDF/EPUB/file hunts, use content_mode: links_only: Googler's deliverable is the indexed URL list, which Apirer can download and File-Extractor/File-Interpreter can read. The direct Multi-Turn googler tool accepts equivalent operators inside its query string, while the structured fields belong to the visual/pool agent. Googler finds only publicly indexed URLs; she does not bypass access controls, and the user remains responsible for copyright, licensing, authorization, and use.

Search execution is also resilient rather than a brittle Google-then-one-fallback path. Tier 0 uses plain urllib HTTP first against four server-rendered routes (DuckDuckGo HTML, Bing, DuckDuckGo Lite, Mojeek), harvesting outbound result links without browser fingerprints, consent dialogs, JavaScript-app failures, or CSS-selector dependence. If those routes return nothing, Tier 1 opens a visible installed Chrome window (headless: false), falls back to bundled Chromium if needed, and tries seven direct-results routes (DuckDuckGo HTML/Lite, Mojeek, Bing, Google, Brave, Startpage) with bounded jittered retries. The log always names the route that answered. site: and filetype: carry broadly, but advanced before:/after:/AROUND()/numeric-range semantics are Google-specific, so a fallback may intentionally return broader candidates; pin engines: [google] when exact full-vocabulary behavior matters (pinning also skips Tier 0). Headless mode remains available for unattended browser fallback but is explicitly the more refusal-prone path.

The new adding-external-mcp skill turns External-MCP setup into one guarded sequence: classify transport, write/import a secret-separated catalog entry, run MCP Doctor, activate only on operator intent, wait for healthy tools, inspect status/list output, then call the remote tool. The Deep Internet Research card is appended as prompt 118 in Getting Started and requests Multi-Turn plus Exec Report for a long, link-rich search that may use Sequential Thinking, Memory, MCPs, and agents.

The previous annotated release, v1.48.17 (2026-08-16), remains fully carried. Its same-day lineage is v1.48.15 (encoding-safe Grepper + the closed Exec-Report status vocabulary), v1.48.16 (themed pop-ups + a post-build frozen-bundle proof), and v1.48.17 (Escape dismissal standardization and the sealed updater). Grepper recognizes BOM-marked UTF-8/16/32 before cp1252/Latin-1 fallbacks, so valid Windows text is searchable while genuine binaries remain skipped.

Exec-Report status handling now uses a closed, source-guarded vocabulary with five disjoint classes: completed diagnostics, intact completed work, degraded work, work not done, and agent errors. Degraded deliverables such as inaudible token-only speech or a compromised PDF are red rather than falsely clean; named completions are auditable greens; an unknown token still fails open but is identified by rule R8b. The repository-wide guard scans every pool-agent status: literal so a newly invented token fails during tests instead of silently defaulting green. Kuberneter now reports numeric returncode, explicit success, and a real ok/failed status token, preventing a failed kubectl call from being painted green.

Updater coverage protects the separately built Uninstaller.exe during self-update and keeps the preserve-list parser from being confused by comments. Public release builders forcibly clear inherited private External-MCP catalog and contact opt-ins; only the explicit keyed/private builder can bundle private state, and it first merges same-machine contact sources into gitignored contacts.private.json without putting PII into a public build or self-modify snapshot. Drift-proof tests derive supervisor counts and prompt rules from source. The source-verified worktree surface is 88 workflow agents, 66 wrapped chat agents, 108 Multi-Turn tools (20 core + 66 wrapped + 12 ACPX/Skill + 10 External-MCP supervisors), 37 JavaScript modules, 29 runtime skills, and 197 migrations. The v1.48.14 private MCP runtimes, inactive Memory/Sequential-Thinking defaults, public/private catalog separation, and lossless diagram restoration remain carried.

Dialog behaviour is now uniform on both pages: Escape dismisses every dialog and means exactly what the titlebar ✕ means, while an outside click still never dismisses anything — so a guarded prompt cannot be lost to a stray click, and no dialog can trap you either. A single dispatcher finds the topmost dialog and activates that dialog's own dismiss control, so an Ask-Execs permission prompt still answers Deny, a confirmation still resolves to "no", scroll locks are still released, and a sealed update step still refuses to close. The last native browser pop-ups are gone: alert() / confirm() inside the contacts book and the External-MCP dialog were replaced by themed tlmAlert / tlmConfirm panels that match the app instead of showing OS chrome over it.

