核心记忆
AI Skill Hub 推荐使用：核心记忆 是一款优质的Agent工作流。AI 综合评分 7.5 分，在同类工具中表现稳健。如果你正在寻找可靠的Agent工作流解决方案，这是一个值得深入了解的选择。
📚 深度解析
核心记忆 工作流的设计遵循"最小配置，最大复用"原则：核心逻辑已经封装好，用户只需配置自己的 API Key 和业务参数即可快速上手。工作流内置错误处理和重试机制，在网络波动或 API 限速等情况下仍能稳定运行，适合作为生产环境的自动化基础设施。
在实际部署时，建议先在测试环境中运行 3-5 次，验证各个环节的输出结果符合预期，再部署到生产环境。AI Skill Hub 评分 7.5 分，是同类 Agent 工作流中的精选推荐。
📋 工具概览
核心记忆 是一套完整的 AI Agent 自动化工作流方案。通过可视化的节点编排，将复杂的多步骤任务拆解为清晰的自动化流程，实现全程无人值守的智能处理。支持与数百种外部服务和 API 无缝集成，适合构建数据处理管线、业务自动化和 AI 辅助决策系统。
📖 中文文档
核心记忆 是一套完整的 AI Agent 自动化工作流方案。通过可视化的节点编排，将复杂的多步骤任务拆解为清晰的自动化流程，实现全程无人值守的智能处理。支持与数百种外部服务和 API 无缝集成，适合构建数据处理管线、业务自动化和 AI 辅助决策系统。
- 可视化 Agent 工作流编排，无需编写复杂代码
- 支持多步骤自动化任务链，实现全流程无人值守
- 与外部 API、数据库和第三方服务无缝集成
- 内置错误处理与自动重试机制，保障稳定运行
- 提供可复用的自动化模板，快速在同类场景部署
- 自动化日常重复性工作，将精力集中于创造性任务
- 构建数据采集 → 处理 → 输出的完整自动化管线
- 实现跨平台、跨系统的数据流转和业务协同
# 方式一：pip 安装（推荐）
pip install core-memory
# 方式二：虚拟环境安装（推荐生产环境）
python -m venv .venv
source .venv/bin/activate # Windows: .venv\Scripts\activate
pip install core-memory
# 方式三：从源码安装（获取最新功能）
git clone https://github.com/JohnnyFiv3r/Core-Memory
cd Core-Memory
pip install -e .
# 验证安装
python -c "import core_memory; print('安装成功')"
- 访问 GitHub 仓库获取工作流文件
- 在对应平台（Dify / Flowise / Make 等）中找到「导入工作流」功能
- 上传工作流文件
- 按照提示配置必要的环境变量和 API Key
- 运行测试确认流程正常后投入使用
# 命令行使用
core-memory --help
# 基本用法
core-memory input_file -o output_file
# Python 代码中调用
import core_memory
# 示例
result = core_memory.process("input")
print(result)
# core-memory 配置文件示例（config.yml） app: name: "core-memory" debug: false log_level: "INFO" # 运行时指定配置文件 core-memory --config config.yml # 或通过环境变量配置 export CORE_MEMORY_API_KEY="your-key" export CORE_MEMORY_OUTPUT_DIR="./output"
简介
<p align="center"> <img src="https://raw.githubusercontent.com/JohnnyFiv3r/Core-Memory/master/docs/assets/core-memory-hero-banner.jpg" alt="Core Memory banner" /> </p>
<p align="center"> <a href="LICENSE"><img src="https://img.shields.io/badge/license-Apache%202.0-blue.svg" alt="Apache-2.0 License"></a> <a href="#"><img src="https://img.shields.io/badge/python-3.10%2B-blue.svg" alt="Python 3.10+"></a> </p>
<p align="center"> <b>Causal memory for AI agents.</b><br> Structured memory objects + causal trace over durable events — so agents can recall <i>why</i>, not just <i>what</i>. </p>
<p align="center"> <a href="#quick-start">Quickstart</a> · <a href="#features">Features</a> · <a href="#supported-clients">Supported Clients</a> · <a href="#contributing">Contributing</a> </p>
Features
Transcript-native storage: Built specifically for conversational data, each turn is normalized into a memory object rather than chunked and indexed alongside authored documents.
