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Agentic AI
agent-memory
long-term agent memory
Claude memory files, MemGPT
System
Techniques · 16
Primitives · 49
agent-memory
Memory consolidation
agentic-ai · medium: Periodically merge near-duplicate memories, resolve contradictions, decay the unused and cap the store, so an agent's long-term memory stays small, current and cheap to search.
›
Prompt injection defense
agentic-ai · medium: Treat every untrusted string as data, track where values came from, and make tool calls and rendered output depend on provenance, not on what the model says.
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Entity linking
machine-learning · medium: Map each mention to a knowledge-base id, or to NIL, by generating candidates from an alias table, scoring them on prior and context, and thresholding.
›
Near-duplicate detection with MinHa…
machine-learning · medium: Estimate set Jaccard similarity from k min-hashes and find similar pairs with banded LSH buckets; the standard dedup for web crawls, training corpora and sequence collections.
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Nearest-neighbour search with filte…
search-sort · medium: Combine approximate nearest-neighbour search with attribute predicates by pre-filtering, post-filtering or filter-aware graph traversal, chosen by how many items pass.
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HNSW
search-sort · medium: Hierarchical Navigable Small World: a layered proximity graph searched greedily from a sparse top layer; the default in-memory approximate nearest-neighbour index.
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Entity resolution
storage · medium: Decide which records describe the same real-world thing: block to cut the pair space, score pairs with a probabilistic model, cluster the matches transitively.
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Embeddings
machine-learning · low: A learned table mapping discrete ids to dense vectors: gather forward, sparse scatter-add backward, row-wise optimizers to keep huge tables cheap.
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Time decay scoring
search-sort · low: Scoring items by recency with exponential decay or half-life, combined with relevance
BM25 ranking
search-sort · low: The standard lexical relevance score: IDF-weighted term frequency with saturation (k1) and length normalization (b); default in Lucene, Elasticsearch and SQLite FTS5.
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Agent memory
agentic-ai · unknown:
Context window management
agentic-ai · low: Keep an agent's growing history inside the model's window by truncating, clearing and summarizing old turns in a cheap-to-lossy ladder, at turn boundaries.
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Prompt caching
agentic-ai · low: Reuse the attention keys/values of a repeated prompt prefix, so an agent's growing context is prefilled once and later turns pay only for the new tokens.
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Text chunking
machine-learning · low: Cut documents into token-bounded pieces along structure boundaries, keeping offsets and section paths, so each piece embeds well and can be cited.
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Hybrid search and rank fusion
search-sort · low: Merge a keyword (BM25) result list and a vector (ANN) list into one ranking, by ranks (RRF) or by normalized scores (convex combination).
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Optimistic concurrency control
storage · low: Run transactions without locks, record what they read, and validate at commit that nothing changed; fastest when conflicts are rare.
›
Html sanitization
security · low: Allowlist-based HTML sanitizing of untrusted markup
Least privilege and isolation
security · low: Assume every component will be compromised and limit what it can then reach: narrow identities, separate accounts, sandboxed processes.
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Fast hash functions
data-structures · low: Non-cryptographic hashes built from 64×64→128-bit multiplies and xor-shifts; pick by key size, trust level and whether the hash is persisted.
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Rolling hash
data-structures · low: Rabin-Karp polynomial rolling hashes and winnowing for substring fingerprints and document matching
Swiss table
data-structures · low: Open-addressing hash table that filters 8-16 slots at once with 1-byte hash tags and SIMD, giving fast lookups at 87.5 % load.
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Roaring bitmaps
compression · low: Compressed set of 32-bit integers in 64K-value chunks, each a sorted array, bitmap or run list; fast intersections, the standard for bitmap indexes.
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d-ary heap
data-structures · low: Array heap with d children per node (d = 4 typical): half the binary heap's depth, siblings in one cache line, faster priority queues.
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+ 9 more techniques
+ 33 more primitives
System
Techniques · 16
Primitives · 49
agent-memory
Memory consolidation
agentic-ai · medium: Periodically merge near-duplicate memories, resolve contradictions, decay the unused and cap the store, so an agent's long-term memory stays small, current and cheap to search.
›
Prompt injection defense
agentic-ai · medium: Treat every untrusted string as data, track where values came from, and make tool calls and rendered output depend on provenance, not on what the model says.
›
Entity linking
machine-learning · medium: Map each mention to a knowledge-base id, or to NIL, by generating candidates from an alias table, scoring them on prior and context, and thresholding.
›
Near-duplicate detection with MinHa…
machine-learning · medium: Estimate set Jaccard similarity from k min-hashes and find similar pairs with banded LSH buckets; the standard dedup for web crawls, training corpora and sequence collections.
