GitHub - XinyueZ/TinyAgent: 🛠️ Keep it Simple, a minimalist framework for developing agentic applications and solutions for learning, idea generation, and paper replication.

A personal hobby project for building various agentic applications. The whole point is to make it easy to spin up new agentic apps, so the agent components are built from scratch — implemented wherever I feel the need during development, rather than chasing excessive code reuse or textbook "design patterns". Designed with a keep-it-simple philosophy — currently only supports:

Google GenAI SDK (including Vertex AI)
Ollama

Whether it will be expanded in the future? We'll see. No promises.

Environment Setup

Copy apps/env_sample to apps/.env and fill in your values:

cp apps/env_sample apps/.env

This configures Google GenAI, Ollama, and Tavily API keys. See apps/env_sample for all available variables.

Google GenAI: Vertex AI vs Google AI Studio (Mutually Exclusive)

For Google models, you can choose exactly one of the following authentication modes:

Vertex AI (recommended for GCP users)
- Set GOOGLE_GENAI_USE_VERTEXAI=true
- Set GOOGLE_CLOUD_PROJECT and GOOGLE_CLOUD_LOCATION
- Authenticate via ADC (see the troubleshooting section below)
Google AI Studio API Key
- Set GOOGLE_GENAI_USE_VERTEXAI=false
- Set GOOGLE_AI_STUDIO_API_KEY

These two modes are mutually exclusive: when GOOGLE_GENAI_USE_VERTEXAI=true, the AI Studio API key will not be used.

Ollama

When using the Ollama backend, configure the server address. The resolution order is:

OLLAMA_BASE_URL — Ollama on the host machine (checked first). Typical value: http://host.docker.internal:11434 when the agent runs inside a Docker container but Ollama runs on the host.
OLLAMA_HOST — Ollama inside the container (fallback). Typical value: http://localhost:11434.
Default: http://localhost:11434.

This matches the lookup logic used in apps (e.g. apps/single-ollama-agent/agent.py):

os.environ.get("OLLAMA_BASE_URL", os.environ.get("OLLAMA_HOST", "http://localhost:11434"))

These are only needed when using ollama_stuff in TinyAgent.

Tavily

TAVILY_API_KEY_0 is required at minimum.
You can register multiple Tavily API keys — number them starting from 0 (e.g. TAVILY_API_KEY_0, TAVILY_API_KEY_1, TAVILY_API_KEY_2, ...).

🚀 Build and local deploy

docker compose build
docker compose up -d

Enter container

docker exec -it TinyAgentDev /bin/bash

📋 Logging

docker logs -f TinyAgentDev

Agent Use

TinyAgent supports two mutually exclusive backends — Google GenAI (Vertex AI or AI Studio) and Ollama. You must provide exactly one of genai_stuff or ollama_stuff when creating an agent.

Google GenAI (Vertex AI / AI Studio)

See apps/single-tavily-search-agent/agent.py for a full working example.

from tiny_agent.agent.tiny_agent import TinyAgent
from tiny_agent.agent.agent_manager import AgentManager

agent = TinyAgent(
    name="my_agent",
    model="gemini-2.5-flash",
    output_root="./output",
    genai_stuff={
        # Provider — pick one (mutually exclusive):
        # Vertex AI
        "vertexai": True,
        "vertexai_project": "<your-project-id>",
        "vertexai_location": "europe-west4",
        # Google AI Studio
        # "vertexai": False,
        # "google_ai_studio_api_key": "<your-api-key>",
    },
    tools=[...],            # list of tool functions
    # subagents=[...],      # optional sub-agents
    # Model options passed as **kwargs
    temperature=1.0,
    seed=42,
)

try:
    result = agent(contents="Your task or question here")
finally:
    AgentManager().unregister(agent.agent_id)

genai_stuff accepts the following keys (all optional except the provider choice):

