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  1. Home
  2. 🧠 Knowledge & Memory
  3. Agendum
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Agendum

User RatingsBe the first to rate and review this MCP server! Enrichment pendingWe haven’t run our AI enrichment pass on this listing yet, so the overview, use cases, and FAQ below may be sparse or missing. We work through the catalog over time β€” check back soon.
View Repository

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

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent β€” or use 1-click editor setup below.

Add to CursorAdd to VS Code
Manual Client & Custom JSON ConfigExpand JSON β–Ύ

Client Config & Setup

Choose your client or environment
Target File:~/Library/Application Support/Claude/claude_desktop_config.json
claude_desktop_config.json
{
  "mcpServers": {
    "agendum": {
      "command": "npx",
      "args": [
        "-y",
        "agendum"
      ]
    }
  }
}

πŸ’‘ Paste the JSON block into your client's configuration file under mcpServers, then restart the application.

Install Directory Badge Claim listing Alternatives🧠 More in Knowledge & Memory

Documentation Overview

agendum

PyPI version Downloads Python 3.13+ Tests License: MIT

Project memory and scoping engine for AI coding agents.

AI coding agents are stateless β€” 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 agendumWith agendum
Agent forgets everything between sessionsPicks up exactly where it left off
No scope β€” agent modifies random filesBounded work packages with file lists, acceptance criteria, constraints
Decisions lost β€” same mistakes repeatedDecisions and patterns persist in searchable memory
No task ordering β€” agent picks randomlyDependency graph with auto-unblocking and priority scoring
Learning locked inside one projectCross-project learnings carry patterns forward

Quick Start

Terminal
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 β€” 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 β€” context compounds across sessions.

Example session:

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

Agent:
  β†’ 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:
  β†’ 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:
  β†’ 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

ToolPurpose
pm_initInitialize board directory (optional β€” auto-initializes on first use)
pm_projectCreate, list, or get projects
pm_statusDashboard β€” item counts, recent progress, memory health, suggested next task

Planning & Backlog

ToolPurpose
pm_addAdd an item with type, priority, tags, dependencies, acceptance criteria
pm_boardView and filter the project board
pm_ingestImport a Markdown plan file into bounded board items with dependencies

Execution Loop

ToolPurpose
pm_nextGet the next scoped work package with complexity signal and enriched context
pm_doneComplete an item β€” record decisions, patterns, learnings; auto-extract from git; auto-unblock dependents
pm_blockReport a task as blocked with reason

Knowledge & Search

ToolPurpose
pm_memoryRead, write, append, or search project memory (decisions, patterns, project knowledge)
pm_learnRecord global or project-scoped learnings with tags and topic entities
pm_searchHybrid search across all knowledge β€” memory, learnings, completed items
pm_consolidateClean memory corruption, deduplicate learnings, detect contradictions
pm_supersedeSoft-invalidate a learning β€” 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 β€” auth matches authentication, config matches configuration. Always on, zero config.
  • Vector search (optional) β€” Install agendum[vectors] to add semantic similarity via fastembed + sqlite-vec. Activates automatically alongside FTS5.
  • Entity graph β€” 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 β€” no data loss if it gets corrupted.

Key Capabilities

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

config.json
{
  "mcpServers": {
    "agendum": {
      "command": "uvx",
      "args": ["agendum", "--home", "serve"]
    }
  }
}
ClientConfig location
Claude CodeRun: claude mcp add agendum -- uvx agendum --home serve
Cursor.cursor/mcp.json in project root
Windsurf~/.codeium/windsurf/mcp_config.json
ClineSettings β€Ί MCP Servers β€Ί Edit
Roo CodeMCP settings file
Claude Desktopclaude_desktop_config.json

VS Code (GitHub Copilot): Uses "servers" instead of "mcpServers". Add to .vscode/mcp.json:

config.json
{
  "servers": {
    "agendum": {
      "command": "uvx",
      "args": ["agendum", "--home", "serve"]
    }
  }
}

CLI (standalone)

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

Architecture

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

Read the full README β†’View source on GitHub β†’

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Reviews

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Frequently Asked Questions about Agendum

Add the following block to your claude_desktop_config.json under mcpServers: "mcpServers": { "agendum": { "command": "npx", "args": ["-y", "Agendum"] } }

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Technical Specs & Signals

Category🧠Knowledge & Memory
More technical detailsExpand β–Ύ
TransportSTDIO
RuntimeNode.js
Last updatedSep 7, 2026
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27Quality signal: Emerging Β· 27/100How this signal is calculated β–Ύ
Server availabilityNot measured

Not scored for repo-hosted servers β€” we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership8/20
Documentation & tools11/30
Adoption & activity1/15
Community engagement0/10

A guidance signal from public completeness & health data β€” not a user rating. New listings start lower and rise as they add docs, get verified, and grow adoption. Signals we can't observe for a listing are skipped, not counted against it.

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