Skip to content

haochencheng/Agents-Memory

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

134 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

created_at 2026-03-26
updated_at 2026-04-13
doc_status active

Agents-Memory

License: MIT Python 3.10+ MCP Docs Check

Release CI Changelog Release Checklist Contributors Last Commit Repo Size

Read in: English | 简体中文


English

Agents-Memory is a Shared Engineering Brain for AI coding agents. It gives teams a reusable runtime for engineering memory, standards, planning bundles, and validation gates across real repositories.

Why Teams Install Agents-Memory

Operational Memory Workflow Discipline Safer Delivery
Capture repeated failures, promote rules, and carry engineering context across repos. Turn freeform requests into bootstrap, planning bundles, and validation-first execution. Gate changes with docs-check, profile-check, plan-check, CI, and release discipline.

System Architecture

%%{init: {"theme": "default", "flowchart": {"nodeSpacing": 80, "rankSpacing": 100}} }%%
flowchart LR
  U[User / Product Team] --> B[amem bootstrap]
  U --> T[amem start-task]
  A[AI Agent] --> S1[index.md]
  A --> S2[standards]
  A --> S3[profile-managed instructions]
  A --> S4[onboarding-state.json]
  A --> S5[task bundle]

  B --> R[Project Registration]
  B --> P[Profile Install / Refresh]
  B --> I[Bridge and MCP Setup]
  B --> D[Doctor and Onboarding Export]

  P --> PM[profile-manifest.json]
  D --> OS[.agents-memory/onboarding-state.json]
  D --> BC[docs/plans/bootstrap-checklist.md]
  D --> RW[docs/plans/refactor-watch.md]

  T --> TB[docs/plans/task-slug/]
  TB --> SPEC[spec.md]
  TB --> PLAN[plan.md]
  TB --> GRAPH[task-graph.md]
  TB --> VAL[validation.md]

  A --> NX[amem do-next]
  NX --> OS
  NX --> TB

  A --> V[amem validate]
  V --> DC[docs-check]
  V --> PC[profile-check]
  V --> PLC[plan-check]
  V --> TG[focused test gate]
  V --> CG[complexity gate]

  A --> C[amem close-task]
  C --> BG[bundle exit gate]
  C --> GG[global validate gate]
  BG --> TB
  GG --> OS

  A --> L[amem promote-learning]
  L --> ERR[errors]
  L --> RULES[memory/rules.md]
  L --> STD[standards]
  L --> VG[validation rules]
  L --> EVAL[workflow and eval tests]

  classDef user fill:#dbeafe,stroke:#3b82f6,color:#1e3a8a
  classDef agent fill:#ede9fe,stroke:#7c3aed,color:#2e1065
  classDef cmd fill:#d1fae5,stroke:#059669,color:#064e3b
  classDef file fill:#fef3c7,stroke:#d97706,color:#78350f
  classDef gate fill:#fee2e2,stroke:#dc2626,color:#7f1d1d

  class U user
  class A agent
  class B,T,NX,V,C,L cmd
  class S1,S2,S3,S4,S5,PM,OS,BC,RW,TB,SPEC,PLAN,GRAPH,VAL,ERR,RULES,STD,VG,EVAL file
  class R,P,I,D,DC,PC,PLC,TG,CG,BG,GG gate
Loading

Five layers inside the Brain:

Layer Responsibility
Memory Tiered hot / warm / cold error records; keyword + vector hybrid search
Standards Shared Python, docs, planning, validation standards; profile-managed instructions
Planning spec → plan → task graph → validation bundle; start-task / do-next / close-task
Validation Unified delivery gate: docs-check, profile-check, plan-check, test gate, complexity gate
Learning Bus Promote errors and good practices to standards, validation rules, and eval cases

Knowledge Layer (Wiki) Architecture

The Memory layer includes a structured wiki knowledge graph. This is a zoom-in on the Memory layer, not a separate system.