Key	Description
`system_instruction`	Custom system prompt (defaults to built-in)
`vertexai`	`True` for Vertex AI, `False` for AI Studio
`vertexai_project`	GCP project ID (required when `vertexai=True`)
`vertexai_location`	GCP region (required when `vertexai=True`)
`google_ai_studio_api_key`	API key (required when `vertexai=False`)
`http_options`	`types.HttpOptions` for retry/timeout config

Ollama

See apps/single-ollama-agent/agent.py for a full working example.

from tiny_agent.agent.tiny_agent import TinyAgent
from tiny_agent.agent.agent_manager import AgentManager

agent = TinyAgent(
    name="my_agent",
    model="qwen3:8b",
    output_root="./output",
    ollama_stuff={
        "host": "http://localhost:11434",
    },
    tools=[...],            # list of tool functions
    # subagents=[...],      # optional sub-agents
    # Model options passed as **kwargs
    think=True,
    options={"temperature": 0.5, "top_p": 0.5, "top_k": 10},
)

try:
    result = agent(contents="Your task or question here")
finally:
    AgentManager().unregister(agent.agent_id)

ollama_stuff accepts the following keys (all optional):

Key	Description
`system_instruction`	Custom system prompt (defaults to built-in)
`host`	Ollama server URL (defaults to `http://localhost:11434`). See Environment Setup > Ollama for env var configuration.

Common Notes

genai_stuff and ollama_stuff are mutually exclusive — providing both raises ValueError.
tools — a list of functions decorated with @tool() (see Tools).
subagents — optional list of sub-agent instances (see Sub-Agents).
**kwargs — additional model config options (e.g. temperature, seed for GenAI; think, options for Ollama).
Wrap the agent call in try/finally and call AgentManager().unregister(agent.agent_id) to clean up the agent from the singleton registry after execution.
The agent writes artifacts (work plan, memory, reflection, result) to <output_root>/<agent-name>-<agent-id>/ (see Agent Output Artifacts).

Apps

App	Backend	Description	🐳 Inside Container	💻 Local Computer (CLI)
`apps/single-tavily-search-agent`	Google GenAI	Single agent with Tavily web search	`cd apps/single-tavily-search-agent` `python ./agent.py --output ./agent-output` More ↓	`CLIs/single-tavily-search-agent.sh` `--output ./my-output --tasks ./my-tasks` More ↓
`apps/single-ollama-agent`	Ollama	Single agent with web search via Ollama	`cd apps/single-ollama-agent` `python ./agent.py --output ./agent-output --tasks ./tasks` More ↓	`CLIs/single-ollama-agent.sh` `--output ./my-output --tasks ./my-tasks` More ↓
`apps/deep-research-multi-agents-tool-tavily-search`	Google GenAI	Deep research via tool calls that spawn multiple TinyAgents concurrently with Tavily search	`cd apps/deep-research-multi-agents-tool-tavily-search` `python ./deep-research.py --output ./deep-research-output --tasks ./my-tasks` More ↓	`CLIs/deep-research-multi-agents-tool-tavily-search.sh` `--output ./my-output --tasks ./my-tasks` More ↓
`apps/deep-agents-research`	Google GenAI	Deep agents research with a lead agent coordinating multiple sub-agents	`cd apps/deep-agents-research` `python ./deep-research.py --output ./deep-research-output --tasks ./my-tasks` More ↓	`CLIs/deep-agents-research.sh` `--output ./my-output --tasks ./my-tasks` More ↓
`apps/sequential-reflection-agent`	Google GenAI	Sequential agent that runs step-by-step with analysis, reflection, revision, and a final compose-report stage	`cd apps/sequential-reflection-agent` `python ./sequential-reflection-agent.py --output ./sequential-reflection-agent-output --tasks ./my-tasks` More ↓	`CLIs/sequential-reflection-agent.sh` `--output ./my-output --tasks ./my-tasks` More ↓
`apps/app-builder`	Google GenAI	Builds a CLI `.sh` script for any app given the path to its main file. Takes `--main` pointing to the app's entry-point `.py` file, reads its `argparse` definition, references existing `CLIs/*.sh` scripts, and generates a new matching `.sh` under `CLIs/`. 👍 The `CLIs/app-builder.sh` script itself was built by this app!	`cd apps/app-builder` `python ./app-builder.py --main /path/to/apps/my-app/main.py` More ↓	`CLIs/app-builder.sh` `--main /path/to/apps/my-app/main.py` More ↓

