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Recalld

FreemiumAPI

Building your own memory pipeline means wiring a vector database, a re-ranker, and a deduplication layer together — and then debugging why your agent confidently cites the old payment provider after you migrated. Recalld replaces that stack with three API endpoints that extract facts, reconcile contradictions, and return only what answers the question.

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Pricing

Model
Usage-Based

Summary

The core loop is write once, retrieve precisely: you POST a conversation turn or document, Recalld extracts atomic facts and timestamps them, and when new information supersedes old it updates or merges automatically. The recall endpoint runs a curation pass on the vendor's infrastructure before anything lands in your context window — the benchmark reports 88.7% accuracy on 243 tokens per query versus 1,627 tokens for raw vector search at 88.2% accuracy. That token reduction matters most when you are paying per token at scale across many agent sessions. The search endpoint skips the LLM curation pass entirely, returning raw dense-vector candidates for teams that want to filter with their own model. Self-hosting is not available, so teams with data-residency requirements outside EU or US regions hit a wall immediately.

Bottom line: Pick Recalld when you need persistent, deduplicated agent memory without building the retrieval pipeline yourself — but plan a different architecture the moment your compliance team rules out a cloud-only memory store you do not control.

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Pros

  • Automatic fact reconciliation on write — when new information arrives, older contradicting facts are updated or superseded without manual intervention, so your agent stops citing the payment provider you migrated away from three months ago.
  • LLM curation runs on Recalld's infrastructure, not in your context window, which means the token cost of the selection pass does not appear on your model bill — the benchmark reports 243 average tokens landing in the agent versus 1,627 for raw vector retrieval.
  • Three-endpoint API surface (add, recall, search) replaces the vector database, re-ranker, and deduplication pipeline you would otherwise maintain separately, so teams that would spend a sprint wiring those components together can ship against the Recalld API instead.
  • Raw search endpoint skips the LLM curation pass entirely and returns dense-vector candidates directly, so teams with their own retrieval models get a fast, predictable lookup without paying for compute they do not need.
  • Benchmark methodology is fully public — the harness, answer prompts, and judge were built by a competitor, and every patch the vendor applied is marked in an open results repo — so you are not accepting a vendor-written accuracy claim at face value.

Cons

  • No self-hosted deployment option exists: all memory is stored on Recalld's cloud infrastructure across EU and US regions only. Teams subject to data-residency requirements outside those regions, or under compliance frameworks that prohibit third-party memory stores, cannot use this service and move to self-hosted alternatives such as a managed Qdrant or Weaviate cluster with a custom reconciliation layer.
  • Multi-hop reasoning — questions that require synthesizing facts scattered across many sessions — benchmarks at 80.5% accuracy, the lowest category, while single-hop sits at 91.7%. Agents built around complex cross-session inference will hit retrieval misses at a rate that compounds across high-volume deployments, and teams managing those agents end up adding their own post-retrieval synthesis step, partially rebuilding the pipeline Recalld was meant to replace.
  • The curation pass in the recall endpoint is an LLM call running on Recalld's infrastructure, which means latency and availability depend on a third-party inference layer outside your control. Teams with strict p99 latency requirements or SLA obligations that cover memory retrieval have to fall back to the search endpoint and absorb the token cost themselves, or accept that the curated path is not an option under load.

About

API Available
Yes
Self-Hosted
No
Last Updated
September 30, 2026

Best For

Who it's for

  • Production AI agents needing persistent memory
  • Reducing context tokens while maintaining accuracy
  • MCP-compatible tools and IDEs

What it does well

  • Long-term memory for conversational agents
  • Fact extraction and deduplication from documents
  • Time-aware retrieval across multiple sessions

Integrations

MCP clientsClaudeCursorVS Code

Frequently Asked Questions

Is Recalld free?
Recalld offers a free option alongside paid upgrades. Check the current allowances and terms before choosing a plan.
Does Recalld have an API?
Yes. Recalld exposes a developer API. See the official documentation at https://recalld.ai for details.

Curated lists that include this category

Stale facts in agent memory

Teams wiring their own memory stack often debug why an agent still cites the old payment provider months after migration. Recalld replaces that stack with three API endpoints that extract atomic facts on write, timestamp them, and reconcile contradictions automatically when newer information arrives.

Core operation

POST a conversation turn or document and the service extracts facts, updates or merges superseded entries, then stores everything on its cloud infrastructure. The recall endpoint runs a curation pass before results reach the context window. The vendor states this delivers 88.7 percent accuracy on an average 243 tokens per query, compared with 1,627 tokens for raw vector search at 88.2 percent accuracy. The search endpoint skips the LLM curation step when a lighter lookup is sufficient.

Trade-offs

Multi-hop questions that require facts spread across sessions reach 80.5 percent accuracy while single-hop questions reach 91.7 percent. All storage stays on Recalld infrastructure in EU and US regions only; no self-hosted option exists.

Who it is for / who should skip it

Production agents that need persistent memory and lower token counts benefit from the automatic reconciliation and three-endpoint surface. Teams with data-residency rules outside EU or US regions, or those requiring self-hosted storage, should skip it and keep a managed vector database with a custom reconciliation layer instead.

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