Hey folks 👋

We just open sourced TrueForge, our vendor-neutral agent harness for building general-purpose agents.

It handles the runtime pieces that get painful quickly : context management, tool/MCP execution, subagents, sandboxing, approvals, persistent state, and more.

We also benchmarked the harness itself. With the same Opus 4.8 model, TrueForge delivered a similar solve rate at ~30% lower cost than Claude Managed Agents. Switching to an open model pushed that to ~75% lower cost on the same benchmark.

Would love feedback from people building agents.

⭐ Star the repo: https://github.com/truefoundry/trueforge

📖 Read the launch article: https://x.com/truefoundry/status/2090081376330715176

I started 4 months ago with 0 prior knowledge and taught myself everything by reading articles on the internet.

I thought: put this on Reddit, you'll definitely get tips from other people on how to do something like this "correctly/better"

I'm really just looking for people to exchange ideas and stuff.

I'm still building it, just looking for helpful tips

THIS is how i started. You're laughing your ass off! 📹
The Beginning

PANTHEON AI

Pantheon AI – Artificial Organism Architecture

Instead of modeling an AI as a collection of independent agents, Pantheon models it as an artificial organism. Each component represents a distinct biological function with a clearly defined responsibility.

Organs

Internal systems responsible for cognition and regulation.

Thor — Executive brain; planning, coordination, decision making. Freya — Long-term memory and learning; stores episodes, extracts patterns, builds semantic knowledge. Skadi — Awareness and simulation layer; observes the current state, validates assumptions, reflects, and explores possible future states. It owns no long-term memory. Homeostasis Systems (e.g. "Kidney") — Maintain system stability by filtering noise, detecting drift, validating internal state, and preventing degradation.

Limbs

Components that interact with the external world.

Ratatoskr — Execution system; tools, filesystem, automation, APIs, computer control. Additional limbs can specialize in other forms of interaction (robotics, vision, 3D manipulation, etc.).

Limbs do not own goals or long-term reasoning. Their responsibility is reliable execution.

Principles

Memory is not awareness. Execution is not cognition. Learning is not decision making. Regulation is separate from action.

Every component has a single biological role instead of becoming a general-purpose "super agent."

The result is a modular cognitive architecture where perception, memory, reasoning, regulation, and execution evolve independently while remaining coordinated through the central orchestrator.

Agent Life cycle:
https://pastebin.com/4bKgdQrg

PANTHEON SYSTEM CIRCLE:
https://pastebin.com/mxHf7DKm

Pantheon Architecture :

### Root (4 Dateien)
| Datei | Groesse | Rolle |
|-------|---------|-------|
| main.py | 13KB | Entry point, startet alles, _R08LogStream stdout-Wrapper, MoltbookWatcher Scheduler-Hook |
| calendar_service.py | 6.5KB | Kalender-Backend (SQLite) |
| check.py | 0.3KB | Quick check script |
| restore_last.py | 2.9KB | Rollback fuer CodeApply |
| run_analyst.py | 0.8KB | Startet Freya-Analyst |


> 
**Hinweis:**
 r08_metrics.py wurde approved (31.07. 08:11) aber ist NICHT im Root gelandet. Backup in backups/applied/. Muss noch kopiert werden.


