io.github.sralli/agendum

Official

by sralli · Python

Agendum

AI agent memory: task tracking, scoped work packages, decisions, and cross-session continuity

Tracks tasks, work packages, and decisions with memory persistence across sessions.

Topics

ai-agentsclimcpmodel-context-protocolproject-managementpython

README.md10 KBView on GitHub

agendum

Project memory and scoping engine for AI coding agents.

AI coding agents are stateless — they forget between sessions, lose decisions, and have no way to scope complex work. agendum is an MCP server that gives any agent (Claude Code, Cursor, Windsurf, Cline, and others) persistent project state, bounded work packages, and cross-session continuity.

Without agendum	With agendum
Agent forgets everything between sessions	Picks up exactly where it left off
No scope — agent modifies random files	Bounded work packages with file lists, acceptance criteria, constraints
Decisions lost — same mistakes repeated	Decisions and patterns persist in searchable memory
No task ordering — agent picks randomly	Dependency graph with auto-unblocking and priority scoring
Learning locked inside one project	Cross-project learnings carry patterns forward

Quick Start

bash

pip install agendum                                    # or: uvx agendum
claude mcp add agendum -- uvx agendum --home serve     # add to Claude Code
# Done. pm_* tools are now available in your agent.

Works with any MCP client — see setup for Cursor, Windsurf, VS Code, and others.

How It Works

mermaid

flowchart LR
    A["PLAN\nwrite plan file"] --> B["pm_ingest\nboard items + deps"]
    B --> C["pm_next\nwork package + context"]
    C --> D["EXECUTE\nagent implements"]
    D --> E["pm_done\ndecisions + patterns"]
    E -->|next task| C
    E -->|new session| F["pm_status\nresume context"]
    F --> C

Each pm_done records decisions and patterns that enrich future pm_next calls — context compounds across sessions.

Example session:

code

You: I have a plan file for the API rewrite. Ingest it.

Agent:
  → pm_ingest(project="api-rewrite", plan_file="plan.md")

  Ingested 4 board items from plan:
    item-001: Schema design [high]
    item-002: Resolver layer (depends on item-001)
    item-003: Auth middleware (depends on item-001)
    item-004: Integration tests (depends on item-002, item-003)

You: What should I work on?

Agent:
  → pm_next(project="api-rewrite")

  Work package for item-001 "Schema design":
    Context: project rules, memory from last session
    Scope: Define GraphQL schema types
    Acceptance criteria: Types for User, Product, Order

You: Done with the schema. Here's what I decided...

Agent:
  → pm_done(project="api-rewrite", item_id="item-001",
      decisions="Using code-first with Strawberry",
      patterns="N+1 queries need DataLoader",
      verified=True)

  Marked item-001 as done. Unblocked: item-002, item-003
  > Next: pm_next("api-rewrite") to continue with newly unblocked tasks

14 MCP Tools

Setup & Orientation

Tool	Purpose
`pm_init`	Initialize board directory (optional — auto-initializes on first use)
`pm_project`	Create, list, or get projects
`pm_status`	Dashboard — item counts, recent progress, memory health, suggested next task

Planning & Backlog

Tool	Purpose
`pm_add`	Add an item with type, priority, tags, dependencies, acceptance criteria
`pm_board`	View and filter the project board
`pm_ingest`	Import a Markdown plan file into bounded board items with dependencies

Execution Loop

Tool	Purpose
`pm_next`	Get the next scoped work package with complexity signal and enriched context
`pm_done`	Complete an item — record decisions, patterns, learnings; auto-extract from git; auto-unblock dependents
`pm_block`	Report a task as blocked with reason

Knowledge & Search

Tool	Purpose
`pm_memory`	Read, write, append, or search project memory (decisions, patterns, project knowledge)
`pm_learn`	Record global or project-scoped learnings with tags and topic entities
`pm_search`	Hybrid search across all knowledge — memory, learnings, completed items
`pm_consolidate`	Clean memory corruption, deduplicate learnings, detect contradictions
`pm_supersede`	Soft-invalidate a learning — excluded from all future searches

Hybrid Search

pm_search combines three signals to find relevant knowledge across memory, learnings, and completed board items:

FTS5 with Porter stemming — auth matches authentication, config matches configuration. Always on, zero config.
Vector search (optional) — Install agendum[vectors] to add semantic similarity via fastembed + sqlite-vec. Activates automatically alongside FTS5.
Entity graph — Topics and tags form a knowledge graph. Entries sharing 2+ entities are linked automatically. Graph expansion surfaces related knowledge that keyword search misses.

