GitHub - Kohaku-Lab/KohakuTerrarium: KohakuTerrarium is a general-purpose AI agent framework and batteries-included app for building, running, and composing self-contained agents and multi-agent teams, with built-in tools, sub-agents, persistent sessions, TUI, and web UI.

The machine for building agents — so you stop rebuilding the machine every time you want a new one.

See it run (60 seconds)

pip install kohakuterrarium                                         # install
kt login codex                                                      # authenticate
kt install https://github.com/Kohaku-Lab/kt-biome.git            # get OOTB creatures
kt run @kt-biome/creatures/swe --mode cli                        # run one

You get an interactive shell with a full coding agent — file tools, shell access, web search, sub-agents, resumable sessions. Ctrl+D exits; kt resume --last picks back up.

Want more hand-holding? Getting Started. Want to build your own? First Creature.

Is this for you?

You probably want KohakuTerrarium if you need a new agent shape and don't want to rebuild the substrate; you want OOTB creatures you can customise; you want to embed agent behaviour in existing Python; your requirements are still evolving.

You probably don't if an existing agent product (Claude Code, Codex, …) already fits your stable needs; your mental model doesn't map onto controller / tools / triggers / sub-agents / channels; you need sub-50 ms per-operation latency. More honesty at boundaries.

What KohakuTerrarium is

KohakuTerrarium is a framework for building agents — not another agent.

The last two years produced a striking number of agent products: Claude Code, Codex, OpenClaw, Gemini CLI, Hermes Agent, OpenCode, and many more. They are genuinely different, and they all re-implement the same substrate from scratch: a controller loop, tool dispatch, trigger system, sub-agent mechanism, sessions, persistence, multi-agent wiring. Every new agent shape costs a new ground-up reimplementation of the plumbing.

KohakuTerrarium's job is to put that substrate in one place so the next agent shape costs a config file and a few custom modules, not a new repo.

The core abstraction is the creature: a standalone agent with its own controller, tools, sub-agents, triggers, memory, and I/O. Creatures are hosted by a Terrarium engine: a graph runtime for channels, lifecycle, output wiring, hot-plug, and session attachment. A Studio layer sits above that for catalog, identity, active sessions, persistence, live traces, and web/desktop/API management. Everything is Python, so agents can be embedded inside tools, triggers, plugins, and outputs of other agents.

For out-of-the-box creatures you can try today, see kt-biome — the showcase pack of useful agents and plugins built on top of the framework.

Where it fits

	Product	Framework	Utility / Wrapper
LLM App	ChatGPT, Claude.ai	LangChain, LangGraph, Dify	DSPy
Agent	kt-biome, Claude Code, Codex, OpenCode, OpenClaw, Hermes Agent…	KohakuTerrarium, smolagents	—
Multi-Agent	kt-biome	KohakuTerrarium	CrewAI, AutoGen

Most tooling sits below the agent layer or jumps straight to multi-agent orchestration with a thin idea of what an agent is. KohakuTerrarium starts with the agent itself.

A creature is made of:

Controller — the reasoning loop
Input — how events enter the agent
Output — how results leave the agent
Tools — what actions it can take
Triggers — what wakes it up
Sub-agents — internal delegation for specialised tasks

A terrarium composes multiple creatures horizontally through channels, lifecycle management, and observability.

Key features

Agent-level abstraction. The six-module creature model is the first-class concept. Every new agent shape is "write a config and maybe a few custom modules," not "rebuild the runtime."
Built-in session persistence and resume. Sessions store operational state, not just chat history. Resume a run hours later with kt resume.
Searchable session history. Every event is indexed. kt search and the search_memory tool let you (and the agent) look up past work.
Non-blocking context compaction. Long-running agents keep working while context is compacted in the background.
Comprehensive built-in tools and sub-agents. File, shell, web, JSON, notebook/Jupyter, search, editing, planning, review, research, terrarium management.
MCP support. Connect stdio, streamable HTTP, or legacy SSE/HTTP MCP servers per-agent or globally; tools surface in the prompt automatically.
Package system. Install creatures / terrariums / plugins / LLM presets from Git or local paths; compose installed packages with inheritance.
Python-native. Agents are async Python objects. Embed them inside tools, triggers, plugins, or outputs of other agents.
Composition algebra. >>, &, |, *, .iterate operators for stitching agents into pipelines programmatically.
Multiple runtime surfaces. CLI, TUI, web dashboard, and desktop app out of the box.
Useful OOTB creatures via kt-biome. Start by running strong default agents; customise or inherit from them later.

