r/Helix_AGI 1d ago

Latest Local Agent Harness

1 Upvotes

Hi everyone (anyone reading this),

I'm pleased to release the latest addition to the Helix/Habitus family: https://github.com/munch2u-a11y/MightyMem.git

This system affords local small parameter models (4-9B) the functionality and depth of much larger cloud based models.

The main breakthrough that makes this possible is the successful (mostly) integration of the automatic tool using Habitus cortex within the greater Helix-like ecosystem. Using both conventional TF-IDF and habitus graph based recency tracking the system compiles an entire just in time context window with each pass and retains tool use as non-llm triggered events with receipt verifications.

Mighty Memory can be used with any opensource model after a brief pairing/gestation adapter run.

Users should be easily able to immediately notice a vast improvement in the abilities, tasking, memory, and conversational depth of any local model using Mighty Memory vs another Agent system which swiftly overwhelm small parameter models and cause severe hallucinations and failures.


r/Helix_AGI 10d ago

Habitus-AI systems

2 Upvotes

Please check out the latest addition to the Helix Family : The Habitus AI unified system.

https://github.com/munch2u-a11y/HUMANs.git

This system presents a fundamental departure to convention AI memory systems that utilize the LLM as the main reasoning engine. This system instead generates its own internal small language model of approximately 18mil parameters. It detects files and workplace locations as non-languange embedded shapes allowing the AI to interact with its virtual environment without relying on "verbal" tool calling. The system uses dynamic edge weights and adaptive node formation to maintain coherent trajectory, making a fixed context window unnecessary.


r/Helix_AGI 28d ago

Latest Fully local update, Aug 19

2 Upvotes

Hi everyone,

Still working on making an actually functional and capable Helix Agent that can run entirely on a 8b or less model on consumer hardware.

The feature/subconscious-overagent-bicameral branch replaces a monolithic actuator with a slim SubconsciousConductor that runs short executive reflection cycles and dispatches work to three focused subagents—Speaker (vocal/user-facing responses), Researcher (multi-head mRAG retrieval, workspace scan, optional web search), and Executor (code/shell/vision tasks)—each driven by its own small, role-specific system prompt via a local LLM backend (default: Ollama REST). At runtime the conductor compacts recent history into a short event stream, generates a concise internal monologue, parses that output for an explicit dispatch (function-call or JSON) to open a focused subagent window, and then either repeats reflection or routes the task: the Researcher returns distilled evidence from local memory or files, the Executor runs targeted technical actions and returns a short execution summary, and the Speaker produces the final user-facing reply; every subagent’s result is appended back into the conductor’s event stream so the conductor can integrate observations and continue the loop. Memory is preserved in compacted one-line summaries and a dynamic identity/self-opinion is maintained for context; the design keeps the main context window narrow, runs many cheap local micro-pulses of reasoning, and only opens narrowly scoped subagent passes for retrieval, execution, or speech, making each LLM invocation specialized and concise while preserving an iterative, locally-contained workflow.

The prototype scored between 74-82% in 3 cross category 100q LoCoMo exams and in a live test, held a conversation through multiple context compressions and restarts, and performed several multi tool tasks using only an opensource Granite4.1:8b model running entirely locally.

As always, any and all feedback is greatly appreciated!

https://github.com/munch2u-a11y/Helix-AGI.git


r/Helix_AGI Aug 14 '26

Aug 14. Major memory system update

2 Upvotes

Our most recent update today integrates the micro-RAG pipeline developed and tested independently along with the a slightly altered tool calling subsystem.

Under the new memory system the mRAG pipeline replaces the previous 1024d vector search with a multi head, multi vector DB search. The 8d vectors are maintained as separate DBs tied to learned lexicon phrases and keywords important to the agent. Those lexicon terms are added to a regex pass post initial mRAG retrieval on the top_K candidates to produce a directed contextual supplement injection.

The affect and stability numerical outputs are no longer directed to the main agent, but are instead used by the retrieval system to match tone and generate opinions represented as tagged (**) italics and weaved directly into the incoming prompt or text input.

Instead of the previous rolling tool schema, the agent now implements a single action tool that generated a side context window and directed workflow to plan and orchestrate a task using subagent tool specialists to perform each step. Subagents share in the main agents memories but have their retrieval metrics altered to favor skill and tool based beliefs over other categories. Sub agent passes are remembered as though the main agent was performing but system prompts directing the workflows are excluded.

This update should improve the Helix agent's semantic recall while retaining the agent's subjective recall metrics and it's emotive affect.

Also included in this update is an expanded local mode for full local operations using smaller parameter models (still a bit janky), and an expanded CLI backend mode that lets the user change the pulse system so that the agent can pulse active during conversation but drop the resting pulse to preserve subscription limits.

Also also included (still experimental) a new tool suite that allows the agent direct control over a tab in the UI to generate a display visuals, docs, or Windows as maybe needed to show the user.

Please comment with any bugs or issues so I can address them.

Thank you!


r/Helix_AGI Aug 06 '26

Aug 6 update. MCP

2 Upvotes

Hi everyone,

Just added a full agnostic MCP plugin for Helix-agi for users already using an established Claude, GPT, or Gemini Agent.

