conversation.context.get_context_for_prompt

await sdk.conversation.context.get_context_for_prompt(
    conversation_id: str,
    style: str = "structured",
) -> ContextForPromptResponse

Returns a single formatted_context string that stitches together the compacted history with any messages that arrived after the last compaction cutoff. Drop the result straight into your prompt. No further assembly required. If no compaction has run yet, every recorded message comes back as a recent message, so this method is useful from the very first turn.

Parameters

str

required

The conversation to assemble context for. Must be a valid UUID registered via record_message.

str

default:"structured"

Formatting style for the compacted portion:

"structured": labelled sections (facts, decisions, preferences, current state)
"narrative": prose summary
"bullet_points": bulleted list

Returns

A ContextForPromptResponse ready for prompt injection.

str | None

The combined compacted + recent context, formatted per style. Inject directly into your LLM prompt.

bool

Whether usable context (compacted or recent) was found.

bool

Whether the compacted portion is stale relative to newer messages.

float | None

Compression ratio of the compacted portion.

float | None

Quality validation score of the compacted portion.

int | None

How long ago the compaction completed.

bool

True when the compacted portion was flagged for quality concerns.

List[Dict[str, Any]]

Raw un-compacted messages since the last compaction. Use if you want to format them yourself.

int

Number of un-compacted messages included.

int

Number of messages covered by the compacted portion.

int

Total messages in the conversation.

Example

import uuid
from maximem_synap import MaximemSynapSDK

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

# conversation_id must be a valid UUID; reuse the conversation's id
conversation_id = str(uuid.uuid4())

ctx = await sdk.conversation.context.get_context_for_prompt(
    conversation_id=conversation_id,
    style="structured",
)

system_prompt = f"""You are a helpful assistant.

Conversation context so far:
{ctx.formatted_context or "(no prior context)"}
"""

print(system_prompt)
print(f"Covered {ctx.compacted_message_count} compacted + {ctx.recent_message_count} recent messages.")

Raises

AuthenticationError: when the API key is missing or invalid.
NetworkError: when the SDK cannot reach Synap.

Introduction

Lifecycle

Memory ingestion

Memory management

Conversation