Key takeaways
- $7.5M seed led by Pebblebed (Pamela Vagata, ex-OpenAI; Keith Adams, ex-Facebook AI Research) in February 2026, with 42CAP, Vermilion Ventures, and angels from Google DeepMind, n8n, and Snowplow
- The differentiator is the ECL (Extract-Cognify-Load) pipeline: data from 38+ sources is structured into a knowledge graph with embeddings, then a "memify" layer applies feedback-driven refinement — the self-improving part — over unified relational, vector, and graph storage
- Traction inflected hard: pipeline runs grew from about 2,000 in 2025 to over 1 million in 2026 (500x), with 70+ companies deployed including Bayer for scientific research workflows
- Apache-2.0 open source with 17.7K+ GitHub stars and a hosted cloud from $35/month, so the self-host-to-managed path is cheap to start and auditable end to end
FAQ
What is Cognee?
Cognee is an open-source memory engine for AI agents that ingests scattered data and builds a persistent, self-improving knowledge graph agents can query across sessions.
How much does Cognee cost?
The open-source engine is free under Apache-2.0; the hosted cloud runs $35/month (Developer, 1,000 documents), $200/month (Cloud, 2,500 documents, 10 users), with custom-priced on-prem enterprise deployment.
How does the ECL pipeline work?
Extract pulls data from 38+ sources, Cognify structures it into a knowledge graph with embeddings and relationships, and Load serves it to agents — with a "memify" layer refining the graph from feedback over time.
How is Cognee different from Zep?
Both build graph-based agent memory, but Zep centers on a temporal knowledge graph of conversational facts, while Cognee is a general data-to-memory pipeline that unifies relational, vector, and graph storage and emphasizes bulk document ingestion.
Executive Summary
Cognee is an open-source memory engine for AI agents: it ingests scattered data — documents, databases, conversations — and turns it into a persistent, self-improving knowledge graph that agents query across sessions.[1][2] The mechanism is its ECL pipeline — Extract, Cognify, Load — which pulls from 38+ data sources, structures content into a graph with embeddings and relationships, and layers a "memify" step that refines the graph from feedback over time; underneath, it unifies relational, vector, and graph storage rather than bolting a vector index onto a chat log.[3] The pitch to the agent-self-improvement category is that memory should be a structured, evolving substrate, not a pile of retrieved snippets.
Berlin-based Topoteretes (founded 2024 by CEO Vasilije Markovic; the repo dates to August 2023) closed a $7.5M seed on February 19, 2026, led by Pebblebed — the fund of OpenAI co-founder Pamela Vagata and Facebook AI Research founder Keith Adams — with 42CAP, Vermilion Ventures, and angels from Google DeepMind, n8n, and Snowplow.[3][1] The traction curve behind the raise: pipeline runs grew from roughly 2,000 in 2025 to over 1 million in 2026 — 500x year over year — across 70+ company deployments including Bayer, which uses Cognee for scientific research workflows.[3] The repo stands at 17.7K+ stars as of June 2026, up from the 12K+ cited at the raise.[1][3]
| Attribute | Value |
|---|---|
| Company | Topoteretes UG (Berlin)[3] |
| Founder | Vasilije Markovic (CEO); early co-creators Boris and Laszlo per the Show HN posts[3][4] |
| Founded | 2024 (repository created August 2023)[3][1] |
| Funding | $7.5M seed (Feb 19, 2026) led by Pebblebed; 42CAP, Vermilion Ventures, angels[3] |
| GitHub Stars | 17.7K+ (June 2026); 1,880+ forks[1] |
| License | Apache-2.0; primary language Python[1] |
| Named Users | Bayer, University of Wyoming, Dilbloom, dltHub[3] |
Product Overview
The core loop: point Cognee at your data, call cognify, and get back a knowledge graph that agents query for grounded, relationship-aware answers instead of raw chunk retrieval. Documents become both searchable by meaning (embeddings) and connected by typed relationships (graph), and the "memify" layer applies feedback-driven refinement so retrieval quality compounds with use — the self-improvement claim in concrete form.[3][2] Memory separates into a session layer (short-term working memory) and a permanent layer (long-term knowledge artifacts such as user data and interaction traces), continuously cross-connected inside the graph.[2]
The team's own evaluation, published as a paper alongside the June 2025 Show HN launch, reported nearly 90% on standard industry benchmarks, with the eval harness in the open repository.[4][5]
Key Capabilities
| Capability | Description |
|---|---|
| ECL pipeline | Extract → Cognify → Load: ingestion from 38+ sources into a structured memory graph[3] |
| Unified storage | Relational, vector, and graph databases behind one abstraction[3] |
| Memify refinement | Feedback-driven layer that improves the graph over time[3] |
| Node sets | Tag-like grouping for filtering and multi-tenant memory separation, with per-user graphs and permissions[4] |
| Graph completion retrieval | Graph-aware retrieval with weighting and self-improving feedback[6] |