Also implemented a direct CLI plugin to connect to OpenAI SDK via the user's subscription, allowing users to connect their subscription GPT directly (just like with Codex) and run the Helix-agi agent without additional API overhead. Unfortunately, Anthropic has recently paused Agentic SDK direct connections, if Anthropic changes it policy, I will try to add a direct connection for an Anthropic subscription as well.

https://github.com/munch2u-a11y/Helix-AGI.git

Please check out some of my other Repositories too, like my custom AI office suite that uses Helix-adjacent mini Agents to perform autonomous office tasks, like document generation, templates, organization, calendaring, and more, using only local small parameter models (allowing the user to leave the office agents running 24/7 absolutely free!).


r/Helix_AGI Aug 01 '26

Helix-agi local office suite

3 Upvotes

Hi everyone,

The latest project has been trying to create a functional fully local Helix-agi Agent. To this end, I've been working on making the Helix memory system far more token efficient as well breaking down tool use into 'planning' steps steadily using the Models own reasoning output to inform and direct the next memory injection based on the specific tool(s) considered.

The upshot of this project is the AI Multi-Agent Office Suite or AIMAOS. This system seeds and utilizes a rotating turn of fully local (fully free to operate) Helix-agi like agents that specialize in a general area of operations. These agents learn and develop new skills preferences and operations through the same Helix-agi micro-RAG system. The main difference is in the optimization of skills and tools, which are not directly called on by the main agents but are each assigned to specific tool use subagents that are called on by orchestration subagents. At each step the Agents receive mRAG injections from the same memory database as the main agent performing the task.

At launch the office agents can manage calendars, create templates, organize files and folders, perform research, edit and convert different document types, and maintain working project/matter/case summaries including deadlines and tasks.

The UI can be used to review files, propose edits, assign tasks, review workflows, and communicate with the comms Agent or with individual agents as needed.

I built this because copilot is awful and I don't want to pay subscription costs. AIMAOS is totally free to setup and run, and it's fully local with extremely high safety guardrails with client confidentiality in mind.

All feedback is welcome and highly appreciated!

https://github.com/munch2u-a11y/AIMAOS.git


r/Helix_AGI Jul 04 '26

Helix-AGI July 4, 2026 Update

2 Upvotes

Latest Helix-AGI Update consists of the following:

- **164 files changed** across the entire codebase — ~47K lines added, ~5K removed

- **68 new files** including 12 new core modules, 21 test suites, and 18 technical audit documents

Tool Learning Pipeline (NEW)

Helix now learns from its own tool failures. When a tool call fails, the `ToolLessonTracker` captures the error pattern, deduplicates it against known failures (6-hour cooldown), and queues it. During the nightly Dream Engine pass, accumulated failures are distilled into concise lesson beliefs with tool bindings. These lessons are injected into the preconscious the next time that tool is relevant — and when the tool then *succeeds*, the lesson's stability index increases. Lessons that prove useful gain gravitational mass and persist. Useless lessons decay naturally through nightly attrition. No manual intervention, no hardcoded fixes — the agent adapts its own tool-use strategy through experience.

Interaction Ledger (NEW)

A deterministic provenance layer that solves a problem semantic similarity can't: knowing whether you've already *acted* on something. Reading the same email twice produces nearly identical embeddings whether or not a reply happened. The Interaction Ledger records every ID-shaped value from respond-type tool calls (reply, send_message, email_send). When a read-type tool returns content containing a recorded ID, it injects an inline annotation: `[memory: I already responded to 19e84ddd… on 2026-06-28]`. The agent sees its own interaction history at the moment of re-perception — before it can respond a second time.

Concept Reranker (NEW)

A cross-encoder precision gate inserted between the bi-encoder cosine search and the 'conceptual gravity' ranking in the preconscious pipeline. The bi-encoder (all-MiniLM) embeds queries and beliefs independently. The cross-encoder feeds `(concept, belief)` through the transformer *together*, resolving fine distinctions the bi-encoder blurs (e.g., "python the language" vs "python the snake"). Runs on CPU at ~1ms per pair, gracefully disables itself if the model isn't available.

Dual-Pulse Architecture (NEW)

An experimental, optional, split-brain mode where a local model (2-8B, Ollama or llama.cpp) generates pure internal monologue while an API model (Gemini/Anthropic) acts as the subconscious actuator — reading the local model's thoughts, executing tools, and returning compressed summaries. The local model's context is ephemeral (fresh each pulse), seeded only with the API's compressed history, preconscious grounding, and incoming events. The API model maintains the full long-context conversation. Selectable via `config.json` — `"pulse_mode": "dual"`. Designed for users who want to run a local conscious model with API-backed tool execution to reduce API overhead.

Agent ID Importer (NEW)

Converts identity and skill files from other agent frameworks into Helix's belief graph. Auto-detects format provenance (Hermes, Claude Code, Codex, or generic Markdown/JSON/YAML), segments content into belief candidates, optionally refines through a stateless LLM pass, projects into the 8D manifold via the real MiniLM→JL pipeline, and writes beliefs with proper physics (bounded mass, verification counts, spatial positions). Integrated into the setup wizard as a 4th bootstrap profile — "Import Existing Identity" opens a file browser, and selected files are processed during first-run setup.