The build now also proves what it ships. Several modules reach the frozen app only through fail-open imports, which cannot report their own absence — the app would boot perfectly and silently lose the capability. After PyInstaller succeeds, build.py opens the archive it just produced and verifies that the runtime provisioner, the External-MCP defaults and client, the verdict engine, the path guard, the self-update module, and the version resolver are all really inside it, aborting the build if any is missing.

💎 The jewels — what nothing else can do

Claude Code, Codex, Cursor, Gemini — they edit text files. Tlamatini does that and reaches into the physical and creative world, then lets you wire it all together visually:

The headline no competitor can copy: Tlamatini is the only local-first AI dev assistant where you design the agent workflow visually, then have it flash firmware, drive Unreal/Blender, run security tools, and command any external MCP — all from one machine.

🔒 And it's yours alone

Tlamatini's application, database, workflows, agent code, credentials, and embedding model remain under your control on your machine. Its intended chat, planning, vision, and tool-calling configuration uses Ollama cloud models, so the prompts and context sent to those configured models are processed through Ollama's cloud service. Direct Claude API use, external coding-agent CLIs, remote MCP servers, and other third-party integrations remain separately configurable. Review each provider's privacy terms and decide what project material you authorize Tlamatini to send outside your machine.

⚠️ CLEAR DISCLAIMER — USER CONTROL, JURISDICTION, AND RESPONSIBILITY FOR AGENTS

Every agent in Tlamatini/agent/agents/ is intentionally provided as a plain-Python program so its operating code can be read, audited, edited, restricted, or disabled by the user. This transparency is a user-control mechanism, not a warranty that an agent is secure or suitable for a particular environment. The agents do not have independent authority or jurisdiction: the user alone decides whether, where, how, and with which permissions they run.

When you enable, configure, modify, chain, or execute an agent, that agent and its execution are under your control and your jurisdiction. You are solely responsible for reviewing its code and configuration; protecting and limiting its secrets, credentials, and permissions; selecting and authorizing every file, folder, network target, browser, shell, API, external MCP server, machine, hardware device, and downstream system it can access; supervising its output; and complying with every law, policy, license, contract, and authorization that applies to your use.

BY RUNNING AN AGENT, YOU ACCEPT RESPONSIBILITY FOR ITS ACTIONS AND CONSEQUENCES. TO THE FULLEST EXTENT PERMITTED BY APPLICABLE LAW, ANY SECURITY BREACH, DATA EXPOSURE OR LOSS, UNAUTHORIZED ACTION, CREDENTIAL LEAK, UNSAFE AUTOMATION, POLICY OR LEGAL VIOLATION, SYSTEM COMPROMISE, DEVICE DAMAGE, FINANCIAL LOSS, OR OTHER HARM ARISING FROM YOUR USE, CONFIGURATION, MODIFICATION, OR EXECUTION OF AN AGENT OR AGENT WORKFLOW IS THE RESPONSIBILITY OF THE USER WHO RUNS IT. Tlamatini's orchestration, documentation, examples, and guardrails do not authorize access to third-party systems and cannot replace the user's own security review, permission controls, monitoring, or legal compliance.

📋 The full capability list

Everything Tlamatini can do, grouped:

🧩 Orchestration & design

• Visual Workflow Designer (ACP) — 88 drag-and-drop agent types wired into runnable flows; save/load .flw files; Flow Compiler validates the canvas into config.yaml.
• Multi-Turn orchestration — a tool-calling loop with 108 built-in tools and a global execution planner; Step-by-Step mode paces hands-on setup one action at a time; self-healing model steps mean a network/model hiccup never freezes her — she retries under a watchdog, finishes gracefully from work already done, and always tells you what happened.
• FlowCreator / FlowHypervisor — let an LLM design a flow; a watchdog monitors flow health. FlowCreator is now also callable from chat (chat_agent_flowcreator): describe a flow in plain words and it writes a real, canvas-loadable .flw file to disk.
• Parametrizer / Gatewayer / Gateway-Relayer / Node Manager — chain agent outputs into the next agent's config; trigger flows from webhooks, folder-drops, or GitHub/GitLab.
• ACPX — spawn external coding-agent CLIs (Claude Code, Codex, Cursor, Gemini, Qwen, and more) as tools and relay between them.

