Catalog Systems Dedicated memory layers Backboard

Backboard

https://backboard.io/products/api

Stateful AI API combining persistent memory, RAG, web search and routing across many LLMs behind one interface, with SDKs for Python and npm; benchmarks its memory on LoCoMo and LongMemEval. As of Oct 2026 Backboard positions itself as an AI compute efficiency lab selling an inference-optimization engine; the memory product continues as its Stateful API.

Active · Verified 2026-10-29 · fresh as of 2026-10-29 · 39 cited facts, 2 with a third-party source · 2 recorded changes · Suggest a correction

At a glance

Type
Memory + LoCoMo evaluation harness repo fix
Tier
T2
Section
Dedicated memory layers
Created
2025-11 repo fix
Latest release
no releases fix
License
Unlicensed repo fix
GitHub
Backboard-io/Backboard-Locomo-Benchmark — ~18 stars (org's largest repo is Backboard-R-CLI, ~54 stars) repo fix
Pricing
Free $0; Education $5; Developer $20; Scale $400 (billing period not stated on page) vendor fix
Funding
undisclosed pre-seed (2026-01), led by Mistral Venture Partners + N49P; Garage Capital, DevCap participated. The $3M figure could not be confirmed: BetaKit and the company press release give no amount third-party fix
Freshness
2026-10-29 fix
Layer
agent-infra repo fix
Deployment locus
cloud vendor fix
Platform vs product
both vendor fix
SDK languages
python, rest, typescript vendor fix
Compliance
none-published vendor fix
Maintainer health
0 issues in 90 days · 0 releases in 12 months · bus factor 1 — as of 2026-10-31 · concern: one contributor wrote 100% of sampled commits repo

Memory taxonomy

storage
vector
retrieval
similarity
persistence
long-term
update
extraction
unit
episode
governance
inspectable
conflict
none

When to use

Optimised for: LoCoMo benchmark performance (long-conversation accuracy)

Anti-fit: searched not found

Pros & cons

Pros

Lightweight managed memory layer with simple SDK; quick to integrate into greenfield agents.

Cons

Newer entrant; thin track record; small team.

Claims & capabilities

Ships its own LoCoMo benchmark fork ( Backboard-Locomo-Benchmark ) for transparent evaluation.

Technical surface

API surface
searched not found
Backend storage
searched not found
Deployment
Managed cloud API; vendor also advertises private-cloud, on-prem and on-device deployment
Embedding model
searched not found
Multi-tenancy
searched not found
MCP
not documented publicly
A2A
not documented publicly
OpenTelemetry
not documented publicly

Alternatives to Backboard → live options ranked by shared design, licence, hosting and MCP support

Similar systems

Other dedicated memory layers in the catalog, ranked by inbound references.

  • Mem0 T1

    Universal memory layer for AI agents. Three concurrent stores (vector + graph + KV); LLM-extracted facts; concurrent retrieval via ThreadPoolExecutor.

  • Zep & Graphiti T1

    Bi-temporal knowledge graph (event time + ingestion time). Strong on chronological reasoning and contradiction tracking. Graphiti is the open-source core.

  • Cognee T1

    "Extract–Cognify–Load" pipeline that turns raw input into a typed, queryable knowledge graph for agent recall.

  • Hindsight (Vectorize) T1

    Standalone memory service from Vectorize. Open source. Biomimetic four-network design (World, Bank, Observation, Opinion). Ships an MCP memory server.

  • Letta / MemGPT T1

    Originated as MemGPT (UC Berkeley), where the model pages its own memory between context, recall and archival tiers. The product now centres on Letta Code, a stateful agent harness whose agents rewrite their own memory, skills and prompts over time.

  • Memori T2

    Structured, persistent memory layer for AI agents. Open source.

Sources (38 cited facts, 2 third-party)

Each fact links to the page it came from; the badge says who published it (how to read sources).

What changed

Changes to this record's lifecycle, licence, release, pricing and hosting facts, from the catalog's git history. Subscribe (Atom)

  1. 2026-10-29
    • Homepage “https://github.com/Backboard-io” → “https://backboard.io/products/api”
    #309
  2. 2026-10-22
    • Pricing “Usage-based tiered pricing; free production-ready tier with promo; paid tiers for scale” → “Free $0; Education $5; Developer $20; Scale $400 (billing period not stated on page)” source
    • Funding / ownership “$3M pre-seed Jan 2026; co-led Mistral Venture Partners + N49P; Garage Capital + DevCap participated” → “undisclosed pre-seed (2026-01), led by Mistral Venture Partners + N49P; Garage Capital, DevCap participated. The $3M figure could not be confirmed: BetaKit and the company press release give no amount” source
    • Deployment “Cloud-hosted (managed SaaS; multi-model API)” → “Managed cloud API; vendor also advertises private-cloud, on-prem and on-device deployment” source
    #195

Related systems

References (3)