Quick Start

HTTP Service Startup

reme \
  backend=http \
  http.port=8002 \
  llm.default.model_name=qwen3-30b-a3b-thinking-2507 \
  embedding_model.default.model_name=text-embedding-v4 \
  vector_store.default.backend=local

MCP Server Support

reme \
  backend=mcp \
  mcp.transport=stdio \
  llm.default.model_name=qwen3-30b-a3b-thinking-2507 \
  embedding_model.default.model_name=text-embedding-v4 \
  vector_store.default.backend=local

Core API Usage

Task Memory Management

python(http)

import requests

# Experience Summarizer: Learn from execution trajectories
response = requests.post("http://localhost:8002/summary_task_memory", json={
    "workspace_id": "task_workspace",
    "trajectories": [
        {"messages": [{"role": "user", "content": "Help me create a project plan"}], "score": 1.0}
    ]
})

# Retriever: Get relevant memories
response = requests.post("http://localhost:8002/retrieve_task_memory", json={
    "workspace_id": "task_workspace",
    "query": "How to efficiently manage project progress?",
    "top_k": 1
})

python(import)

import asyncio
from reme_ai import ReMeApp

async def main():
    async with ReMeApp(
        "llm.default.model_name=qwen3-30b-a3b-thinking-2507",
        "embedding_model.default.model_name=text-embedding-v4",
        "vector_store.default.backend=memory"
    ) as app:
        # Experience Summarizer: Learn from execution trajectories
        result = await app.async_execute(
            name="summary_task_memory",
            workspace_id="task_workspace",
            trajectories=[
                {
                    "messages": [
                        {"role": "user", "content": "Help me create a project plan"}
                    ],
                    "score": 1.0
                }
            ]
        )
        print(result)

        # Retriever: Get relevant memories
        result = await app.async_execute(
            name="retrieve_task_memory",
            workspace_id="task_workspace",
            query="How to efficiently manage project progress?",
            top_k=1
        )
        print(result)

if __name__ == "__main__":
    asyncio.run(main())

curl

# Experience Summarizer: Learn from execution trajectories
curl -X POST http://localhost:8002/summary_task_memory \
  -H "Content-Type: application/json" \
  -d '{
    "workspace_id": "task_workspace",
    "trajectories": [
      {"messages": [{"role": "user", "content": "Help me create a project plan"}], "score": 1.0}
    ]
  }'

# Retriever: Get relevant memories
curl -X POST http://localhost:8002/retrieve_task_memory \
  -H "Content-Type: application/json" \
  -d '{
    "workspace_id": "task_workspace",
    "query": "How to efficiently manage project progress?",
    "top_k": 1
  }'

Node.js

// Experience Summarizer: Learn from execution trajectories
fetch("http://localhost:8002/summary_task_memory", {
  method: "POST",
  headers: {
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
    workspace_id: "task_workspace",
    trajectories: [
      {messages: [{role: "user", content: "Help me create a project plan"}], score: 1.0}
    ]
  })
})
.then(response => response.json())
.then(data => console.log(data));

// Retriever: Get relevant memories
fetch("http://localhost:8002/retrieve_task_memory", {
  method: "POST",
  headers: {
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
    workspace_id: "task_workspace",
    query: "How to efficiently manage project progress?",
    top_k: 1
  })
})
.then(response => response.json())
.then(data => console.log(data));

Personal Memory Management

python(http)

import requests

# Memory Integration: Learn from user interactions
response = requests.post("http://localhost:8002/summary_personal_memory", json={
    "workspace_id": "task_workspace",
    "trajectories": [
        {"messages":
            [
                {"role": "user", "content": "I like to drink coffee while working in the morning"},
                {"role": "assistant",
                 "content": "I understand, you prefer to start your workday with coffee to stay energized"}
            ]
        }
    ]
})

# Memory Retrieval: Get personal memory fragments
response = requests.post("http://localhost:8002/retrieve_personal_memory", json={
    "workspace_id": "task_workspace",
    "query": "What are the user's work habits?",
    "top_k": 5
})

python(import)

import asyncio
from reme_ai import ReMeApp

async def main():
    async with ReMeApp(
        "llm.default.model_name=qwen3-30b-a3b-thinking-2507",
        "embedding_model.default.model_name=text-embedding-v4",
        "vector_store.default.backend=memory"
    ) as app:
        # Memory Integration: Learn from user interactions
        result = await app.async_execute(
            name="summary_personal_memory",
            workspace_id="task_workspace",
            trajectories=[
                {
                    "messages": [
                        {"role": "user", "content": "I like to drink coffee while working in the morning"},
                        {"role": "assistant",
                         "content": "I understand, you prefer to start your workday with coffee to stay energized"}
                    ]
                }
            ]
        )
        print(result)

        # Memory Retrieval: Get personal memory fragments
        result = await app.async_execute(
            name="retrieve_personal_memory",
            workspace_id="task_workspace",
            query="What are the user's work habits?",
            top_k=5
        )
        print(result)

if __name__ == "__main__":
    asyncio.run(main())

curl

# Memory Integration: Learn from user interactions
curl -X POST http://localhost:8002/summary_personal_memory \
  -H "Content-Type: application/json" \
  -d '{
    "workspace_id": "task_workspace",
    "trajectories": [
      {"messages": [
        {"role": "user", "content": "I like to drink coffee while working in the morning"},
        {"role": "assistant", "content": "I understand, you prefer to start your workday with coffee to stay energized"}
      ]}
    ]
  }'