📟 Firmware & hardware

• STM32er — zero-config STM32 build/flash/observe across the whole ST 32-bit line (Blue Pill → F7/G/L/H7/U5/WB) via a dual backend (PlatformIO ststm32 + the STM32F407VG template MCP), with a critical-mission safety preflight.
• ESP32er — direct PlatformIO build/flash/monitor, zero-config bootstrap.
• Arduiner — direct arduino-cli, auto-installs binary + core, build/upload.
• ESPHomer — ESPHome smart-home device configs (YAML, no C++), zero-config.

🎬 3D & creative engines

• Unrealer — Unreal Engine control from chat.
• Blenderer — Blender scene/object/render/code over the official MCP socket.

🛠️ Code & projects

• PDFer — the document composer: turn Tlamatini's own answer, some Markdown/HTML, plain text, a folder of images, or several existing PDFs into ONE styled PDF — with a cover page, real tables, page numbers and an optional table of contents. It is the WRITE side of the document family (File-Extractor / File-Interpreter read documents; PDFer authors them). Needs no installation — every engine it uses already ships inside Tlamatini. Modes: auto (it sniffs the content for you) / markdown / html / text / images (one-per-page, fit, or grid) / mixed (prose + embedded figures) / merge / info / validate. It reads what you wrote and decides how the document should look, before it renders anything. A piece about science or technology comes out on a near-black page with white type, cyan-to-violet gradients and a generated star field; a document with an abstract and citations comes out pure white, black and justified, set in Palatino the way a journal typesets an article; a contract comes out plain, in Times, with no decoration whatsoever. Twenty treatments in all, chosen from the vocabulary, the structure and the tone of your text — and you can override it with nuance (science, paper, legal, receta … any alias, English or Spanish) or hand it a single predominant_color and watch the whole palette — headings, rules, table headers, callout bars, the cover gradient, the footer — be derived from that one colour. Its tables cannot overlap. Every column width is solved from the real font metrics before a letter is placed, so a long Windows path or a URL wraps inside its own cell instead of printing across the next one; and once the file is written PDFer re-opens it and measures the actual letters on the page for overlapping text, ink running off the sheet, blank pages and real contrast, so it can tell you the layout was verified rather than assumed. It draws its own illustrations — gradient washes, star fields, circuit traces, waves, soft organic shapes — in your document's own colours, with no image files and no downloads, and only where the content makes that safe: a contract, a dosage chart and a financial statement get none. Optionally let an Ollama model tidy the text into clean Markdown first, or art-direct the colours for you — both off by default, and neither can ever lose your document. PDFs land in Documents/TlamatiniPDF with a collision-proof name, and a fail-safe preflight refuses rather than write an empty or wrong file.

• LaTeXer — the LaTeX typesetter, and the typesetting sibling of PDFer: PDFer composes a PDF out of Markdown, HTML and pictures; LaTeXer typesets one from real .tex source — proper mathematics, a real bibliography, numbered cross-references, an index. Hand it a whole folder of .tex files and it finds the master document itself, follows every \input, runs biber/bibtex and makeindex, and keeps re-running the compiler until the cross-references settle — then it turns the famously unreadable LaTeX log into a short list of real errors. You can also hand it a bare fragment (even just $E = mc^2$) and it wraps it in a proper preamble for you. Eight built-in templates (article, report, book, beamer, letter, cv, homework, spanish-article), plus authoring actions to create, edit, read, list, lint and inspect .tex files. PDFs land in Documents/TlamatiniLaTeX.

  ⚠️ LaTeXer needs MiKTeX — and that is the only thing it needs

  Tlamatini does not ship a TeX distribution: a full one is several gigabytes, and the whole release is kept small on purpose. So install MiKTeX once — https://miktex.org/download — and then install Tlamatini. That is it: MiKTeX + Tlamatini = LaTeXer works, forever, with nothing else to configure.

  Why MiKTeX specifically? Because when a document asks for a LaTeX package you have never installed, MiKTeX downloads and installs it by itself, in the middle of the build — so the document still comes out. That single feature is what lets LaTeXer typeset anything out of the box. TeX Live and MacTeX are detected and used if you already have one, but neither can do that, so you would have to hunt down missing packages yourself. MiKTeX is the recommended choice.

  No LaTeX installed? LaTeXer tells you so plainly and refuses — it never crashes and never pretends a PDF was made. Ask it to run action: validate to see exactly what it found, or action: install and it will download and launch the official MiKTeX installer for you.

