Search and synthesize context from the user’s past AI conversations. This is the primary recall endpoint — it performs semantic search, then uses AI to synthesize matching results into a coherent summary with source citations. This is the same function powering the recall_chat_history tool in the MCP server.
Supports parallel queries for complex topics that benefit from multiple search angles (e.g., timeline, decisions, people).
POST
/
api
/
chat-history
/
inject
Recall chat history
curl --request POST \ --url https://www.memoryplugin.com/api/chat-history/inject \ --header 'Authorization: Bearer <token>' \ --header 'Content-Type: application/json' \ --data '{ "query": "What decisions did I make about the database schema?", "maxTokens": 600, "conversationContext": "<string>", "conversationHistory": [ { "content": "<string>" } ], "queries": [ { "query": "<string>", "maxTokens": 123 } ]}'
This is the primary endpoint for retrieving context from a user’s past AI conversations. It searches across all imported chat history, then uses AI to synthesize the results into a coherent summary with source citations.This is the same function that powers the recall_chat_history tool in the MCP server.
curl -X POST https://www.memoryplugin.com/api/chat-history/inject \ -H "Authorization: Bearer YOUR_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "query": "what did I decide about the API rate limiting approach?", "maxTokens": 800 }'
Array of parallel queries for complex topics (max 15). Each query is searched and synthesized independently. Use when a topic benefits from multiple search angles.
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