Turn data into shared, structured memory
Owletto gives all your agents the same durable graph: connectors, recall, and managed auth without leaking credentials to the runtime.
How it works
Turn scattered prompts, tools, and application data into a shared context layer your agents can use everywhere.
Model the world
Represent incidents, services, deploys, and pull requests as first-class objects so on-call context survives after the thread scrolls away.
Connect sources
Turn live operational signals into structured incident memory.
Turn live operational signals into structured incident memory.
| Entity | Events | Sources | Added context |
|---|---|---|---|
| Alerts | Triggered, resolved, severity changed | PagerDuty, Datadog | State, urgency, impact |
| Code | PRs, fixes, rollbacks | GitHub, GitLab | Change timeline |
| Deploys | Started, failed, rolled back | CI/CD, Argo, Kubernetes | Rollout history |
| Notes | Updates, handoffs, comments | Slack, incident tools | Decisions and context |
Let users connect their data
Let teams bring the tools they already use, while keeping credentials outside the worker.
Let teams bring the tools they already use, while keeping credentials outside the worker.
| Account | Brought by | Access | Used for |
|---|---|---|---|
| GitHub / GitLab | User | OAuth | PRs, commits, diffs |
| Slack / Linear / Notion | User | OAuth | Notes, tickets, team context |
| PagerDuty / Datadog | User or admin | OAuth / token | Alerts and incident state |
| AWS / GCP / Kubernetes | Org admin | Service account | Infra and deploy metadata |
| Incident history | Org | Import / sync | Memory bootstrap |
Reuse context across agents
The same incident memory powers operational agents wherever teams work.
The same incident memory powers operational agents wherever teams work.
Keep it fresh
Watchers pull in new alerts, deploy state, and merged fixes so the runtime sees the latest impact and rollback options.
A scheduled watcher keeps this memory current as new source changes arrive.
{ incident_state, impacted_regions[], rollback_ready, blocking_prs[] }Latest blog posts
Filesystem vs Database for Agent Memory
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Beats other memory systems on public benchmarks
Apples-to-apples comparison on public memory datasets. Same answerer (glm-5.1) and same questions.
LongMemEval (oracle-50)
Single-session knowledge retention.
| System | Overall | Answer | Retrieval | Latency |
|---|---|---|---|---|
| Lobu | 87.1% | 78.0% | 100.0% | 237ms |
| Supermemory | 69.1% | 56.0% | 96.6% | 702ms |
| Mem0 | 65.7% | 54.0% | 85.3% | 753ms |
LoCoMo-50
Multi-session conversational memory.
| System | Overall | Answer | Retrieval | Latency |
|---|---|---|---|---|
| Lobu | 57.8% | 38.0% | 79.5% | 121ms |
| Mem0 | 41.5% | 28.0% | 66.9% | 606ms |
| Supermemory | 23.2% | 14.0% | 36.5% | 532ms |
Start building shared memory
Model the right entities, connect your sources, and keep long-term context available across every agent workflow.