• Editor / Grepper / Globber — surgical find-and-replace, regex content search, and filename glob (Claude-Edit/Grep/Glob equivalents). Grepper searches UTF-8, BOM-marked UTF-16/32, cp1252, and Latin-1 text without confusing valid Windows text with NUL-bearing binary data.

• File-Creator / Mover / Deleter / File-Interpreter / File-Extractor — create, move, delete, read-and-interpret, extract from PDF/DOCX.

• Executer / Pythonxer — run shell commands and gated Python.

• Gitter — full git control. Googler — resilient two-tier search + extraction: plain-HTTP server-rendered routes first, then visible Chrome/bundled Chromium across seven browser routes, plus a structured Google-dork builder, lawful-source presets, grouped site/filetype filters, and links_only output for downstream file retrieval.

• Hybrid RAG — FAISS + BM25 retrieval, metadata extraction, context budgeting, grounded in your codebase.

• Skills — SKILL.md packages: code-review, security-audit, kali-pentest, flow-making, skill-creator, summarize, audit/lint/refactor helpers, and integration stubs (GitHub, Gmail, Slack, Jira, Notion, Todoist, Trello, Weather).

🛡️ Security

• Kalier — authorized Kali Linux / MCP-Kali-Server offensive-security assessments.
• Discoverer — ProjectDiscovery recon suite (subfinder/httpx/naabu/katana/nuclei/cvemap — the CVE search runs ProjectDiscovery's vulnx, since cvemap's own API was retired Aug 2025) via a self-installing private Go toolchain in <install_dir>/Go; authorized recon, attack-surface mapping & vulnerability discovery. The ProjectDiscovery Cloud (PDCP) key is OPTIONAL (lifts cvemap/vulnx rate limits, enables nuclei -ai/cloud upload) — set it once in Config ▸ Access Keys Wizard ▸ "Security Recon (ProjectDiscovery)" (auto-injected into every run; redacted from .flw exports and by regen_secrets.py before a push).
• Nmapper — LOCAL, use-only nmap bridge for pentesters / CTF: runs a real nmap the user installed themselves (Nmapper NEVER bundles or redistributes nmap — nmap's NPSL forbids embedding it in a product without a paid OEM licence), resolving it from PATH → C:\Program Files\Nmap → a %LOCALAPPDATA%\Tlamatini\nmap copy; if it's absent it refuses gracefully and action='install' fetches the OFFICIAL free nmap installer (admin/UAC; also brings Npcap). The default is an UNPRIVILEGED TCP connect scan (-sT, no Npcap, no admin) so a fresh install scans immediately; SYN / -O / UDP auto-downgrade to a connect scan on Windows without Npcap. Actions: quick / full / top_ports / version / scripts (NSE) / host_discovery / udp / custom / validate / install; emits INI_SECTION_NMAPPER. Distinct from Kalier (a remote Kali box) and Discoverer (ProjectDiscovery). Authorized targets only.
• NetSpeed-Calculator — measures your Internet connection and gives you the answer with its error bar: download, upload, latency, jitter, packet loss and bufferbloat. It does not trust a single speed-test site — it measures against several keyless public providers at once (Cloudflare, Ookla, Fast.com, LibreSpeed, Hetzner, CacheFly; no account, no API key) and then fuses them with a real random-effects meta-analysis, so you get a 95% confidence interval and a plain statement of whether the providers actually agreed. It follows RFC 6349: several parallel TCP streams, the slow-start ramp thrown away, throughput sampled as a derivative instead of the naive total÷elapsed, outliers rejected. Bufferbloat is the one most people are missing — it is graded A+ to F and it is usually the real reason a "fast" connection has choppy video calls. Dead or moved endpoints are skipped with a named reason, never as a silent 0.00 Mbps. ⚠️ It consumes real, possibly metered bandwidth (~100-200 MB per full run), so it asks before running.
• Zavuerer — Zavu unified messaging: SMS / WhatsApp / Telegram / Email / Voice from ONE API key (channel: auto smart-routes to the best channel with auto-fallback). Set the key once in Config ▸ Access Keys Wizard ▸ "Unified Messaging (Zavu)"; direct HTTP, fail-safe preflight, refuses safely when no key is set. Zavu pricing: sign-up is free (no card), but sending is pay-as-you-go — Zavu charges per message.
• Blue-hat security toolkit — the path-independent security/ v2.1 assets configure persistent Windows visibility/exceptions, validate their own syntax/classifier in a visible non-destructive harness, and monitor ten signal families from Defender health through ransomware and account abuse. Begin with -DetectOnly; armed modes can create persistent IP firewall blocks or force-stop process-name matches. Defender/firewall components remain running, but the whitelist creates real scan/enforcement exceptions, so read the complete Blue-hat enablement section before elevation.
• security-audit / kali-pentest skills.