All three signals are fused via Reciprocal Rank Fusion (RRF), then reranked by recency and access frequency. The index rebuilds from Markdown files — no data loss if it gets corrupted.

Key Capabilities

Adaptive context budget — enrichment scales with task complexity: 4K chars for trivial tasks, up to 10K for large ones
Verification gate — pm_done(verified=True) distinguishes tested from untested completions
Git auto-extract — pm_done reads git diff and git log automatically when no files are specified
Pluggable enrichment pipeline — four context sources injected into every work package: project rules (CLAUDE.md/AGENTS.md), memory, dependency context, learnings
Dependency resolution — topological ordering with cycle detection; dependents unblock automatically when upstream tasks complete
Memory health — pm_status warns about corrupted entries; pm_consolidate strips XML fragments, deduplicates, and flags contradictions
Zero config — auto-initializes on first tool call, derives board name from git remote
Git-native storage — all state is human-readable Markdown + YAML in .agendum/, diffable and committable

Installation

All MCP clients except VS Code use the same config. Add to the appropriate file:

json

{
  "mcpServers": {
    "agendum": {
      "command": "uvx",
      "args": ["agendum", "--home", "serve"]
    }
  }
}

Client	Config location
Claude Code	Run: `claude mcp add agendum -- uvx agendum --home serve`
Cursor	`.cursor/mcp.json` in project root
Windsurf	`~/.codeium/windsurf/mcp_config.json`
Cline	Settings › MCP Servers › Edit
Roo Code	MCP settings file
Claude Desktop	`claude_desktop_config.json`

VS Code (GitHub Copilot): Uses "servers" instead of "mcpServers". Add to .vscode/mcp.json:
json
{
  "servers": {
    "agendum": {
      "command": "uvx",
      "args": ["agendum", "--home", "serve"]
    }
  }
}

CLI (standalone)

bash

pip install agendum
agendum project create my-app   # Create a project
agendum status                  # Dashboard overview
agendum next my-app             # Suggest next task

Storage Layout

All state lives in ~/.agendum/ (or .agendum/ in your project if you prefer local storage):

code

~/.agendum/
├── .cache/
│   └── search.db               # FTS5 + vector search index (auto-rebuilt)
├── config.yaml
├── projects/
│   └── webapp/
│       ├── project.yaml         # Project metadata
│       ├── board/
│       │   ├── item-001.md      # Markdown + YAML frontmatter
│       │   └── item-002.md
│       └── learnings/           # Project-scoped learnings
│           └── learning-001.md
├── learnings/                   # Cross-project learnings
│   └── learning-001.md
└── memory/
    ├── decisions.md             # Key decisions + rationale
    └── patterns.md              # Discovered conventions

Architecture

code

src/agendum/
├── server.py              # MCP server wiring (FastMCP)
├── tools.py               # 14 MCP tools
├── models.py              # Pydantic models (BoardItem, WorkPackage, SearchResult)
├── task_graph.py          # Dependency resolution + topological levels
├── config.py              # Shared configuration
├── env_context.py         # Git diff/log auto-extraction
├── cli.py                 # CLI interface
├── enrichment/
│   ├── pipeline.py        # ContextEnricher, budget allocation
│   └── sources.py         # ProjectRules, Memory, Dependency, Learnings sources
└── store/
    ├── board_store.py     # BoardItem CRUD
    ├── board_format.py    # Markdown <-> BoardItem serialization
    ├── project_store.py   # Project metadata
    ├── memory_store.py    # Scoped memory storage
    ├── learnings_store.py # Global and project-scoped learnings
    ├── search_index.py    # FTS5 + vector + entity graph + RRF
    ├── embedding.py       # Lazy fastembed wrapper (optional)
    └── locking.py         # get_lock() + atomic_write()

Development

bash

git clone https://github.com/sralli/agendum.git
cd agendum
uv sync
uv run pytest tests/ -v     # all tests
uv run ruff check .          # lint
uv run ruff format --check . # format check

License

MIT

Install

claude_desktop_config.json

{
  "mcpServers": {
    "agendum": {
      "command": "uvx",
      "args": [
        "agendum"
      ]
    }
  }
}