Quick start

1. Install KohakuTerrarium

# From PyPI
pip install kohakuterrarium
# Optional extras: pip install "kohakuterrarium[full]"

# Or from source (for development — uv is the project convention)
git clone https://github.com/Kohaku-Lab/KohakuTerrarium.git
cd KohakuTerrarium
uv pip install -e ".[dev]"

# Build the web frontend (required for `kt web` / `kt app` from source)
npm install --prefix src/kohakuterrarium-frontend
npm run build --prefix src/kohakuterrarium-frontend

2. Install OOTB creatures and plugins

# Official showcase pack
kt install https://github.com/Kohaku-Lab/kt-biome.git

# Any third-party package
kt install <git-url>
kt install ./my-creatures -e        # editable install

3. Authenticate a model provider

# Codex OAuth (ChatGPT subscription)
kt login codex
kt model default gpt-5.4

# Or native Anthropic / OpenAI-compatible providers via `kt config llm add`

Supports Codex OAuth, OpenRouter/OpenAI, native Anthropic, Google Gemini, and any OpenAI-compatible API.

4. Run something

# Single creature
kt run @kt-biome/creatures/swe --mode cli
kt run @kt-biome/creatures/researcher

# Multi-agent terrarium
kt terrarium run @kt-biome/terrariums/swe_team

# Web dashboard
kt serve start

# Native desktop
kt app

Choose your path

I want to run something now

I want to build my own creature

I want multi-agent composition

I want to embed it in Python

I want to understand what's going on

I want to work on the framework itself

Development home
Internals
Testing
Package READMEs under src/kohakuterrarium/

Core mental model

Creature

    List, Create, Delete  +------------------+
                    +-----|   Tools System   |
      +---------+   |     +------------------+
      |  Input  |   |          ^        |
      +---------+   V          |        v
        |   +---------+   +------------------+   +--------+
        +-->| Trigger |-->|    Controller    |-->| Output |
User input  | System  |   |    (Main LLM)    |   +--------+
            +---------+   +------------------+
                              |          ^
                              v          |
                          +------------------+
                          |    Sub Agents    |
                          +------------------+

A creature is a standalone agent with its own runtime, tools, sub-agents, prompts, and state.

kt run path/to/creature
kt run @package/path/to/creature

Runtime hierarchy

User / API / Desktop
        |
        v
+----------------------+     no agent reasoning
| Studio / App Layer   |  catalog, identity, active sessions,
|                      |  persistence, attach, editors, live traces
+----------------------+
        |
        v
+----------------------+     no LLM, no decisions
| Terrarium Engine     |  creature graph, topology, channels,
|                      |  lifecycle, output wiring, session attach
+----------+-----------+
           |
   +-------+----------------+
   |                        |
Root Creature           Worker Team Creatures
(user-facing)           swe / coder / reviewer / ...
   |
   v
Sub-agents inside each creature
(vertical/private delegation)

Studio is the management facade used by the web dashboard, desktop app, and HTTP API. It owns catalog views, identity/settings, active sessions, persistence, attach/resume, editors, and live traces. It does not reason.
Terrarium is the runtime engine that hosts every running creature in the process. A standalone agent is a one-creature graph; a multi-agent team is a connected graph. The engine manages topology, channels, lifecycle, hot-plug, output wiring, and session attachment. No LLM, no decisions.
Root creature is optional. It is a normal creature hosted by the same Terrarium engine, conceptually outside the worker team and user-facing through terrarium management tools.
Creature owns reasoning: controller, tools, triggers, sub-agents, plugins, memory, I/O, prompts, and private state. Creatures do not need to know whether they are alone or part of a graph.
Sub-agents are vertical/private delegation inside one creature. Prefer them when one controller can decompose the task internally; use Terrarium when multiple peer creatures need horizontal cooperation.