| Integrated evals | Evaluation tooling ships with the platform, including the free tier[7][5] |
Product Surfaces
| Surface | Description | Availability |
|---|---|---|
| Python library | Open-source engine, self-hosted | GA, Apache-2.0[1] |
| Cognee Cloud | Hosted API on AWS, GCP, and Azure | GA, from $35/month[7] |
| On-prem | Private-cloud or on-premises enterprise deployment | Custom[7] |
Technical Architecture
Cognee is a Python engine that orchestrates three storage families — relational, vector, and graph — behind a single pipeline abstraction, so teams can swap underlying databases without rewriting memory logic.[1][3] Development is active: 166 contributors (including anonymous) per the GitHub API, release v1.1.2 shipped May 30, 2026, and commits landed the day of this profile.[1] The seed round's stated roadmap points at the architecture's next moves: a Rust engine for edge and on-device agents, expanded multi-database support, and 30+ new connectors.[3]
Key Technical Details
| Aspect | Detail |
|---|---|
| Deployment | Self-hosted (open source), hosted cloud (AWS/GCP/Azure), or on-prem enterprise[1][7] |
| Model(s) | Bring-your-own LLM for extraction and cognify steps; model-agnostic[1] |
| Storage | Pluggable relational + vector + graph backends, unified[3] |
| Open Source | Apache-2.0, Python, 17.7K+ stars, 1,880+ forks, 166 contributors[1] |
Strengths
- Verified hockey-stick usage — pipeline runs went from ~2,000 in 2025 to 1M+ in 2026 (500x), a trajectory corroborated by the founder's mid-2025 HN job post citing 116K runs/month and "projecting north of a million."[3][6]
- Credible deep-tech backing — Pebblebed is run by an OpenAI co-founder (Pamela Vagata) and Facebook AI Research's founder (Keith Adams); angels span Google DeepMind, n8n, and Snowplow.[3]
- A real enterprise anchor — Bayer runs Cognee in scientific research workflows, with the University of Wyoming, Dilbloom, and dltHub also named, across 70+ company deployments.[3]
- Open core with a cheap managed on-ramp — Apache-2.0 engine plus a $35/month hosted tier means evaluation costs nothing and migration paths run in both directions.[1][7]
- Published evals, not just claims — the team shipped a paper (arXiv 2505.24478) and keeps the benchmark harness in the repo, unusual transparency in a category with disputed benchmarks.[5][4]
Cautions
- LLM-in-the-loop economics — like Mem0 and Zep, Cognee sends text through an LLM for extraction and structuring, which competitors attack on cost at scale; budget for per-document API spend on top of subscription.[6]
- Temporal evolution is unfinished — when a Show HN commenter asked how Cognee handles facts that change over time, the founder answered that temporal resolution mechanisms "are being built"; as of the June 2025 thread this remained roadmap.[4]
- Thin independent community signal — the best-performing Show HN drew 9 points and 2 comments; most substantive HN mentions of Cognee come from founders of competing tools or from Cognee's own founder.[6][4]
- Document-count pricing climbs fast — the hosted tiers meter by documents (1,000 at $35/month), and add-on packs run to $750 for 15,000 documents, which can surprise teams with large corpora.[7]
- Crowded, contested category — agent memory has many funded rivals (Mem0, Zep, Letta) plus newer entrants, and independent reviewers note most systems converge on extract-store-retrieve with weak mechanisms for deciding what is worth remembering.[6]
What Developers Say
Community discussion is modest as of June 2026 — Cognee's Show HN threads drew single-digit comment counts, and the most detailed independent commentary comes from an eight-system comparison and from builders of competing memory tools (read those as adversarial). Founder Vasilije Markovic (vasa_) is active in HN memory threads, so some framing is vendor voice.[6][4]
"Hey, this looks super interesting - nice work! ... How does cognee handle temporal evolution of memory (e.g. when facts change over time)?" — an HN commenter on the Show HN launch[4]
"every AI memory system I tried (mem0, Cognee, Zep) makes 2-3 LLM API calls just to store a single memory." — a competing memory-tool builder on Hacker News[6]
"Structure without selection pressure is art. Many of these systems build elaborate relationship schemas with no mechanism to decide what's worth remembering." — an HN commenter who prototyped eight memory systems including Cognee[6]
Pricing & Licensing
| Tier | Price | Includes |
|---|---|---|
| Free | $0 | Build and run memory workflows, auto-generated knowledge structures, integrated evals, 28+ data sources, community support |
| Developer | $35/month | 1,000 documents or 1GB, 1 user, hosted on AWS/GCP/Azure, 10,000 API calls |
| Cloud | $200/month | 2,500 documents or 2GB, 10 users, multi-tenant architecture, per-user/domain memory grouping, dedicated Slack channel, 10,000 API calls |
| On-Prem (Enterprise) | Custom | On-premises or private cloud, architecture review, premium support/SLA, AI FDE engineers, roadmap prioritization |
Add-on document packs: +1,000 for $35, +3,000 for $100, +15,000 for $750. All pricing as of June 2026.[7]
