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[GH-ISSUE #14750] Integration #35296

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opened 2026-04-22 19:42:06 -05:00 by GiteaMirror · 0 comments
GiteaMirror commented 2026-04-22 19:42:06 -05:00
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Originally created by @frankie336 on GitHub (Mar 10, 2026).
Original GitHub issue: https://github.com/ollama/ollama/issues/14750

Hi Ollama team,

I have developed an Open Source orchestration layer compatible with OpenAI's Assistants API that seamlessly integrates with Ollama.

It provides state management, universal function call handling, and built-in platform tools that give users fast access to code_interpreter, computer, web_search, file_search (RAG), and deep_research. Aside from serving as a general SDK for Ollama, it provides enterprise-grade orchestration, compliance, and logging.

Here is a quick, stripped-down example demonstrating how easy it is to set up an assistant, handle tool calls, and stream inference directly with Ollama:

import json
from dotenv import load_dotenv
from projectdavid import Entity, ToolCallRequestEvent

load_dotenv()

# Universal Function call handler
def get_flight_times(tool_name: str, arguments: dict) -> str:
    return json.dumps({
        "status": "success",
        "departure": arguments.get("departure", "UNK"),
        "arrival": arguments.get("arrival", "UNK"),
        "duration": "4h 30m",
    })

TOOL_REGISTRY = {
    "get_flight_times": get_flight_times,
}

client = Entity()

# 1. Create the Assistant and attach tools
assistant = client.assistants.create_assistant(
    name="Test Assistant",
    model="gpt-oss-120b",
    instructions="You are a helpful AI assistant, your name is Nexa.",
    tools=[{
        "type": "function",
        "function": {
            "name": "get_flight_times",
            "description": "Get flight times",
            "parameters": {
                "type": "object",
                "properties": {
                    "departure": {"type": "string"},
                    "arrival": {"type": "string"},
                },
                "required": ["departure", "arrival"],
            },
        },
    }],
)

thread = client.threads.create_thread()

message = client.messages.create_message(
    thread_id=thread.id,
    role="user",
    content="Please provide the flight times for a trip departing from Tokyo and arriving in Sydney?",
    assistant_id=assistant.id,
)

run = client.runs.create_run(assistant_id=assistant.id, thread_id=thread.id)

# 2. Setup Streaming
stream = client.synchronous_inference_stream
stream.setup(
    thread_id=thread.id,
    assistant_id=assistant.id,
    message_id=message.id,
    run_id=run.id,
)

# 3. Stream events via Ollama and handle tool execution dynamically
for event in stream.stream_events(model="ollama/qwen3:4b"):
    print(f"{event.__class__.__name__:<25} | {json.dumps(event.to_dict())}")
    
    if isinstance(event, ToolCallRequestEvent):
        handler = TOOL_REGISTRY.get(event.tool_name)
        if handler:
            print(f"\n[TOOL CALL] {event.tool_name} → executing...")
            event.execute(handler)
            print("[✓] Result submitted — resuming stream...\n")
        else:
            print(f"\n[!] Unknown tool: '{event.tool_name}'\n")

I have raised PR https://github.com/ollama/ollama/pull/14644 to be featured in the integrations section of your README. As per the CONTRIBUTING.md, I wanted to open this issue to provide context and start a discussion.
I am here to address any questions or concerns you might have. Thank you for your time and for the amazing work on Ollama!
Best,
Francis

Originally created by @frankie336 on GitHub (Mar 10, 2026). Original GitHub issue: https://github.com/ollama/ollama/issues/14750 Hi Ollama team, I have developed an Open Source orchestration layer compatible with OpenAI's Assistants API that seamlessly integrates with Ollama. It provides state management, universal function call handling, and built-in platform tools that give users fast access to `code_interpreter`, `computer`, `web_search`, `file_search` (RAG), and `deep_research`. Aside from serving as a general SDK for Ollama, it provides enterprise-grade orchestration, compliance, and logging. Here is a quick, stripped-down example demonstrating how easy it is to set up an assistant, handle tool calls, and stream inference directly with Ollama: ```python import json from dotenv import load_dotenv from projectdavid import Entity, ToolCallRequestEvent load_dotenv() # Universal Function call handler def get_flight_times(tool_name: str, arguments: dict) -> str: return json.dumps({ "status": "success", "departure": arguments.get("departure", "UNK"), "arrival": arguments.get("arrival", "UNK"), "duration": "4h 30m", }) TOOL_REGISTRY = { "get_flight_times": get_flight_times, } client = Entity() # 1. Create the Assistant and attach tools assistant = client.assistants.create_assistant( name="Test Assistant", model="gpt-oss-120b", instructions="You are a helpful AI assistant, your name is Nexa.", tools=[{ "type": "function", "function": { "name": "get_flight_times", "description": "Get flight times", "parameters": { "type": "object", "properties": { "departure": {"type": "string"}, "arrival": {"type": "string"}, }, "required": ["departure", "arrival"], }, }, }], ) thread = client.threads.create_thread() message = client.messages.create_message( thread_id=thread.id, role="user", content="Please provide the flight times for a trip departing from Tokyo and arriving in Sydney?", assistant_id=assistant.id, ) run = client.runs.create_run(assistant_id=assistant.id, thread_id=thread.id) # 2. Setup Streaming stream = client.synchronous_inference_stream stream.setup( thread_id=thread.id, assistant_id=assistant.id, message_id=message.id, run_id=run.id, ) # 3. Stream events via Ollama and handle tool execution dynamically for event in stream.stream_events(model="ollama/qwen3:4b"): print(f"{event.__class__.__name__:<25} | {json.dumps(event.to_dict())}") if isinstance(event, ToolCallRequestEvent): handler = TOOL_REGISTRY.get(event.tool_name) if handler: print(f"\n[TOOL CALL] {event.tool_name} → executing...") event.execute(handler) print("[✓] Result submitted — resuming stream...\n") else: print(f"\n[!] Unknown tool: '{event.tool_name}'\n") ``` --- I have raised PR https://github.com/ollama/ollama/pull/14644 to be featured in the integrations section of your README. As per the CONTRIBUTING.md, I wanted to open this issue to provide context and start a discussion. I am here to address any questions or concerns you might have. Thank you for your time and for the amazing work on Ollama! Best, Francis
GiteaMirror added the documentation label 2026-04-22 19:42:06 -05:00
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