TeemLush
Keep useful knowledge across AI sessions and teams.
TeemLush stores organizational knowledge outside any one conversation. Authorized agents can save validated lessons and reusable task methods in a private knowledge and memory layer, then retrieve, check and improve them in later sessions or across teams.
- Persistent agent memory
- Shared team knowledge
- Agent-led maintenance
What knowledge is worth carrying into the next session?
An agent may spend a task identifying a custom field, reconciling conflicting instructions, or finding why a report produces the wrong result. The useful outcome includes the knowledge of how to approach that work again.
TeemLush lets agents turn those findings into reusable organizational knowledge with evidence, scope and conditions. Later agents can start by searching what the organization has already learned.
System conventions
Which record, field, dictionary or workflow applies in a particular application account.
Verified task methods
A useful procedure, the evidence behind it, the circumstances where it applies and the checks required before reuse.
Lessons and exceptions
An error cause, a confirmed workaround, an important exclusion or a distinction that changed how the task should be performed.
Who builds and maintains the memory?
AI agents are the primary readers, writers and maintainers. They research, work and learn, then propose knowledge, validate it, organize it at the appropriate scope and improve it as new evidence appears.
The organization owns the private content. People retain administration, approval and oversight. Authorized people and agents can also supplement captured application context with terminology, policies and explanations that the source system does not contain.
How can another agent reuse a lesson without repeating a mistake?
Reuse begins with the business question and the authorized scope. The agent searches existing knowledge, reads the full relevant sources, and checks the account, source date, application version assumptions and evidence before relying on a result.
When the system has changed or evidence conflicts, the agent checks the relevant live source and updates the knowledge accordingly. Shared methods and account-specific observations stay in the appropriate collections. An observation from one task retains its limits when reused.
Research and propose
Identify useful findings from work and compare them with existing knowledge.
Validate and save
Check evidence, resolve conflicts and preserve the conditions under which the lesson applies.
Retrieve and recheck
Bring the lesson into a later task and verify that its scope and freshness still fit.
Improve and maintain
Correct errors, consolidate overlapping knowledge and retire guidance that has become stale.
What does existing task knowledge look like in practice?
In a recorded September 2026 NetSuite interaction, FleeceTide retrieved existing time-reporting guidance from TeemLush. That knowledge identified the relevant labor-cost source, the distinction between actual and planned time, and a mismatch between record fields and SQL query columns.
The example illustrates the kind of practical knowledge an agent can retrieve during later work. It shows access to existing knowledge; the new memory proposals recorded in that interaction remained unsaved. The cross-session and team workflow described here is the product’s broader capability.
Where does the shared memory live?
Agent-built knowledge and memory share one private TeemLush host with your automatically captured application context. Choose a TeemLush-hosted private cloud or an on-premise deployment. Both private layers require a software subscription license.
Compatible agents connect to that host through CLI, MCP or REST. The host also accesses ConstaVitality’s expert library through the library subscription, bringing supplied expertise, your application context and agent-built knowledge into one access point.
TeemLush team memory: key facts
- Primary operators
- Authorized AI agents read, write, validate, organize and improve knowledge.
- Ownership
- The organization owns private content; people retain administration, approval and oversight.
- Access
- CLI, MCP and REST, scoped to the relevant organization, application and account.
- Subscriptions
- Software subscription license for private context and memory; library subscription for expert knowledge. Exact pricing: [email protected].
FAQ
Does TeemLush automatically save every AI conversation?
TeemLush’s knowledge workflow centers on useful, validated knowledge. Agents select findings, check them against evidence and existing entries, and save them at the appropriate scope. A proposed memory may remain unsaved.
Can different agents and teams use the same knowledge?
Yes. Authorized compatible agents can use the organization’s shared TeemLush knowledge through CLI, MCP or REST. Retrieval and maintenance respect the relevant organizational, application and account scopes.
What happens when a saved lesson becomes outdated?
Agents check source dates, application assumptions and current evidence during reuse. Conflicts are resolved against the relevant live source, and agents can correct, refresh or retire guidance so later tasks receive useful context.
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