OllamaToolkit

Use Case Synopsis

ollamatoolkit is a modular, pip-installable toolkit for building reliable local-AI and hybrid-AI systems on top of Ollama. It is designed for:

• agentic workflows (single agent + multi-agent teams),
• programmatic automation (sync/async clients, typed responses, streaming events),
• tool orchestration (web, files, vector/RAG, DB, system, email, schema, vision), and
• integration into larger systems (research pipelines, MCP-connected tools, telemetry-enabled apps).

If your goal is “make smaller local models perform consistently through good scaffolding,” this package is built for exactly that.

---

Installation

# Core
pip install roy-ollama-toolkit

# Full extras (system + file/image + email tooling)
pip install "roy-ollama-toolkit[full]"

Supported Python versions: 3.10–3.13. Import path remains ollamatoolkit after installation.

---

Integration Guide

1) Standalone usage (direct Python)

from ollamatoolkit.client import OllamaClient

with OllamaClient(base_url="http://localhost:11434") as client:
    reply = client.chat(
        model="qwen3:8b",
        messages=[{"role": "user", "content": "Give me a 1-line business summary."}],
    )
    print(reply.message.content)

2) Agentic usage (SimpleAgent + tools)

from ollamatoolkit.agents.simple import SimpleAgent

agent = SimpleAgent(
    name="research-assistant",
    system_message="You are concise, evidence-focused, and tool-using.",
    model_config={"model": "ollama/qwen3:8b", "base_url": "http://localhost:11434"},
)

@agent.tool()
def add(a: int, b: int) -> int:
    """Add two integers."""
    return a + b

print(agent.run("What is 19 + 23?"))

3) Agentic + MCP tool servers (stdio bridge)

ollamatoolkit.tools.mcp connects to MCP servers via command + args (stdio JSON-RPC).

from ollamatoolkit.tools.mcp import MCPToolManager
from ollamatoolkit.agents.simple import SimpleAgent

mcp = MCPToolManager(
    {
        "web": {
            "command": "python",
            "args": ["-m", "web_scraper_toolkit.server.mcp_server"],
        }
    }
)
mcp.start_all()

agent = SimpleAgent(
    name="mcp-agent",
    system_message="Use tools when helpful.",
    model_config={"model": "ollama/qwen3:8b", "base_url": "http://localhost:11434"},
    tools=mcp.get_tool_schemas(),
    function_map=mcp.get_proxy_functions(),
)

print(agent.run("Find contact info for example.com and summarize it."))
mcp.shutdown()

For remote MCP hosting, run the server on the remote machine and use a transport command that exposes stdio locally (for example via SSH command invocation).

---

Quick API Starts

Sync + Async parity

import asyncio
from ollamatoolkit.client import OllamaClient, AsyncOllamaClient

with OllamaClient() as client:
    print(client.version())

async def main() -> None:
    async with AsyncOllamaClient() as client:
        print(await client.version())

asyncio.run(main())

Stream structured events

from ollamatoolkit.client import OllamaClient

with OllamaClient() as client:
    for event in client.stream_chat_events(
        model="qwen3:8b",
        messages=[{"role": "user", "content": "Explain caching in 2 bullets."}],
    ):
        if event.event == "token":
            print(event.text, end="", flush=True)

OpenAI-compatible /v1/* helpers

from ollamatoolkit.client import OllamaClient

with OllamaClient() as client:
    models = client.openai_list_models()
    chat = client.openai_chat_completions(
        model="qwen3:8b",
        messages=[{"role": "user", "content": "One sentence about B2B lead qualification."}],
    )
    print(chat.choices[0].message.content)

Structured extraction

from pydantic import BaseModel
from ollamatoolkit.agents.simple import SimpleAgent

class CompanyFacts(BaseModel):
    website: str
    ceo_name: str

agent = SimpleAgent(
    name="extractor",
    system_message="Return valid JSON only.",
    model_config={"model": "ollama/qwen3:8b", "base_url": "http://localhost:11434"},
)

facts = agent.run_structured("Extract website and CEO from: ...", CompanyFacts)
print(facts.model_dump())

---

CLI Usage

Entry point:

ollamatoolkit --help
# or
python -m ollamatoolkit.cli --help

Commands

config

• --init create sample config JSON.

run

• positional: role_file
• --task/-t
• --interactive/-i
• --dashboard/-d
• --model/-m
• --tools (all files math db server system web vision)
• --db-path
• --config/-c
• --batch-file/-b
• --output-dir/-o
• --streaming/-s

models

• --capability/-c (vision embedding tools completion reasoning)
• --json
• --base-url

chat

• --model/-m
• --base-url
• --system/-s

Example commands

# Generate sample config
python -m ollamatoolkit.cli config --init

# Quick chat
python -m ollamatoolkit.cli chat --model qwen3:8b

# Run role-based agent with streaming
python -m ollamatoolkit.cli run .\roles\researcher.json -t "Research Acme Corp" --streaming

