| What it does | Open-source memory platform for AI agents
| Production infrastructure for AI applications with observability, governance, and multi-model support
|
|---|
| Stage | Not given
| Not given
|
|---|
| Founded | Not given
| Not given
|
|---|
| Based in | DE
| Not given
|
|---|
| Pricing model | Not given
| Not given
|
|---|
| 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.
| Personal $15/month (25,000 credits); Starter $25/month (50,000 credits); Team $75/month (100,000 credits); Pro $375/month (500,000 credits). Additional credits available at $0.10 per 1,000 credits.
|
|---|
| 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
| - Step-level observability with traces showing status, latency, credits, and governance for every step
- Natural-language governance policies with flag and block thresholds
- Model portability: swap 200+ models at any time without code changes
- Automatic content ingestion and vector database indexing for RAG
- Persistent memory banks for conversation context
- Cost tracking per model, agent, and step with credit-based pricing
- Structured output validation ensuring quality responses
- HTTP endpoints for every agent with SDKs for Python, JavaScript, Go, and C#
- ML-based prompt scanner blocking injection attacks in real time
- Evaluation framework with evals that run locally, in CI, and in production
|
|---|
| 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
| - All production LLM features unified in one runtime with no orchestration stack to assemble
- Local development environment matches production exactly for debugging and verification
- Swap between 200+ models without changing code or workflow logic
- Governance and compliance built-in with real-time prompt injection blocking and policy evaluation
- Transparent credit-based pricing where all operations consume predictable, tracked credits
|
|---|
| 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) | 70 ● no change since last check | 8 ▼ −1 (down) since last check |
|---|
| Trust Flow (Majestic) | 14 | 6 |
|---|
| Citation Flow (Majestic) | 43 | 20 |
|---|
| Referring domains (Majestic) | 1089 | 22 |
|---|
Seclai