One discovery, every project smarter
A gotcha fixed in one repo should not be rediscovered in the next. TRW propagates validated learnings across your projects — so your third repo benefits from what your first two already learned.
Depth versus breadth
Memory compounds one project over sessions. Sharing extends that compound across every sibling project. Complementary, not the same thing.
Knowledge scoped to one context window does not compound
By default, AI-agent memory is scoped narrowly — to a single conversation, a single user, or a single project. A gotcha you solved in one repo stays locked there. The next repo rediscovers the same Pydantic v2 migration quirk, the same SQLAlchemy session-management trap, the same timezone-offset bug. Every rediscovery burns tokens, session time, and the trust the team places in the agent.
TRW lifts validated learnings out of a single project and replicates them into every sibling project that shares the same workspace. What one repo discovers, the rest inherit.
SCOPE
Org-wide network
Cross-validated entries surface in every sibling project session-start recall.
TRIGGER
Every trw_learn()
Deduplication and cross-validation run on each store call. No manual sync.
OUTCOME
0 rediscoveries
A solved gotcha stays solved across every future repo in the workspace.
How cross-validation works
When the same learning content is independently written in two or more projects with cosine similarity above 0.92, TRW marks both entries cross_validated: True and applies a +0.05 importance boost. High-importance cross-validated entries are surfaced in all sibling projects' session-start recall. See the trw_learn tool reference for call signature and parameters.
The standalone memory engine
Cross-project sharing is powered by trw-memory — a standalone Python package built on SQLite and sqlite-vec. It handles hybrid retrieval (BM25 + dense vectors), knowledge-graph traversal, semantic deduplication, and tiered storage lifecycle. Installable and usable independently of the rest of TRW.
pip install trw-memorySQLite + sqlite-vec. No external infrastructure. Works fully offline.
Note: this installs only the standalone memory engine. The full TRW framework installs via install.sh — see the quickstart.
- LangChain memory adapter
- LlamaIndex reader/writer
- CrewAI component
- OpenAI-compatible endpoint
The knowledge-sync tool
Manual sync across workspace projects. Run periodically by the trw-lead agent during the DELIVER phase. Syncs go through semantic deduplication — the same gotcha written many times across projects gets collapsed to a single canonical entry with evidence links attached. Full call signature in the trw_knowledge_sync tool reference.
trw_knowledge_sync clusters learnings by tag co-occurrence, deduplicates near-duplicates across namespaces, and writes canonical topic documents back to each project's memory store.
The numbers add up differently
Without cross-project sharing
With TRW cross-project sharing
What sharing does not do
No cloud sync by default — sharing is filesystem-level within a local workspace. Remote sync via the TRW platform is opt-in.
No automatic sync across user account boundaries without the hosted platform.
Different embedding models across projects can confuse cosine matching. Stick to one model family per workspace for consistent deduplication.
Pairs with
Memory
Sharing is Memory applied across project boundaries. Single-project memory is the foundation; sharing is what lifts it into a network.
Agent teams
Teams usually own multiple repos. Cross-project sharing is where team-scale TRW really pays off.
Requirements
Cross-project PRDs — shared requirement templates — get auto-indexed into shared memory.
Common questions
Does cross-project sharing require the hosted platform?
What counts as the "same" learning?
Can I keep projects siloed?