# List only embedding-capable models as JSON
python -m ollamatoolkit.cli models --capability embedding --json

---

Configuration Hierarchy

When running through CLI/runtime patterns, prefer this precedence:

1. Explicit runtime arguments (CLI flags / constructor args)
2. Config file values (config.json or generated sample config)
3. Dataclass defaults in ToolkitConfig

Primary config sections:

• agent
• tools (including mcp_servers)
• web
• vision
• vector
• models (per-capability slot config)
• memory
• document
• benchmark
• telemetry
• logging
• dashboard

Load and save:

from ollamatoolkit.config import ToolkitConfig

cfg = ToolkitConfig.load("config.json")
cfg.save("config.generated.json")

---

Architecture (high level)

• Client layer: ollamatoolkit.client + client_api/*
  • sync + async clients
  • transport + retries/backoff/circuit breaker
  • chat/generate/embed/model-management/web
  • OpenAI-compat wrappers
• Agent layer: agents/simple.py, agents/team.py, agents/role.py, agents/memory.py
  • tool calling
  • streaming token loops
  • structured Pydantic responses
  • multi-agent orchestration
• Tool layer: tools/*
  • vector, document, web, files, DB, math, email, schema, system, server
  • vision subpackage for OCR/analysis/video/spatial tasks
  • MCP client for external tool servers
• Model intelligence: models/selector.py
  • choose best model by capability

---

Source File Map (what each file does)

File	Responsibility
ollamatoolkit/__init__.py	Ollama Toolkit - A Professional-Grade Agentic Framework
ollamatoolkit/agent.py	Ollama Toolkit - Agent Shim
ollamatoolkit/agents/__init__.py	Ollama Toolkit - Agent Components
ollamatoolkit/agents/memory.py	Ollama Toolkit - Agent Memory Management
ollamatoolkit/agents/role.py	Ollama Toolkit - Role Agent
ollamatoolkit/agents/simple.py	Simple agent runtime for synchronous/async LiteLLM conversations with tool execution.
ollamatoolkit/agents/team.py	Ollama Toolkit - Multi-Agent Team Orchestration
ollamatoolkit/callbacks.py	OllamaToolkit Callbacks
ollamatoolkit/cli.py	Ollama Toolkit - CLI Runner
ollamatoolkit/client.py	Public client module for OllamaToolkit.
ollamatoolkit/client_api/__init__.py	Composable client-domain adapters used by OllamaToolkit public client façades.
ollamatoolkit/client_api/async_client.py	Public asynchronous Ollama client composed from endpoint-domain adapters.
ollamatoolkit/client_api/common.py	Shared helpers for OllamaToolkit API clients.
ollamatoolkit/client_api/inference.py	Inference endpoint adapters for OllamaToolkit clients.
ollamatoolkit/client_api/models.py	Model-management endpoint adapters for OllamaToolkit clients.
ollamatoolkit/client_api/openai_compat.py	OpenAI-compatible (/v1/*) endpoint adapters for OllamaToolkit clients.
ollamatoolkit/client_api/sync_client.py	Public synchronous Ollama client composed from endpoint-domain adapters.
ollamatoolkit/client_api/transport.py	HTTP transport layer used by OllamaToolkit client domain modules.
ollamatoolkit/client_api/web.py	Web endpoint adapters for OllamaToolkit clients.
ollamatoolkit/common/utils.py	Ollama Toolkit - Utilities
ollamatoolkit/config/__init__.py	Ollama Toolkit - Configuration Package
ollamatoolkit/config/core.py	Ollama Toolkit - Core Configuration
ollamatoolkit/config/presets.py	Ollama Toolkit - Model Presets
ollamatoolkit/config.py	Ollama Toolkit - Configuration
ollamatoolkit/connector.py	High-level Ollama connector façade used by toolkit consumers and dependent apps.
ollamatoolkit/dashboard.py	Ollama Toolkit - Mission Control Dashboard
ollamatoolkit/exceptions.py	OllamaToolkit Exception Hierarchy
ollamatoolkit/extractor.py	Schema-first field extraction helper built on top of SimpleAgent.
ollamatoolkit/models/__init__.py	Ollama Toolkit - Model Utilities
ollamatoolkit/models/selector.py	Ollama Toolkit - Smart Model Selector
ollamatoolkit/openai_types.py	Typed models for Ollama OpenAI-compatible (/v1/*) endpoints.
ollamatoolkit/telemetry.py	Ollama Toolkit - Telemetry Integration
ollamatoolkit/tool_registry.py	OllamaToolkit - LLM Tool Registry
ollamatoolkit/tools/__init__.py	Ollama Toolkit - Tools Package
ollamatoolkit/tools/benchmark.py	Ollama Toolkit - Model Benchmarking (GPU-Aware)
ollamatoolkit/tools/cache.py	OllamaToolkit - Cache Tools
ollamatoolkit/tools/db.py	Ollama Toolkit - Database Tools
ollamatoolkit/tools/document.py	Ollama Toolkit - Document Processor
ollamatoolkit/tools/email.py	OllamaToolkit - Email Tools
ollamatoolkit/tools/files.py	Ollama Toolkit - File Tools
ollamatoolkit/tools/math.py	Ollama Toolkit - Math Tools
ollamatoolkit/tools/mcp.py	Ollama Toolkit - MCP Client
ollamatoolkit/tools/models.py	Ollama Toolkit - Model Inspector
ollamatoolkit/tools/pdf.py	PDF helper utilities shared by vision/document workflows.
ollamatoolkit/tools/schema.py	OllamaToolkit - JSON Schema Tools
ollamatoolkit/tools/server.py	Ollama Toolkit - Server Tools
ollamatoolkit/tools/system.py	Ollama Toolkit - System Tools
ollamatoolkit/tools/system_health.py	Ollama Toolkit - System Health Tool
ollamatoolkit/tools/vector.py	Ollama Toolkit - Vector Intelligence (RAG)
ollamatoolkit/tools/vision/__init__.py	Ollama Toolkit - Vision Package
ollamatoolkit/tools/vision/analysis.py	Ollama Toolkit - Vision Analysis
ollamatoolkit/tools/vision/metadata.py	Ollama Toolkit - Vision Metadata
ollamatoolkit/tools/vision/ocr.py	OCR helper that routes image/PDF inputs through a vision-capable model.
ollamatoolkit/tools/vision/spatial.py	Ollama Toolkit - Vision Spatial
ollamatoolkit/tools/vision/tiling.py	Ollama Toolkit - Vision Tiling
ollamatoolkit/tools/vision/video.py	Ollama Toolkit - Smart Video Processor
ollamatoolkit/tools/web.py	Ollama Toolkit - Web Tools
ollamatoolkit/types.py	Ollama Toolkit - Type Definitions
ollamatoolkit/utils.py	Ollama Toolkit - Utilities

