AxAgent Memory And Skills Rules (@ax-llm/ax)

Use this skill when an agent needs a persistent context map, task-relevant memory retrieval, or skill guides loaded into the executor prompt on demand. For ordinary agent setup use ax-agent. For RLM runtime policy use ax-agent-rlm. For callbacks and telemetry use ax-agent-observability.

Use These Defaults

• Use a static skillsCatalog / memoriesCatalog when the skill guides or memories fit in a plain array — Ax then backs discover({ skills }) / recall(...) with a built-in deterministic local search and no host search code is needed.
• Use onSkillsSearch / onMemoriesSearch when retrieval needs a real backend (vector DB, BM25 service, KV). A host callback always takes precedence over the catalog's built-in search.
• Use contextMap when repeated runs inspect the same long external context and should accumulate a small orientation cache automatically.
• recall(...) is available to distiller and executor stages when onMemoriesSearch or a non-empty memoriesCatalog is set.
• discover({ skills }) is available to the executor when onSkillsSearch or a non-empty skillsCatalog is set.
• With skillsCatalog, the executor prompt also gains a static ### Available Skills index (id + name + description), so skill discovery is targeted instead of blind.
• Both recall(...) and discover({ skills }) return void. The loaded content appears on the next turn.
• Use onLoadedMemories / onLoadedSkills to observe what got loaded.
• Use onUsedMemories / onUsedSkills to track what the actor says it actually relied on.
• Child agents do not inherit memory or skills search callbacks; wire them explicitly on every agent that needs the capability.

Context Map

Use contextMap when repeated runs ask different questions over the same long context, document set, or repository. The map is prompt-resident orientation knowledge: structure, concepts, constants, parsing schema, reusable aggregate results, and concrete error patterns. It is not a task-specific answer cache.

Runnable example: src/examples/rlm-context-map-live.ts demonstrates a provider-backed context-map update, onUpdate snapshot persistence, finite evolve, and frozen map reuse.

When contextMap is configured:

• Ax injects the current map into the distiller prompt.
• Ax updates the map once after each successful completed forward(...).
• By default the map evolves forever. For a finite warmup, create the map with { infiniteEvolve: false, evolveSteps: N }; after N successful updates it is still injected but no longer updated.
• Failed runs, aborts, and clarification requests do not update the map.
• Use onUpdate to persist result.map.snapshot() outside the agent.

import { agent, AxAgentContextMap } from '@ax-llm/ax';

const map = new AxAgentContextMap(savedSnapshot, {
  maxChars: 4000,
  infiniteEvolve: false,
  evolveSteps: 10,
});

const myAgent = agent('context:string, query:string -> answer:string', {
  contextFields: ['context'],
  contextMap: {
    map,
    onUpdate: ({ map }) => saveSnapshot(map.snapshot()),
  },
});

Types:

type AxAgentContextMapConfig = {
  map?: AxAgentContextMap | AxAgentContextMapSnapshot | string;
  onUpdate?: (result: AxAgentContextMapUpdateResult) => void | Promise<void>;
};

type AxAgentContextMapOptions = {
  maxChars?: number;
  infiniteEvolve?: boolean;
  evolveSteps?: number;
};

Memory Search

Use onMemoriesSearch when the agent needs to pull task-relevant context such as user preferences, prior decisions, project facts, or past conversations from an external store (vector DB, BM25, KV). The actor decides what to load, when, and how much.

When onMemoriesSearch is set, the distiller and executor stages gain:

1. An inputs.memories field. In JS this is an array of { id, content } entries the actor reads directly. In the prompt, the same entries render as markdown blocks with ID: \...`lines, matching the Loaded Skills ID style. Eachcontent` is opaque markdown; frontmatter is not parsed.
2. A recall(searches: string[]): void global the actor awaits to load more entries. Recalled entries are appended to inputs.memories and visible from the next turn onward. recall() returns nothing.

The responder stage does not receive memories.

