TL;DR
NucleusIQ is an agent-first Python framework (not a chat wrapper). As of v0.7.12, you get three execution modes, a context engine that compacts before overflow, parallel sub-agents in Autonomous mode, and stable MCP + file tools — with research/analysis agents as the primary fit.
What’s not shipped yet: Agent-as-Tool for the LLM, A2A, a sandboxed shell tool, and published benchmark scorecards. Those are on the v0.8–v0.9 plan — not “maybe someday,” but explicit backlog items with IDs in the scorecard.
What “coverage” means here
Each row in the interactive map answers one question:
If I want to build this kind of agent on NucleusIQ, how ready is the framework today — and how ready will it be after the next multi-agent release?
Scores are 0–100% (subjective but tied to the repo):
| Score | Meaning |
|---|---|
| Support today | What you can run now on v0.7.12 without forking core |
| Support planned | Expected after v0.8–v0.9 (Agent-as-Tool, structured sub-agent handoff, A2A, benchmark proof) |
| Readiness | Plain label: Ready now · Partial · On roadmap · Not planned |
This is not a leaderboard against LangGraph or CrewAI. It’s an honest inventory of NucleusIQ’s agent surface area.
What we’re planning (v0.8–v0.9)
- Agent-as-Tool — wrap an agent so the LLM can call it like a tool
- Better multi-agent handoff — structured synthesis (fix 2K truncation), optional cheaper sub-agent model
- A2A — thin client/server for remote agents (not a full graph engine)
- Public proof — Context Report + benchmark scorecards (challenge runner already in the repo)
Not on the roadmap: Graph/DAG orchestration, Swarm/handoff chains (LangGraph-style) — intentional.
NucleusIQ — Full Agent Taxonomy & Coverage
Audited against nucleusiq 0.7.12 (May 29, 2026) from src/nucleusiq + provider packages.
Scores 0–100 = support for building each agent type today vs planned (v0.8–v0.9).
Overview — how many agent “types” exist?
There is no single global count. Industry uses 14 taxonomy categories below; only some map to what NucleusIQ ships.
Bar: blue = strong now · green = planned · grey = partial · red = deferred
- A. Classical decision agents (5)
- B. LLM runtime architectures (8)
- C. Multi-agent orchestration (7)
- D. Application roles (7)
- E. Cognitive functions (7)
- F. Execution topologies (6)
- G. 28 named design patterns (7×6 matrix)
- H. Interop protocols (3)
- I. Environments (5)
- J. Gearbox execution modes (3)
- K. Builtin tool classes (5)
- L. Provider-native capabilities (4)
- M. Benchmark / proof types (4)
- N. Strands menu mapping (5)
Stat cards — click to expand
Category-level checklist — every agent type you can build
Each row: can you build this agent on NucleusIQ today? Planned? Gap ID if missing.
28 named design patterns (7 cognitive × 6 topology)
From Huang & Zhou (2026) — Cognitive Function × Execution Topology. Full matrix = 42 cells; 28 named, 14 empty. NucleusIQ column = how well our runtime implements that pattern (not whether the pattern exists in literature).
| Cognitive ↓ / Topology → | Chain | Route | Parallel | Orchestrate | Loop | Hierarchy |
|---|
Gap analysis — current landscape vs future landscape
Current landscape (v0.7.12 — what we ship)
Future landscape (v0.8–v0.9 — backlog)
Intentionally out of scope
Coverage radars
Multi-agent orchestration (7)
Runtime & platform (9)
Application roles (7)
Cognitive functions (7)
Agent coverage scorecard
Each row is one kind of agent (or agent architecture) you might build — for example “research agent”, “parallel sub-agents”, or “coding agent”.
Support today = v0.7.12 codebase (May 29, 2026 audit).
Support planned = v0.8–v0.9 backlog (MA-01/02/03, A2A, BMK-02/04/06).
Readiness is a plain label. Update scores in the TAXONOMY / MATRIX arrays when you ship features.
| Cat | Agent type | Support today | Support planned | Readiness | Backlog | What it means on NucleusIQ |
|---|
Scores are audited against the open-source repo (v0.7.12, May 2026). Star or watch NucleusIQ on GitHub for updates. When we ship MA-01 / benchmark proof, we’ll bump the audit date in the HTML and refresh this post’s intro.
When to update
| You change… | Update… |
|---|---|
| Coverage scores / new agent type | agent-coverage-radar.html (TAXONOMY, MATRIX, AUDIT.date) |
| Published blog URL | README link + optional line in Section E |
| Major release (e.g. v0.8) | Section B/C bullets + HTML audit stamp |
Do not copy tables from HTML back into this markdown — keep one source of truth (the HTML).
Written by Nucleusbox. More tutorials: Machine Learning archive. Code: GitHub — ml-beginners-python.
Footnotes:
Additional Reading
- GitHub: NucleusIQ
- AI Agents: The Next Big Thing in 2025
- Logistic Regression for Machine Learning
- Cost Function in Logistic Regression
- Maximum Likelihood Estimation (MLE) for Machine Learning
- ETL vs ELT: Choosing the Right Data Integration
- What is ELT & How Does It Work?
- What is ETL & How Does It Work?
- Data Integration for Businesses: Tools, Platform, and Technique
- What is Master Data Management?
- Check DeepSeek-R1 AI reasoning Papaer
OK, that’s it, we are done now. If you have any questions or suggestions, please feel free to comment. I’ll come up with more topics on Machine Learning and Data Engineering soon. Please also comment and subscribe if you like my work. Any suggestions are welcome and appreciated.
