Enable Function Calling in Mistral Agents Using Standard JSON Schema

 This updated tutorial guides developers through enabling function calling in Mistral Agents via the standard JSON Schema format Function calling allows agents to invoke external APIs or tools (like weather or flight data services) dynamically during conversation—extending their reasoning capabilities beyond text generation.

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🧩 Why Function Calling?

• Seamless tool orchestration: Enables agents to perform actions—like checking bank interest rates or flight statuses—in real time.

• Schema-driven clarity: JSON Schema ensures function inputs and outputs are well-defined and type-safe.

• Leverage MCP Orchestration: Integrates with Mistral's Model Context Protocol for complex workflows 

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🛠️ Step-by-Step Implementation

1. Define Your Function

Create a simple API wrapper, e.g.:

python
def get_european_central_bank_interest_rate(date: str) -> dict:
    # Mock implementation returning a fixed rate
    return {"date": date, "interest_rate": "2.5%"}

2. Craft the JSON Schema

Define the function parameters so the agent knows how to call it:

python
tool_def = {
  "type": "function",
  "function": {
    "name": "get_european_central_bank_interest_rate",
    "description": "Retrieve ECB interest rate",
    "parameters": {
      "type": "object",
      "properties": { "date": {"type": "string"} },
      "required": ["date"]
    }
  }
}

3. Create the Agent

Register the agent with Mistral's SDK:

python
agent = client.beta.agents.create(
  model="mistral-medium-2505",
  name="ecb-interest-rate-agent",
  description="Fetch ECB interest rate",
  tools=[tool_def],
)

The agent now recognizes the function and can decide when to invoke it during a conversation.

4. Start Conversation & Execute

Interact with the agent using a prompt like, "What's today's interest rate?"

• The agent emits a function.call event with arguments.

• You execute the function and return a function.result back to the agent.

• The agent continues based on the result.

This demo uses a mocked example, but any external API can be plugged in—flight info, weather, or tooling endpoints 

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✅ Takeaways

• JSON Schema simplifies defining callable tools.

• Agents can autonomously decide if, when, and how to call your functions.

• This pattern enhances Mistral Agents’ real-time capabilities across knowledge retrieval, action automation, and dynamic orchestration.

OpenAI Unveils Four Major Enhancements to Its AI Agent Framework

 OpenAI has announced four pivotal enhancements to its AI agent framework, aiming to bolster the development and deployment of intelligent agents. These updates focus on expanding language support, facilitating real-time interactions, improving memory management, and streamlining tool integration.

1. TypeScript Support for the Agents SDK

Recognizing the popularity of TypeScript among developers, OpenAI has extended its Agents SDK to include TypeScript support. This addition allows developers to build AI agents using TypeScript, enabling seamless integration into modern web applications and enhancing the versatility of agent development.

2. Introduction of RealtimeAgent with Human-in-the-Loop Functionality

The new RealtimeAgent feature introduces human-in-the-loop capabilities, allowing AI agents to interact with humans in real-time. This enhancement facilitates dynamic decision-making and collaborative problem-solving, as agents can now seek human input during their operation, leading to more accurate and context-aware outcomes.

3. Enhanced Memory Capabilities

OpenAI has improved the memory management of its AI agents, enabling them to retain and recall information more effectively. This advancement allows agents to maintain context over extended interactions, providing more coherent and informed responses, and enhancing the overall user experience.

4. Improved Tool Integration

The framework now offers better integration with various tools, allowing AI agents to interact more seamlessly with external applications and services. This improvement expands the functional scope of AI agents, enabling them to perform a broader range of tasks by leveraging existing tools and platforms.

These enhancements collectively represent a significant step forward in the evolution of AI agents, providing developers with more robust tools to create intelligent, interactive, and context-aware applications.