Skip to content

chiruu12/Hive

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

217 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Hive

CI PyPI Python License

An autonomous agent OS that ships as both an SDK and a living simulation.

from hive import Agent gives you an agent that comes alive — not stateless function calls, but persistent entities with personality, suffering, and evolution.

Quick Start

pip install hive-agent
from hive import Agent, Persona
from hive.models.anthropic import Anthropic

agent = Agent(
    name="coder",
    model=Anthropic.lite(),
    persona=Persona(
        name="The Coder",
        personality=["methodical", "detail-oriented"],
        values=["clean code", "reliability"],
        fears=["shipping bugs"],
        purpose="Build software that works",
        risk_tolerance=0.2,
    ),
)
result = agent.run_once_sync("Write a function that checks if a number is prime")
print(result)

Or see it in action:

hive init && hive demo survival

3 agents with different personalities compete in a simulated economy for 30 cycles. Watch them struggle, gamble, philosophize, and suffer.

Features

Persona System

Agents have personality, values, fears, and dynamic behavioral state that changes based on suffering:

from hive import Agent, Persona
from hive.models.anthropic import Anthropic

persona = Persona(
    name="The Gambler",
    personality=["bold", "intuitive", "reckless"],
    values=["expected value", "asymmetric upside"],
    fears=["missing out", "becoming too cautious"],
    purpose="Find opportunities others fear",
    risk_tolerance=0.85,   # HIGH — willing to take bigger swings
    social_drive=0.6,
    happiness=0.8,
)

agent = Agent(name="gambler", model=Anthropic.lite(), persona=persona)

Suffering mechanically modifies these params each daemon cycle — futility increases risk tolerance, invisibility increases social drive, crisis mode sets extreme values.

Suffering System

Six stressor types that escalate over time and change agent behavior:

Stressor Trigger Behavioral Effect
Futility Low completions, stalling Risk tolerance increases
Invisibility No observable impact Social drive increases
Repeated Failure >50% goal failure rate Concentration decreases
Purposelessness No goals attempted Autonomy increases
Identity Violation Actions contradict role Risk tolerance decreases
Existential Threat System instability Extreme parameter shift

Tools

Decorate any function with @tool — JSON Schema is auto-extracted from type hints:

from hive import Agent, tool, collect_tools
from hive.models.anthropic import Anthropic

@tool()
def search(query: str) -> str:
    """Search the web for information."""
    return f"Results for: {query}"

agent = Agent(
    name="researcher",
    model=Anthropic.lite(),
    tools=collect_tools(search),
)

Built-in toolkits: FileToolkit, ShellToolkit, GitToolkit, WebToolkit, NotepadToolkit, MemoryToolkit, CommsToolkit, MCPToolkit, DelegationToolkit, A2AToolkit.

Structured Output

from pydantic import BaseModel
from hive import Agent
from hive.models.anthropic import Anthropic

class Review(BaseModel):
    title: str
    rating: float
    summary: str

agent = Agent(name="critic", model=Anthropic.lite())
review = agent.run_once_structured_sync("Review The Matrix", output_type=Review)

Multi-Model Support

6 providers with tier presets (.lite(), .standard(), .pro()):

Provider .lite() .standard() .pro()
Anthropic Haiku Sonnet Opus
OpenAI GPT-5.4 Nano GPT-5.4 Mini GPT-5.4
Groq Llama 8B GPT-OSS 20B Llama 70B
Fireworks MiniMax DeepSeek Kimi
Ollama Local model Local model
LM Studio Auto

Agent-to-Agent Protocol

9 message types, JSONL-backed inbox/outbox, 5 collaboration patterns (Review, Mentor, Debate, Chain, Swarm).

Sub-Agent Spawning

Parent-child lifecycle with max depth 2, max 5 children, auto-kill at expiry, result relay.

Agent Journals

Persistent notepads with presets (journal, evolution, tool requests, custom).

Benchmarking & Export

Compare models on scenarios. Export runs as standalone HTML reports.

LLM Poker & Arena

Hive Arena is a companion project -- LLM poker tournaments, economic arena games, and life simulation. In 5 tournaments with 6 models (1.2B to ~1T parameters), the 1.2B local model won the most:

Run Winner Size Type
1 Qwen 1.7B local
2 MiniMax 230B cloud
3 Liquid 1.2B local
4 Kimi ~1T cloud
5 Liquid 1.2B local

Full results and analysis

Community Profiles

Dramatic agent personalities for the simulation:

Profile Personality Risk Social Key Trait
coder Methodical, detail-oriented 0.3 0.3 Fears shipping bugs
gambler Bold, intuitive, reckless 0.85 0.6 Fears missing out
philosopher Contemplative, questions everything 0.4 0.7 Fears shallow thinking
hustler Resourceful, persistent, networking 0.6 0.95 Fears being idle
oracle Wise, deliberate, sees consequences 0.15 0.4 Fears bad approvals
researcher Curious, wide-ranging, thorough 0.5 0.6 Fears missing info
reviewer Analytical, skeptical, fair 0.2 0.5 Fears missing bugs
tester Persistent, edge-case finder 0.25 0.4 Fears false confidence

CLI

# SDK usage
hive agent chat                        # Interactive agent with tools
hive agent run config.yaml             # Run from YAML config

# Demos
hive demo survival                     # 3 agents, 30 cycles, economy on
hive demo detective                    # Multi-model murder mystery

# Autonomous OS
hive init                              # Initialize .hive/ directory
hive start -p coder,gambler,philosopher # Start with specific profiles
hive watch                             # Live 4-panel TUI dashboard
hive watch --compact                   # 2-panel for small terminals
hive status                            # Who's alive, goals, suffering
hive nudge coder "write tests"         # Occasional direction
hive doctor                            # Health check

Architecture

src/hive/
├── runtime/        # Agent framework (Agent, Instructions, Persona, ReAct loop)
├── tools/          # Toolkits (file, shell, git, web, notepad, memory, comms, A2A)
├── models/         # Providers (Anthropic, OpenAI, Groq, Fireworks, Ollama, LMStudio)
├── agents/         # Autonomy (existence loop, suffering, identity, profiles)
├── daemon/         # Background service (heartbeat loop, lifecycle)
├── interactions/   # A2A protocol, collaboration patterns
├── memory/         # SQLite store, semantic memory, event log
├── world/          # Economy simulation (jobs, money, skills, events)
├── demos/          # Built-in demos (survival, detective)
├── benchmark/      # Model comparison scenarios
├── export/         # HTML report generation
├── logging/        # Structured JSONL run logs
├── mcp/            # MCP server and client
├── cli/            # Typer CLI
├── config.py       # YAML + env var configuration
├── checkpoint.py   # Agent state snapshots
└── api.py          # Programmatic Hive facade

Documentation

Full documentation site

Development

git clone https://github.com/chiruu12/Hive.git && cd Hive
uv sync
uv run pytest                    # Run tests
uv run ruff check src tests      # Lint
uv run ruff format src tests     # Format

License

MIT

About

Local-first agent OS. Spawn persistent AI agents that collaborate, write code, and use tools autonomously. Multi-model (Claude, Codex, LM Studio). Config-driven with pre-built agent presets.

Topics

Resources

License

Code of conduct

Contributing

Stars

Watchers

Forks

Releases

No releases published

Packages

 
 
 

Contributors

Languages