Captures every turn automatically: The LLM applies causal labels from a fixed taxonomy rather than judging importance, so nothing is filtered before storage and no explicit "remember this" is required.
Rolling context injection on a budget: Compacted memory objects carry only their title, type, and causal associations, fitting 10+ sessions of history into a fraction of the token cost of naive loading.
Causal graph, not a flat index: Memory objects are linked by typed relationships (caused_by, contradicts, supports, and more), so recall follows reasoning chains instead of ranking similarity scores.
Claims tracked and superseded: Statements like "user prefers PostgreSQL" are monitored and updated when later turns contradict them. Memory stays truthful, not just full.
Full context is always retrievable: Full transcripts are preserved and linked via turn and session_ID references, so full context is always a tool call away.
Inspectable retrieval with provenance: Every recall() returns the source conversation, the traversal path that found it, and a verifiable hash. Retrieval is never a black box.
Depth on demand: recall(query, effort="low" | "medium" | "high") scales from fast lookup to full causal traversal. The orchestrator decides what the question needs.
Self-hosted MCP: Streamable-HTTP server at /mcp with a canonical agent guide that loads automatically at connection. No system prompt changes needed.
Auto-detected embedding model: Picks up OPENAI_API_KEY, GEMINI_API_KEY, or GOOGLE_API_KEY from your environment. Runs in degraded mode with one hint if none are set.
Plug and play adoption: Your data stays on your infrastructure. No cloud dependencies. A single MCP server setup works across any MCP-compatible client.
<p align="center"> <img src="https://raw.githubusercontent.com/JohnnyFiv3r/Core-Memory/master/docs/assets/core-memory-causal-graph.png" alt="Core Memory causal graph alongside the grounded bead JSON returned by recall()" width="100%" /> </p>
<p align="center"><i>The causal memory graph (left) and the grounded bead JSON returned by <code>recall()</code> (right) — type, entities, session_id, and source_turn_ids make every retrieval inspectable.</i></p>
---
Quick Start
Core Memory auto-detects your embeddings provider from OPENAI_API_KEY, GEMINI_API_KEY, or GOOGLE_API_KEY. No configuration needed.
uvx "core-memory[mcp]" mcp serveCore Memory starts on http://localhost:8000/mcp and stores data in ~/.core-memory/store.
For Claude Code, add to your MCP config:
{
"mcpServers": {
"core-memory": {
"type": "streamable-http",
"url": "http://localhost:8000/mcp"
}
}
}Start a new conversation. MCP-capable agents can capture and recall through the bundled Core Memory tools and agent guide.
Or install directly from PyPI for Python SDK use:
pip install "core-memory[mcp]"To ingest existing transcripts, use the CLI command:
core-memory ingest transcript my-transcript.jsonl
Or call the ingest tool directly from any connected MCP client. Accepts JSONL or JSON with user/assistant, human/ai, or customer/agent roles.
See the full setup guide for MCP client configuration and adapter configurations for OpenClaw, PydanticAI, LangChain, and SpringAI.
---
Recall Example
Request
from core_memory import recall
result = recall(
"what database did we decide on for Project Heron?",
effort="high",
root="~/.core-memory"
)Response
{
"contract": "recall_result",
"schema_version": "recall_result.v1",
"status": "answered",
"answer": "PostgreSQL",
"why": "Decision recorded in session 2026-04-12: PostgreSQL selected for Project Heron tenant config",
"evidence": [
{
"bead_id": "b_a3f9c2",
"type": "decision",
"title": "PostgreSQL selected for Project Heron tenant config",
"content_excerpt": "We decided to use PostgreSQL for the main tenant config database.",
"score": 0.94,
"grounding_hash": "sha256:e3b0c44..."
