| What it does | Open-source memory platform for AI agents
| The agentic OS built for the enterprise sales function
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| Stage | Not given
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| Founded | Not given
| 2023
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| Based in | DE
| US
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| Pricing model | Not given
| Paid
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| Pricing | Free: $0/month with 1M tokens and 1 workspace (no card required). Standard: $1.00 per 1M tokens plus $5 per additional workspace. Enterprise: contact for pricing with bring-your-own-cloud deployment. Open-source engine available free to self-host.
| Build free. Take agents to production: $100K a year to start, includes software and team support. Govern every agent: customized pricing. Agent-months billing: Assistant (½ agent-month), Specialist (1 agent-month), Expert (2 agent-months). No token billing.
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| Key features | - Unified vector search, knowledge graphs, and relational storage
- Automatic knowledge graph generation from data sources
- Multi-tenant support with isolated workspaces
- Data source integrations (Slack, Notion, Google Drive, GitHub, etc.)
- Self-improving memory that learns from use
- Multiple deployment options (cloud, self-hosted, air-gapped)
- Agent framework integrations (LangGraph, CrewAI, OpenAI SDK, MCP)
- Support for multiple vector and graph databases
- GDPR compliance and encryption at rest and in transit
- Built-in graph visualization of company brain
| - Account prioritization ranked on fit, intent, timing and prior engagement
- Prospect research with deep briefs including funding, hiring, tech stack, and stakeholders
- Personalized outbound email, LinkedIn, and call scripts
- Multi-channel outreach sequencing across email, LinkedIn, phone and ads
- Automatic meeting booking with context sharing
- Call intelligence and scoring against sales methodology
- Automatic CRM updates for contacts, activities, and deal stages
- Pipeline risk detection and deal velocity analysis
- Real-time deal coaching with battlecards and objection handling
- Expansion and renewal workflow automation
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| What makes it different | - Combines vectors with graph relationships and generated data models for connected context retrieval
- State of the art performance on BEAM benchmark (79% vs 73.4% at 100k tokens)
- Keeps memory costs flat as data grows compared to baseline LLM approaches
- Fully open-source with no database lock-in
- Updates memory as source data changes, not just static snapshots
| - Stays in your environment - deploys in AWS, Azure, GCP or on-premise with no data leaving
- Revenue-first reporting that traces activity back to revenue goals rather than activity metrics
- No lock-in with ability to export agents and workflows
- Works with any AI model or framework
- Production deployment in 30 days with co-built implementation
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| Free plan | Not given
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| Open source | Not given
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| Platforms | Not given
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| Public API | Not given
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| Community votes | 0 | 0 |
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| DR (Domain Rating by Ahrefs) | 70 ● no change since last check | 71 ● no change since last check |
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| Trust Flow (Majestic) | 14 | 18 |
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| Citation Flow (Majestic) | 43 | 42 |
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| Referring domains (Majestic) | 1089 | 1710 |
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Jazon