Adaptive-RAG

https://github.com/starsuzi/Adaptive-RAG

Smaller classifier LM predicts query complexity, then routes to no-retrieval / single-step / iterative retrieval as appropriate. NAACL 2024.

Research code · Verified 2026-10-23 · fresh as of 2026-10-23 · 28 cited facts, 1 with a third-party source · 1 recorded change · Suggest a correction

At a glance

Tier
T3
Created
2024-04 repo fix
Latest release
no releases fix
License
Apache-2.0 repo fix
Pricing
searched not found fix
Funding
not applicable — not commercial fix
Freshness
2026-10-23 fix
Layer
agent-infra repo fix
Licensing
open-source repo fix
Deployment locus
local repo fix
Platform vs product
platform repo fix
SDK languages
python repo fix

Memory taxonomy

storage
vector
retrieval
similarity
persistence
long-term
update
read-only
unit
chunk
governance
inspectable
conflict
n/a

When to use

Optimised for: retrieval quality / accuracy on long-context QA

Anti-fit: most are research papers; production fitness varies by maintainer

Pros & cons

Pros

Query-adaptive routing across retrieval strategies — picks the right method per question.

Cons

Routing decisions can be wrong; adds inference cost.

Claims & capabilities

Improves overall efficiency and accuracy of QA systems vs adaptive baselines, across query complexity levels.

Technical surface

API surface
not applicable — research paper
Backend storage
not applicable — research paper
Deployment
searched not found
Embedding model
not applicable — research paper
Multi-tenancy
not applicable — research paper
MCP
not applicable — research paper, no deployed product
A2A
not applicable — research paper, no deployed product
OpenTelemetry
not applicable — research paper, no deployed product

Similar systems

Other retrieval-as-memory hybrids in the catalog, ranked by inbound references.

  • GraphRAG (Microsoft) T1

    Modular graph-based RAG. Leiden community detection over knowledge graph; community summaries at multiple levels. DRIFT Search hybrid (Oct 2024). The GraphRAG repo is now largely in maintenance mode: no new features or PRs, only bug fixes and dependency/CVE updates.

  • RETRO T3

    DeepMind. Retrieval-Enhanced Transformer with chunked cross-attention over a 2T-token retrieval database. Frozen BERT retriever + differentiable encoder.

  • Atlas T3

    Meta AI. Jointly fine-tunes retriever + LM for few-shot tasks. The facebookresearch/atlas repo is no longer maintained; the code is provided as is.

  • RAPTOR T3

    Stanford recursive-summarisation tree over the corpus — clusters and summarises documents into a hierarchical index; retrieves at multiple levels. ICLR 2024.

  • Self-RAG T4

    Trains an LLM end-to-end to emit reflection tokens that decide whether to retrieve, assess passage relevance, and critique output quality — all within a single model.

  • HippoRAG / HippoRAG2 T3

    Hippocampus-inspired RAG. LLMs + knowledge graphs + Personalized PageRank to mimic neocortex/hippocampus roles. NeurIPS 2024.

Sources (27 cited facts, 1 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. 2026-10-22
    • Lifecycle first recorded as “research-complete”
    #195

Related systems

References (1)

  • Self-RAG cites — S2 isInfluential citation