Cognee vs Hindsight (Vectorize)
Cognee vs Hindsight (Vectorize): side-by-side comparison of two dedicated memory layers systems — architecture, taxonomy, license, pricing, MCP/A2A support, and direct edges.
Cost & capability
| Cognee | Hindsight (Vectorize) | |
|---|---|---|
| Capability band | competent | competent |
| Capability composite | 65 | 68 |
| Cost tier | free | free |
| $/Mtok input | 0 | 0 |
| $/Mtok output | 0 | 0 |
| Use cases | Long Running Session, Memory Augmented Chat, Analytical Summarization, Multi Agent Coordination | Long Running Session, Memory Augmented Chat, Offline Capable |
Where they differ (16)
Rows where both sides have data and the values disagree — the shortlist of dimensions that actually distinguish these two systems.
| Cognee | Hindsight (Vectorize) | |
|---|---|---|
| Capability composite | 65 | 68 |
| Use cases | Long Running Session, Memory Augmented Chat, Analytical Summarization, Multi Agent Coordination | Long Running Session, Memory Augmented Chat, Offline Capable |
| Type | Knowledge graph + ECL pipeline | Vector + reflection / summarisation |
| Created | 2023-08 | 2025-10 |
| Latest release | v1.6.5 (2026-10-16) | v1.1.0 (2026-10-21) |
| License | Apache-2.0 | MIT |
| GitHub | topoteretes/cognee — ~31.2k stars, last commit 2026-10 | vectorize-io/hindsight — ~44.6k stars, last commit 2026-10 |
| Pricing | Free $0 (1 workspace, 1M tokens); Standard $1 per 1M tokens + $5/workspace/mo; Enterprise BYOC custom | OSS self-host free (MIT); Hindsight Cloud pay-as-you-go credits ($5 free credit on GitHub sign-up); Enterprise single-tenant custom |
| Funding | $10M total Seed · 2026-02 | total $3.6M (Seed led by True Ventures, 2024-04) |
| Backend storage | hybrid (vector + graph) | custom |
| Deployment | Both (OSS self-hosted; Cognee Cloud managed; Enterprise BYOC) | Both — managed Hindsight Cloud; Enterprise single-tenant (Vectorize cloud, customer AWS/GCP/Azure, or on-prem); self-host via Docker, Helm or pip (Postgres + pgvector) |
| API surface | REST, SDK: Python | REST, SDK: Python, TS |
| Multi-tenancy | Logical namespace per project/dataset; self-hosted OSS deployment | namespace |
| MCP | native (first-party) — cognee-mcp | native (first-party) — Hindsight MCP |
| Optimised for | typed knowledge graph extraction (ECL pipeline) over RAG | retrieval engineering + reflection / summarisation as service |
| Anti-fit | no anti-fit explicitly stated | searched not found |
At a glance
| Cognee | Hindsight (Vectorize) | |
|---|---|---|
| Section | Dedicated memory layers | Dedicated memory layers |
| Tier | T1 | T1 |
| Type | Knowledge graph + ECL pipeline | Vector + reflection / summarisation |
| Created | 2023-08 | 2025-10 |
| Latest release | v1.6.5 (2026-10-16) | v1.1.0 (2026-10-21) |
| License | Apache-2.0 | MIT |
| GitHub | topoteretes/cognee — ~31.2k stars, last commit 2026-10 | vectorize-io/hindsight — ~44.6k stars, last commit 2026-10 |
| Pricing | Free $0 (1 workspace, 1M tokens); Standard $1 per 1M tokens + $5/workspace/mo; Enterprise BYOC custom | OSS self-host free (MIT); Hindsight Cloud pay-as-you-go credits ($5 free credit on GitHub sign-up); Enterprise single-tenant custom |
| Funding | $10M total Seed · 2026-02 | total $3.6M (Seed led by True Ventures, 2024-04) |
| Backend storage | hybrid (vector + graph) | custom |
| Deployment | Both (OSS self-hosted; Cognee Cloud managed; Enterprise BYOC) | Both — managed Hindsight Cloud; Enterprise single-tenant (Vectorize cloud, customer AWS/GCP/Azure, or on-prem); self-host via Docker, Helm or pip (Postgres + pgvector) |
| API surface | REST, SDK: Python | REST, SDK: Python, TS |
| Embedding | multiple supported | multiple supported |
| Multi-tenancy | Logical namespace per project/dataset; self-hosted OSS deployment | namespace |
| MCP | native (first-party) — cognee-mcp | native (first-party) — Hindsight MCP |
| A2A | not documented publicly | not documented publicly |
| OpenTelemetry | not documented publicly | not documented publicly |
| Optimised for | typed knowledge graph extraction (ECL pipeline) over RAG | retrieval engineering + reflection / summarisation as service |
| Anti-fit | no anti-fit explicitly stated | searched not found |
Taxonomy
| Axis | Cognee | Hindsight (Vectorize) |
|---|---|---|
| storage | graph | vector |
| retrieval | graph-traversal | similarity |
| persistence | long-term | long-term |
| update | extraction | consolidation |
| unit | fact | episode |
| governance | inspectable | opaque |
| conflict | llm-arbitrate | none |
Pros & cons
Cognee
Pros: Pipeline-as-code ECL (extract/cognify/load) makes the memory build path inspectable and replayable; fully OSS.
Cons: Smaller community than Mem0/Letta; more end-to-end engineering required to deploy.
Hindsight (Vectorize)
Pros: Built on Vectorize's RAG pipeline expertise — retrieval tuning is a first-class concern; strong Pydantic-AI integration story.
Cons: Newer entrant with smaller adoption; positioning straddles RAG and memory which can muddy the value prop vs pure-play layers.