flowchart TD
  subgraph Sources["Sources"]
    E[errors/*.md]
    PR[PR reviews / meeting notes]
    DEC[decision records]
  end

  subgraph Ingest["Ingest Pipeline"]
    ING["amem ingest --type pr-review / decision / meeting"]
    WC[amem wiki-compile --topic X --scope errors]
  end

  subgraph Wiki["memory/wiki/*.md — Knowledge Graph"]
    WP["Wiki Page: topic / compiled_truth / timeline"]
  end

  subgraph Search["Hybrid Search"]
    FTS[FTS index]
    VEC[vector index / LanceDB]
    HS["amem hybrid-search: fts x0.4 + vec x0.6"]
  end

  subgraph Lint["Wiki Lint"]
    L1[orphan pages]
    L2[stale compiled_truth]
    L3[missing cross-links]
  end

  E --> ING
  PR --> ING
  DEC --> ING
  ING --> WC
  WC -- LLM synthesises --> WP
  WP --> FTS
  WP --> VEC
  FTS --> HS
  VEC --> HS
  WP --> L1
  WP --> L2
  WP --> L3

  style Sources fill:#fef3c7,stroke:#d97706
  style Ingest fill:#d1fae5,stroke:#059669
  style Wiki fill:#ede9fe,stroke:#7c3aed
  style Search fill:#dbeafe,stroke:#3b82f6
  style Lint fill:#fee2e2,stroke:#dc2626
Loading

Wiki page anatomy (compiled_truth + append-only timeline):

---
topic: finance-safety
compiled_at: 2026-04-07
confidence: high
sources: [AME-001, AME-007]
links:
  - topic: smart-contract-errors
    context: "Reentrancy overlaps with precision rules"
---

## Compiled Truth        ← LLM rewrites on each wiki-compile run
> Consolidated finding: use Decimal, never float for on-chain values.

## Known Patterns
- AME-001: USDT precision loss on transfer

---

## Timeline             ← append-only, never rewritten
- **2026-04-07** | wiki-compile — synthesised from 7 error records
- **2026-03-20** | AME-007 — first recorded precision failure

Wiki commands:

Command Purpose
amem wiki-compile --topic X LLM synthesises compiled_truth from latest errors
amem wiki-link <from> <to> Add cross-reference between topics
amem wiki-backlinks <topic> Show all pages linking to this topic
amem wiki-lint Detect orphans, stale truths, missing links
amem ingest <file> --type ... Structured ingest of PR / meeting / decision records

Code layout:

agents_memory/
├── app.py            # CLI entry point
├── mcp_app.py        # MCP Server entry point
├── runtime.py        # AppContext, path resolution
├── commands/         # CLI dispatch → services
├── services/         # Business logic (records, search, wiki, planning, …)
├── integrations/     # Agent adapters (GitHub Copilot, ChatGPT, Claude)
├── web/              # FastAPI REST API backend (port 10100)
└── frontend/         # React SPA frontend (port 10000)

Workflow

connect project
  -> amem bootstrap .          # register + profile + MCP + doctor
  -> amem start-task "<task>"  # spec + plan + task graph + validation bundle
  -> implement with shared standards
  -> amem validate .           # docs + profile + plan + tests + complexity
  -> amem close-task .         # gate + bundle close + onboarding state update
  -> amem promote-learning .   # promote errors / good practices to global defaults

Quick Start

git clone https://github.com/haochencheng/Agents-Memory.git
cd Agents-Memory
python3 -m pip install -e .

# Verify installation
amem bootstrap . --dry-run
amem validate .

# Run tests
python3.12 -m unittest discover -s tests -p 'test_*.py'

# Start web UI (optional)
bash scripts/web-start.sh restart   # FastAPI :10100, React :10000
bash scripts/web/manage.sh --env staging restart
bash scripts/runtime/manage.sh --env prod config
bash scripts/local/web.sh restart

Trust Signals

  1. CI is public and mirrors the local install, compile, test, and docs-check path.
  2. CI is split into tests and docs jobs for clearer branch protection.
  3. CHANGELOG.md and docs/ops/release-checklist.md govern public release execution.
  4. SECURITY.md, SUPPORT.md, and CONTRIBUTING.md define collaboration paths up front.

