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Using Ollama with Skillware

Skillware natively supports Ollama, enabling you to run open-source models completely locally while seamlessly utilizing skills. Ollama's tool-calling format is directly compatible with Skillware's manifest structure.

Prerequisites

  1. Install Ollama: Follow the instructions at ollama.com to install Ollama on your machine.
  2. Pull a Model: You need a model that supports tool calling. We recommend llama3 or llama3.1. 
    ollama run llama3
  3. Install Python Client: Install the official Ollama Python package. 
    pip install ollama

Example Usage

Here is a simple example demonstrating how to load a skill and execute it using a local model running via Ollama.

import json
import re
import ollama
from skillware.core.loader import SkillLoader
from skillware.core.env import load_env_file

# Load Env for API Keys if any needed by skills
load_env_file()

# 1. Load the Skill dynamically
SKILL_PATH = "finance/wallet_screening"
skill_bundle = SkillLoader.load_skill(SKILL_PATH)
WalletScreeningSkill = getattr(skill_bundle["module"], "WalletScreeningSkill")
wallet_skill = WalletScreeningSkill()

print(f"Loaded Skill: {skill_bundle['manifest']['name']}")

# 2. Build the System Prompt with Tool Rules
tool_description = SkillLoader.to_ollama_prompt(skill_bundle)

system_prompt = f"""You are an intelligent agent equipped with specialized capabilities.
To use a skill, you MUST output a JSON code block in the EXACT following format:
```json
{{
  "tool": "the_tool_name",
  "arguments": {{
    "param_name": "value"
  }}
}}
```
Wait for the system to return the result of the tool before proceeding.

Available skills:
{tool_description}
Instructions: {skill_bundle.get('instructions', '')}
"""

messages = [
    {"role": "system", "content": system_prompt},
    {"role": "user", "content": "Please screen this ethereum wallet: 0xd8dA6BF26964aF9D7eEd9e03E53415D37aA96045"}
]

# 3. Call the Ollama Model
model_name = "llama3"
print(f"🤖 Calling Ollama model: {model_name}...")
response = ollama.chat(
    model=model_name,
    messages=messages
)

message_content = response.get("message", {}).get("content", "")
print(f"\n[Model Output]:\n{message_content}")

# 4. Handle Text-based Tool Calls
tool_match = re.search(r"```json\s*({.*?})\s*```", message_content, re.DOTALL)
if tool_match:
    tool_call = json.loads(tool_match.group(1))
    fn_name = tool_call.get("tool")
    fn_args = tool_call.get("arguments", {})

    if fn_name == "finance/wallet_screening":
        print(f"⚙️ Executing skill '{fn_name}' locally...")
        api_result = wallet_skill.execute(fn_args)

        # Give result back to model
        messages.append({"role": "assistant", "content": message_content})
        messages.append({
            "role": "user",
            "content": f"SYSTEM RESPONSE (Result from {fn_name}):\n```json\n{json.dumps(api_result)}\n```\nPlease continue."
        })

        print("\n🤖 Sending tool results back to Agent...")
        final_resp = ollama.chat(model=model_name, messages=messages)
        print("\n💬 Final Answer:")
        print(final_resp.get("message", {}).get("content", ""))