Botpress LLMz vs Flowise Memory
Botpress LLMz vs Flowise Memory: side-by-side comparison of two framework-embedded memory systems — architecture, taxonomy, license, pricing, MCP/A2A support, and direct edges.
Botpress LLMz · Flowise Memory
Cost & capability
| Botpress LLMz | Flowise Memory | |
|---|---|---|
| Capability band | competent | competent |
| Capability composite | 55 | 55 |
| Cost tier | premium | mid |
| Use cases | Memory Augmented Chat, Scoped Agentic, Long Running Session | Memory Augmented Chat, Scoped Agentic |
Where they differ (14)
Rows where both sides have data and the values disagree — the shortlist of dimensions that actually distinguish these two systems.
| Botpress LLMz | Flowise Memory | |
|---|---|---|
| Cost tier | premium | mid |
| Use cases | Memory Augmented Chat, Scoped Agentic, Long Running Session | Memory Augmented Chat, Scoped Agentic |
| Type | Vector DB + LLMz engine + KB | Buffer + Buffer-Window + Conversation-Summary nodes |
| Created | 2017 (Botpress open-sourced on GitHub January 2017, founded by Sylvain Perron and Justin Watson) | 2023 (Flowise launched 2023; YC S23) |
| Pricing | Free 500 msgs/mo; Plus $89/mo 1GB Vector DB; Team $495/mo 2GB; Enterprise custom; AI Spend billed separately per LL… | Pre-acquisition: Free OSS; Starter $35/mo; Pro $65/mo; Enterprise custom. Post-Workday acquisition Aug 2025: pricin… |
| Funding | $40M total; Series A $15M 2021; Series B $25M Jun 2025; Inovia Capital backed | $500K total Seed (YC) · 2023-01 |
| Backend storage | searched not found | pluggable |
| Deployment | Both (OSS self-hosted; Botpress Cloud SaaS; private cloud option) | Both (OSS self-hosted; Flowise Cloud managed; air-gapped deployment supported) |
| API surface | searched not found | REST, SDK: JS/TS |
| Embedding | searched not found | multiple supported |
| Multi-tenancy | searched not found | namespace |
| MCP | via official adapter — Botpress MCP integration | via official adapter — Flowise MCP node |
| Optimised for | conversational AI + per-plan vector quota + KB | visual LangChain canvas + memory nodes |
| Anti-fit | searched not found | not for production-grade SLA workloads |
At a glance
| Botpress LLMz | Flowise Memory | |
|---|---|---|
| Section | Framework-embedded memory | Framework-embedded memory |
| Tier | T1 | T1 |
| Type | Vector DB + LLMz engine + KB | Buffer + Buffer-Window + Conversation-Summary nodes |
| Created | 2017 (Botpress open-sourced on GitHub January 2017, founded by Sylvain Perron and Justin Watson) | 2023 (Flowise launched 2023; YC S23) |
| Pricing | Free 500 msgs/mo; Plus $89/mo 1GB Vector DB; Team $495/mo 2GB; Enterprise custom; AI Spend billed separately per LL… | Pre-acquisition: Free OSS; Starter $35/mo; Pro $65/mo; Enterprise custom. Post-Workday acquisition Aug 2025: pricin… |
| Funding | $40M total; Series A $15M 2021; Series B $25M Jun 2025; Inovia Capital backed | $500K total Seed (YC) · 2023-01 |
| Backend storage | searched not found | pluggable |
| Deployment | Both (OSS self-hosted; Botpress Cloud SaaS; private cloud option) | Both (OSS self-hosted; Flowise Cloud managed; air-gapped deployment supported) |
| API surface | searched not found | REST, SDK: JS/TS |
| Embedding | searched not found | multiple supported |
| Multi-tenancy | searched not found | namespace |
| MCP | via official adapter — Botpress MCP integration | via official adapter — Flowise MCP node |
| A2A | searched not found | searched not found |
| OpenTelemetry | searched not found | searched not found |
| Optimised for | conversational AI + per-plan vector quota + KB | visual LangChain canvas + memory nodes |
| Anti-fit | searched not found | not for production-grade SLA workloads |
Taxonomy
| Axis | Botpress LLMz | Flowise Memory |
|---|---|---|
| storage | vector | vector |
| retrieval | similarity | similarity |
| persistence | cross-session | session |
| update | extraction | append-only |
| unit | document | episode |
| governance | inspectable | inspectable |
| conflict | llm-arbitrate | append-only |
Pros & cons
Botpress LLMz
Pros: Focused on conversational bot deployments at scale — memory is structured around conversation lifecycle (handoff, escalation, return).
Cons: Bot-builder market positioning narrows applicability; less relevant for general agentic apps.
Flowise Memory
Pros: Visual builder lowers the bar for non-engineers to design memory pipelines; LangChain-compatible nodes.
Cons: Memory is as good as the LangChain primitive underneath — no novel architecture; less appealing to engineers building from code.