Adaptive Recall
Adaptive Recall is a hosted memory system for AI applications that goes far beyond simple vector search. It stores, recalls, and manages long-term memory for agents and apps over MCP or a plain REST API, and — unlike a static embeddings store — it actively learns. Four retrieval strategies run in parallel (vector similarity, temporal recency, full-text keyword, and knowledge-graph traversal), and the system learns which to prioritize for each query type. Results are ranked with ACT-R cognitive scoring from 30 years of cognitive-science research, factoring in recency, access frequency, entity connections, and validated confidence. A knowledge graph is built automatically from stored memories, memories move through a confidence-based lifecycle and fade when unused, and an ML pipeline trains on your usage patterns — validating every parameter change against real query history before adopting it. A simple eight-tool API (store, recall, update, forget, graph, status, snapshot, feedback) covers everything, with Bearer-token auth and JSON in/out. Free, Starter, Pro, and Business plans are available.
✅ Pros / Advantages
- • Four retrieval strategies learned per query
- • ACT-R cognitive scoring surfaces the right memory
- • Automatic knowledge graph from stored memories
- • Self-improving ML with statistically-validated changes
- • Simple 8-tool API over MCP or REST
❌ Cons / Limitations
- • Hosted SaaS — data leaves your infrastructure
- • Young product, patent-pending, roadmap risk
- • Pricing tiers unclear for heavy use
- • Vendor lock-in to its memory format
- • Requires integration effort to see value
💰 Pricing Plans
Free (Freemium)
Pricing details are gathered from public sources and are subject to change. Please visit the official website for real-time rates.
Last updated: July 2026 · 9bests editorial review
✅ Who should use Adaptive Recall
- • Four retrieval strategies learned per query
- • ACT-R cognitive scoring surfaces the right memory
- • Automatic knowledge graph from stored memories
- • Self-improving ML with statistically-validated changes
- • Simple 8-tool API over MCP or REST
⚠️ Who should look elsewhere
- • Hosted SaaS — data leaves your infrastructure
- • Young product, patent-pending, roadmap risk
- • Pricing tiers unclear for heavy use
- • Vendor lock-in to its memory format
- • Requires integration effort to see value
🎯 Common use cases
Web scraping and extraction
Building AI data pipelines
Enrichment and cleaning
⚖️ Adaptive Recall vs Crawl4AI
| Adaptive Recall | Crawl4AI | |
|---|---|---|
| Rating | 4/5 | 4.3/5 |
| Pricing | Free (Freemium) | Free (Open Source) |
| Key strength | Four retrieval strategies learned per query | LLM-first output format |
See the full head-to-head in our Adaptive Recall vs Crawl4AI comparison.
❓ Frequently asked questions
Is Adaptive Recall free?
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Adaptive Recall offers a free tier (Free (Freemium)). Paid plans unlock higher limits and advanced features.
What is Adaptive Recall best for?
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Adaptive Recall is best for Four retrieval strategies learned per query and ACT-R cognitive scoring surfaces the right memory. Adaptive Recall is a hosted memory system for AI applications that goes far beyond simple vector search. It stores, recalls, and manages long-term memory for agents and apps over MCP or a plain REST API, and — unlike a static embeddings store — it actively learns. Four retrieval strategies run in parallel (vector similarity, temporal recency, full-text keyword, and knowledge-graph traversal), and the system learns which to prioritize for each query type. Results are ranked with ACT-R cognitive scoring from 30 years of cognitive-science research, factoring in recency, access frequency, entity connections, and validated confidence. A knowledge graph is built automatically from stored memories, memories move through a confidence-based lifecycle and fade when unused, and an ML pipeline trains on your usage patterns — validating every parameter change against real query history before adopting it. A simple eight-tool API (store, recall, update, forget, graph, status, snapshot, feedback) covers everything, with Bearer-token auth and JSON in/out. Free, Starter, Pro, and Business plans are available.
How does Adaptive Recall compare to Crawl4AI?
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Adaptive Recall (4/5) and Crawl4AI (4.3/5) serve overlapping needs. Adaptive Recall stands out for Four retrieval strategies learned per query, while Crawl4AI is stronger at LLM-first output format. Choose based on your priority.
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ParseHawk
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