Talos is the autonomous, fully-agentic platform I designed and built. It detects opportunities, produces assets, runs commerce, sales, trading, and creative work, talks to its operator by voice or chat, and improves its own capabilities over time - across a four-node fleet, mostly on local models. It behaves less like a collection of scripts and more like an operating system for agents.
- 4-node compute fleet, specialized by role (control/business, two GPU nodes, support).
- ~55 agents and 85+ services, discovered through a single registry.
- 54 local language models served via a hot-swapping gateway, plus cloud models.
- MQTT event bus (60+ event namespaces) wiring every component together.
- Cumulative vector memory: 640,000+ points across 12 collections.
- Real-time voice stack, full observability, and self-healing.
These figures are measured on the live fleet, not estimated - see metrics/snapshot-2026-06-20.md for the exact commands and their outputs.
The control surface: a real-time view of every node, agent, pipeline, and KPI, with a built-in voice and chat assistant.
A few of the operational panels:
Commerce & marketing. Async integrations with Shopify, WooCommerce, Stripe, Printful, dropshipping suppliers, and TikTok Shop, plus ML engines for pricing, demand forecasting, recommendation, customer-lifetime-value, fraud detection, sentiment, product enrichment, and visual search. Storefronts, catalogs, and campaigns run with little to no human input.
Autonomous sales. A pipeline with prospection and qualification live, and negotiation and closing built - backed by a deal ledger and a compliance guard. Self-hosted e-signature and full end-to-end validation are in progress.
Trading. A research-and-execution stack running in paper/testnet mode: strategy models, time-series forecasting, and a reinforcement-learning world model that simulates business and market decisions before they are taken.
Creative & content. Long-form and social content generation, SEO articles, community management, image and video generation, and scheduled multi-channel publication.
Voice & multimodal assistant. A real-time conversational assistant drives the platform by voice or chat: streaming speech in and out, speaker identification, vision, and document understanding (OCR, parsing). Ask it for a status, a report, or an action and it routes the request to the right agent.
Builders. Generators that produce web apps, games, and storefront themes, plus self-improving dev pipelines (audit / build / architect) that scan, generate, and refactor code and infrastructure under execution verifiers.
A cognitive layer keeps the system improving rather than merely running, built as a stack. An analytics hub is the strategic brain coupled to the Command Center: it consolidates KPIs, finance, and ML predictions, supervises the fleet, and acts on agents directly. Its predictive level goes beyond Monte Carlo, with two live stages: time-series forecasting (Chronos-class) and a reinforcement-learning world model (DreamerV3-class) that imagines the consequences of a decision before it is taken, trained on the platform's own real outcomes; a causal-reasoning stage is in progress. Above it, adaptive governance switches strategic mode from live metrics, a shared strategic-state graph is the common ground, and a reality-alignment check recalibrates it against actual impact. Around it, a closed self-evaluation loop: recursive validators, multi-layer QA with self-healing tests, a reflection mechanism that learns from past mistakes, a learning engine (A/B canary and evolutionary search), and experiment and drift monitors. A skill manager turns gaps into new reusable capabilities. Long-horizon planning and a meta-research engine are being added.
Four complementary systems, not one store: a structured knowledge wiki (raw to wiki to index, link graph, full-text search, note lifecycle) compiled by a librarian; an episodic memory of intents, decisions, errors, and reflections with graceful decay; a shared, versioned strategic-state graph used by the cognitive modules as common ground; and a retention manager that ages, archives, and purges under time-to-live policies. A vector knowledge base and embeddings give every agent durable, searchable context.
Command center & dashboard. A control plane classifies each request, routes it to the right agent and model, manages lifecycle, and surfaces everything in a live dashboard with the built-in voice/chat assistant. A mobile controller and Notion sync extend control beyond the desk.
Research & knowledge. An autonomous research agent gathers and synthesizes external sources; a knowledge backbone (embeddings, a librarian, vector search, document ingestion) gives every agent durable, searchable context.
Reliability & observability. An autonomous reliability layer (SOC plus SRE) detects anomalies, correlates them, drives incidents through a state machine, and remediates from runbooks with LLM-assisted investigation, on top of a metrics/logs/alerting stack and data-governance services (schema registry, validators, retention, aggregation), plus defensive security and compliance monitoring. Business-support agents handle finance, accounting, legal, and customer service.
Talos is organized into planes: a control plane (orchestration, routing, lifecycle, dashboard), a knowledge plane (a four-part memory of knowledge wiki, episodic store, strategic state, and retention, plus research and skill generation), an execution plane (business, creative, and trading agents), a cognitive layer (a stack from prediction and governance to shared state, with long-horizon planning and meta-research being added), and a reliability plane (observability and self-healing). An MQTT event bus and a local-model gateway connect them across the fleet.
Python (async-first) · local LLM ops (llama.cpp / GGUF, model routing, VRAM-aware hot-swap) · multi-agent orchestration · graph-RAG memory · vector database · MQTT event bus · real-time voice (streaming STT/TTS, speaker ID) · Prometheus-compatible metrics, logs, and alerting · reverse-proxy + WAF ingress · systemd · CI with ruff, pytest, and pre-commit.
- Execution-verified generation over single-shot prompting.
- Constrained decoding for valid output; ensemble judges to catch semantic false positives.
- Defense-in-depth - multiple independent checks, never one prompt.
- Metrics-driven: every change is measured; if it isn't reproducible, it isn't done.
Parts of the infrastructure are extracted, generalized, and released as standalone libraries:
- agent-resilience - circuit breaker, Redis-backed DLQ, and offline MQTT buffer.
- async-api-client - resilient async REST client with rate limiting, retries, and pagination.
- multi-agent-orchestrator - capability-based task routing template.
The platform itself is private; the components above are the parts released under MIT.