Documentation

Doc Purpose
docs/guides/getting-started.md Local install, startup, and baseline verification
docs/guides/integration.md How another repo integrates Agents-Memory
docs/guides/commands.md CLI command map and parameter reference
docs/ops/runbook.md Operations, recovery, and troubleshooting
docs/architecture/overview.md Repo-level ADRs and technical decisions
docs/architecture/modular.md Code layering, agent adapters, extension points
docs/product/ai-engineering-operating-system.md Full product baseline and implementation status

Full documentation map: docs/README.md

Open-Source Boundary

Public repository content is limited to code, templates, standards, profiles, and docs. The following are local runtime artifacts and should not be committed:

index.md
memory/projects.md
memory/rules.md
errors/*.md
.vscode/mcp.json
logs/
vectors/

The repository uses public examples under templates/ so real project context, private paths, and runtime data do not leak into the open-source tree.

Contributing

Before shipping behavior changes, update code, matching docs, and matching tests or validation scripts together.

Contribution guidance lives in CONTRIBUTING.md. Pull requests should follow PULL_REQUEST_TEMPLATE.md. Community expectations live in CODE_OF_CONDUCT.md. Security issues should follow SECURITY.md. Support paths are listed in SUPPORT.md. Releases should update CHANGELOG.md and docs/ops/release-checklist.md.

Back to top


简体中文

Agents-Memory 是面向 AI coding agents 的 Shared Engineering Brain。它把工程记忆、工程标准、planning bundle 和 validation gate 收敛成可复用的共享运行层,服务真实仓库而不是零散 prompt 片段。

为什么团队会安装 Agents-Memory

团队记忆沉淀 工作流约束 更稳的交付
记录重复错误、升级规则,把工程上下文跨仓库复用。 把自由需求收敛成 bootstrap、planning bundle 和 validation-first 执行路径。 用 docs-check、profile-check、plan-check、CI 和 release discipline 约束交付。

系统架构全图

%%{init: {"theme": "default", "flowchart": {"nodeSpacing": 80, "rankSpacing": 100}} }%%
flowchart LR
  U[用户 / 产品团队] --> B[amem bootstrap]
  U --> T[amem start-task]
  A[AI Agent] --> S1[index.md]
  A --> S2[standards 工程标准]
  A --> S3[profile 受管 instructions]
  A --> S4[onboarding-state.json]
  A --> S5[task bundle 任务工件]

  B --> R[项目注册]
  B --> P[Profile 安装 / 刷新]
  B --> I[Bridge 与 MCP 安装]
  B --> D[Doctor 与 Onboarding 导出]

  P --> PM[profile-manifest.json]
  D --> OS[.agents-memory/onboarding-state.json]
  D --> BC[docs/plans/bootstrap-checklist.md]
  D --> RW[docs/plans/refactor-watch.md]

  T --> TB[docs/plans/task-slug/]
  TB --> SPEC[spec.md]
  TB --> PLAN[plan.md]
  TB --> GRAPH[task-graph.md]
  TB --> VAL[validation.md]

  A --> NX[amem do-next]
  NX --> OS
  NX --> TB

  A --> V[amem validate]
  V --> DC[docs-check]
  V --> PC[profile-check]
  V --> PLC[plan-check]
  V --> TG[focused test gate]
  V --> CG[complexity gate]

  A --> C[amem close-task]
  C --> BG[bundle exit gate]
  C --> GG[global validate gate]
  BG --> TB
  GG --> OS