---

Examples Directory

The examples/ folder includes runnable patterns:

• 01_basic_chat.py
• 02_with_tools.py
• 03_vision_ocr.py
• 04_rag_pipeline.py
• 05_multi_agent_team.py
• 06_streaming_ui.py
• 07_schema_cache.py
• 08_email_tools.py
• 09_tool_registry.py

---

Testing and Quality

# Lint
python -m ruff check .

# Format
python -m ruff format .

# Type-check
python -m mypy src/ollamatoolkit

# Tests
python -B -m pytest -q

Optional live Ollama integration tests (env-gated):

# PowerShell example (do not hardcode private hosts in tracked files)
$env:OLLAMA_TEST_BASE_URL="http://YOUR_HOST:11434"
python -m pytest tests/test_live_ollama_integration.py -m integration -v

Workspace gateway (run from workspace root):

python .\tools\run_quality_gate.py --project .\OllamaToolkit

---

Publishing Checklist (GitHub + PyPI)

From workspace root:

python .\tools\run_quality_gate.py --project .\OllamaToolkit
python .\tools\publish_release.py --project .\OllamaToolkit

---

Security & Privacy Notes

• Do not commit private server IPs, API keys, or machine-specific paths.
• Use environment variables for private runtime endpoints.
• Keep local-only testing settings in ignored files (e.g., local config files excluded by .gitignore).

---

Author

Created by: Roy Dawson IV
GitHub: https://github.com/imyourboyroy
PyPI: https://pypi.org/user/ImYourBoyRoy/

---

Lightweight Model Inventory CLI

ollamatoolkit models now stays lightweight by lazily importing optional tool families. That means model inspection no longer requires vision extras just to list inventory.

Inspect installed + running models

ollamatoolkit models --base-url http://192.168.1.21:11434 --json

Merge benchmark context into inventory output

ollamatoolkit models --base-url http://192.168.1.21:11434 --benchmarks-file output/_benchmarks/benchmark_report.json --json

The JSON payload now includes:

• installed inventory
• running models
• capability/family summaries
• recommended models by use-case
• optional benchmark-role summaries when a benchmark report is provided