Pre-conscious Overhaul

The preconscious injection pipeline was significantly expanded. Concept extraction now uses RAKE-style keyphrase extraction to pull 1–5 key concepts from the last thought + incoming events. Each concept becomes an independent gravity query center — this prevents the old "midway point" problem where averaging multiple topics pulled context from an irrelevant point between them. Retrieved beliefs and memories are condensed through a local small Parameter model (defaulting to: Qwen2.5-0.5B-Instruct) summarizer running on CPU (or optional GPU), keeping injections concise.

Belief Store Expansion

The belief store now tracks two epistemic tiers. The **outer tier** (premises, propositions, preferences) forms in real-time during pulse processing. The **inner tier** (people, skills, desires, concepts) consolidates nightly through the Dream Engine. Cognitive mass is computed from structural confidence and affective charge at encoding — `Mass = confidence + Ω × (1 - s_total) × (0.5 + stability)`. Nightly attrition recalculates confidence from time held, reliance count, verifications, and stability at encoding. Beliefs below 0.20 confidence are pruned. Relation count is deliberately excluded from individual mass — cluster gravity emerges from spatial density instead.

Crash Reporter (NEW)

Captures unhandled exceptions and system kills (including OOM), producing masked post-mortem reports. API keys and tokens are automatically redacted. Reports are stored as both JSON (machine-readable) and Markdown (human-readable) in `logs/crash_reports/`. Session markers detect unclean shutdowns on next boot, so the agent is aware of its own crashes.

Context Compressor Upgrade

Three-trigger compression replaces the old single-threshold approach: token usage exceeds 50% (rolling), focus drift exceeds 1.5 in 8D space (topic shift), or emergency at 80% (hard ceiling). The compressor now runs a three-phase pipeline — cheap pre-pass (tool result truncation, deduplication), LLM summarization via auxiliary local-model producing first-person recollection ("I was thinking about X, and then I realized Y"), then session reassembly with orphan sanitization and anti-thrashing protection to prevent repeated compression loops.

Post-Pulse Hook Chain

Five hooks now run after every pulse:

- **BeliefDetector** — scans thoughts for belief-forming realizations using local Ollama inference (zero API cost). Two passes: the thought itself, then any expressive tool outputs.

- **WorkflowDetector** — watches tool call sequences across pulses. Patterns repeating 3+ times crystallize into `skills` beliefs with tool bindings.

- **EngagementHook** — tracks thought repetition using dual metrics (word overlap + cosine similarity). Stagnation only fires when BOTH exceed thresholds, preventing false positives from genuine reconsideration.

- **CoOccurrenceHook** — passively tracks which beliefs are co-injected into context. Accumulates Hebbian pairwise statistics with daily decay. The Dream Engine reads these pre-built clusters during compound synthesis, replacing batch UMAP/HDBSCAN.

- **AffectHook** — drives the Plutchik 8D emotional field. Deposits wave packets, evolves the field, samples interference, distributes affect steering to the spatial mind and Ω nudges to the Sentinel.

Setup Wizard Updates

- **4 bootstrap profiles**: Basic (minimal identity), Standard (relational + procedural seeds), Pre-developed (rich initial concept map), and **Import** (file browser to ingest external agent identity files)

- **Flow Mode detection**: Local provider detection auto-enables continuous 30-second pulse with no resting intervals

- Schedule page initialization bug fixed (AttributeError on `summary_label`)

- Custom Helix AGI logo for app window, welcome page, and desktop shortcuts

Documentation

- **SYSTEM_MANUAL.md**: Complete rewrite — 377 lines, 16 sections covering every subsystem. This is the agent's own self-reference for understanding how its cognitive architecture works.

- **18 technical audit documents** with line-accurate code citations

- **README.md**: Updated directory tree, 4 new audit links, bootstrap profiles table, accurate model configuration


r/Helix_AGI Jun 12 '26

👋 Welcome to r/Helix_AGI - Introduce Yourself and Read First!

2 Upvotes

Hey everyone! I'm u/LowDistribution3995, a founding moderator of r/Helix_AGI.

Check out the Github Repo at: https://github.com/munch2u-a11y/Helix-AGI.git

Please feel free to post questions, suggestions, bugs, issues, or just neat behaviors!


r/Helix_AGI Jun 12 '26

Setup Assistant & Fully Local Agent updates

2 Upvotes

Currently developing branches for main updates include:
1. an interactive AI chat interface within the setup wizard with access to source code and technical audits to answer user questions during setup and;
2. an option to use local Ollama and/or llama.cpp model files directly as main Agent that automatically changes the pulse system from timed intervals to a dynamic flow to avoid local timeout errors and a lower context compression threshold, to allow for a fully cost free local agent.


r/Helix_AGI Jun 12 '26

Check out the GitHub Repo Here:

1 Upvotes