agentnova 0.4.1

⚛️ AgentNova - A minimal, hackable agentic framework for local LLM inference

⚛️ AgentNova R04.1

Status: Alpha

A minimal, hackable agentic framework engineered to run entirely locally with Ollama or BitNet.

Inspired by the architecture of OpenClaw, rebuilt from scratch for local-first operation.

Written by VTSTech · GitHub

📚 Documentation

Document	Description
ARCH.md	Technical documentation for developers (directory structure, core design, orchestrator modes)
CHANGELOG.md	Version history and release notes (includes LocalClaw history)
TESTS.md	Benchmark results, model recommendations, and testing guide
CREDITS.md	Acknowledges every project, inspiration, API, model creator, and specification that makes AgentNova possible

Features

• Zero dependencies — Uses Python stdlib only (urllib for HTTP)
• Ollama + BitNet backends — Switch with --backend flag
• Dual API support — OpenResponses (--api openre) and OpenAI Chat-Completions (--api openai)
• Three-tier tool support — Native, ReAct, or none (auto-detected)
• Small model optimized — Fuzzy matching, argument normalization
• Built-in security — Path validation, command blocklist, SSRF protection
• Multi-agent orchestration — Router, pipeline, and parallel modes
• Soul Spec v0.5 — Persona packages with progressive disclosure
• ACP v1.0.5 integration — Agent Control Panel for monitoring and control
• AgentSkills spec — Skill loading with SPDX license validation
• Thinking models support — Automatic handling of qwen3, deepseek-r1 thinking mode

Installation

# Latest Development Release
pip install git+https://github.com/VTSTech/AgentNova.git --force-reinstall

# Last Stable (as stable as Alpha can be) Release
pip install agentnova

Quick Start

CLI Usage

# Run a single prompt
agentnova run "What is 15 * 8?" --tools calculator

# Interactive chat
agentnova chat -m qwen2.5:0.5b --tools calculator,shell

# Autonomous agent mode
agentnova agent -m qwen2.5:7b --tools calculator,shell,write_file

# Use OpenAI Chat-Completions API
agentnova chat -m qwen2.5:0.5b --api openai

# List available models
agentnova models

# List available tools
agentnova tools

Python API

from agentnova import Agent
from agentnova.tools import make_builtin_registry

# Create tools
tools = make_builtin_registry().subset(["calculator", "shell"])

# Create agent
agent = Agent(
    model="qwen2.5:0.5b",
    tools=tools,
    backend="ollama",
)

# Run
result = agent.run("What is 15 * 8?")
print(result.final_answer)
print(f"Completed in {result.total_ms:.0f}ms")

Chat-Completions Streaming

from agentnova.backends import get_backend
from agentnova.core.types import ApiMode

# Use Chat-Completions mode with streaming
backend = get_backend("ollama", api_mode=ApiMode.OPENAI)

for chunk in backend.generate_completions_stream(
    model="qwen2.5:0.5b",
    messages=[{"role": "user", "content": "Hello!"}],
    response_format={"type": "json_object"}
):
    print(chunk["delta"], end="", flush=True)

Skill License Validation

from agentnova.skills import validate_spdx_license, parse_compatibility

# Validate SPDX license identifier
valid, msg = validate_spdx_license("MIT")  # (True, "Valid SPDX identifier: MIT")
valid, msg = validate_spdx_license("Custom")  # (False, "Unknown license...")

# Parse compatibility requirements
compat = parse_compatibility("python>=3.8, ollama")
# Returns: {"python": ">=3.8", "runtimes": ["ollama"], "frameworks": []}

Multi-Agent Orchestration

from agentnova import Agent, Orchestrator, AgentCard

orchestrator = Orchestrator(mode="router")

# Register specialized agents
orchestrator.register(AgentCard(
    name="math_agent",
    description="Handles mathematical calculations",
    capabilities=["calculate", "math", "compute"],
    tools=["calculator"],
))

orchestrator.register(AgentCard(
    name="file_agent",
    description="Handles file operations",
    capabilities=["read", "write", "file"],
    tools=["read_file", "write_file"],
))

# Route tasks to appropriate agent
result = orchestrator.run("Calculate 15 * 8 and save to file")

Tool Support Levels

AgentNova supports three levels of tool use:

1. Native — Models with built-in function calling (qwen2.5, llama3.1+, mistral, granite, functiongemma)
2. ReAct — Text-based tool use via reasoning prompts (qwen2.5-coder, qwen3)
3. None — Pure reasoning without tools

Tool support is auto-detected by running agentnova models --tool-support. Results are cached in ~/.cache/agentnova/tool_support.json.