🔌 External integration

• Universal External-MCP client — connect to any MCP server over 4 transports, up to 5 active, with 10 supervisor tools and an MCP Doctor agent that triages a server before you wire it.
• External MCP Adder skill — follow the complete classify → configure/import → doctor → activate → wait → status/list → call lifecycle instead of guessing at a transport or declaring an unproven server ready.
• Memory + Sequential Thinking included, inactive by default — open External ▸ MCPs, select memory or sequential-thinking, and activate only what you want. Memory stores its graph in %LOCALAPPDATA%\Tlamatini\memory\memory.json, outside the install directory so updates preserve it. Removing a shipped default records a tombstone instead of resurrecting it on the next launch.
• Private MCP runtime, no admin required — npx/uvx servers can trigger one-time provisioning of Node/npm/npx/pnpm or uv/uvx into %LOCALAPPDATA%\Tlamatini\runtimes. The External MCP dialog's runtime strip shows exactly which managers are ready and whether Tlamatini is using her private copy; Install now performs the same operation explicitly. Nothing is added to the system PATH, nothing is bundled into the installer, and a failed download never prevents Tlamatini from starting.
• Companion-app discovery (Tlamatini-FlowPills) — sister XAIHT apps locate Tlamatini's agent-template catalog instantly, with no Python and no drive scan: at install and on every launch Tlamatini publishes a per-user HKCU\Software\XAIHT\Tlamatini registry key + an _tlamatini_agents_manifest.json (each agent's sha256) next to the agents, and leaves a preserved-agents marker if you uninstall but keep the agents. HKCU-only, no admin, fail-open.

🖥️ Desktop & browser automation

• Playwrighter — scripted browser automation.
• Windower — Win32 window manager (focus/move/resize/tile/close).
• Shoter / Mouser / Keyboarder — screenshots, mouse, keyboard.

🎙️ Audio, video, vision & speech

• Talker (TTS) — text-to-speech via Ollama. Whisperer (STT) — speech-to-text (faster-whisper local + cloud fallback).

• Recorder / Camcorder — microphone and webcam capture.

• AudioPlayer / VideoPlayer — audio and video playback with volume/loop control.

• Image-Interpreter — triple-model vision analysis: qwen3.5:cloud + gemma4:cloud interpret each image in parallel on two dedicated Ollama connections, then glm-5.2:cloud merges both interpretations into one definitive report (mockup/GUI inventories in % coordinates, full OCR, people described exhaustively with identity clues taken from the image file name).

• Screenshot → chat (paste or drop) — hit Print Screen (or snip), Alt+Tab back to Tlamatini and press Ctrl+V — or drag image files onto the chat column. She saves the image into her own Temp folder as image_<timestamp>.jpg, shows a thumbnail above the input, and drops the full path into your message at the cursor, so you can finish the sentence — "…what's wrong in this screenshot?" — and send. The path is what Image-Interpreter reads.

📨 Messaging, bridges & platform

• Telegrammer — Telegram send/receive that can send under two identities, picked per message with provider: as the bot (provider=bot, Bot API + a @BotFather token) or as your own account (provider=user, official Telegram user session). Plain English works — say "send it as me" (→ your account) or "as the bot". auto (the default) uses your account for private @usernames/+phone and the bot for numeric ids/channels. Sending as you needs a one-time login; human configs stay readable as @username.
• Whatsapper — WhatsApp send/receive with a provider switch for which number sends: cloud (default, the official Meta WhatsApp Cloud API — business number, templates, System User) or web (say "send it as me" / "from my own WhatsApp") which sends from your own personal number by automating WhatsApp Web after a one-time QR login — no templates, no System User. The web path is unofficial (it drives WhatsApp Web) and carries Meta-ban risk; the cloud path remains the official, supported route.
• Instant Messaging Doctor — automatically diagnoses Telegrammer/Whatsapper failures and can be called directly before critical sends; validates official tokens, contacts, readable @username routing, Meta templates/webhooks, and emits Parametrizer-ready repair actions.
• TeleTlamatini — Telegram bridge into the full chat.
• Multi-model — Ollama (local), Anthropic Claude (cloud), Qwen (vision).
• Self-knowledge & self-modification — can read, modify, and rebuild her own source.
• PyInstaller packaging — ships as a standalone Windows .exe.