›
Nearest-neighbour search with filte…
search-sort · medium: Combine approximate nearest-neighbour search with attribute predicates by pre-filtering, post-filtering or filter-aware graph traversal, chosen by how many items pass.
›
HNSW
search-sort · medium: Hierarchical Navigable Small World: a layered proximity graph searched greedily from a sparse top layer; the default in-memory approximate nearest-neighbour index.
›
Entity resolution
storage · medium: Decide which records describe the same real-world thing: block to cut the pair space, score pairs with a probabilistic model, cluster the matches transitively.
›
Inverted index
storage · medium: Map each term to a sorted, compressed list of the documents containing it; the core of full-text search and other fast set queries.
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Named entity recognition
machine-learning · medium: Find the character spans of entities in text and give each a type, by labelling every token and decoding a valid label sequence.
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Text classification
machine-learning · medium: Assign one or more labels to a text; climb a ladder from regexes to linear models on n-grams, embeddings, fine-tuned encoders and LLM prompts, only as far as the data and budget demand.
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Cross-encoder reranking
search-sort · medium: Re-score the top-k candidates of a cheap first stage with a model that reads query and candidate together; more accurate than separate embeddings, but one model call per pair.
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IVF-PQ
search-sort · medium: Inverted file plus product quantization: cluster vectors into lists, store each as a few bytes of codes, scan only the nearest lists via table lookups.
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Record linkage
search-sort · medium: Probabilistic record matching (Fellegi-Sunter) with blocking for patient or entity indexes
Taint tracking
security · medium: Track untrusted sources to sensitive sinks through dataflow
Unicode confusables
security · medium: UTS #39 maps every string to a "skeleton" so look-alikes collide, and restricts script mixing so most spoofs never get registered.
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Dataflow analysis
compilers · medium: Compute a fact per program point (live variables, reaching definitions, available expressions) by iterating transfer functions over the CFG to a fixed point.
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Embeddings
machine-learning · low: A learned table mapping discrete ids to dense vectors: gather forward, sparse scatter-add backward, row-wise optimizers to keep huge tables cheap.
›
Time decay scoring
search-sort · low: Scoring items by recency with exponential decay or half-life, combined with relevance
BM25 ranking
search-sort · low: The standard lexical relevance score: IDF-weighted term frequency with saturation (k1) and length normalization (b); default in Lucene, Elasticsearch and SQLite FTS5.
›
Agent memory
agentic-ai · unknown:
Context window management
agentic-ai · low: Keep an agent's growing history inside the model's window by truncating, clearing and summarizing old turns in a cheap-to-lossy ladder, at turn boundaries.
›
Prompt caching
agentic-ai · low: Reuse the attention keys/values of a repeated prompt prefix, so an agent's growing context is prefilled once and later turns pay only for the new tokens.
›
Text chunking
machine-learning · low: Cut documents into token-bounded pieces along structure boundaries, keeping offsets and section paths, so each piece embeds well and can be cited.
›
Hybrid search and rank fusion
search-sort · low: Merge a keyword (BM25) result list and a vector (ANN) list into one ranking, by ranks (RRF) or by normalized scores (convex combination).
›
Optimistic concurrency control
storage · low: Run transactions without locks, record what they read, and validate at commit that nothing changed; fastest when conflicts are rare.
›
Html sanitization
security · low: Allowlist-based HTML sanitizing of untrusted markup
Least privilege and isolation
security · low: Assume every component will be compromised and limit what it can then reach: narrow identities, separate accounts, sandboxed processes.
›
Fast hash functions
data-structures · low: Non-cryptographic hashes built from 64×64→128-bit multiplies and xor-shifts; pick by key size, trust level and whether the hash is persisted.
›
Rolling hash
data-structures · low: Rabin-Karp polynomial rolling hashes and winnowing for substring fingerprints and document matching
Swiss table
data-structures · low: Open-addressing hash table that filters 8-16 slots at once with 1-byte hash tags and SIMD, giving fast lookups at 87.5 % load.
›
Roaring bitmaps
compression · low: Compressed set of 32-bit integers in 64K-value chunks, each a sorted array, bitmap or run list; fast intersections, the standard for bitmap indexes.
›
d-ary heap
data-structures · low: Array heap with d children per node (d = 4 typical): half the binary heap's depth, siblings in one cache line, faster priority queues.
›
SIMD programming
hardware-os · low: Process 4-16 lanes per instruction with AVX2, AVX-512 or NEON via Rust std::arch, with runtime dispatch and a portable fallback.
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PRNGs (PCG, xoshiro)
numerics · low: Small, fast, statistically strong non-cryptographic generators: PCG32 and xoshiro256**, with seeding, parallel streams and unbiased ranges.
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Scalar quantization
search-sort · low: Per-dimension int8/binary quantization of vectors with rescoring
Union-find
graphs · low: Disjoint-set forest with union by size and path compression; merges sets and answers same-set queries in effectively constant time.
