Skip to main content

Documentation Index

Fetch the complete documentation index at: https://docs.maximem.ai/llms.txt

Use this file to discover all available pages before exploring further.

sdk.as_tool(*, scope="user", user_id=None, customer_id=None, conversation_id=None, name=None, description=None, style="openai")
Produces a tool definition dict you can pass straight into an OpenAI or Anthropic tool-calling loop. The returned dict carries the JSON schema, a scope-appropriate description, and a bound async handler coroutine the host runtime invokes when the LLM calls the tool. Scope identifiers (user_id, customer_id, conversation_id) are closed over inside the handler so the LLM cannot accidentally drop them — the per-user privacy filter holds regardless of what the model passes at call time. Prefer sdk.fetch(...) as the default integration; reach for as_tool only when the LLM genuinely needs agency over when context is fetched mid-reasoning.

​
Parameters

​
scope
str
Which scope the tool fetches from. One of "conversation", "user", "customer", "client", or "unified" (cross-scope). Defaults to "user".
​
user_id
str
Closed-over user identifier. Required when scope="user", and strongly recommended for "conversation" and "unified" so the per-user privacy filter applies.
​
customer_id
str
Closed-over customer identifier. Required when scope="customer".
​
conversation_id
str
Optional closed-over conversation identifier. When provided, the tool always fetches for this conversation; when omitted, the LLM supplies it per call.
​
name
str
Override the tool name exposed to the LLM. Defaults to synap_fetch_{scope}_context.
​
description
str
Override the tool description. Defaults to a scope-specific blurb that primes the LLM to call it for context retrieval.
​
style
str
Output dict shape. "openai" returns {"type": "function", "function": {...}}; "anthropic" returns {"name", "description", "input_schema"}. Defaults to "openai".

​
Returns

A tool definition dict shaped for the requested style. The dict always carries an async handler key with the bound coroutine; host runtimes that don’t use it can ignore it.
​
type
str
Present in "openai" style only. Always "function".
​
function
dict
Present in "openai" style only. Contains name, description, and parameters (the JSON schema).
​
name
str
Present in "anthropic" style. The tool name.
​
description
str
Present in "anthropic" style. The tool description.
​
input_schema
dict
Present in "anthropic" style. JSON schema describing the tool’s call-time arguments.
​
handler
Callable[..., Awaitable[dict]]
Async coroutine the host runtime should await with the LLM’s tool-call arguments. Returns the same shape sdk.fetch(...) produces.

​
Example

from maximem_synap import MaximemSynapSDK

sdk = MaximemSynapSDK(api_key="synap_your_key_here")
await sdk.initialize()

# OpenAI-style tool, bound to a specific user.
tool = sdk.as_tool(scope="user", user_id="user-456")

# Hand the schema to the LLM, then route tool calls through the handler.
tool_call_args = {"search_query": ["dietary preferences"]}
result = await tool["handler"](**tool_call_args)
print(result["formatted_context"])

# Anthropic-style, cross-scope.
anthropic_tool = sdk.as_tool(
    scope="unified",
    user_id="user-456",
    customer_id="cust-789",
    style="anthropic",
)

​
Raises

  • InvalidInputError — when scope is not one of the accepted values, when scope="user" is requested without user_id, when scope="customer" is requested without customer_id, or when style is not "openai" or "anthropic".
See Error Codes for the full SDK exception hierarchy.

​
See also

  • fetch — the recommended pre-fetch integration path.
  • initialize — required before calling as_tool’s handler.