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Mem0 Agent with Kimi K3 for LOCOMO Benchmark / Mem0 Agent 与 LOCOMO 评测

Companion material for AI Agents in Depth, Chapter 3 — Mem0 memory framework + Kimi for long-context multi-session memory (Experiment 3-2 comparison track).
配套《深入理解 AI Agent》第 3 章——Mem0 记忆框架 + Kimi，长上下文多会话记忆（实验 3-2 对照实现之一）。

← Chapter 3 index / 返回第 3 章目录

English

Overview

An agent that combines the Mem0 memory framework with the Kimi language model for LOCOMO-style long-context multi-agent / multi-session tasks:

  • Persistent memory via Mem0 across sessions
  • Kimi integration (experiment caps context budget below the model’s full window)
  • LOCOMO benchmark scenarios
  • Multi-session and multi-agent collaboration with shared memory

Features

Core: Mem0 v3 ADD-only extraction and hybrid retrieval; context preservation; metrics (consistency, coherence, latency, memory use); local or cloud memory backend.

LOCOMO scenarios: collaborative planning; information sharing; multi-step problem solving; negotiation; teaching & learning.

Installation

Prerequisites: Python 3.12 with the root ch3 extra (including Mem0's NLP support for entity/BM25 signals), Kimi API key; optional Mem0 cloud key.

# From the repository root: use the shared Chapter 3 environment
uv sync --locked --python 3.12 --extra ch3

# Activate it before changing directories:
# macOS/Linux:
source .venv/bin/activate
# Windows PowerShell: .venv\Scripts\Activate.ps1
# Windows cmd: .venv\Scripts\activate.bat

# pip fallback when uv is not installed:
# python -m pip install -e ".[ch3]"

cd chapter3/mem0

# Single-project compatibility path, still supported during migration:
# python -m pip install -r requirements.txt

cp env.example .env
# Edit .env with API keys

Required env:

  • KIMI_API_KEY
  • MODEL_NAME (default kimi-k3) — raw Moonshot model id (e.g. kimi-k3, kimi-k2.5); do not use provider/model slash form; Mem0 uses OpenAI-compatible provider pointed at Moonshot base_url and forwards the string verbatim (kimi/k3 → “Not found the model”)
  • MEMORY_BACKEND: local / cloud
  • MAX_TOKENS (default 128000)

Quick start

python quickstart.py

Shows basic chat with memory, multi-session persistence, multi-agent collaboration.

Memory pipeline demo (ADD-only extraction + hybrid retrieval)

Demonstrates Mem0 v3's append-only history and cross-session recall:

python main.py --mode demo --user-id demo_user

Book example: a user lives in Beijing and later moves to Shanghai. Mem0 preserves both dated facts, while hybrid, time-aware retrieval ranks the current one. Same routine: memory_pipeline_example() in quickstart.py.

Direct memory operations CLI

python main.py --help   # Chinese descriptions

python main.py --mode memory --op add   --text "我住在北京，是一名后端工程师" --user-id u1
python main.py --mode memory --op search --query "这个用户住在哪里？" --user-id u1
python main.py --mode memory --op get-all --user-id u1 --output mem.json
python main.py --mode memory --op history --memory-id <id>
python main.py --mode memory --op delete --memory-id <id>

Flags: --op {add,search,get-all,history,delete}, --text, --query, --memory-id, --user-id, --agent-id, --model, --output. --text may be a raw string or path to a JSON message list.

Demo, memory ops, and chat modes need a working LLM key (KIMI_API_KEY) and vector store. Without a key the CLI parses args then reports the missing key—no fabricated memory output.

Interactive / batch

python main.py --mode interactive
# commands: help, memories, metrics, save, load, new, exit

python main.py --mode batch --input conversations.json --output results.json

Batch input format:

[
  {
    "session_id": "session_001",
    "user_id": "user_001",
    "agent_id": "agent_001",
    "turns": ["First user message", "Second user message"]
  }
]

LOCOMO benchmark

python experiment.py --scenarios 10 --output results/

Metrics: consistency, coherence, memory retention, response time, context utilization. Results JSON under results/ with per-scenario and overall metrics.

Architecture

  • agent.py: Mem0Agent, KimiK3Client, AgentContext
  • config.py: Kimi / Mem0 / LOCOMO config
  • experiment.py: LOCOMOBenchmark

Mem0 provides append-only extraction, hybrid retrieval, and multi-level (user/agent/session) organization.

