Two lists, individual capacities

Mentor Mentee MatchingGenerator

Give each learner one mentor while respecting different mentoring loads and excluded pairs. Review the complete proposal before confirming relationships.

Match mentors and mentees Read the guide

Free • Local processing • No account

Adult mentor reading with three learners on mint stools in a small clay library alcove

Prepare your mentoring allocation

Enter separate lists, set each mentor’s capacity and exclude any unsuitable pairs.

Mentees and mentors

One learner per line. 1–200 learners; 120 characters per name; 30,000 input characters. Duplicate names keep separate IDs.

One mentor | capacity per line, or a tab. 1–40 mentors; 0–200 places each; 120 characters per name; 30,000 input characters. Zero means no new learners.

UTF-8 TXT up to 120,000 bytes per file. Editing or importing either list clears exclusions and the previous proposal.

Mentee and mentor ID reference
  • Mentee 1: Alex
  • Mentee 2: Blair
  • Mentee 3: Casey
  • Mentee 4: Drew
  • Mentor 1: Morgan
  • Mentor 2: Jordan
  • Mentor 3: Taylor
4
Mentees
—
Assigned / total
—
Unmatched

One mentee ID, mentor ID per line, e.g. 1,2. Chinese comma accepted. No line means no exclusion; repeated pairs and unknown IDs are rejected. 30,000 characters maximum.

Maximize assigned learners within capacities and exclusions, with random alternatives. No skill scoring, equal-load guarantee or uniform-matching guarantee.

Four learners, three mentors, four available places. Sample exclusion: mentee 1 cannot match mentor 2.

Matches by mentee

Generate to see each learner’s mentor, unmatched learners and remaining capacity.

Lists and exclusions stay in this tab and local worker without input uploads or persistent storage by this tool. Export before refreshing.

Connect two different participant pools

Capacity is a limit you set, not a measure of mentoring quality.

One mentor can support several mentees

A mentor mentee matching generator connects a learner roster to a separate mentor roster. Each learner can receive at most one mentor, while every mentor has an individual limit on how many learners they can accept. This supports a training cohort, an onboarding program or a volunteer mentoring round after the organizer has checked eligibility. Ordinary random pairs put everybody in the same pool, so they cannot express this arrangement accurately. Here the two roles stay separate throughout the result, the capacity review and the exported records.

Try the mentoring sample
Three mentor portrait tokens connected to different numbers of learner tokens on an ivory tabletop

Review unused places alongside unmatched learners

A full headcount does not always mean enough usable places exist. Several learners might be restricted to the same mentor, while another mentor has unused capacity. The result therefore includes both directions: each learner’s proposed mentor, and each mentor’s learner list with remaining places. Excluded pairs remain forbidden even when this leaves someone unmatched. Read that gap as a planning problem to resolve with the participants. A second random draw can change who gets a scarce place, but cannot make the maximum possible coverage exceed the limits you entered.

Review mentor capacity
Two coordinators review a mentoring binder and portrait folders beside an empty waiting chair

How to match mentors and mentees

Start with agreed availability, then review the proposal.

  1. 01

    Enter two rosters and realistic limits

    Put one learner on each line. Enter mentors as name | capacity, with either a pipe or a tab separating the two fields. Capacity is required and is a whole number from zero to two hundred; zero temporarily keeps a mentor in the list without accepting learners. There can be one to two hundred learners and one to forty mentors. Names allow up to one hundred twenty characters. Each text field allows thirty thousand characters, and separate UTF-8 TXT imports use the same formats with a limit of one hundred twenty thousand bytes per file.

  2. 02

    Exclude unsuitable pairs by their IDs

    Open the ID reference and write one learner ID, mentor ID per line. For example, 1,2 prevents learner one from receiving mentor two. A Chinese comma is also accepted. The two number spaces are independent, so learner one and mentor one are not assumed to be the same real person. Unknown IDs and repeated exclusion lines are errors. Blank lines are ignored, and identical names keep separate IDs. Editing or importing either roster clears the exclusions so an old number cannot silently refer to a different person.

  3. 03

    Generate, inspect and export both views

    Generate the assignment and inspect the assigned total, unmatched count and every mentor’s load. The calculation runs in a local worker so you can cancel it. An input change cancels pending work and clears the previous proposal. Copy and print include both the learner view and mentor rosters. CSV contains one learner record for every learner, including unmatched learners, followed by one capacity summary for every mentor. Use the row-type column when filtering the spreadsheet so capacity summaries are not mistaken for additional matches.

Rules the allocation actually enforces

Useful limits are visible in the result.

Maximum coverage with hard restrictions

The calculation finds the largest number of learners that can be assigned using the permitted connections and individual mentor capacities. It can move an earlier tentative pairing to free a place for somebody with fewer options. This avoids the avoidable gaps that a simple first-come shuffle can create. If the final result is partial, no additional learner can be included without changing the existing rules or rearranging within the same maximum total. No excluded pair is used as a fallback, and nobody is counted twice.

Random alternatives, without a compatibility score

Secure browser randomness varies the costs of permitted pairings within the maximum-coverage calculation. This produces alternative proposals; it does not give every feasible complete assignment an equal probability. The tool does not optimize equal mentor workloads or matching percentages. It does not collect expertise, interests, schedule availability, consent or relationship history. If those matters affect eligibility, review them beforehand and exclude unsuitable pairs explicitly. A generated assignment is an administrative proposal for people to review, not a recommendation about personal suitability.