}
],
"sources": [
{
"turn_id": "turn_042",
"session_id": "session_2026_04_12",
"bead_id": "b_a3f9c2",
"speaker": "user",
"ts": "2026-04-12T14:23:00Z"
}
],
"steps": [
{ "tier": "semantic", "status": "ok", "result_count": 3, "why": "anchor search" },
{ "tier": "causal", "status": "ok", "result_count": 1, "why": "causal chains resolved" }
],
"planning": {
"selected_effort": "high",
"reason": "full orchestration: search + trace + goal resolution + claim enrichment"
},
"claim_slots": {
"project_heron.database": {
"subject": "project_heron",
"slot": "database",
"current_value": "PostgreSQL",
"status": "active",
"current_claim_id": "claim_b3f1a9",
"chain_seq": 1,
"grounding_hash": "sha256:e3b0c44..."
}
},
"resolved_goals": [],
"warnings": []
}---
高质量的开源AI工作流，提供了创新性的解决方案
- 需要让 Claude / Cursor 操作本地工具的 AI 工程师
- 构建多智能体协作系统的 Agent 开发者
- 构建企业知识库 / RAG 检索应用的团队
- 配置 MCP 服务器时建议使用 stdio 传输 + JSON-RPC，避免暴露公网
- Agent 任务先做 dry-run 验证工具调用链，再开启自主执行
- API key 直接提交到 git 仓库（请用 .env 并加入 .gitignore）
- MCP 配置路径拼错或权限不足，重启 Claude Desktop 才生效
- Python 依赖冲突：建议用 venv / uv 隔离环境
- CLI：直接 npm install -g / pip install，命令行调用
- 云端托管：可放在 Vercel / Railway / Fly.io 等 PaaS 平台
⚡ 核心功能
- 可视化 Agent 工作流编排，无需编写复杂代码
- 支持多步骤自动化任务链，实现全流程无人值守
- 与外部 API、数据库和第三方服务无缝集成
- 内置错误处理与自动重试机制，保障稳定运行
- 提供可复用的自动化模板，快速在同类场景部署
- 需要让 Claude / Cursor 操作本地工具的 AI 工程师
- 构建多智能体协作系统的 Agent 开发者
- 构建企业知识库 / RAG 检索应用的团队
- 配置 MCP 服务器时建议使用 stdio 传输 + JSON-RPC，避免暴露公网
- Agent 任务先做 dry-run 验证工具调用链，再开启自主执行
- API key 直接提交到 git 仓库（请用 .env 并加入 .gitignore）
- MCP 配置路径拼错或权限不足，重启 Claude Desktop 才生效
- Python 依赖冲突：建议用 venv / uv 隔离环境
👥 适合人群
🎯 使用场景
- 自动化日常重复性工作，将精力集中于创造性任务
- 构建数据采集 → 处理 → 输出的完整自动化管线
- 实现跨平台、跨系统的数据流转和业务协同
⚖️ 优点与不足
- +Apache-2.0 协议，可免费商用
- +大幅减少重复性人工操作
- +可视化流程，清晰直观
- +可扩展性强，支持复杂场景
- −初始配置和调试需投入一定时间
- −强依赖外部服务的稳定性
- −复杂场景需具备一定技术基础
AI Skill Hub 为第三方内容聚合平台，本页面信息基于公开数据整理，不对工具功能和质量作任何法律背书。
建议在沙箱或测试环境中充分验证后，再部署至生产环境，并做好必要的安全评估。
✅ Apache 2.0 — 宽松开源协议，可商用，需保留版权声明和 NOTICE 文件，含专利授权条款。
🔗 相关工具推荐
❓ 常见问题 FAQ
总体来看，核心记忆 是一款质量良好的Agent工作流，在同类工具中具备一定竞争力。AI Skill Hub 将持续追踪其更新动态，建议收藏备用，结合自身场景选择合适时机引入使用。
| 原始名称 | Core-Memory |
| 原始描述 | 开源AI工作流：Drop-in causal memory for AI agents. Append-only event store with associations a。⭐16 · Python |
| Topics | ai-agentsevent-sourcingpython |
| GitHub | https://github.com/JohnnyFiv3r/Core-Memory |
| License | Apache-2.0 |
| 语言 | Python |
收录时间：2026-05-28 · 更新时间：2026-05-30 · License：Apache-2.0 · AI Skill Hub 不对第三方内容的准确性作法律背书。