Licensing model: Apache-2.0 open-source engine; hosted cloud and on-prem enterprise are commercial.[1][7]
Hidden costs: Extraction and cognify steps consume your own LLM API tokens on top of subscription fees; document-metered tiers mean large corpora require add-on packs.[6][7]
Competitive Positioning
Direct Competitors
| Competitor | Differentiation |
|---|---|
| Zep | The closest graph-memory rival — Zep's temporal knowledge graph (Graphiti) specializes in tracking conversational facts as they change over time, exactly the temporal-evolution problem Cognee lists as in-progress; Cognee counters with broader data-to-memory pipelines and unified tri-store architecture |
| Mem0 | The category's mindshare leader by GitHub stars; minimal-structure memory layer (LLM extraction, no rich schema) versus Cognee's full knowledge-graph construction — simpler to adopt, less structured to reason over |
| Hindsight | Runs four-way parallel retrieval with cross-encoder reranking on a single PostgreSQL database — a one-database simplicity bet against Cognee's pluggable relational + vector + graph stack |
| Letta | A full stateful agent runtime with tiered memory rather than a memory engine you attach to your own agents |
When to Choose Cognee Over Alternatives
- Choose Cognee when: the memory problem is document- and data-heavy — turning corpora, databases, and multiple sources into one queryable graph — and you want an Apache-2.0 core you can self-host with a managed escape hatch.
- Choose Zep when: conversational memory with first-class temporal fact tracking (what was true when) is the center of the workload.
- Choose Mem0 when: you want the simplest framework-agnostic memory layer and the largest community, without knowledge-graph overhead.
- Choose Hindsight when: you want agent memory that lives entirely in PostgreSQL you already operate.
Ideal Customer Profile
Best fit:
- Teams building agents over large, heterogeneous corpora — research, scientific, legal, enterprise documents — where relationship-aware retrieval beats chunk similarity (the Bayer pattern)[3]
- Engineering organizations that require a self-hostable, Apache-2.0 memory layer for data-residency or audit reasons[1]
- Builders who want to customize graph ingestion, generation, and retrieval logic rather than accept a fixed memory schema[6]
Poor fit:
- Chat products whose memory needs are simple user-preference recall — lighter layers cost less per stored memory
- Workloads dominated by rapidly changing facts where temporal resolution is the core requirement, until Cognee's in-progress mechanisms ship[4]
- Token-cost-sensitive deployments storing memories at very high volume, given LLM-in-the-loop extraction[6]
Viability Assessment
| Factor | Assessment |
|---|---|
| Financial Health | Solid for stage — $7.5M seed (Feb 2026) led by Pebblebed with notable angels[3] |
| Market Position | Credible challenger — 17.7K+ stars and a Bayer anchor, but Mem0, Zep, and Letta hold more mindshare in a crowded category[1][3] |
| Innovation Pace | High — v1.1.2 in May 2026, daily commits, published evals, and a funded roadmap (Rust edge engine, 30+ connectors)[1][3] |
| Community/Ecosystem | Growing but thin on independent voices — 166 contributors yet single-digit HN engagement on launches[1][6] |
| Long-term Outlook | Depends on converting 500x usage growth into paid cloud/enterprise revenue before better-funded memory rivals consolidate the category[3] |
The 500x year-over-year pipeline-run growth is the standout number, and it is partially corroborated by the founder's own contemporaneous HN posts rather than only the funding announcement.[3][6] The structural question is positioning: Cognee's tri-store, graph-first architecture is more ambitious than the lightweight memory layers, but ambition cuts both ways — heavier to adopt, and competing against both simpler tools (Mem0) and deeper specialists (Zep on temporal facts).[6]
Bottom Line
Cognee is the strongest open-source bet on memory-as-knowledge-graph: a genuinely permissive Apache-2.0 engine, published evaluations, an enterprise anchor in Bayer, and a usage curve (2K to 1M+ pipeline runs in a year) that earned a $7.5M seed from ex-OpenAI and ex-FAIR investors. The trades are LLM-in-the-loop costs at scale, temporal fact-tracking that still trails Zep, and a community footprint where most of the loudest commentary comes from competitors.
Recommended for: Teams turning large multi-source corpora into agent memory, especially with self-hosting or data-residency requirements; builders who want to own and customize their memory graph's construction and retrieval logic.
Not recommended for: Simple preference-recall chat memory, high-volume cost-sensitive memory writes, or workloads where temporal fact evolution is the core problem today.
Outlook: Watch whether the Rust edge engine and 30+ promised connectors ship on the funded roadmap, whether temporal resolution lands as a first-class feature, and whether the 1M+ pipeline runs convert into paying cloud customers — the metric the next round will be priced on.
Research by Ry Walker Research • methodology