🧹 Your context stays clean — automatic binary detection

When you point Tlamatini at a folder (Context ▸ Set directory as context), real projects are full of files that are not text: compiled binaries, images, archives, model weights, databases, build artefacts. Feeding those into an embedding index is pure damage — it wastes VRAM and time, and it buries your real code under noise.

Tlamatini screens every file by its actual bytes before loading it, and silently skips the binary ones. It is on by default and needs no setup.

• Fast by design — at most one 8 KiB read per file, and known binary extensions are never opened at all. Screening a 4 GB video costs the same as screening a README.
• Content-based, not name-based — a PNG renamed notes.md is still caught. This works alongside Context ▸ Set file type omissions, which stays exactly as it was for the files you choose to ignore.
• Never silent — every skipped file is listed in tlamatini.log with the reason it was skipped, so you always know why something is not in your context:

--- [BINARY-GUARD] 3 binary file(s) OMITTED from the context / embedding chain
--- [BINARY-GUARD]   ✗ OMITTED C:\proj\assets\logo.png  [extension: known binary extension .png]

• Safe by default — if anything is uncertain or unreadable, the file is loaded as text rather than dropped. Your context is never removed on a guess. Accented and legacy-encoded text files (Spanish, French, cp1252 …) are always kept.

Turn it off with "binary_context_detection": false in config.json; tune it with binary_detection_control_ratio, or rescue a specific extension with binary_detection_force_text_extensions.

See it work

• ▶️ One-minute teaser · 🎬 more demos on xaiht.org.

Enable Tlamatini as a Blue-hat agent

"Blue-hat" means using Tlamatini as an operator-controlled defensive monitor and incident-response helper on a Windows machine you own or are explicitly authorised to defend. The security/ directory is a host-side toolkit, not a new chat tool or visual Agent row: a human administrator launches it, reviews its evidence, and decides when automated response is appropriate.

Security assets

Pause before enabling

The enablement script requires Windows 10/11, Microsoft Defender PowerShell cmdlets, and Administrator approval. Its settings persist after the script exits. Core Defender and firewall services remain enabled, but enforcement is deliberately relaxed around Tlamatini: the install tree and selected processes are added to Defender exclusions, Tlamatini is allowed through Controlled Folder Access, six ASR rules are changed to Audit rather than Block, and broad outbound allow rules are created for Tlamatini/Python. These exceptions reduce protection if malicious code reaches an excluded path or process.

The six ASR rules are: Office applications creating child processes; LSASS credential stealing; WMI event-subscription persistence; executable content from email/webmail; untrusted or unsigned USB processes; and process creation through PSExec/WMI. The script uses Microsoft's published rule IDs, reads the effective Defender configuration back after each write, and reports [OK] only when action 6 (Audit) is verified; otherwise it emits [WARN]. Audit mode records matching behavior but does not block it. See the Microsoft ASR rules reference.

The script also enables locale-neutral audit subcategories by GUID: success/failure Logon, Credential Validation, Sensitive Privilege Use, and User Account Management, plus success Process Creation. It checks auditpol's exit status rather than assuming each command worked. Process-command-line and PowerShell Script Block Logging can capture sensitive arguments, so protect the Windows event logs as carefully as Tlamatini's own logs.

Before enabling, create a restore point or record your current Defender exclusions, ASR configuration, execution policies, audit policy, Security-log permissions, and matching firewall rules. The repository currently provides no automatic rollback script; restore the recorded baseline through your organisation's Windows security policy if you later disable the toolkit.

