Migrate to Tallyfy
You can move from many popular workflow and form tools into Tallyfy using our open-source migrator scripts. Start with a dry run, review the report, then run the full migration when you’re ready. The code lives here: Tallyfy Migrator on GitHub ↗[1].
The migrator runs a five-phase flow that matches how data moves from a source vendor into Tallyfy:
- Discovery: Connects to the source, catalogs available data, and gathers counts.
- Mapping: Translates vendor concepts to Tallyfy concepts (for example, project → template, task → step/task, custom field → form field).
- Transformation: Converts structures to Tallyfy format and prepares users, fields, and metadata.
- Migration: Creates data in Tallyfy. Supports dry-run and checkpoint resume.
- Validation: Verifies integrity and outputs a report you can keep.
-
Clone and install.
Terminal window git clone https://github.com/tallyfy/migrator.gitcd migratorpip install -r requirements.txt -
Configure environment variables.
Terminal window cp .env.template .env# Edit .env with: TALLYFY_API_KEY, TALLYFY_ORGANIZATION, and the source vendor's API keys -
Run a dry run first.
Terminal window # Example: Asanacd asana/srcpython main.py --dry-run --verbose -
Review the report. Confirm mappings, spot errors, and check rate-limiting notes. Adjust batch sizes or delays if needed.
-
Run the actual migration.
Terminal window python main.py -
Validate and spot-check. Use the generated report and sample records in Tallyfy. If anything fails mid-way, use
--resume
. -
(Optional) Enable AI-assisted mapping. Add
AI_ENABLED=true
and an AI API key in.env
to improve field and structure mapping for complex vendors.
Each folder below links directly to the open-source code for that vendor, with a quick summary of scope and common considerations.
-
- Scope: Projects, tasks/subtasks, sections, custom fields → map to Tallyfy templates, steps, and form fields.
- Caveats: Dependencies and sections may flatten or translate into conditional steps. Tags often become metadata. Respect API rate limits.
-
- Scope: To-dos and lists → procedure templates and steps; message docs are typically referenced via links.
- Caveats: Threads/comments are not 1:1 with Tallyfy task comments; attachments may be copied by URL.
-
- Scope: Lists/folders/spaces, tasks, custom fields.
- Caveats: Deep hierarchy is flattened into templates and steps. Use rules-based flattening; consider AI mapping for complex setups.
-
- Scope: Forms and submissions → kick-off forms and variables.
- Caveats: Field validation and conditional logic translate to Tallyfy where possible; verify complex branching.
-
- Scope: Questions and responses.
- Caveats: Simple structure migrates well; logic jumps may require manual checks. Low complexity.
-
- Scope: Forms, fields, and submissions.
- Caveats: Rich widgets may need custom mapping. Watch for attachment handling and rate limits.
-
- Scope: Workflows and forms.
- Caveats: Some constructs (like advanced states) may map to conditional steps or automations; review validation results carefully.
-
- Scope: Boards, groups, items, column types.
- Caveats: 30+ field types via GraphQL. Use AI for field mapping where possible. High complexity; test thoroughly.
-
- Scope: Processes and tasks.
- Caveats: Step logic and automations may need manual review. Confirm user-role mapping.
-
- Scope: Pipes, phases, fields.
- Caveats: Paradigm shift from card/connection model to templates/steps. Use AI for workflow conversion. High complexity.
-
- Scope: Checklists, tasks, conditional logic.
- Caveats: Complex conditions translate to Tallyfy automations. Medium complexity; validate branching.
-
- Scope: Project templates and tasks.
- Caveats: Timeline and resource views do not directly map; focus on steps and assignments.
-
- Scope: Boards, lists, cards → templates and steps.
- Caveats: Checklists and labels map to steps/metadata. Butler automations require manual recreation.
-
- Scope: Forms and responses.
- Caveats: Logic jumps translate where feasible. Low complexity overall.
-
- Scope: Projects/folders, tasks, custom fields.
- Caveats: Folder/project distinctions flatten into template/process structure. Validate dependencies and custom field mappings.
- Python 3.10+ and
pip
installed - Tallyfy credentials:
TALLYFY_API_KEY
andTALLYFY_ORGANIZATION
- Source vendor API credentials
- Sufficient disk space for logs and reports
- Always dry-run first. Review the migration report. Fix obvious mapping gaps before moving on.
- Mind rate limits. Tune
MIGRATION_RATE_LIMIT_DELAY
and batch sizes for large datasets. - Use checkpoints. For long runs,
--resume
saves time after transient failures. - AI assistance helps. For complex vendors (Monday.com, Pipefy), enable AI mapping to improve field translations.
- Verify outcomes. Spot-check templates, steps, users, and sample processes in Tallyfy after migration.
Migrated structures typically become Tallyfy templates, then you launch processes you can track as live processes with individual tasks. Use automations to recreate conditional behavior from the source system.
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