| What it does | Open-source memory platform for AI agents
| Standing AI teams that run whole business functions around the clock
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| Stage | Not given
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| Based in | DE
| NZ
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| Pricing model | Not given
| Freemium
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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.
| Free: 200 turns, 1 running team, up to 5 agents. Pay as you go: US$20 buys 2,000 turns. Starter: US$29/month with 2 running teams, up to 15 agents, 5,800 agent turns monthly. Business: US$99/month with 5 running teams, up to 50 agents, 19,800 agent turns monthly. Company: US$399/month with 20 runnin
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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
| - Shadow mode for verification before live operation
- Learns from documents, help centres and past tickets
- Hard spending caps per team and job
- Works with 102+ integrations
- Kill switch to stop any team
- Approval workflows for risky actions
- Team memory and learning across jobs
- MCP server and REST API for AI agents
- Encrypted credential storage per connection
- 26+ ready-made teams for different functions
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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
| - Teams learn from your actual business documents and past work, not generic templates
- Shadow mode proves capability before autonomous operation
- You bring your own model key and pay providers directly, no markup
- Granular security controls: ask-before actions, credentials encryption, kill switches
- Works with AI agents through MCP or REST API
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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 | 1 ● no change since last check |
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| Trust Flow (Majestic) | 14 | 0 |
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| Citation Flow (Majestic) | 43 | 18 |
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| Referring domains (Majestic) | 1089 | 5 |
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Jugg.ai