  A --> L[amem promote-learning]
  L --> ERR[errors 错误记录]
  L --> RULES[memory/rules.md]
  L --> STD[standards 工程标准]
  L --> VG[validation rules]
  L --> EVAL[workflow & eval tests]

  classDef user fill:#dbeafe,stroke:#3b82f6,color:#1e3a8a
  classDef agent fill:#ede9fe,stroke:#7c3aed,color:#2e1065
  classDef cmd fill:#d1fae5,stroke:#059669,color:#064e3b
  classDef file fill:#fef3c7,stroke:#d97706,color:#78350f
  classDef gate fill:#fee2e2,stroke:#dc2626,color:#7f1d1d

  class U user
  class A agent
  class B,T,NX,V,C,L cmd
  class S1,S2,S3,S4,S5,PM,OS,BC,RW,TB,SPEC,PLAN,GRAPH,VAL,ERR,RULES,STD,VG,EVAL file
  class R,P,I,D,DC,PC,PLC,TG,CG,BG,GG gate
Loading

五层核心能力:

层级 职责
Memory 三层分级记忆(热 / 温 / 冷);关键词 + 向量混合搜索
Standards 共享 Python、docs、planning、validation 标准;profile 受管 instructions
Planning spec → plan → task graph → validation bundle;start-task / do-next / close-task
Validation 统一交付门禁:docs-check、profile-check、plan-check、test gate、complexity gate
Learning Bus 把错误和最佳实践升级为跨项目 standards、validation rules、eval cases

知识层(Wiki)架构

Memory 层内置结构化 Wiki 知识图谱,是对上方系统架构图中 Memory 层的纵向展开,不是独立系统

flowchart TD
  subgraph Sources["输入源"]
    E[errors/*.md 错误记录]
    PR[PR review / 会议记录]
    DEC[决策记录]
  end

  subgraph Ingest["Ingest 流水线"]
    ING["amem ingest --type pr-review / decision / meeting"]
    WC[amem wiki-compile --topic X --scope errors]
  end

  subgraph Wiki["memory/wiki/*.md — 知识图谱"]
    WP["Wiki 页面: topic / compiled_truth / timeline"]
  end

  subgraph Search["混合搜索"]
    FTS[FTS 全文索引]
    VEC[向量索引 / LanceDB]
    HS["amem hybrid-search: fts x0.4 + vec x0.6"]
  end

  subgraph Lint["Wiki 健康检查"]
    L1[孤岛页面检测]
    L2[过期结论检测]
    L3[缺失交叉引用检测]
  end

  E --> ING
  PR --> ING
  DEC --> ING
  ING --> WC
  WC -- LLM合成 --> WP
  WP --> FTS
  WP --> VEC
  FTS --> HS
  VEC --> HS
  WP --> L1
  WP --> L2
  WP --> L3

  style Sources fill:#fef3c7,stroke:#d97706
  style Ingest fill:#d1fae5,stroke:#059669
  style Wiki fill:#ede9fe,stroke:#7c3aed
  style Search fill:#dbeafe,stroke:#3b82f6
  style Lint fill:#fee2e2,stroke:#dc2626
Loading

Wiki 页面结构(compiled_truth 结论区 + append-only 时间线):

---
topic: finance-safety
compiled_at: 2026-04-07
confidence: high
sources: [AME-001, AME-007]
links:
  - topic: smart-contract-errors
    context: "Reentrancy 与精度规则有重叠"
---

## 结论(Compiled Truth)        ← LLM 每次 wiki-compile 时整体重写
> 综合评估:链上金额运算必须用 Decimal,禁止使用 float。

## 已知 Pattern
- AME-001:USDT transfer 精度丢失

---

## 时间线                        ← 只追加,永不改写
- **2026-04-07** | wiki-compile — 从 7 条错误记录合成
- **2026-03-20** | AME-007 — 首次记录精度问题

Wiki 命令:

命令 作用
amem wiki-compile --topic X LLM 从最近错误合成 compiled_truth
amem wiki-link <from> <to> 建立 topic 之间的交叉引用
amem wiki-backlinks <topic> 查看哪些页面引用了当前 topic
amem wiki-lint 检测孤岛页、过期结论、缺失链接
amem ingest <file> --type ... 结构化导入 PR / 会议记录 / 决策文档