Enabling

import { agent } from '@ax-llm/ax';
import type { AxAgentMemoriesSearchFn } from '@ax-llm/ax';

const onMemoriesSearch: AxAgentMemoriesSearchFn = async (
  searches,
  alreadyLoaded
) => {
  // `searches` is the full array passed to recall(...). Batch your
  // store lookup in one round-trip.
  // `alreadyLoaded` is the current inputs.memories snapshot. Filter
  // against it to skip duplicates.
  const skip = new Set(alreadyLoaded.map((m) => m.id));
  const fresh = await myVectorDB.searchBatch(searches, { topK: 3 });
  return fresh.filter((m) => !skip.has(m.id));
};

const myAgent = agent('task:string -> answer:string', {
  contextFields: [],
  onMemoriesSearch,
});

Each memory result must be:

type AxAgentMemoryResult = {
  id: string;
  content: string;
};

Static catalog (no callback)

If the memory set fits in a plain array, skip the callback entirely: pass memoriesCatalog and Ax backs recall(...) with a built-in deterministic local search (idf-weighted token overlap over id + content; not regex, not embeddings). The alreadyLoaded contract is preserved — entries already on inputs.memories are excluded before ranking.

const myAgent = agent('task:string -> answer:string', {
  contextFields: [],
  memoriesCatalog: [
    { id: 'deploy-window', content: 'Prod deploys only on Tuesday afternoons.' },
    { id: 'user-prefs', content: 'User prefers concise answers.' },
  ],
});

Rules:

• If both memoriesCatalog and onMemoriesSearch are set, the host callback handles all recall(...) searches; the catalog still powers the advisory relevanceRanking hint.
• Catalog content is NOT preloaded into the prompt; entries load only when recalled.
• The built-in search is lexical. For semantic retrieval over large stores, supply onMemoriesSearch instead.

Preloading memories at forward time

To seed specific memories for one run (no recall round-trip), pass them as the memories input value. They render on inputs.memories from the first turn and merge with anything recalled later (deduped by id).

await myAgent.forward(ai, {
  task: 'Plan the deploy',
  memories: [{ id: 'deploy-window', content: 'Prod deploys only on Tuesday afternoons.' }],
});

Actor usage

// Turn 1: kick off one batched lookup.
await recall(['user preferences', 'project constraints']);

// Turn 2+: matched entries are now visible on inputs.memories.
const prefs = inputs.memories.find((m) => m.id === 'user-prefs-v2');

Rules:

• Pass all memory queries in one await recall([...]) call.
• Do not loop recall() calls or wrap them in Promise.all(...).
• Read inputs.memories on the next turn to see what landed.
• recall() invokes onMemoriesSearch with (searches, alreadyLoaded) and returns void.
• Results land on inputs.memories for subsequent turns and render in the prompt as:

### Memory

ID: `mem:user-prefs-v2`

...

• Entries are deduped by id (last-write-wins) and sorted by id for prefix-cache stability.
• Memories loaded by the distiller thread automatically to the executor. No second recall() is needed for those entries.
• recall() may be called multiple times across turns; results accumulate for that run.
• inputs.memories lifetime is one .forward() call. It resets between calls.

Carrying Memories Across .forward() Calls

To preserve continuity across calls, persist memories in your store and recall them again on the next call. If you want to replay anything loaded on a prior run, observe loads with onLoadedMemories.

const carried = new Map<string, string>();

const myAgent = agent('task:string -> answer:string', {
  contextFields: [],
  onMemoriesSearch: async (searches) => {
    const fresh = await myVectorDB.searchBatch(searches, { topK: 3 });
    const carriedAsResults = [...carried.entries()].map(([id, content]) => ({
      id,
      content,
    }));
    return [...carriedAsResults, ...fresh];
  },
  onLoadedMemories: (results) => {
    for (const r of results) carried.set(r.id, r.content);
  },
});

Skills Search

Use onSkillsSearch when the agent needs to load skill guides such as usage instructions, operational guides, or domain conventions into the executor's system prompt on demand. The actor decides which skills to fetch and when, so you do not pre-render every skill into every prompt.

When onSkillsSearch is set, the distiller and executor stages gain:

1. A "Loaded Skills" section in the system prompt that renders matched skill bodies with stable ID: values sorted by id.
2. A discover({ skills }) path the actor awaits to load more skills. Loaded entries appear in the next turn's prompt. discover(...) returns nothing.

Skills the distiller loads carry over to the executor automatically. The responder does not see skills.