Channels and output wiring

Channels and output wiring are the horizontal cooperation substrate between creatures:

Queue — one consumer receives each message.
Broadcast — all subscribers receive each message.
Output wiring — deterministic pipeline edges that auto-deliver a creature's turn-end output to named targets.

Modules

A creature has six conceptual modules. Five of them are user-extensible — you swap their implementations in config or in Python. The sixth, the controller, is the reasoning loop that drives them; you rarely swap it (and when you do, you're writing the framework's successor).

Module	What it does	Example custom use
Input	Receives external events	Discord listener, webhook, voice input
Output	Delivers agent output	Discord sender, TTS, file writer
Tool	Executes actions	API calls, database access, RAG retrieval
Trigger	Generates automatic events	Timer, scheduler, channel watcher
Sub-agent	Delegated task execution	Planning, code review, research

Plus plugins, which modify the connections between modules without forking them (prompt plugins, lifecycle hooks). See plugins guide.

Environment and session

Environment — shared terrarium state (shared channels).
Session — private creature state (scratchpad, private channels, sub-agent state).

Private by default, shared by opt-in.

Practical capabilities

KohakuTerrarium already ships:

Built-in file, shell, web, JSON, notebook/Jupyter, channel, trigger, and introspection tools, including single-edit and multi-edit file mutation primitives.
Built-in sub-agents for exploration, planning, implementation, review, summarisation, and research.
Background tool execution and non-blocking agent flow.
Session persistence with resumable operational state.
FTS + vector memory search (model2vec / sentence-transformer / API embedding providers).
Non-blocking auto-compaction for long-running agents.
MCP (Model Context Protocol) integration — stdio, streamable HTTP, and legacy SSE/HTTP transports.
Package manager for creatures, plugins, terrariums, and reusable agent packs (kt install, kt update).
Python embedding through the Terrarium engine plus lower-level Agent access.
HTTP and WebSocket serving.
Web dashboard and native desktop app.
Custom module and plugin systems.

Programmatic usage

Agents are async Python values. One Terrarium engine per process hosts every running creature — a standalone agent is just a 1-creature graph in the engine.

import asyncio
from kohakuterrarium import Terrarium

async def main():
    # Solo creature
    engine, alice = await Terrarium.with_creature("@kt-biome/creatures/swe")
    try:
        async for chunk in alice.chat("Explain what this codebase does."):
            print(chunk, end="", flush=True)
    finally:
        await engine.shutdown()

    # Multi-agent recipe
    engine = await Terrarium.from_recipe("@kt-biome/terrariums/swe_team")
    try:
        async for chunk in engine["swe"].chat("Fix the auth bug."):
            print(chunk, end="", flush=True)
    finally:
        await engine.shutdown()

asyncio.run(main())

Composition algebra

Because agents are Python values, they compose with operators. >> (sequence), & (parallel), | (fallback), *N (retry), .iterate (async loop):

import asyncio
from kohakuterrarium.compose import agent, factory
from kohakuterrarium.core.config import load_agent_config

def make_agent(name, prompt):
    config = load_agent_config("@kt-biome/creatures/general")
    config.name, config.system_prompt, config.tools, config.subagents = name, prompt, [], []
    return config

async def main():
    # Persistent agents (accumulate conversation)
    async with await agent(make_agent("writer", "You are a writer.")) as writer, \
               await agent(make_agent("reviewer", "You are a strict reviewer. Say APPROVED if good.")) as reviewer:

        pipeline = writer >> (lambda text: f"Review this:\n{text}") >> reviewer

        async for feedback in pipeline.iterate("Write a haiku about coding"):
            print(f"Reviewer: {feedback[:100]}")
            if "APPROVED" in feedback:
                break

    # Parallel ensemble with retry + fallback
    fast = factory(make_agent("fast", "Answer concisely."))
    deep = factory(make_agent("deep", "Answer thoroughly."))
    safe = (fast & deep) >> (lambda results: max(results, key=len))
    safe_with_retry = (safe * 2) | fast
    print(await safe_with_retry("What is recursion?"))

asyncio.run(main())

More: Programmatic Usage, Composition, Python API, and examples/code/.