--output (required): Output directory for results.
--tasks: Directory containing task files (.md). Required when running from host via CLI. Optional inside container (defaults to ./tasks/ in the app folder).
Inside container: Enter with docker exec -it TinyAgentDev /bin/bash first.
From host: CLI scripts handle Google Cloud ADC authentication and resolve --output/--tasks paths relative to your current directory automatically.

Model & Provider Configuration

Each app defines its own model names, model options, and provider settings directly in its entry-point .py file (the file where the app main lives). Web search tools (tiny_agent/tools/web/) are configured by injecting attributes onto the tool functions (e.g. google_search.search_model, tavily_search.summarize_model) inside that app entry file.

# Provider (Vertex AI vs Google AI Studio — mutually exclusive)
PROVIDER_CONFIG = {
    "vertexai": bool(os.environ.get("GOOGLE_GENAI_USE_VERTEXAI", True)),
    "vertexai_location": os.environ.get("GOOGLE_CLOUD_LOCATION", "europe-west4"),
    "vertexai_project": os.environ.get("GOOGLE_CLOUD_PROJECT", "<your-project-id>"),
    "google_ai_studio_api_key": os.environ.get("GOOGLE_AI_STUDIO_API_KEY", ""),
}

# Search agent — used by web tools (see tiny_agent/tools/web/)
SEARCH_AGENT_MODEL = "gemini-2.5-flash-lite"
SEARCH_AGENT_MODEL_CONFIG = { "temperature": 1.0, "seed": 42, ... }

# Summarization — used by web tools (see tiny_agent/tools/web/)
SUMMARIZE_MODEL = "gemini-2.5-flash-lite"
SUMMARIZE_MODEL_CONFIG = { "temperature": 0.0, "seed": 42, ... }

See the following files for examples of how to define these:

apps/single-tavily-search-agent/agent.py — single GenAI agent config
apps/single-ollama-agent/agent.py — single Ollama agent config
apps/deep-research-multi-agents-tool-tavily-search/deep-research.py — multi-agent config (main + research agents)
apps/app-builder/app-builder.py — app-builder agent config

Run Examples

📝 Run Tasks

You can organize research tasks in separate .md files. Each file contains prompts that guide the agent's research focus.

Example directory structure:

labubuVShellokitty/
├── labubu.md
├── hellokitty.md
└── compare.md

Example task file (labubu.md):

What is Labubu?
- Provide an introduction to Labubu and its creator (Pop Mart)
- Analyze the current market performance and collector demand
- Discuss the future outlook and growth potential of the brand

Example task file (hellokitty.md):

What is Hello Kitty?
- Provide a brief introduction to Hello Kitty and its origins
- Summarize the history and evolution of the Hello Kitty brand
- Analyze the current market presence and commercial performance
- Discuss the future outlook and potential growth of the brand

Example task file (compare.md):

Compare Labubu and Hello Kitty from multiple perspectives:
- Market positioning and global reach
- Branding strategies and visual identity
- Popularity and cultural impact among young consumers

🏗️ Run App Builder

Create a new app directory under apps/ with a Python entry-point. The app can have multiple .py files, but only one main (the file with argparse and the if __name__ == "__main__" block).

Example app structure:

apps/my-new-app/
├── my-new-app.py    ← main (only one per app)
├── helper.py
└── utils.py

Then generate its CLI shell script inside the container:

$APP_BUILDER --main apps/my-new-app/my-new-app.py

The generated CLIs/my-new-app.sh is ready to use immediately.