代码分层:

agents_memory/
├── app.py            # CLI 总入口
├── mcp_app.py        # MCP Server 总入口
├── runtime.py        # AppContext,路径解析
├── commands/         # CLI 分发 → services
├── services/         # 业务逻辑(records、search、wiki、planning 等)
├── integrations/     # Agent adapters(GitHub Copilot、ChatGPT、Claude)
├── web/              # FastAPI REST API 后端(端口 10100)
└── frontend/         # React SPA 前端(端口 10000)

工作流

连接项目
  -> amem bootstrap .          # 注册 + profile + MCP + doctor
  -> amem start-task "<task>"  # spec + plan + task graph + validation bundle
  -> 按共享标准实现
  -> amem validate .           # docs + profile + plan + tests + complexity
  -> amem close-task .         # gate + bundle 关闭 + onboarding state 更新
  -> amem promote-learning .   # 把错误 / 最佳实践升级到全局默认

快速开始

git clone https://github.com/haochencheng/Agents-Memory.git
cd Agents-Memory
python3 -m pip install -e .

# 验证安装
amem bootstrap . --dry-run
amem validate .

# 运行测试
python3.12 -m unittest discover -s tests -p 'test_*.py'

# 启动 Web UI(可选)
bash scripts/web-start.sh restart   # FastAPI :10100,React :10000
bash scripts/web/manage.sh --env staging restart
bash scripts/runtime/manage.sh --env prod config
bash scripts/local/web.sh restart

可信信号

  1. CI 是公开的,执行的就是本地会跑的安装、编译、测试和 docs-check
  2. CI 已拆成独立的 testsdocs jobs,便于 branch protection 单独要求通过。
  3. CHANGELOG.mddocs/ops/release-checklist.md 共同约束公开发版流程。
  4. SECURITY.mdSUPPORT.mdCONTRIBUTING.md 让协作路径在首页即可追溯。

文档入口

文档 说明
docs/guides/getting-started.md 首次安装、启动、基础验证
docs/guides/integration.md 目标项目如何接入 Agents-Memory
docs/guides/commands.md CLI 命令总表与参数参考
docs/ops/runbook.md 日常运维、恢复与排障
docs/architecture/overview.md 仓库级 ADR 与技术决策
docs/architecture/modular.md 代码分层、agent adapter 扩展点
docs/product/ai-engineering-operating-system.md 完整产品基线与实施状态矩阵

完整文档地图见 docs/README.md

最新架构设计见 docs/product/ai-engineering-operating-system.md。 安装与启动细节见 docs/guides/getting-started.md。 接入其他项目见 docs/guides/integration.md。

公开仓库只包含代码、模板、标准、profiles 和文档。以下内容属于本地运行数据,默认不应提交

index.md
memory/projects.md
memory/rules.md
errors/*.md
.vscode/mcp.json
logs/
vectors/

仓库使用 templates/ 下的公开样例初始化本地文件,以避免把真实项目上下文、私有路径或运行时数据带进开源仓库。

贡献

提交行为变更前,请同步更新代码、对应文档以及对应测试或验证脚本。

贡献说明见 CONTRIBUTING.md。提交合并请求时请按 PULL_REQUEST_TEMPLATE.md 补齐验证信息;协作行为遵循 CODE_OF_CONDUCT.md;安全问题请按 SECURITY.md 私下报告;支持入口见 SUPPORT.md;发布时同步维护 CHANGELOG.mddocs/ops/release-checklist.md

返回顶部


License / 许可证

This project is released under the MIT License.

本项目采用 MIT License

About

Shared Engineering Brain

Resources

License

Code of conduct

Contributing

Security policy

Stars

2 stars

Watchers

1 watching

Forks

Releases

No releases published

Packages

 
 
 

Contributors