Enabling

import { agent } from '@ax-llm/ax';
import type { AxAgentSkillsSearchFn } from '@ax-llm/ax';

// Each result is { id?: string; name: string; content: string }.
// If id is omitted, Ax falls back to name.
const onSkillsSearch: AxAgentSkillsSearchFn = async (searches) => {
  return mySkillStore.resolveBatch(searches, {
    // Recommended backend order: exact id, exact name, then broader search.
    // This lets the actor pass one simple string and keeps lookup policy host-side.
    strategy: ['id', 'name', 'search'],
    topK: 2,
  });
};

const myAgent = agent('task:string -> answer:string', {
  contextFields: [],
  onSkillsSearch,
});

Each skill result is:

type AxAgentSkillResult = {
  id?: string;
  name: string;
  content: string;
};

Static catalog (no callback)

If the skill set fits in a plain array, skip the callback entirely: pass skillsCatalog and Ax backs discover({ skills }) with a built-in deterministic local search (idf-weighted token overlap over id + name×2 + description×2 + the first 600 chars of content; not regex, not embeddings). The executor prompt also gains a static, cache-stable ### Available Skills index (id + name + description, sorted by id), so the actor searches by known ids instead of guessing.

import type { AxAgentCatalogSkill } from '@ax-llm/ax';

const catalog: AxAgentCatalogSkill[] = [
  {
    id: 'release-checklist',
    name: 'Release checklist',
    description: 'Steps for shipping a package release safely', // high-signal for matching
    content: '1. Bump version\n2. Run tests\n3. Tag and publish',
  },
];

const myAgent = agent('task:string -> answer:string', {
  contextFields: [],
  skillsCatalog: catalog,
});

type AxAgentCatalogSkill = {
  id: string;
  name: string;
  description?: string;
  content: string;
};

Rules:

• If both skillsCatalog and onSkillsSearch are set, the host callback handles all discover({ skills }) searches; the catalog still powers the ### Available Skills index and the advisory relevanceRanking hint.
• Catalog content is NOT preloaded into the prompt (unlike skills); entries load only when matched. Use skills for guides that must always be in context, skillsCatalog for a larger set loaded on demand.
• The built-in search is lexical. For semantic retrieval over large stores, supply onSkillsSearch instead.

Actor usage

// Pass all skill queries in one call.
await discover({ skills: ['release-checklist', 'incident-response'] });

// Next turn: loaded skill bodies render under the "Loaded Skills"
// system-prompt section.

Rules:

• discover({ skills }) invokes onSkillsSearch with the raw search strings and returns void.
• Resolve each raw string backend-side: prefer an exact id match, then an exact name match, then fuzzy/full-text search. The actor should not have to choose id: vs name: syntax.
• Matched skills land under "Loaded Skills" for the next turn.
• Entries are deduped by id (last-write-wins) and sorted by id for prefix-cache stability.
• If a skill result omits id, its trimmed name is used as the id for backwards compatibility.
• Skills persist on the agent's currentSkillsPromptState across .forward() calls, unlike memories.
• Use agent.getState() / setState(...) to serialize/restore loaded skills.
• discover({ skills }) may be called multiple times across turns. Within one turn, batch all skill queries in a single call.
• Child agents do not inherit onSkillsSearch; wire it explicitly per agent.

Preloading Skills

If the caller already knows which skills are relevant, pass them up front instead of round-tripping through discover({ skills }).

• Init-time: skills on AxAgentOptions seeds the executor prompt at agent creation. They survive setState(...) resets.
• Forward-time: skills on forward(ai, values, { skills }) merge in at the start of that call. Distiller and responder ignore forward-time skills.

Both accept the same shape onSkillsSearch returns: readonly AxAgentSkillResult[]. Forward-time skills override init-time skills by id. onLoadedSkills is not fired for preset skills; that callback is for runtime discover({ skills }) analytics.

const releaseAgent = agent('task:string -> answer:string', {
  contextFields: [],
  skills: [
    {
      id: 'release-checklist',
      name: 'release-checklist',
      content: '...',
    },
  ],
});

await releaseAgent.forward(
  ai,
  { task: 'Prepare release notes' },
  {
    skills: [
      {
        id: 'incident-response',
        name: 'incident-response',
        content: '...',
      },
    ],
  }
);

You can use skills without setting onSkillsSearch at all. That is useful for static guides where the actor never needs to fetch more.