Run Inside Container

docker exec -it TinyAgentDev /bin/bash

# Single agent
cd apps/single-tavily-search-agent
python ./agent.py --output ./agent-output --tasks /path/to/labubuVShellokitty

# Deep research
cd apps/deep-research-multi-agents-tool-tavily-search
python ./deep-research.py --output ./deep-research-output --tasks /path/to/labubuVShellokitty

# Deep agents research
cd apps/deep-agents-research
python ./deep-research.py --output ./deep-agents-research-output --tasks /path/to/labubuVShellokitty

# Sequential reflection agent
cd apps/sequential-reflection-agent
python ./sequential-reflection-agent.py --output ./sequential-reflection-agent-output --tasks /path/to/labubuVShellokitty

# App builder
cd apps/app-builder
python ./app-builder.py --main /path/to/apps/my-app/main.py

Run From Host

cd labubuVShellokitty

# Deep research
.../TinyAgent/CLIs/deep-research-multi-agents-tool-tavily-search.sh --output deep-research-multi-agents-tool-tavily-search --tasks .

# Deep agents research
.../TinyAgent/CLIs/deep-agents-research.sh --output deep-agents-research --tasks .

# Sequential reflection agent
.../TinyAgent/CLIs/sequential-reflection-agent.sh --output sequential-reflection-agent --tasks .

# Or single agent
.../TinyAgent/CLIs/single-tavily-search-agent.sh --output single-tavily-search-agent/ --tasks .

# Or single ollama agent
.../TinyAgent/CLIs/single-ollama-agent.sh --output single-ollama-agent/ --tasks .

# App builder
.../TinyAgent/CLIs/app-builder.sh --main /path/to/apps/my-app/main.py

🛠️ Developing Apps

All apps are developed under the apps/ directory. To create a new app, add a new subdirectory there, and put your model/provider configuration (including web search tool attribute injection) in the app entry-point .py file (the one that contains the if __name__ == "__main__" block). To publish a new app for host-side CLI usage, add a corresponding shell script under CLIs/ and a service entry in docker-compose.yml, following the existing ones as a reference.

🏗️ App Builder

The App Builder (apps/app-builder) is a meta-app — a single TinyAgent that builds CLI shell scripts for other apps.

Every app in this repo follows a convention: a Python entry-point with argparse, a Docker Compose service, and a wrapper .sh script under CLIs/ that handles host-side concerns (ADC authentication, path resolution, volume mounts). Writing these .sh scripts by hand is repetitive, so the App Builder automates it.

How it works:

You point it at an app's main .py file via --main.
The agent reads all existing CLIs/*.sh scripts to learn the common structure and conventions.
It reads the target file's argparse section to extract every argument — name, type, required/optional, and whether it represents a directory path, file path, or plain value.
It generates a new .sh script that mirrors the reference scripts but adapts the argument handling to match the target app's argparse definition:
- Directory paths → resolved, mounted as Docker volumes.
- File paths → resolved, parent directory mounted as a Docker volume.
- Plain values → forwarded as-is.
The generated script is saved to CLIs/<parent-dir-name>.sh, named after the target app's directory.

This means publishing a new app to the CLI is as simple as running:

python apps/app-builder/app-builder.py --main apps/my-new-app/main.py

Inside the container, the environment variable $APP_BUILDER points to /app/CLIs/app-builder.sh, so you can also run:

$APP_BUILDER --main apps/my-new-app/main.py

Note: If the image hasn't been rebuilt since adding the chmod +x step in the Dockerfile, you may need to run chmod +x /app/CLIs/app-builder.sh manually inside the container first.

🧪 Experiments

Note: Experiment results are for reference only. Actual performance may vary depending on hardware, network conditions, model versions, and other factors.