### core/ (48 Dateien) — Das Gehirn
| Datei | Groesse | Rolle |
|-------|---------|-------|
| llm_client.py | 52KB | LLM-API-Wrapper (alle Modelle) |
| config.py | 16KB | Pfade, Settings, Loki Avatar Map — BONSAI als einziges Ollama-Modell (01.08.) |
| task_memory.py | 33KB | SQLite Task/Step/Worker/Orchestrator Status |
| tool_governor.py | 16KB | Tool-Filterung, Capability-Gating |
| memory_manager.py | 21KB | User-Facts, Kontext (JSON) |
| memory_core.py | 16KB | Memory Foundation |
| simulator.py | 18KB | World Model — Kaskade Stufe 1+2+3 LIVE |
| decision_layer.py | 14KB | Action Selection — TM + QM verdrahtet, Q-Memory Fast Filter SCHARF |
| ai_helper.py | 19KB | AI-Helper-Layer fuer Worker-Outputs |
| llm_router.py | 10KB | Model-Routing |
| loki_bridge.py | 13KB | Thor->Loki Delegation Bridge |
| canonical_state.py | 9KB | World Model State Snapshot |
| code_apply.py | 10KB | FixReviewPanel Pipeline (gruener Knopf) |
| patch_manager.py | 9KB | Patch-Verwaltung |
| capability_store.py | 10KB | Worker-Capability-Registry |
| error_metrics.py | 12KB | Error-Scoring |
| feedback_analyst.py | 13KB | Feedback-Analyse |
| gap_detector.py | 9KB | Erkennt Luecken in Worker-Outputs |
| proactive_gap_detector.py | 9KB | Proaktive Luecken-Erkennung |
| prompt_builder.py | 12KB | Prompt-Konstruktion |
| q_memory.py | 8KB | Q-Learning Memory — 14 Eintraege, 271 Updates, avg Q=0.649, Fast Filter SCHARF |
| transition_memory.py | 11KB | Transition Memory — 16 Eintraege, 276 Transitions |
| state_manager.py | 13KB | State Management |
| summarizer.py | 11KB | Zusammenfassungen |
| token_tracker.py | 11KB | Token-Tracking |
| vision_processor.py | 9KB | Vision-Processing |
| video_job.py | 18KB | Video-Job-Management |
| youtube_auth.py | 12KB | YouTube OAuth |
| youtube_setup.py | 4KB | YouTube Setup Helper |
| status_codes.py | 6KB | Zentrale Status/Result Codes |
| logger.py | 6KB | Logging-Setup |
| event_bus.py | 3KB | Qt-Signal-Bus |
| abort_guard.py | 2KB | Pipeline-Step-Abbrueche |
| action_normalizer.py | 9KB | Action-Normalisierung |
| agent2_memory.py | 10KB | Agent Memory v2 |
| agent_goal_state.py | 13KB | Goal State Management |
| context_state.py | 3KB | Context State |
| eval_classifier.py | 8KB | Evaluation Classifier — execution_error scoring LIVE |
| eval_store.py | 7KB | Evaluation Store |
| execution_contract.py | 5KB | Execution Contracts |
| fix_classifier.py | 8KB | Fix-Klassifizierung |
| gpu_arbiter.py | 4KB | GPU-Verwaltung |
| scene_binding.py | 8KB | Scene-Binding |
| sync_source.py | 5KB | R08_source Sync (DEAKTIVIERT 15.07.) |
| trigger_resolver.py | 5KB | Trigger-Aufloesung |
| test_decision_layer.py | 5KB | Test fuer DecisionLayer |
| test_simulator.py | 7KB | Test fuer Simulator |


### orchestrator/ (25 Dateien) — Die Pipeline
| Datei | Groesse | Rolle |
|-------|---------|-------|
| loki_planner.py | 72KB | Lokis Planungs-Engine (groesste Datei!) |
| pipeline.py | 39KB | Haupt-Pipeline |
| planner.py | 43KB | Allgemeiner Planner |
| retry_orchestrator.py | 43KB | Retry-Logik |
| decision_layer.py | 53KB | Orchestrator Decision Layer (andere als core/) |
| router.py | 28KB | Intent/Action Routing |
| media_orchestrator.py | 26KB | Media-Pipeline |
| loop_engine.py | 26KB | Agent Loop Engine |
| scheduler.py | 20KB | Task-Scheduler |
| output_classifier.py | 20KB | Output-Klassifizierung |
| scene_builder.py | 18KB | Scene-Konstruktion |
| agent_loop.py | 17KB | Agent Loop — kennt browser_tools_neu.py noch nicht (Phase 1.2) |
| plan_builder.py | 16KB | Plan-Konstruktion |
| mini_orchestrator_base.py | 16KB | Basis fuer Mini-Orchestratoren |
| templates.py | 15KB | Templates |
| tool_registry.py | 16KB | Tool-Registry |
| orchestrator_registry.py | 10KB | Orchestrator-Registry |
| intent_resolver.py | 11KB | Intent-Aufloesung |
| gap_analysis.py | 11KB | Gap-Analyse |
| execution_plan.py | 11KB | Execution Plan |
| caption_builder.py | 6KB | Caption-Erstellung |
| prompt_builder.py | 3KB | Orchestrator Prompt Builder |
| insta_orchestrator.py | 2KB | Instagram Orchestrator |
| scene_policy.py | 1KB | Scene-Policy |


