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 market
Represent brands and products as first-class objects with positioning, features, and competitive relationships.
Connect sources
Ingest from Product Hunt, review sites, news sources, and social mentions through supported connectors and MCP proxying.
Brand and product memory comes from monitoring the channels where competitive signals appear.
| Type | Source | Added context |
|---|---|---|
| Product Hunt | Launch tracking | Monitor product launches, feature announcements, and upvotes for competitive positioning signals. |
| Crunchbase | Funding database | Pull funding rounds, investor syndicates, and valuation changes for company growth tracking. |
| Review sites | Customer feedback | Aggregate reviews and comparisons to understand how products are positioned against alternatives. |
| News & social | Market chatter | Track mentions, feature announcements, and strategic moves across news and social channels. |
Let users connect their data
Support OAuth for review platforms, RSS feeds for news, and API keys for private sources while keeping credentials scoped outside the agent runtime.
Connect product and brand data sources while keeping API keys outside the agent runtime.
| Access | System | How it works |
|---|---|---|
| API key | Premium databases | Store Crunchbase, PitchBook, or other research platform keys centrally for company and funding data. |
| RSS feeds | News and reviews | Pull industry news, blog coverage, and review updates through RSS without per-request auth. |
| Web scraping | Public websites | Monitor company blogs, changelogs, and pricing pages for product and positioning updates. |
| Agent boundary | Scoped access | The market agent receives extracted insights, not raw credentials or database dumps. |
Reuse context everywhere
Market intelligence powers competitive analysis agents in Slack, strategy tools, and MCP clients like OpenClaw, ChatGPT, and Claude.
The same brand and product memory powers competitive analysis wherever teams work.
Keep it fresh
Watchers turn new product launches, feature announcements, and market shifts into updated brand and product memory.
A scheduled watcher keeps this memory current as new source changes arrive.
{ new_features[], pricing_changes[], positioning_shifts[], competitive_mentions[] }Latest blog posts
Filesystem vs Database for Agent Memory
Agents need a workspace to think in and a warehouse to remember in. The filesystem is for ephemeral work. The memory layer is for durable organizational knowledge.
Add OpenClaw to your Next.js app in 10 minutes
Embed OpenClaw agents in a Next.js project with per-user sandboxing, MCP tools, and network isolation — no Docker required.
MCP Is Overengineered, Skills Are Too Primitive
MCP HTTP is great for external services. MCP stdio is redundant. And most skill systems are just prompt text with no reproducibility. Here's what we built instead.
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.