Experiment I: Single Agent vs Tool-Driven Multi-Agent

This experiment compares two design patterns:

apps/single-tavily-search-agent: a single agent that iterates on a task and uses Tavily search. It processes tasks sequentially — seemingly straightforward, but this simplicity makes it the easiest pattern to reason about and debug.
apps/deep-research-multi-agents-tool-tavily-search: a supervisor-style deep-research workflow (tool-driven), where a lead agent decomposes work and orchestrates multiple concurrent research agents.

In this experiment, the tool-driven multi-agent pattern is generally better than a single-agent loop:

Coverage: parallel sub-agents can explore different angles and sources.
Speed: concurrency reduces wall-clock time for wide research.
Robustness: even if one sub-agent underperforms, the overall result can still be strong.

⚠️ Warning

Both apps rely on random task decomposition (randomly splitting the task into topics/subtopics). As a result, each run can produce different intermediate topics and the overall output quality/performance can vary from run to run.

🎥 YouTube walkthrough (click the preview):

Experiment II: Single Agent vs Tool-Driven Multi-Agent vs Sub-Agent Multi-Agent

This experiment extends Experiment I by adding a third pattern, comparing all three:

apps/single-tavily-search-agent: a single agent that iterates on a task and uses Tavily search.
apps/deep-research-multi-agents-tool-tavily-search: a tool-driven multi-agent workflow where the lead agent decomposes the task into as many subtasks as there are CPU cores, then spawns concurrent research agents via multithreading to handle them in parallel.
apps/deep-agents-research: a sub-agent multi-agent workflow where a fixed number of sub-agents (equal to the CPU core count) are pre-created, and the lead agent dispatches subtasks to them hierarchically, then synthesizes a final report.

Results

Tool-driven multi-agent (deep-research-multi-agents-tool-tavily-search) was the fastest by a clear margin. Its implementation decomposes the task into a number of subtasks matching the available CPU cores, then launches research agents concurrently via multithreading — maximizing parallelism.
Single agent (single-tavily-search-agent) came in second. It processes tasks sequentially one by one — seemingly straightforward, but this simplicity makes it the easiest to understand and debug. Despite this, it was still faster than the sub-agent approach.
Sub-agent multi-agent (deep-agents-research) was the slowest. Like the tool-driven variant, it also decomposes the task based on CPU core count and pre-creates that many sub-agents. However, the lead agent randomly decides how to assign subtasks to those sub-agents — it may assign 3–4 sub-agents to a single subtask, or have one sub-agent handle a task alone. This unpredictable allocation leads to uneven workload distribution and longer overall execution time.

Takeaways

Structured, deterministic parallelism (tool-driven, CPU-core-based decomposition) significantly outperforms random agent orchestration.
Sub-agent flexibility comes at a cost: without a deliberate allocation strategy, random assignment can lead to worse performance than a simple single-agent loop.
As noted in Experiment I, all apps rely on random task decomposition, and results will vary across runs depending on hardware, network conditions, and the nature of the task.
Additionally, both multi-agent variants (tool-driven and sub-agent) rely on parallel threading, which inherently introduces non-determinism — thread scheduling, network latency per thread, and API rate limits can all cause fluctuations between runs. This is a contributing factor to the variability observed across experiments.

🎥 YouTube walkthrough (click the preview):

📦 Tools

Tools are the callable capabilities exposed to agents. In this repo there are two main categories:

Note: web search tools require configuration to be injected in an app entry file under apps/ (the file where the app main lives). This is one reason app development is coupled with the repo's library code, and why new apps are typically developed under the apps/ directory.

