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 member graph
Represent members, companies, projects, repos, posts, topics, and introductions as linked objects so the community can remember who is building what and why they should meet.
Connect sources
Ingest GitHub, LinkedIn, newsletters, personal websites, and manual profile forms through MCP proxying, public feeds, and Connector SDK integrations.
Member context comes from the places people already publish work, identity, and intent.
| Type | Source | Added context |
|---|---|---|
| GitHub | Code and repo activity | Track maintained repositories, contribution patterns, and technical areas of focus from connected GitHub accounts. |
| Role and company context | Pull current title, company, and professional background to keep the member graph aligned with real-world changes. | |
| Newsletter / blog | Public writing and interests | Use Substack, RSS, and personal blogs to capture what members are actively thinking and writing about. |
| Profile import | Member-provided context | Collect explicit goals, interests, and who the member wants to meet through forms or manual imports. |
Let members connect their data
Use MCP login and OAuth for connected accounts, support RSS and public-site ingestion for newsletters and blogs, and allow manual profile imports without exposing credentials to agents.
Members connect accounts through MCP auth flows and operators can supplement that with public feeds or imports.
| Access | System | How it works |
|---|---|---|
| MCP login | Connected accounts | Use MCP/OAuth login for sources like GitHub and LinkedIn without exposing raw credentials to agents. |
| Public feeds | Websites and newsletters | Pull RSS, Substack, and public website content directly when a source does not require a private login. |
| Manual import | Profile setup | Let members or operators fill in a profile form or upload a structured import for goals, tags, and intro preferences. |
| Agent boundary | Scoped access | The community agent works with structured member context and approved workflows, not raw account credentials. |
Reuse context everywhere
The same member graph powers community concierge agents in Slack, internal dashboards, and MCP clients like OpenClaw, ChatGPT, and Claude.
The same member graph can power discovery, concierge, and intro workflows wherever the community already operates.
Keep it fresh
A scheduled watcher turns new launches, posts, project updates, and hiring signals into suggestions about which members might care and which warm introductions to draft next.
A scheduled watcher keeps this memory current as new source changes arrive.
{ signals:[{ type, source, related_topics[], interested_members[], reason, suggested_action }] }Latest blog posts
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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.