Recommended enablement sequence

1. Review every file in security/, especially the whitelist's persistent changes and the defender's process/IP response rules.
2. Validate the shipped assets without changing security policy: python security\automated_tests_of_security_assets.py. The test intentionally opens a foreground PowerShell window and headed Chrome, and writes proof under security\security_logs\asset_tests\.
3. Record the current Defender/ASR/CFA, execution-policy, audit-policy, Security-log, and matching firewall-rule baseline.
4. From <Tlamatini-root>\security, double-click enable_tlamatini_v2.bat or run it as administrator, approve UAC, and restart Tlamatini/PowerShell after it completes.
5. Establish a false-positive baseline with an elevated detect-only scan:

cd <Tlamatini-root>\security
powershell -NoProfile -ExecutionPolicy Bypass -File .\tlamatini_defender.ps1 -DetectOnly

6. Review security_logs\alerts.log and security_logs\monitor.log. WARNING, ALERT, and CRITICAL entries are investigation leads, not proof of compromise.
7. Only after reviewing the baseline, use run_defender.bat for a default armed one-shot scan or start a deliberate watch session from an elevated PowerShell window.

Defender modes

-IntervalSeconds accepts 5 through 86400; invalid values are rejected before monitoring begins. The defender monitors ten areas: Defender health/detections; failed and remote/network logons; established connections and suspicious listeners; process names and launch paths; non-Microsoft scheduled tasks; services; Run/RunOnce, Winlogon, AppInit and IFEO persistence; recent executable/script files in critical directories; ransomware/recovery-tampering indicators; and account/admin-group changes.

Response boundaries and review

• Default armed mode blocks an IP inbound and outbound after the scanned Security-log sample contains at least five failed logons from that address. The resulting Tlamatini Block <IP> Inbound|Outbound firewall rules persist; there is no automatic expiry or unblock.
• Process response is name-pattern based. Recognised Tlamatini paths are protected; known attacker-tool patterns are force-stopped, while dual-use tools only alert unless -Aggressive is supplied. Basename heuristics and suspicious-directory checks can produce false positives.
• Suspicious ports, persistence entries, ransomware indicators, account events, and many path findings are alerts only. The script is not an antivirus engine, EDR, SIEM, forensic conclusion, or replacement for Microsoft Defender and professional incident response.
• Inspect created block rules with Get-NetFirewallRule -DisplayName "Tlamatini Block *". Remove a specific inbound/outbound pair only after validating the incident and the IP; keep an audit record of that decision.
• Logs append under security/security_logs/, are intentionally git-ignored, are excluded from public builds/source snapshots, and can contain usernames, process paths, command lines, IP addresses, administrator-group membership, and other sensitive host telemetry. Protect and retain them according to your policy.

The batch launchers resolve their own full path with %~f0, pass it to the UAC relaunch through an environment variable so directories with spaces remain intact, and locate companion scripts with %~dp0. They also return the companion PowerShell process's failure code. The PowerShell scripts use $PSScriptRoot and Split-Path -Parent. This keeps the toolkit path-independent across source checkouts and installed builds. build.py ships the complete security/ directory but omits runtime logs, while copy_source_assets.py carries its source into self-modify snapshots and excludes security_logs/. On self-update, security/ is replaced as application code, but your security_logs/ evidence is preserved rather than deleted: apply_update.ps1 stashes it to Temp/_security_logs_carryover before the swap and restores it into the new security/ afterward, failing open (on any error the evidence is left in the carryover directory instead of being removed).

Authorisation boundary: use these assets only on systems you own or are explicitly authorised to defend. Tlamatini's Blue-hat toolkit helps collect and react to signals; the human operator remains responsible for scope, policy changes, false positives, containment decisions, evidence preservation, and recovery.

Created by Angela López Mendoza (@angelahack1) — Tlamatini, the one who knows.

Installation

See the full docs for complete setup — cloud models (Ollama Pro/Max, Claude API), the visual workflow designer, and building a frozen Windows distribution with PyInstaller. In short: install Ollama → clone, venv, pip install -r requirements.txt, migrate → runserver (--noreload optional since 2026-07-11) → open http://127.0.0.1:8000/.

Tech stack

Python 3.12 · Django 5.2.4 · Django Channels (Daphne ASGI) · LangChain / LangGraph · FAISS + rank-bm25 · Ollama / Anthropic Claude / Qwen vision · SQLite · PyInstaller. Platform: Windows 10/11.

Contributing

Tested it on your board, in your engine, or on the canvas? Open an issue and tell me what worked and what didn't — that feedback is the most useful thing right now. PRs welcome.

License

MIT · Built by @XAIHT · xaiht.org