Legra is the memory artificial intelligence needs.

GitHub became the shared corpus for coding AI. Legra is that corpus for every other domain: a sovereign, versioned graph of business work that every session inherits and improves.

Map repository to workspace

The next productivity revolution is a corpus revolution.

Models read, write, reason, and call tools. Their impact compounds when the work improves a shared body of knowledge that later sessions trust and extend.

Read knowledge graphs for AI

From software to every domain

A workspace is the repository for business knowledge.

Code repositoryLegra workspace

Source, schemas, docs, and testsGraph knowledge, models, documents, media, policies, and tests

Issues and project contextUse Cases, Stories, tasks, conversations, and decisions

Branches and working treesProposals, experiments, agent work, and what-if analysis

Builds and continuous validationConstraints, rules, inference, tests, and validation

Review and mergeHuman and agent review before knowledge joins the corpus

Commit history and attributionVersion history, provenance, task trees, and evidence

Beyond retrieval

Retrieval finds a passage. The substrate runs the work.

The corpus preserves meaning, authority, change, and evidence across people, agents, applications, and organizations.

Shared meaning

Objects, concepts, rules, and Use Cases stay linked.

Enforceable authority

Identity, permissions, budgets, and review paths bound what an agent reads, spends, or changes.

Reviewable change

Agents propose on branches. Shared state changes after review.

Durable evidence

Inputs, actors, authority, decisions, costs, and results stay connected to the work.

One connected corpus

The graph holds every kind of content.

Structured knowledge and files stay organized around the objects, agreements, and Use Cases they concern. A conversation becomes reusable context.

Enterprise dataOntologies and taxonomiesRules and policiesUse Cases and StoriesTasks and decisionsDocuments and datasetsImages, audio, and videoMessages and emailHuman-agent conversationsTests and validationProvenance and historyRights and economic evidence

Explore the connected corpus

Perpetual learning machine

Every accepted contribution improves the starting point.

The learning lives in the corpus.

Graph, content, history, tests, and evidence accrue. Models and agent frameworks stay replaceable.

Put the corpus to work

Give the next session something worth inheriting.

One bounded workspace. One valuable Use Case. Each completed cycle improves the shared corpus.

Define the agent boundary