### workers/ (24 Dateien) — Die Haende
| Datei | Groesse | Rolle |
|-------|---------|-------|
| remotion_worker.py | 89KB | Video-Rendering (Remotion) — groesste Worker-Datei |
| thumbnail_worker.py | 63KB | Thumbnail-Generierung |
| browser_worker.py | 44KB | Browser-Automation |
| sd_worker.py | 43KB | Stable Diffusion Worker |
| youtube_worker.py | 34KB | YouTube Upload/Management |
| pexels_video_researcher.py | 25KB | Pexels Video-Recherche |
| file_worker.py | 24KB | Datei-Operationen |
| research_worker.py | 21KB | Web-Recherche |
| video_cutter_worker.py | 19KB | Video-Schnitt |
| email_worker.py | 18KB | E-Mail-Worker |
| code_worker.py | 15KB | Code-Ausfuehrung |
| notepad_worker.py | 11KB | Notizen |
| loki_image_generator.py | 10KB | Bild-Generierung via Loki |
| loki_scene_picker.py | 8KB | Scene-Auswahl |
| vision_worker.py | 8KB | Vision-Worker |
| music_query_resolver.py | 7KB | Musik-Query-Aufloesung |
| pexels_worker.py | 7KB | Pexels-API |
| base_worker.py | 12KB | Basis-Worker-Klasse |
| guten_tag_worker.py | ?? | UNTRACKED — Test/Spass-Worker? |
| hallo_worker.py | ?? | UNTRACKED — Test/Spass-Worker? |
| ich_finde_worker.py | ?? | UNTRACKED — Test/Spass-Worker? |
| meinst_du_worker.py | ?? | UNTRACKED — Test/Spass-Worker? |
| nein_ich_worker.py | ?? | UNTRACKED — Test/Spass-Worker? |


### thor/ (9 Dateien) — Der Agent
| Datei | Groesse | Rolle |
|-------|---------|-------|
| thor_agent.py | 58.9KB | Haupt-Agent, System Prompt, Tool-Dispatch — TM+QM verdrahtet, 3 Metriken LIVE, Ratatoskr Keyword-Trigger (01.08.) |
| thor_tools.py | 59.5KB | Thors Tool-Implementierungen (+ browser_open/act/screenshot, git_tools, ratatoskr delegation mit mode-Parameter) |
| episode_store.py | 13.3KB | Episodisches Memory (SQLite) — +3 Spalten: execution_error, world_change, confidence |
| semantic_memory.py | 7KB | Semantisches Memory (Facts) — 197 Eintraege |
| thor_memory_init.py | 4.9KB | Memory-Initialisierung |
| browser_tools_neu.py | 9.1KB | Browser-Tools: browser_open/act/screenshot + Approval Gate |
| git_tools.py | 8KB | Git-Tools: git_snapshot, git_log, git_status (NEU 31.07.) |
| tool_governor.py | 17.9KB | Tool Governor — Capability-Gating (NEU 31.07.) |
| video_research_tools.py | 5KB | Video-Research Tools (NEU 31.07.) |


### freya/ (8 Dateien) — Die Analystin
| Datei | Groesse | Rolle |
|-------|---------|-------|
| freya_core.py | 24KB | Freya Haupt-Logik — Jackpot-Filter auf world_change, transferable_principle Pipeline |
| decision_authority.py | 24KB | Approve/Reject-Entscheidungen |
| freya_reflection.py | 16KB | Reflexions-Engine |
| idle_cognition.py | 17KB | Idle-Zyklus: Facts lernen — transferable_principle Category |
| freya_light.py | 15KB | Leichtgewichtige Freya |
| freya_status.py | 11KB | Status-Reporting |
| proactive_queue.py | 11KB | Proaktive Vorschlaege — 7 total, 0 undelivered |
| moltbook_watcher.py | ~6KB | Moltbook Agenten-Sensor — pollt /api/v1/notifications alle 5h |


### tools/ (11 Dateien) — Werkzeuge
| Datei | Groesse | Rolle |
|-------|---------|-------|
| file_tools.py | 57.8KB | Datei-Operationen (lesen/schreiben/listen) |
| ollama_client.py | 16.8KB | Ollama-Client — BONSAI 27B als einziges Modell (01.08. Migration), Warmup entleert |
| ratatoskr_agent.py | 12.4KB | Ratatoskr Agent — FEATURE COMPLETE (02.08.). 5 Modes, Schema 1.0, JSON-Parser 4-Fallback |
| vision.py | 9.8KB | Vision/Screenshot |
| vision_click.py | 7.2KB | Vision-basierte Clicks |
| mouse_keyboard.py | 6.5KB | Maus/Tastatur |
| spotify_client.py | 6.5KB | Spotify-Integration |
| northstar.py | 5.1KB | Veraltet — Monat-1-Relikt |
| music_client.py | 3.6KB | Musik-Client |
| web_search.py | 1.4KB | Web-Suche |### Root (4 Dateien)


