| What it does | Long-term memory for AI agents that organizes itself and corrects itself
| Agent harness infrastructure that gives AI agents memory for each user, connected accounts, action limits and an audit record.
|
|---|
| Stage | MVP
| Not given
|
|---|
| Founded | Not given
| Not given
|
|---|
| Based in | SE
| CA
|
|---|
| Pricing model | Free
| Freemium
|
|---|
| Pricing | Personal: Free during early access. Unlimited memories, all agents with separate memories, no card required. Enterprise: On request, set up by the team for departmental organization with cross-department sharing and direct support.
| Free: first 500 monthly-active end users per month, free forever. Paid: $0.30 per monthly-active end user above 500, billed monthly.
|
|---|
| Key features | - Self-correcting memory that updates when facts change
- Historical memory queries with time-based retrieval
- Automatic topic organization and hierarchy
- Separate memory for each agent with controlled sharing
- Multi-agent support via MCP, REST API, or LangGraph
- AES-256 encryption at rest and in backups
- OAuth 2.1 authentication and API key management
- Data export and deletion capabilities
- 37 MCP tools with automated verification on every code change
| - Timestamped memory ingestion with backdating capability
- Per-user data isolation and privacy scoping
- Ranked memory recall with dated evidence
- Connected accounts integration per user
- Long-running agent jobs with folder-level operations
- Configurable token ceiling per API call
- Enforced action limits on agent operations
- Complete action audit trail with hash chaining
- MCP server integration for Claude and other clients
|
|---|
| What makes it different | - Understands what changed, when it was true, and where it belongs - beyond vector database similarity
- Never contradicts itself by automatically replacing outdated facts
- Fully GDPR aligned with built-in privacy by design
- Two-minute integration with Claude, ChatGPT, Cursor and any MCP client
- Data stays in Europe on AWS eu-north-1 and never used for model training
| - Highest published BEAM benchmark score at 128K tokens: 85.1, compared to competing memory systems
- 98% fewer tokens required than raw tool output, making responses 4× cheaper
- Works with 20+ agent frameworks including LangChain, LangGraph and LlamaIndex
- First 500 monthly-active end users free forever
- Per-person billing model scales with user growth, not API calls
|
|---|
| Free plan | Not given
| Not given
|
|---|
| Open source | Not given
| Not given
|
|---|
| Platforms | Not given
| Not given
|
|---|
| Public API | Not given
| Not given
|
| Community votes | 0 | 0 |
| DR (Domain Rating by Ahrefs) | Not checked yet ● no change since last check | Not checked yet ● no change since last check |
| Trust Flow (Majestic) | 0 | 0 |
| Citation Flow (Majestic) | 0 | 0 |
| Referring domains (Majestic) | 0 | 1 |