Memory backends

# Local Chroma
config.mem0.backend = "local"
config.mem0.vector_store_config = {
    "provider": "chroma",
    "config": {"collection_name": "my_collection", "path": "./data/chroma_db"}
}

# Cloud
config.mem0.backend = "cloud"
config.mem0.api_key = "your_mem0_api_key"

Troubleshooting

  1. API key: set valid KIMI_API_KEY in .env
  2. Local backend: write permission under ./data/
  3. Cloud: valid MEM0_API_KEY
  4. Debug: export LOG_LEVEL=DEBUG

Project structure

mem0/
├── agent.py, config.py, experiment.py, main.py, quickstart.py
├── requirements.txt, env.example, README.md

Limitations

Needs network for APIs; memory grows with use; context capped in experiment config; quality depends on model availability.

License / acknowledgments

Part of AI Agent Book materials. Mem0 by Mem0 AI; Kimi by Moonshot AI.

中文

概述

将 Mem0 记忆框架与 Kimi 语言模型结合，面向 LOCOMO 风格长上下文、多会话 / 多 Agent 任务：

  • 跨会话持久记忆
  • Kimi 集成（实验中会限制上下文预算）
  • LOCOMO 场景评测
  • 多会话、多 Agent 共享记忆协作

功能

核心： Mem0 v3 的 ADD-only 抽取与混合检索；跨会话上下文保持；一致性、连贯性、时延、记忆利用率等指标；本地或云端记忆后端。

LOCOMO 场景： 协作规划、信息共享、多步解题、谈判、教与学。

安装

Python 3.12 与根目录 ch3 extra（包含实体 / BM25 信号所需的 Mem0 NLP 支持）、Kimi API Key；可选 Mem0 云端 Key。

# 在仓库根目录使用统一的第 3 章环境
uv sync --locked --python 3.12 --extra ch3

# 切换目录前先激活环境：
# macOS/Linux：
source .venv/bin/activate
# Windows PowerShell：.venv\Scripts\Activate.ps1
# Windows cmd：.venv\Scripts\activate.bat

# 未安装 uv 时可用 pip 兜底：
# python -m pip install -e ".[ch3]"

cd chapter3/mem0

# 迁移期间仍支持单项目兼容路径：
# python -m pip install -r requirements.txt

cp env.example .env
# 编辑 .env 填入 API Key

环境变量：

  • KIMI_API_KEY
  • MODEL_NAME（默认 kimi-k3）——原始 Moonshot 模型 id，不要用 provider/model 斜杠形式
  • MEMORY_BACKEND：local / cloud
  • MAX_TOKENS（默认 128000）

快速开始

python quickstart.py

记忆管线演示（仅追加提取 + 混合检索）

python main.py --mode demo --user-id demo_user

书中示例：先说住在北京，后来说搬到上海。Mem0 保留两条带时间的事实，由混合、时间感知检索优先返回当前事实。

直接记忆操作 CLI

python main.py --help

python main.py --mode memory --op add   --text "我住在北京，是一名后端工程师" --user-id u1
python main.py --mode memory --op search --query "这个用户住在哪里？" --user-id u1
python main.py --mode memory --op get-all --user-id u1 --output mem.json
python main.py --mode memory --op history --memory-id <id>
python main.py --mode memory --op delete --memory-id <id>

无 Key 时 CLI 会解析参数后明确报错，不会伪造记忆输出。

交互 / 批处理

python main.py --mode interactive
python main.py --mode batch --input conversations.json --output results.json

LOCOMO 基准

python experiment.py --scenarios 10 --output results/

指标：一致性、连贯性、记忆保持、响应时间、上下文利用等。

架构与后端

  • agent.py / config.py / experiment.py
  • 本地 Chroma 或 Mem0 Cloud（配置见 English 节代码块）

故障排查

检查 KIMI_API_KEY、./data/ 写权限、MEM0_API_KEY；LOG_LEVEL=DEBUG。

项目结构

mem0/
├── agent.py, config.py, experiment.py, main.py, quickstart.py
├── requirements.txt, env.example, README.md

局限与许可

需联网调用 API；记忆随使用增长；实验中上下文有上限。教学材料许可。

Notes / 说明

OpenRouter 通用回退 / Universal OpenRouter fallback

  • Primary provider keys unchanged if set.
  • Else OPENROUTER_API_KEY routes chat LLM via https://openrouter.ai/api/v1 with automatic model id mapping; OPENROUTER_MODEL forces a specific id.
  • Note: Mem0’s embedder still uses OpenAI embeddings (OpenRouter has no embeddings endpoint), so OPENAI_API_KEY is still required for store/retrieve. OpenRouter only covers the chat LLM (ADD-only fact extraction and answering).

Add OPENROUTER_API_KEY=... to .env (see env.example).