# Memory Retrieval: Get personal memory fragments
curl -X POST http://localhost:8002/retrieve_personal_memory \
  -H "Content-Type: application/json" \
  -d '{
    "workspace_id": "task_workspace",
    "query": "What are the user's work habits?",
    "top_k": 5
  }'

Node.js

// Memory Integration: Learn from user interactions
fetch("http://localhost:8002/summary_personal_memory", {
  method: "POST",
  headers: {
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
    workspace_id: "task_workspace",
    trajectories: [
      {messages: [
        {role: "user", content: "I like to drink coffee while working in the morning"},
        {role: "assistant", content: "I understand, you prefer to start your workday with coffee to stay energized"}
      ]}
    ]
  })
})
.then(response => response.json())
.then(data => console.log(data));

// Memory Retrieval: Get personal memory fragments
fetch("http://localhost:8002/retrieve_personal_memory", {
  method: "POST",
  headers: {
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
    workspace_id: "task_workspace",
    query: "What are the user's work habits?",
    top_k: 5
  })
})
.then(response => response.json())
.then(data => console.log(data));

Tool Memory Management

python(http)

import requests

# Record tool execution results
response = requests.post("http://localhost:8002/add_tool_call_result", json={
    "workspace_id": "tool_workspace",
    "tool_call_results": [
        {
            "create_time": "2025-10-21 10:30:00",
            "tool_name": "web_search",
            "input": {"query": "Python asyncio tutorial", "max_results": 10},
            "output": "Found 10 relevant results...",
            "token_cost": 150,
            "success": True,
            "time_cost": 2.3
        }
    ]
})

# Generate usage guidelines from history
response = requests.post("http://localhost:8002/summary_tool_memory", json={
    "workspace_id": "tool_workspace",
    "tool_names": "web_search"
})

# Retrieve tool guidelines before use
response = requests.post("http://localhost:8002/retrieve_tool_memory", json={
    "workspace_id": "tool_workspace",
    "tool_names": "web_search"
})

python(import)

import asyncio
from reme_ai import ReMeApp

async def main():
    async with ReMeApp(
        "llm.default.model_name=qwen3-30b-a3b-thinking-2507",
        "embedding_model.default.model_name=text-embedding-v4",
        "vector_store.default.backend=memory"
    ) as app:
        # Record tool execution results
        result = await app.async_execute(
            name="add_tool_call_result",
            workspace_id="tool_workspace",
            tool_call_results=[
                {
                    "create_time": "2025-10-21 10:30:00",
                    "tool_name": "web_search",
                    "input": {"query": "Python asyncio tutorial", "max_results": 10},
                    "output": "Found 10 relevant results...",
                    "token_cost": 150,
                    "success": True,
                    "time_cost": 2.3
                }
            ]
        )
        print(result)

        # Generate usage guidelines from history
        result = await app.async_execute(
            name="summary_tool_memory",
            workspace_id="tool_workspace",
            tool_names="web_search"
        )
        print(result)

        # Retrieve tool guidelines before use
        result = await app.async_execute(
            name="retrieve_tool_memory",
            workspace_id="tool_workspace",
            tool_names="web_search"
        )
        print(result)

if __name__ == "__main__":
    asyncio.run(main())

curl

# Record tool execution results
curl -X POST http://localhost:8002/add_tool_call_result \
  -H "Content-Type: application/json" \
  -d '{
    "workspace_id": "tool_workspace",
    "tool_call_results": [
      {
        "create_time": "2025-10-21 10:30:00",
        "tool_name": "web_search",
        "input": {"query": "Python asyncio tutorial", "max_results": 10},
        "output": "Found 10 relevant results...",
        "token_cost": 150,
        "success": true,
        "time_cost": 2.3
      }
    ]
  }'

# Generate usage guidelines from history
curl -X POST http://localhost:8002/summary_tool_memory \
  -H "Content-Type: application/json" \
  -d '{
    "workspace_id": "tool_workspace",
    "tool_names": "web_search"
  }'

# Retrieve tool guidelines before use
curl -X POST http://localhost:8002/retrieve_tool_memory \
  -H "Content-Type: application/json" \
  -d '{
    "workspace_id": "tool_workspace",
    "tool_names": "web_search"
  }'

Node.js

// Record tool execution results
fetch("http://localhost:8002/add_tool_call_result", {
  method: "POST",
  headers: {
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
    workspace_id: "tool_workspace",
    tool_call_results: [
      {
        create_time: "2025-10-21 10:30:00",
        tool_name: "web_search",
        input: {query: "Python asyncio tutorial", max_results: 10},
        output: "Found 10 relevant results...",
        token_cost: 150,
        success: true,
        time_cost: 2.3
      }
    ]
  })
})
.then(response => response.json())
.then(data => console.log(data));

// Generate usage guidelines from history
fetch("http://localhost:8002/summary_tool_memory", {
  method: "POST",
  headers: {
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
    workspace_id: "tool_workspace",
    tool_names: "web_search"
  })
})
.then(response => response.json())
.then(data => console.log(data));

// Retrieve tool guidelines before use
fetch("http://localhost:8002/retrieve_tool_memory", {
  method: "POST",
  headers: {
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
    workspace_id: "tool_workspace",
    tool_names: "web_search"
  })
})
.then(response => response.json())
.then(data => console.log(data));