synalinks-agents

name	synalinks-agents
description	Use when working with Synalinks agents — FunctionCallingAgent (autonomous / interactive, max_iterations, return_inputs_with_trajectory), Tool definitions (async, type-annotated, register_synalinks_serializable), MCP integration via MultiServerMCPClient, parallel tool calling, or agent execution trajectories.

Synalinks Agents and Tools

Build tool-using AI agents with structured outputs, autonomous or interactive execution, and MCP integration.

Quick Start

import synalinks
import asyncio

class Query(synalinks.DataModel):
    query: str = synalinks.Field(description="The user query")

class FinalAnswer(synalinks.DataModel):
    answer: str = synalinks.Field(description="The final answer")

@synalinks.utils.register_synalinks_serializable()
async def calculate(expression: str):
    """Calculate mathematical expression.

    Args:
        expression: Mathematical expression like '2 + 2'
    """
    try:
        result = eval(expression, {"__builtins__": None}, {})
        return {"result": round(float(result), 4), "log": "Success"}
    except Exception as e:
        return {"result": None, "log": f"Error: {e}"}

async def main():
    lm = synalinks.LanguageModel(model="ollama/mistral")
    tools = [synalinks.Tool(calculate)]

    inputs = synalinks.Input(data_model=Query)
    outputs = await synalinks.FunctionCallingAgent(
        data_model=FinalAnswer,
        tools=tools,
        language_model=lm,
        max_iterations=5,
        autonomous=True,
    )(inputs)

    agent = synalinks.Program(inputs=inputs, outputs=outputs, name="calc_agent")
    result = await agent(Query(query="What is 15 * 7?"))
    print(result.prettify_json())

asyncio.run(main())

Defining Tools

Tool Requirements

1. Async function — must be async def
2. Type annotations — every parameter must have a type hint
3. Docstring — used by the LLM to decide when to call the tool
4. Serializable — decorate with @synalinks.utils.register_synalinks_serializable()
5. Return dict — return a dict with results and a log / status field

Basic Tool

@synalinks.utils.register_synalinks_serializable()
async def calculate(expression: str):
    """Calculate the result of a mathematical expression.

    Args:
        expression: Mathematical expression like '2 + 2' or '(10 * 5) / 2'

    Returns:
        dict with 'result' and 'log' keys
    """
    try:
        result = eval(expression, {"__builtins__": {}})
        return {"result": result, "log": "Success"}
    except Exception as e:
        return {"result": None, "log": f"Error: {e}"}

tool = synalinks.Tool(calculate)

Multiple Parameters

All parameters must be required (no defaults). LLM providers require every parameter in the structured-output JSON schema to be required, so Tool defaults are unsafe.

@synalinks.utils.register_synalinks_serializable()
async def search_database(
    query: str,
    limit: int,
    category: str,
):
    """Search the database for matching records.

    Args:
        query: Search query string
        limit: Maximum number of results (e.g. 10)
        category: Category filter ('all', 'products', 'users')
    """
    results = await db.search(query, limit=limit, category=category)
    return {"results": results, "count": len(results), "log": f"Found {len(results)} results"}

Tool with Validation

@synalinks.utils.register_synalinks_serializable()
async def safe_calculate(expression: str):
    """Safely calculate mathematical expressions.

    Args:
        expression: Expression with numbers and +, -, *, /, (, ), . only
    """
    allowed = set("0123456789+-*/(). ")
    if not all(c in allowed for c in expression):
        return {"result": None, "log": "Error: Invalid characters."}
    try:
        return {"result": round(float(eval(expression, {"__builtins__": None}, {})), 4), "log": "Success"}
    except Exception as e:
        return {"result": None, "log": f"Error: {e}"}

FunctionCallingAgent

synalinks.FunctionCallingAgent(
    data_model=FinalAnswer,              # Required: final output schema
    tools=tools,                         # Required: list of Tool instances
    language_model=lm,                   # Optional if default LM is set
    max_iterations=5,                    # Max tool calls before final answer
    autonomous=True,                     # Run autonomously vs interactive
    return_inputs_with_trajectory=True,  # Include full execution trajectory
    prompt_template=None,
    instructions="",                     # MUST be a string
)

All arguments are keyword-only — FunctionCallingAgent(data_model, tools, lm) (positional) raises TypeError. Same for Generator, ChainOfThought, and other Module subclasses. Tool(my_function) is the exception: its first positional arg is the wrapped function.

language_model may be omitted if synalinks.set_default_language_model(...) was called — defaults flow via ops.predict.

instructions MUST be a string, not a list — see synalinks-modules for the full discussion.

Execution Trajectory

When return_inputs_with_trajectory=True:

{
  "query": "What is 15 * 7?",
  "trajectory": [
    {"tool": "calculate", "input": {"expression": "15 * 7"}, "output": {"result": 105, "log": "Success"}}
  ],
  "answer": "105"
}

Autonomous vs Interactive

Autonomous

Agent runs until max_iterations or final answer:

synalinks.FunctionCallingAgent(autonomous=True, max_iterations=5, ...)

Interactive

Agent pauses for human confirmation at each step:

synalinks.FunctionCallingAgent(autonomous=False, ...)

MCP Integration

MultiServerMCPClient

mcp_client = synalinks.MultiServerMCPClient({
    "math_server":   {"url": "http://localhost:8183/mcp/", "transport": "streamable_http"},
    "search_server": {"url": "http://localhost:8184/mcp/", "transport": "streamable_http"},
})

tools = await mcp_client.get_tools()

outputs = await synalinks.FunctionCallingAgent(
    data_model=FinalAnswer,
    tools=tools,
    language_model=lm,
)(inputs)

MCP Server Example

# mcp_server.py
from mcp.server.fastmcp import FastMCP

mcp = FastMCP("Math Server")

@mcp.tool()
def calculate(expression: str) -> dict:
    """Calculate mathematical expression."""
    return {"result": eval(expression)}

# Run with: uvicorn mcp_server:app --port 8183
app = mcp.streamable_http_app()

Best Practices

Tool design

1. Clear docstrings — the LLM uses these to decide when to call the tool
2. Specific parameter types (int, float, bool) over generic str
3. Informative error messages so the LLM can recover
4. Return a log / status field for debugging

Agent design

1. Cap max_iterations to prevent runaway loops
2. Use a specific data_model to structure the final answer
3. Capture trajectories during development, drop them in production for cost savings
4. Provide instructions as a single string

References

• references/agents-tools.md — Complete agent and tool API with examples

See Also

• synalinks-core — DataModel, Program basics
• synalinks-modules — Generator, ChainOfThought, custom modules
• synalinks-training — Training agents with rewards (e.g. trajectory-aware rewards)
• synalinks-knowledge — Combining KnowledgeBase retrieval with agents
• synalinks-providers — Groq/OpenRouter/LMStudio for the underlying LM