Category	What it provides	Source files
Built-in tools	Local filesystem read/write helpers, datetime helpers, and the agent's on-disk artifacts helpers (work plan, memory, reflection).	`tiny_agent/tools/buildins/core.py` (work plan, memory, reflection), `tiny_agent/tools/buildins/filesys.py` (file read/write/append/exists, list dir), `tiny_agent/tools/buildins/utils.py` (datetime helpers), plus shared wiring in `tiny_agent/tools/decorator.py`
Web tools	Web search and retrieval tools. Tavily search: requires `TAVILY_API_KEY_0` at minimum (optionally `TAVILY_API_KEY_1`, `TAVILY_API_KEY_2`, ...). Google search: uses the Google GenAI SDK and follows the same auth/config described above (Vertex AI vs Google AI Studio via `GOOGLE_GENAI_USE_VERTEXAI`, `GOOGLE_CLOUD_PROJECT`, `GOOGLE_CLOUD_LOCATION`, `GOOGLE_AI_STUDIO_API_KEY`).	`tiny_agent/tools/web/` (`__init__.py`, `tavily_search.py`, `google_search.py`, `base_web_search.py`, `tools.py`)

🤖 Sub-Agents

Sub-agents are specialized TinyAgent instances that a parent agent can delegate tasks to. They are registered as children of a parent agent and invoked via built-in transfer tools during execution.

Defining a Sub-Agent

Use the @subagent decorator on a TinyAgent subclass. The class docstring becomes the sub-agent's description (visible to the parent agent when deciding which sub-agent to use).

So for sub-agents, the TinyAgent class docstring is meaningful: it is not just documentation, it is part of the routing signal the parent agent uses for delegation.

from tiny_agent.subagent.decorator import subagent
from tiny_agent.agent.tiny_agent import TinyAgent

@subagent
class AddAgent(TinyAgent):
    """Perform addition (math)."""
    ...

@subagent(is_async=True)
class ResearchAgent(TinyAgent):
    """A research sub-agent that performs web searches."""
    ...

@subagent or @subagent() — marks a sync sub-agent.
@subagent(is_async=True) — marks an async sub-agent (required for parallel execution).

Registering Sub-Agents

Pass sub-agent instances to the parent agent's subagents parameter:

add_agent = AddAgent(name="add_agent", model="gemini-2.5-flash", ...)
mul_agent = MulAgent(name="mul_agent", model="gemini-2.5-flash", ...)

parent = TinyAgent(
    name="main_agent",
    model="gemini-2.5-flash",
    subagents=[add_agent, mul_agent],
    ...
)

Note: Sub-agent names must be unique and cannot match the parent agent's name.

Transfer Patterns

The parent agent uses two built-in tools to delegate work to sub-agents:

Pattern	Tool	When to use
ONE-TO-ONE	`transfer_to_subagent`	Transfer a task to a single sub-agent. Pass the sub-agent's name as a string.
ONE-TO-MANY	`transfer_to_subagents`	Transfer a task to multiple sub-agents in parallel. Pass a list of sub-agent names. Only sub-agents with `is_async=True` can be used.

ONE-TO-ONE is for sequential delegation — the parent waits for the sub-agent to finish before continuing. ONE-TO-MANY runs all target sub-agents concurrently using threads (worker count is based on CPU cores) and returns a dict mapping each sub-agent name to its result.

Decision Flow

The parent agent is instructed to reflect before every transfer:

Do I need one sub-agent or multiple sub-agents for this task?
If multiple, can they work in parallel, or do they depend on each other's results?
- Parallel → transfer_to_subagents (ONE-TO-MANY)
- Sequential dependency → transfer_to_subagent (ONE-TO-ONE), called one after another
Why this particular sub-agent(s)?
Is the task description clear and complete?

Source Files

File	Description
`tiny_agent/subagent/decorator.py`	`@subagent` decorator
`tiny_agent/tools/buildins/subagents_helper.py`	`transfer_to_subagent` and `transfer_to_subagents` tools
`tiny_agent/agent/agent_manager.py`	Singleton agent registry (enforces unique names)

Agent Output Artifacts

During execution, agents produce several files in the --output directory. These artifacts serve as the agent's "memory" and reasoning trace. The agentic tools are defined in tiny_agent/tools/buildins/core.py, filesystem tools in tiny_agent/tools/buildins/filesys.py, and datetime tools in tiny_agent/tools/buildins/utils.py.