Runtime surfaces

CLI and TUI

cli — rich inline terminal experience
tui — full-screen Textual application
plain — simple stdout/stdin for pipes and CI

See CLI Reference.

Web dashboard

Vue-based dashboard + FastAPI server backed by the Studio management layer.

kt web                       # one-shot, foreground
kt serve start               # long-running daemon
# Frontend dev: npm run dev --prefix src/kohakuterrarium-frontend

See HTTP API, Serving guide, Frontend Architecture.

Desktop app

kt app launches the web UI inside a native desktop window (requires pywebview).

Sessions, memory, and resume

Sessions save to ~/.kohakuterrarium/sessions/ unless disabled.

kt resume            # pick interactively
kt resume --last     # resume most recent
kt resume swe_team   # resume by name prefix

The same store powers searchable history:

kt embedding <session>                       # build FTS + vector indices
kt search <session> "auth bug fix"           # hybrid/semantic/FTS search

And the agent can search its own history via the search_memory tool.

.kohakutr files store conversation, tool calls, events, scratchpad, sub-agent state, channel messages, jobs, resumable triggers, and config metadata.

See Sessions, Memory.

Packages, defaults, and examples

Creatures are meant to be packaged, installed, reused, and shared.

kt install https://github.com/someone/cool-creatures.git
kt install ./my-creatures -e
kt list
kt update --all

Run installed configs with package references:

kt run @cool-creatures/creatures/my-agent
kt terrarium run @cool-creatures/terrariums/my-team

Available resources:

kt-biome/ — official showcase creatures, terrariums, and plugin pack
examples/agent-apps/ — config-driven creature examples
examples/code/ — Python usage examples
examples/terrariums/ — multi-agent examples
examples/plugins/ — plugin examples

See examples/README.md.

Codebase map

src/kohakuterrarium/
  core/              # Agent runtime, controller, executor, events, environment
  bootstrap/         # Agent initialisation factories (LLM, tools, I/O, triggers, plugins)
  cli/               # `kt` command dispatcher
  studio/            # Management facade: catalog, identity, sessions, persistence, attach, editors
  terrarium/         # Runtime engine: creature graph, topology, channels, output wiring, hot-plug
  builtins/          # Built-in tools, sub-agents, I/O modules, TUI, user commands, CLI UI
  builtin_skills/    # Markdown skill manifests for on-demand docs
  session/           # Session persistence, memory search, embeddings
  serving/           # Launch/transport helpers and compatibility streaming wrappers
  api/               # FastAPI HTTP + WebSocket adapters over Studio and Terrarium
  compose/           # Composition algebra primitives
  mcp/               # MCP client manager
  modules/           # Base protocols for tools, inputs, outputs, triggers, sub-agents, user commands
  llm/               # LLM providers, profiles, API key management
  parsing/           # Tool-call parsing and stream handling
  prompt/            # Prompt aggregation, plugins, skill loading
  testing/           # Test infrastructure (ScriptedLLM, TestAgentBuilder, recorders)

src/kohakuterrarium-frontend/   # Vue web frontend
kt-biome/                    # (separate repo) Official OOTB pack
examples/                       # Example creatures, terrariums, code samples, plugins
docs/                           # Tutorials, guides, concepts, reference, dev

Every subpackage has its own README describing files, dependency direction, and invariants.

Documentation map

Full docs live in docs/.

Roadmap

Near-term directions include more reliable terrarium flow, richer UI output / interaction modules across CLI / TUI / web, more built-in creatures, plugins, and integrations, and better daemon-backed workflows for long-running and remote usage. See ROADMAP.md.

Contributing

License

KohakuTerrarium License 1.0: based on Apache-2.0 with naming and attribution requirements.

Derivative works must include Kohaku or Terrarium in their name.
Derivative works must provide visible attribution with a link to this project.

Community

QQ: 1097666427
Discord: https://discord.gg/xWYrkyvJ2s
Forum: https://linux.do/

FAQ

General

What is KohakuTerrarium? KohakuTerrarium is a Python-native AI agent framework for building autonomous agents. The public hierarchy is: Creature for the agent unit, Terrarium for the no-LLM runtime graph, and Studio for catalog/session/persistence/API management.