Local calculation and complete records

This tool keeps the lists, exclusions and proposal in the current browser tab and its local worker. It does not upload that input, save a persistent program history or send invitations. Refreshing or leaving loses the in-memory state. Export an accepted proposal before doing so, and handle the downloaded file according to your organization’s ordinary roster practices. Copy, CSV and print include all records even when the on-screen lists are scrollable. Exporting a name does not contact its owner or confirm acceptance.

Example: four learners and three mentors

The sample has enough capacity with one restricted connection.

  1. 01

    Try a complete cohort

    Use Alex, Blair, Casey and Drew as learners. Add Morgan with capacity two, Jordan with capacity one and Taylor with capacity one. Exclude 1,2, meaning Alex cannot be assigned to Jordan. All four learners can still receive one mentor, and the total assigned count is four out of four. Morgan receives two learners and the other mentors receive one each because all four available places are needed. The exact learner names under each mentor can change on a new draw; Alex never appears under Jordan.

  2. 02

    Create a bottleneck deliberately

    Replace the mentor list with Morgan | 1 and Jordan | 3. Exclude mentor two for all four learners with lines 1,2 through 4,2. Only one learner can receive Morgan, while three remain unmatched and Jordan retains three empty places. The total capacity is four, but only one place is reachable through permitted connections. Raising Jordan’s capacity again cannot solve this restriction. Review whether the exclusions are correct, expand the eligible mentor pool, or increase Morgan’s actual agreed capacity before regenerating.

Make a usable mentoring proposal

Keep the human review connected to the allocation.

  1. 01

    Prepare a training or onboarding round

    Use capacity to express the number of new learners a mentor can accept in this round, after subtracting commitments you already manage elsewhere. The page does not know existing relationships. If a mentor already supports two people and can support three in total, enter one available place for this new cohort. Give repeated names a team label or other appropriate identifier so the exported proposal is understandable. Avoid including unnecessary personal information in a name field; the allocation uses IDs and capacities rather than the meaning of the text.

  2. 02

    Investigate the right kind of shortage

    An unmatched learner with no permitted active mentor is labeled as having no available options. Other unmatched learners are labeled as facing a capacity bottleneck among allowed mentors. These are practical explanations, not a smallest-conflict diagnosis or a first-come waiting queue. The unmatched list has no priority ordering and does not automatically promote people later. If participants withdraw or capacity changes, update the inputs and run a fresh allocation; existing assignments are not locked or remembered across runs.

  3. 03

    Choose the next tool for the next task

    Use the random pair generator when everyone has the same role and you need ordinary pairs. Use workshop session assignment when each person needs several distinct sessions with seat limits and ranked choices. For mentoring, keep this proposal as a separate step: verify willingness, professional requirements, contact details and meeting arrangements through your established process. The generated roster has no calendar, notification channel or approval workflow. Those boundaries help the coordinator explain exactly what was decided by the tool and what still needs agreement.

Mentor matching questions

Practical answers about loads, exclusions and incomplete cohorts.

Can one mentor be assigned several mentees?

Yes. Give each mentor an individual whole-number capacity from zero to two hundred. Each mentee receives at most one mentor, while a mentor can receive several mentees up to that capacity. Enter available places for this round, not a total that already includes relationships outside this list.

How do I prevent a particular mentor and mentee from matching?

Use their role-specific IDs in a learner ID, mentor ID exclusion line. Exclusions are hard restrictions and never become fallback choices. The number order matters. Names shared between the two lists are not automatically recognized as one person, so exclude any unwanted self-pair explicitly.

Why are mentees unmatched when a mentor still has space?

The spare places may belong to mentors those learners are excluded from. Maximum coverage depends on allowed connections as well as total capacity. Inspect the unmatched explanations and the mentor view together. Repeating the draw can change the chosen people, but cannot remove a capacity or eligibility bottleneck.

Does random mentor matching find the most compatible relationship?

No. This tool uses capacities and exclusions only. It does not evaluate qualifications, personalities, goals, consent or schedules. Prepare eligible pools and review the proposal with participants. Random alternatives are not a compatibility score, equal workload guarantee or uniform lottery across all possible assignments.

Can I keep a mentor on the roster with no new places?

Yes. A capacity of zero keeps the mentor in the reference and export while assigning no learners to that mentor. This is useful for someone temporarily unavailable. An all-zero roster produces a clearly partial result with every learner unmatched, rather than silently increasing anybody’s capacity.

Are duplicate names removed from my imported lists?

No. Duplicate names are retained as distinct numbered entries. Blank lines are ignored. Add an appropriate distinguishing label if two people share a name. Changing or importing a roster clears all exclusions and the result because the numbered references may have changed; check and re-enter exclusions afterward.

What is included when I export the matching results?

Copy and print include the learner mapping, unmatched reasons and every mentor’s roster with remaining capacity. CSV includes a learner row for each learner and a separate summary row for each mentor. Filter by row type when counting matches. No records are omitted because they are below a scroll boundary.

Will this tool remember matches or send invitations?

No. It calculates locally in the current tab and worker without persistent tool storage or input uploads. It does not contact participants, lock previous relationships or maintain a waiting queue. Export your reviewed proposal before refreshing, then handle introductions and acceptance through your normal program process.

Mentor Mentee Matching

Give your mentoring round a clear proposal

Set available places for each mentor.

Review every learner, including those still unmatched.

Prepare mentor matches Explore all tools
  • Free to use
  • No signup
  • Runs in your browser

A simple path to your next activity

  1. Prepare two pools

    Learners and mentors with available places.

  2. Exclude unsuitable pairs

    Use the role-specific ID reference.

  3. Review and share

    Check loads, unmatched learners and exports.

Confirm eligibility and willingness before putting the proposal into practice.