File	Description	Read/Write Pattern
`work_plan.md`	The agent's structured work plan (a.k.a. todo list). Created at the start of a task and updated as the agent progresses through sub-tasks.	Created via `create_work_plan`, read via `read_work_plan`, updated via `update_work_plan`
`memory.md`	Accumulated execution context and key findings. The agent appends entries as it discovers new information, acting as a persistent scratchpad across steps.	Read via `read_memory`, appended via `update_memory`
`reflection.md`	The agent's self-reflection and decision-making reasoning. Captures why certain choices were made and lessons learned during execution.	Appended via `reflect`
`result.md`	The final research output or deliverable. Contains the synthesized answer or report produced by the agent for the given task.	Written by the agent at the end of execution

⚠️ Warning

The file names listed above are sensitive — they are hardcoded in the agent's built-in tools. Do not rename them.

Each agent writes to its own output_location directory (pattern: <--output>/<agent-name>-<agent-id>/). To locate a specific artifact, use output_location + the file name (e.g. output_location/result.md). Note: subagents have a default result.md output, but a plain TinyAgent does not enforce a default result filename at the framework level. In this repo we intentionally standardize on result.md: in tiny_agent/patterns/ (except the sequential reflection pattern) you can see output_path being constructed explicitly as f"{agent.output_location}/result.md" (e.g. tiny_agent/patterns/deep_research_multi_agents_tool.py), and the single-agent apps also set result.md from the outside (e.g. apps/single-tavily-search-agent/agent.py and apps/single-ollama-agent/agent.py). The sequential reflection agent's subagents rely on the default result.md.

Not all files are guaranteed to appear. Different models have varying performance — if an agent fails or the model does not follow the expected tool-calling pattern, some artifacts (e.g. work_plan.md, reflection.md) may be missing from the output.

Troubleshooting: Google GenAI Credentials

This section applies when using Google GenAI / Vertex AI (not Ollama).

If you encounter DefaultCredentialsError or any credential-related error during execution:

Inside container: Run the following command inside the container to authenticate:
```
gcloud auth application-default login
```
Follow the prompts to complete the authentication flow.
From host (via CLIs/*.sh): The CLI scripts will automatically detect missing or expired credentials and trigger gcloud auth application-default login. This will open a web browser login dialog — complete the authentication in the browser, then the script will continue automatically.

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License

This project is licensed under the MIT License.

Name		Name	Last commit message	Last commit date
Latest commit History 52 Commits
CLIs		CLIs
apps		apps
media		media
notebooks		notebooks
test		test
tiny_agent		tiny_agent
.dockerignore		.dockerignore
.gitignore		.gitignore
Dockerfile		Dockerfile
LICENSE		LICENSE
README.md		README.md
docker-compose.yml		docker-compose.yml
requirements.txt		requirements.txt

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Table of Contents

Environment Setup

Google GenAI: Vertex AI vs Google AI Studio (Mutually Exclusive)

Ollama

Tavily

🚀 Build and local deploy

Enter container

📋 Logging

Agent Use

Google GenAI (Vertex AI / AI Studio)

Ollama

Common Notes

Apps

Model & Provider Configuration

Run Examples

📝 Run Tasks

🏗️ Run App Builder

Run Inside Container

Run From Host

🛠️ Developing Apps

🏗️ App Builder

🧪 Experiments

Experiment I: Single Agent vs Tool-Driven Multi-Agent

Experiment II: Single Agent vs Tool-Driven Multi-Agent vs Sub-Agent Multi-Agent

Results

Takeaways

📦 Tools

🤖 Sub-Agents

Defining a Sub-Agent

Registering Sub-Agents

Transfer Patterns

Decision Flow

Source Files

Agent Output Artifacts

Troubleshooting: Google GenAI Credentials

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