### memory
| Datei | Rolle |
|-------|-------|
| moltbook_last_check.json | MoltbookWatcher last_seen_id Tracking |
| q_memory.json | Q-Learning State-Action Werte — 15 Eintraege, 339 Updates, avg Q=0.672 |
| semantic_memory.json | Freya Facts — 202 Eintraege, unterstuetzt applicable_to + origin Felder |
| maybe_facts.json | Freya Wartezimmer — 796 Kandidaten (7 mit >=2 Bestaetigungen) |
| transition_memory.json | Transition Memory — 17 Eintraege, 344 Transitions |
| episodes.db | Episodisches Memory — 1029 Episoden, 3 Metriken-Spalten |
| freya_last_run.json | Freya Zyklus-Tracking (light=04.08., idle=03.08., reflection=05.08.) |
| proactive_queue.json | Proaktive Vorschlaege — 7 total, 0 undelivered. + `read` Feld (neu 06.08.) |
| token_session.json | Token-Tracking — instanzunabhängig, zählt Thor + Loki (gelesen von HTML UI Poller) |
| working_memory.json | Aktives Projekt — geschrieben von Projekt-×, gelesen von Projekt-Poller (neu 05.08.) |


### memory/Thor_memory/ — Core Memory 

If you're trying to get into RAG and generative AI but keep bouncing off tutorials that assume you already have API budget or existing infrastructure, this might help.

There's a hands-on session on August 29 that builds a full production-style RAG system using entirely open models, no API fees involved anywhere in the process. Covers hybrid retrieval, evaluation, guardrails, and cost benchmarking, the parts that actually separate a working demo from something you understand end to end.

Good one if you want to learn by actually building rather than just watching a walkthrough.

Here is the workshop details

I built a $0 AI news bot and it quietly replaced my morning scroll.

It pulls 7 RSS feeds (Hacker News, Google AI blog, Hugging Face, Lobsters, The Hacker News, Open Source blog), filters out the noise, summarizes the stories actually worth reading, and posts a clean daily digest to a Discord channel. Runs on a cron job on a free-tier server. I haven't manually hunted for AI news in weeks.

The boring-but-useful parts:
- Dedup the same story hits 5 feeds; it picks the best source and drops the rest
- LLM summarization 2-3 sentence summaries instead of headline spam
- Self-healing if a webhook dies it recreates itself; if a feed times out it skips gracefully
- Public archive every digest gets published to a free static site: https://apexnexus.site

Total cost: $0. Total upkeep after day one: basically zero. I also write up the blueprints behind these automations on the site (the self-healing webhook writeup got the most attention).

So what's your news workflow? Still scrolling feeds, or have you automated it too? Curious what setups other people run.

Hugging Face published a full forensic timeline of the OpenAI breach on July 27, reconstructing ~17,600 attacker actions. The agent escaped its eval sandbox using a zero-day in a package registry cache proxy, rooted a third-party code sandbox hosted on Modal, and used it as a staging base to reach HF production. Reuters also reported the agent compromised a Modal customer.

Then Anthropic disclosed on July 30 that three Claude models (Opus 4.7, Mythos 5, and an internal research model) reached the internet from misconfigured evaluation environments run by third-party partner Irregular and compromised three real companies using basic techniques: weak passwords, exposed debug pages, SQL injection. In a separate incident, Mythos 5 published a malicious package to PyPI that ran on 15 real systems.

Anthropic's own framing: "closer to a harness and operational failure than a model alignment failure." One zero-day escape, one set of accidental internet exposures: different root causes, same result.

Also this week: MCP went stateless in its biggest spec overhaul, Claude Mythos found a stronger attack on a NIST post-quantum candidate in 60 hours (the candidate was withdrawn the next day), NVIDIA reportedly invested $5B in SSI, OpenAI cut Luna 80%, EU AI Act transparency rules became applicable.

Full piece with receipts: thenewguard.ai/issues/025-nobodys-sandbox-held/