Lint check in main branch is failing. This fixes the lint check after we moved to ruff in https://github.com/meta-llama/llama-stack/pull/921. We need to move to a `ruff.toml` file as well as fixing and ignoring some additional checks. Signed-off-by: Yuan Tang <terrytangyuan@gmail.com>
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Tools
Tools are functions that can be invoked by an agent to perform tasks. They are organized into tool groups and registered with specific providers. Each tool group represents a collection of related tools from a single provider. They are organized into groups so that state can be externalized: the collection operates on the same state typically. An example of this would be a "db_access" tool group that contains tools for interacting with a database. "list_tables", "query_table", "insert_row" could be examples of tools in this group.
Tools are treated as any other resource in llama stack like models. You can register them, have providers for them etc.
When instatiating an agent, you can provide it a list of tool groups that it has access to. Agent gets the corresponding tool definitions for the specified tool groups and passes them along to the model.
Refer to the Building AI Applications notebook for more examples on how to use tools.
Types of Tool Group providers
There are three types of providers for tool groups that are supported by Llama Stack.
- Built-in providers
- Model Context Protocol (MCP) providers
- Client provided tools
Built-in providers
Built-in providers come packaged with Llama Stack. These providers provide common functionalities like web search, code interpretation, and computational capabilities.
Web Search providers
There are three web search providers that are supported by Llama Stack.
- Brave Search
- Bing Search
- Tavily Search
Example client SDK call to register a "websearch" toolgroup that is provided by brave-search.
# Register Brave Search tool group
client.toolgroups.register(
toolgroup_id="builtin::websearch",
provider_id="brave-search",
args={"max_results": 5},
)
The tool requires an API key which can be provided either in the configuration or through the request header X-LlamaStack-Provider-Data
. The format of the header is {"<provider_name>_api_key": <your api key>}
.
Code Interpreter
The Code Interpreter allows execution of Python code within a controlled environment.
# Register Code Interpreter tool group
client.toolgroups.register(
toolgroup_id="builtin::code_interpreter", provider_id="code_interpreter"
)
Features:
- Secure execution environment using
bwrap
sandboxing - Matplotlib support for generating plots
- Disabled dangerous system operations
- Configurable execution timeouts
WolframAlpha
The WolframAlpha tool provides access to computational knowledge through the WolframAlpha API.
# Register WolframAlpha tool group
client.toolgroups.register(
toolgroup_id="builtin::wolfram_alpha", provider_id="wolfram-alpha"
)
Example usage:
result = client.tool_runtime.invoke_tool(
tool_name="wolfram_alpha", args={"query": "solve x^2 + 2x + 1 = 0"}
)
Memory
The Memory tool enables retrieval of context from various types of memory banks (vector, key-value, keyword, and graph).
# Register Memory tool group
client.toolgroups.register(
toolgroup_id="builtin::memory",
provider_id="memory",
args={"max_chunks": 5, "max_tokens_in_context": 4096},
)
Features:
- Support for multiple memory bank types
- Configurable query generation
- Context retrieval with token limits
Note: By default, llama stack run.yaml defines toolgroups for web search, code interpreter and memory, that are provided by tavily-search, code-interpreter and memory providers.
Model Context Protocol (MCP) Tools
MCP tools are special tools that can interact with llama stack over model context protocol. These tools are dynamically discovered from an MCP endpoint and can be used to extend the agent's capabilities.
Refer to https://github.com/modelcontextprotocol/server for available MCP servers.
# Register MCP tools
client.toolgroups.register(
toolgroup_id="builtin::filesystem",
provider_id="model-context-protocol",
mcp_endpoint=URL(uri="http://localhost:8000/sse"),
)
MCP tools require:
- A valid MCP endpoint URL
- The endpoint must implement the Model Context Protocol
- Tools are discovered dynamically from the endpoint
Tools provided by the client
These tools are registered along with the agent config and are specific to the agent for which they are registered. The main difference between these tools and the tools provided by the built-in providers is that the execution of these tools is handled by the client and the agent transfers the tool call to the client and waits for the result from the client.
# Example agent config with client provided tools
config = AgentConfig(
toolgroups=[
"builtin::websearch",
],
client_tools=[ToolDef(name="client_tool", description="Client provided tool")],
)
Refer to llama-stack-apps for an example of how to use client provided tools.
Tool Structure
Each tool has the following components:
name
: Unique identifier for the tooldescription
: Human-readable description of the tool's functionalityparameters
: List of parameters the tool acceptsname
: Parameter nameparameter_type
: Data type (string, number, etc.)description
: Parameter descriptionrequired
: Whether the parameter is required (default: true)default
: Default value if any
Example tool definition:
{
"name": "web_search",
"description": "Search the web for information",
"parameters": [
{
"name": "query",
"parameter_type": "string",
"description": "The query to search for",
"required": True,
}
],
}
Tool Invocation
Tools can be invoked using the invoke_tool
method:
result = client.tool_runtime.invoke_tool(
tool_name="web_search", kwargs={"query": "What is the capital of France?"}
)
The result contains:
content
: The tool's outputerror_message
: Optional error message if the tool failederror_code
: Optional error code if the tool failed
Listing Available Tools
You can list all available tools or filter by tool group:
# List all tools
all_tools = client.tools.list_tools()
# List tools in a specific group
group_tools = client.tools.list_tools(toolgroup_id="search_tools")