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Bit packing and PFOR
compression · low: Store blocks of integers with exactly b bits each and patch rare outliers separately (PFOR); the fastest integer codec family, billions of values per second.
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Delta encoding
compression · low: Store differences between consecutive values instead of the values, turning sorted IDs, timestamps and counters into small numbers that pack into few bits.
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Varint (LEB128)
compression · low: Variable-length integers in 7-bit groups with a continuation bit; small numbers take 1 byte, the wire format of Protobuf, WebAssembly and DWARF.
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Sorted-list intersection
search-sort · low: Intersect sorted id lists by merging, galloping from the short list into the long one, and skipping whole compressed blocks, shortest list first.
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Byte-pair encoding (BPE) tokenizati…
machine-learning · low: Learn a subword vocabulary by repeatedly merging the most frequent adjacent pair; byte-level BPE encodes any input losslessly with 32k-200k tokens.
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Transformer block
ml-training · low: The decoder-only pre-norm block (attention plus MLP on a residual stream) behind every modern LLM, with exact shapes, parameter, FLOP and KV-cache counts.
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Convolutional codes and the Viterbi…
signal-processing · low: Streaming error-correcting code: each output bit is a parity of the last K input bits; the Viterbi decoder finds the most likely path through a 2^(K-1)-state trellis.
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Calibration
machine-learning · low: Make predicted probabilities match observed frequencies: measure with ECE, fix post hoc with temperature, Platt or isotonic scaling on held-out data.
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Linear and logistic regression
machine-learning · low: Fit a weighted sum of features to a number (least squares) or a probability (logistic); the baseline every model must beat.
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Top-k selection
search-sort · low: Keep the k best of n items without sorting all n - bounded min-heap for streams, quickselect for arrays, threshold pruning to skip most items.
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Vector quantization
compression · low: Codebook and pyramid vector quantization of coefficient vectors
k-means clustering
machine-learning · low: Partition points into k clusters by alternating nearest-centroid assignment and centroid averaging; k-means++ seeding makes it dependable.
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Fuzzy string matching
search-sort · low: Edit distance, Jaro-Winkler and trigram indexes for approximate name and address matching
Sanitizers
security · low: ASan/UBSan/TSan shadow memory and instrumentation to catch memory bugs
Text normalization
machine-learning · low: Rewrite messy text into one canonical form for matchers and models, or into spoken words for speech output (and back), keeping a map to the original offsets.
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Unicode text tokenization for search
search-sort · low: Turn multilingual text into index terms: segment words (UAX 29 style, CJK bigrams), normalize, case-fold and accent-fold, identically at index and query time.
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Control-flow graph
compilers · low: Split a linear instruction list into basic blocks and connect them by jumps; the graph every optimizer, SSA builder and dataflow pass walks.
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Topological sort
graphs · low: Order the vertices of a DAG so every edge points forward; the core of build systems, schedulers and dependency resolution, with cycle detection for free.
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Reference counting
compilers · low: Keep a count of owners in each object, free it when the count hits zero; deterministic and local, but pays on every copy and leaks cycles.
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Radix trie
data-structures · low: Compressed prefix tree for path routing, domain matching and prefix lookups
BFS and DFS
graphs · low: The two basic graph traversals; BFS gives hop distances and levels, DFS gives discovery/finish order for cycles, components and topological order.
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Loss functions
machine-learning · low: The scalar a model minimizes: cross-entropy for classes and tokens, MSE or Huber for values, each with a stable form and simple gradient.
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AdamW
ml-training · low: Adam's per-parameter adaptive steps with weight decay applied directly to the weights; the default optimizer for transformers and LLM pretraining.
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Paged KV cache
ml-training · low: Store each sequence's attention keys/values in fixed-size blocks mapped through a block table, like virtual memory pages; near-zero fragmentation and copy-on-write sharing.
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Reverse-mode automatic differentiat…
ml-training · low: Record the forward computation on a tape, then sweep it backwards once to get the gradient of a scalar loss for all inputs.
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L-BFGS
numerics · low: Quasi-Newton minimizer that approximates the inverse Hessian from the last m gradient differences via the two-loop recursion; O(m·n) memory and work per step.
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LU and Cholesky factorization
numerics · low: Factor A = PLU (any square matrix, partial pivoting) or A = LLᵀ (SPD) once in O(n³), then solve each right-hand side in O(n²).
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Sparse matrices (CSR)
numerics · low: Compressed sparse row stores only nonzeros as row pointers, column indices and values; SpMV streams them once, so it is memory-bandwidth bound.
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LRU and CLOCK
systems · low: LRU evicts the least recently used entry via hash map plus linked list; CLOCK approximates it with a reference bit, so hits take no lock.
›
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