How does it differ from other agent frameworks? Unlike monolithic frameworks, KohakuTerrarium keeps responsibilities separated: creatures own reasoning and tools, terrariums own graph/channel/lifecycle runtime, and Studio owns management surfaces. Horizontal teams use Terrarium recipes and channels; Python request pipelines can still use composition algebra.

Installation & Setup

What Python version is required? Python 3.10 or higher. Install via pip install kohakuterrarium.

Which LLM providers are supported? Codex OAuth, OpenAI/OpenRouter-style providers, native Anthropic, Google Gemini, local OpenAI-compatible servers (Ollama, vLLM), and other OpenAI-compatible cloud providers. Configure with kt login, kt config llm add, kt config provider add, or provider API keys.

Can I use local models? Yes. Point the LLM endpoint to your local server (Ollama, vLLM, etc.) and configure the model name in your creature configuration.

Core Concepts

What is a "Creature"? A Creature is the standalone agent unit: controller, tools, triggers, sub-agents, plugins, memory, I/O, prompts, and private state. It can run alone or as a node in a Terrarium graph.

What is a "Terrarium"? A Terrarium is the no-LLM runtime engine that hosts creature graphs. It manages topology, channels, output wiring, lifecycle, hot-plug, and session attachment; each creature still owns its controller, tools, memory, and private state.

What are "Plugins"? Plugins extend the framework's capabilities — custom tools, I/O modules, triggers, or behavior hooks. They follow a hook-based system for clean integration.

Development

How do I create a custom Creature? Define a YAML configuration with tools, prompts, and behavior, or use the Python API to build one programmatically. See docs/en/tutorials/first-creature.md for a step-by-step guide.

Can I embed agents in my Python application? Yes. KohakuTerrarium provides a Python-native API for programmatic agent creation and execution. See examples/code/ and docs/en/guides/programmatic-usage.md.

How does multi-agent composition work? Use Terrarium recipes/engine channels/output wiring for horizontal multi-agent teams. Use compose for lightweight Python-side request pipelines (>>, &, |, retry) when you do not need a long-lived graph. See examples/terrariums/ and examples/code/.

Troubleshooting

Why is my creature not responding? Check that your LLM provider is configured correctly with kt login. Verify network connectivity and API key validity.

How do I debug agent behavior? Use kt run --verbose for detailed logs. Resume or inspect prior work with kt resume, search it with kt search, or use the Studio session viewer in the web/desktop UI.

Where can I get help?

QQ Group: 1097666427
Discord: https://discord.gg/xWYrkyvJ2s
Forum: https://linux.do/

Name		Name	Last commit message	Last commit date
Latest commit History 1,044 Commits
.github		.github
docs		docs
examples		examples
images		images
scripts		scripts
src		src
tests		tests
.gitignore		.gitignore
AGENTS.md		AGENTS.md
CLAUDE.md		CLAUDE.md
CODE_OF_CONDUCT.md		CODE_OF_CONDUCT.md
CONTRIBUTING.md		CONTRIBUTING.md
LICENSE		LICENSE
README.md		README.md
README.zh-CN.md		README.zh-CN.md
README.zh.md		README.zh.md
ROADMAP.md		ROADMAP.md
pyproject.toml		pyproject.toml

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See it run (60 seconds)

Is this for you?

What KohakuTerrarium is

Where it fits

Key features

Quick start

1. Install KohakuTerrarium

2. Install OOTB creatures and plugins

3. Authenticate a model provider

4. Run something

Choose your path

I want to run something now

I want to build my own creature

I want multi-agent composition

I want to embed it in Python

I want to understand what's going on

I want to work on the framework itself

Core mental model

Creature

Runtime hierarchy

Channels and output wiring

Modules

Environment and session

Practical capabilities

Programmatic usage

Composition algebra

Runtime surfaces

CLI and TUI

Web dashboard

Desktop app

Sessions, memory, and resume

Packages, defaults, and examples

Codebase map

Documentation map

Tutorials

Guides

Concepts

Reference

Roadmap

Contributing

License

Community

FAQ

General

Installation & Setup

Core Concepts

Development

Troubleshooting

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