Wandering Nomad

9.6.25

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.

🧩 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

🛠️ 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

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

✅ 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.

Google’s MASS Revolutionizes Multi-Agent AI by Automating Prompt and Topology Optimization

Designing multi-agent AI systems—where several AI "agents" collaborate—has traditionally depended on manual tuning of prompt instructions and agent communication structures (topologies). Google AI, in partnership with Cambridge researchers, is aiming to change that with their new Multi-Agent System Search (MASS) framework. MASS brings automation to the design process, ensuring consistent performance gains across complex domains.

🧠 What MASS Actually Does

MASS performs a three-stage automated optimization that iteratively refines:

Block-Level Prompt Tuning
Fine-tunes individual agent prompts via local search—sharpening their roles (think “questioner”, “solver”).
Topology Optimization
Identifies the best agent interaction structure. It prunes and evaluates possible communication workflows to find the most impactful design.
Workflow-Level Prompt Refinement
Final tuning of prompts once the best network topology is set.

By alternating prompt and topology adjustments, MASS achieves optimization that surpasses previous methods which tackled only one dimension

🏅 Why It Matters

Benchmarked Success: MASS-designed agent systems outperform AFlow and ADAS on challenging benchmarks like MATH, LiveCodeBench, and multi-hop question-answering
Reduced Manual Overhead: Designers no longer need to trial-and-error their way through thousands of prompt-topology combinations.
Extended to Real-World Tasks: Whether for reasoning, coding, or decision-making, this framework is broadly applicable across domains.

💬 Community Reactions

Reddit’s r/machinelearningnews highlighted MASS’s leap beyond isolated prompt or topology tuning:

“Multi-Agent System Search (MASS) … reduces manual effort while achieving state‑of‑the‑art performance on tasks like reasoning, multi‑hop QA, and code generation.” linkedin.com

📘 Technical Deep Dive

Originating from a February 2025 paper by Zhou et al., MASS represents a methodological advance in agentic AI

Agents are modular: designed for distinct roles through prompts.
Topology defines agent communication patterns: linear chain, tree, ring, etc.
MASS explores both prompt and topology spaces, sequentially optimizing them across three stages.
Final systems demonstrate robustness not just in benchmarks but as a repeatable design methodology.

🚀 Wider Implications

Democratizing Agent Design: Non-experts in prompt engineering can deploy effective agent systems from pre-designed searches.
Adaptability: Potential for expanding MASS to dynamic, real-world settings like real-time planning and adaptive workflows.
Innovation Accelerator: Encourages research into auto-tuned multi-agent frameworks for fields like robotics, data pipelines, and interactive assistants.

🧭 Looking Ahead

As Google moves deeper into its “agentic era”—with initiatives like Project Mariner and Gemini's Agent Mode—MASS offers a scalable blueprint for future AS/AI applications. Expect to see frameworks that not only generate prompts but also self-optimize their agent networks for performance and efficiency.

7.6.25

Alibaba's Qwen3-Embedding and Qwen3-Reranker: Redefining Multilingual Embedding and Ranking Standards linkedin.com +3

Alibaba's Qwen team has unveiled two groundbreaking models: Qwen3-Embedding and Qwen3-Reranker, aiming to revolutionize multilingual text embedding and relevance ranking. These models are designed to address the complexities of multilingual natural language processing (NLP) tasks, offering enhanced performance and versatility.

Key Features and Capabilities

Multilingual Proficiency:
Both models support an impressive array of 119 languages, making them among the most versatile open-source offerings available today.
Model Variants:
Available in three sizes—0.6B, 4B, and 8B parameters—these models cater to diverse deployment needs, balancing efficiency and performance.
State-of-the-Art Performance:
Qwen3-Embedding and Qwen3-Reranker have achieved top rankings on multiple benchmarks, including MTEB, MMTEB, and MTEB-Code, outperforming leading models like Gemini.
Versatile Applications:
These models are optimized for a range of tasks such as semantic retrieval, classification, retrieval-augmented generation (RAG), sentiment analysis, and code search.

Technical Innovations

The Qwen3 models are built upon a dense transformer-based architecture with causal attention, enabling them to produce high-fidelity embeddings by extracting hidden states corresponding to specific tokens. The training pipeline incorporates large-scale weak supervision and supervised fine-tuning, ensuring robustness and adaptability across various applications.

Open-Source Commitment

In line with Alibaba's commitment to fostering open research, the Qwen3-Embedding and Qwen3-Reranker models are released under the Apache 2.0 license. They are accessible on platforms like Hugging Face, GitHub, and ModelScope, providing researchers and developers with the tools to innovate and build upon these models.

Implications for the AI Community

The introduction of Qwen3-Embedding and Qwen3-Reranker marks a significant advancement in the field of multilingual NLP. By offering high-performance, open-source models capable of handling complex tasks across numerous languages, Alibaba empowers the AI community to develop more inclusive and effective language processing tools.

References: