- name
- cognee-performance
- description
- Use when cognee is slow, expensive, or hitting rate limits — speeding up or throttling ingestion (remember/cognify batching, chunk size, LLM and embedding rate limits, per-stage models), cutting read latency, estimating cost before ingesting, running work in the background, or planning how to scale a deployment.
Tune cognee's performance
The LLM is the bottleneck. Building the graph costs two LLM calls per chunk (graph extraction and summarization), and almost every other tuning question comes down to how many of those calls run, how fast, and on which model. Database and embedding work is small next to that.
Use it
1. Estimate before you ingest
estimate = await cognee.remember("./docs", dry_run=True)
print(estimate) # per-stage token counts and approximate cost; no LLM calls
The estimate covers graph extraction and summarization only, not
improve() or embeddings. Not available with
GLiNER or a remote instance.
2. Ingestion knobs
All are remember() arguments (and cognify() ones):
Worst-case concurrent LLM calls are about
data_per_batch × min(chunks per document, chunks_per_batch) × 2, and there
is no separate concurrency cap. The rate limiter below is the brake.