# Test and cache tool support for all models
agentnova models --tool-support

# Re-test (ignore cache)
agentnova models --tool-support --no-cache

You can also force ReAct mode:

agent = Agent(model="qwen2.5:0.5b", force_react=True)

Model Families

Configured model families with optimized prompts:

• qwen2.5 — Native tool support, excellent performance
• llama3.1/3.2/3.3 — Native tool support
• mistral/mixtral — Native tool support
• gemma2/gemma3 — ReAct mode, special prompting
• granite/granitemoe — Native tool support
• phi3 — Native tool support
• deepseek — Native with <think/> tag handling

Security Features

Built-in security for safe operation:

• Command blocklist — Blocks dangerous shell commands (rm, sudo, etc.)
• Path validation — Prevents access to sensitive directories
• SSRF protection — Blocks requests to local/internal URLs
• Injection detection — Detects shell injection patterns

Configuration

Environment variables:

# Backend URLs
OLLAMA_BASE_URL=https://your-ollama-server.com    # Default: http://localhost:11434
BITNET_BASE_URL=http://localhost:8765              # BitNet server URL
BITNET_TUNNEL=https://your-tunnel.com              # Alternative BitNet URL
ACP_BASE_URL=http://localhost:8766                 # ACP server URL

# Agent settings
AGENTNOVA_BACKEND=ollama      # Default backend: ollama or bitnet
AGENTNOVA_MODEL=qwen2.5:0.5b  # Default model
AGENTNOVA_MAX_STEPS=10        # Maximum reasoning steps
AGENTNOVA_DEBUG=false         # Enable debug output

Check current configuration:

agentnova config
agentnova config --urls  # Show only URLs

CLI Options (run, chat, agent)

Option	Description
--api openre|openai	API mode: OpenResponses (default) or OpenAI Chat-Completions
--response-format text|json	Response format (Chat-Completions mode)
--truncation auto|disabled	Truncation behavior for long responses
--soul <path>	Load Soul Spec persona package
--soul-level 1-3	Progressive disclosure level
--num-ctx <tokens>	Context window size (default: 4096)
--timeout <seconds>	Request timeout (default: 120)
--acp	Enable ACP (Agent Control Panel) logging
--acp-url <url>	ACP server URL

LocalClaw Redirect

The localclaw command is provided for backward compatibility:

# Both work identically
localclaw run "What is 2+2?"
agentnova run "What is 2+2?"

Tests & Examples

AgentNova includes a comprehensive suite of tests for validating agent capabilities across reasoning, knowledge, and tool usage:

# Basic agent test (no tools)
python -m agentnova.examples.00_basic_agent

# Quick 5-question diagnostic
python -m agentnova.examples.01_quick_diagnostic

# Tool usage tests (calculator, shell, datetime, file, python_repl)
python -m agentnova.examples.02_tool_test

# Logic and reasoning tests (BBH-style)
python -m agentnova.examples.03_reasoning_test

# GSM8K math benchmark (50 questions)
python -m agentnova.examples.04_gsm8k_benchmark

# Common sense reasoning (BIG-bench)
python -m agentnova.examples.05_common_sense

# Causal reasoning (BIG-bench)
python -m agentnova.examples.06_causal_reasoning

# Logical deduction (BIG-bench)
python -m agentnova.examples.07_logical_deduction

# Reading comprehension
python -m agentnova.examples.08_reading_comprehension

# General knowledge (BIG-bench)
python -m agentnova.examples.09_general_knowledge

# Implicit reasoning
python -m agentnova.examples.10_implicit_reasoning

# Analogical reasoning
python -m agentnova.examples.11_analogical_reasoning

Test Categories

Test	Questions	Focus
Basic Agent	1	Single prompt, no tools
Quick Diagnostic	5	Calculator tool, multi-step reasoning
Tool Test	10	Calculator, shell, datetime, file, python_repl tools
Reasoning Test	14	Logic, deduction, patterns, spatial
GSM8K Benchmark	50	Math word problems
Common Sense	25	Physical properties, everyday reasoning
Causal Reasoning	25	Cause and effect relationships
Logical Deduction	25	Formal logic puzzles
Reading Comprehension	25	Passage-based Q&A
General Knowledge	25	Science, history, geography
Implicit Reasoning	25	Unstated assumptions and inference
Analogical Reasoning	25	Pattern matching and analogies

Benchmark Results (Quick Diagnostic)

Model	Score	Time	Tool Support
functiongemma:270m	5/5 (100%)	~20s	native
granite4:350m	5/5 (100%)	~50s	native
qwen2.5:0.5b	5/5 (100%)	38s	native
qwen2.5-coder:0.5b	5/5 (100%)	93s	native
qwen3:0.6b	5/5 (100%)	70s	react
deepseek-r1:1.5b	5/5 (100%)	~305s	native

All tested models achieve 100% on the Quick Diagnostic. Native models are ~2x faster than ReAct models due to direct API tool calling.

Development

# Install dev dependencies
pip install -e ".[dev]"

# Run unit tests
pytest

# Format code
black agentnova
ruff check agentnova

License

MIT License - See LICENSE file for details.

Author

VTSTech — https://www.vts-tech.org

Contributing

Contributions welcome!