Skip to main content

🌟 Timeliner - AI's diary. MCP for tracking AI agent work with markdown log

Reason this release was yanked:

old

Project description

Timeliner

Auto-documenting memory layer for AI coding agents. Timeliner logs your AI development sessions as timestamped markdown files, giving AI agents perfect recall of past decisions, implementations, and context.

Feature Highlights

  • Zero-friction logging - AI agents auto-save work via /save command
  • Markdown storage - Human-readable logs with YAML frontmatter
  • MCP integration - Works with Claude Code, Cline, and other MCP-enabled tools
  • Timestamp-based IDs - Precise microsecond task IDs for reliable tracking
  • Rich context - Capture outcomes, tags, metadata (PRs, commits, issues)

Use Cases

Software Development:

  • Document bug fix decisions and attempted solutions
  • Track refactoring steps and rationale across sessions
  • Log feature implementation progress with context for handoffs
  • Record code review findings and resolution strategies

Research & Learning:

  • Maintain experiment logs with results and insights
  • Build a learning journal of concepts explored
  • Track hypothesis evolution in technical investigations
  • Document library/API exploration findings

Installation

Automatic Setup

uvx --from tliner tliner-install

Automatically configures .mcp.json and creates /save command in .claude/commands/. Documents wil be stored in docs/timeline/ by default. To specify a different storage folder, use uvx --from tliner tliner-install --work-folder <PATH_TO_STORE_DOCS>.

Manual Setup

Add to .mcp.json:

{
  "mcpServers": {
    "timeliner": {
      "type": "stdio",
      "command": "uvx",
      "args": ["tliner", "serve"],
      "env": {"TIMELINER_WORK_FOLDER": "${PWD}/docs/timeline"}
    }
  }
}

Create .claude/commands/save.md (see installer output for template).

---
description: "Save findings/outcomes into a Timeline"
---
# Save Command
Execute the save operation according to the next rules.
## Flow
1.  **Generate Content**:
    *   Generate the outcomes for the current step following the "Content Structure" and "Rules".
2.  **Save to Timeliner**:
    *   Call `mcp__timeliner__save_step` with the following parameters:
        *   `task_id`: Use the memorized `task_id` if you have one. If this is the first time saving for this task, send an **empty string** (`""`). The system will create a new task and return the new `task_id`.
        *   `title`: Up to 5 words which represent essence of the step.
        *   `outcomes`: The exact content that you just generated.
    *   **VERY IMPORTANT**: If a new `task_id` is returned, you MUST memorize it for all future `save_step` calls for this task.
    
## Content Structure

1. **Summary**: Describe current step summary and general flow of investigation.
2. **Facts**: Main goal is describing outcomes as facts with GREAT details (not only summary).
3. **User Input**: Note ALL user's input and direction they want to go.
4. **Resources**: Note ALL resources used (files, links, tools, commands, etc) with direct links (full path/URL/command).

## Rules
1. **Avoids**: NO hypothesis, NO assumptions, NO speculations, NO generalizations. Facts ONLY.
2. **Evidence**: Including evidences for statements is mandatory:
    - Link to source files with line numbers: `[cmd line flags](../src/go/flags.go#L94)`
    - Links to external resources: `[config docs](https://example.com/docs/setup.html)`
3. **Structure**: 
    - All main sections within the `outcomes` (e.g., Summary, Facts, User Input) MUST start with a level 2 heading (`##`). Do NOT use level 1 headings.
    - Fit all outcomes in ONE chapter, don't split into several chapters.
    - Use sub-sections inside the `Facts` chapter only. Every fact must be the level 3 heading (`###`). 
    - Do not use level 4 and higher headings. Use multi-level numerated/bullet lists instead ("outliner" style). 

Quick Start

Using the /save Command

  1. Run /save in your Agent Tool, when you feel you have made significant progress or decisions.
  2. File will be created/updated in your configured TIMELINER_WORK_FOLDER.

CLI Commands

For manual inspection and debugging:

# List all tasks
TIMELINER_WORK_FOLDER="${PWD}/docs/timeline" uvx tliner task-list

# Show all steps for a task
TIMELINER_WORK_FOLDER="${PWD}/docs/timeline" uvx tliner task-show <task_id>

# Run MCP server manually
TIMELINER_WORK_FOLDER="${PWD}/docs/timeline" uvx tliner serve

Data Structure

  • Location: specified in TIMELINER_WORK_FOLDER env var
  • Filename: YYYY_MM_DD-HHMMSS-kebab-case-title.md
  • Format: Markdown with YAML frontmatter

Configuration

Environment Variables

  • TIMELINER_WORK_FOLDER: Storage directory (default: work/docs)

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

tliner-0.1.5-py3-none-any.whl (17.2 kB view details)

Uploaded Python 3

File details

Details for the file tliner-0.1.5-py3-none-any.whl.

File metadata

  • Download URL: tliner-0.1.5-py3-none-any.whl
  • Upload date:
  • Size: 17.2 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: uv/0.8.22

File hashes

Hashes for tliner-0.1.5-py3-none-any.whl
Algorithm Hash digest
SHA256 5cd2c549e5fa359fbfff8dc32b990e6f96f9bde75f2f30da2520a410334bb8a7
MD5 4b99a2f93ab4ba568e4c9d180462d3e6
BLAKE2b-256 48b40b7b8d6681e8702c2b54886ce5bd89a5aeeefa2f7e0cc5c380bee8b350fd

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page