Advisory Relevance Hints (relevanceRanking)

relevanceRanking is ON by default — leave it unset; set relevanceRanking: false to opt out. The default was flipped after its A/B gate passed (substance-judged, 49 runs per variant per model: small-model first-lookup precision 24%→90% and answer accuracy 14%→29%; frontier-model control accuracy 63%→88% with fewer turns). The generated language ports implement the same advisory hint contract through AxIR Core.

When enabled, a deterministic local ranker scores the agent's discoverable capabilities against the task once per forward(...) and injects a short advisory ### Likely Relevant shortlist into the executor turn — modules (needs functionDiscovery), catalog skills (needs skillsCatalog), and catalog memories (needs memoriesCatalog). The hint is non-authoritative: the full lists still apply and the actor may discover/recall anything else.

const myAgent = agent('task:string -> answer:string', {
  contextFields: [],
  functionDiscovery: true,
  skillsCatalog: catalog,
  relevanceRanking: true, // or { topK: 3, minScore: 0.08 }
});

Rules:

• Default is ON across TypeScript and generated language ports; domains still self-gate on their prerequisites (functionDiscovery for modules, catalogs for skills/memories), so agents without those see no change. Everything else in this skill (catalog search, the Available Skills index) is independent of the flag.
• The shortlist rides a dynamic, non-cached prompt field; the cached system prompt stays byte-stable across tasks.
• On low confidence the ranker emits nothing rather than guessing.
• Memory hint entries include an ~80-char content snippet; very short memories may be usable from the hint alone without a recall(...) (such use is not visible to onUsedMemories).
• Observe outcomes via the relevance_ranking context event (see ax-agent-observability).

Loaded And Used Tracking

onLoadedMemories reports what recall(...) loaded. onLoadedSkills reports what discover({ skills }) loaded. To track what the actor says it actually relied on, use onUsedMemories / onUsedSkills.

const used: AxAgentUsedMemory[] = [];

await myAgent.forward(
  ai,
  { task: 'Make a personal plan' },
  {
    onUsedMemories: (items) => used.push(...items),
  }
);

used; // [{ id, reason, stage }]

Rules:

• The actor can only report memory IDs already present in inputs.memories.
• The actor can only report skill IDs already present in Loaded Skills.
• Unknown values are dropped.
• When tracking is enabled, the actor sees await used(id, reason?); this is the actor-side declaration mechanism.
• used(...) resolves against loaded memory IDs and loaded skill IDs.
• If memory IDs and skill IDs can collide, namespace them in your application, for example mem:abc and skill:planning.
• Python, Go, and Java accept these observers directly in their agent option maps. C++ wraps them with register_agent_observer(...); Rust wraps them with agent_observer(...). The returned marker can be used in constructor or forward option maps, and observer failures are ignored in every language.

Types:

onMemoriesSearch?: AxAgentMemoriesSearchFn;
onLoadedMemories?: (
  results: readonly AxAgentMemoryResult[]
) => void | Promise<void>;
onUsedMemories?: (
  usedMemories: readonly AxAgentUsedMemory[]
) => void | Promise<void>;

onSkillsSearch?: AxAgentSkillsSearchFn;
onLoadedSkills?: (
  results: readonly AxAgentSkillResult[]
) => void | Promise<void>;
onUsedSkills?: (
  usedSkills: readonly AxAgentUsedSkill[]
) => void | Promise<void>;

contextMap?: AxAgentContextMapConfig;
skills?: readonly AxAgentSkillResult[];
skillsCatalog?: readonly AxAgentCatalogSkill[];
memoriesCatalog?: readonly AxAgentMemoryResult[];
relevanceRanking?: boolean | { topK?: number; minScore?: number };

Persisting Agent State Across Languages

TypeScript uses getState() / setState() for the actor runtime snapshot. The generated packages keep their legacy GetState / SetState (or language-shaped equivalents) as bare-runtime compatibility methods. Use ExportRuntimeState / RestoreRuntimeState in generated packages when you need the complete portable agent snapshot, including loaded skills and constructor-preset reapplication after restore. Do not interchange the two snapshot shapes.

Examples

Fetch this for full working code:

• RLM Memories and Skills - onMemoriesSearch + recall() and onSkillsSearch + discover({ skills }) with load observability and actual usage tracking via onUsedMemories / onUsedSkills
• Skills + Memory Ops Assistant - an on-call assistant that recalls past decisions from a memory store and loads the right runbook skill on demand (also ported to Python, Go, Rust, Java, and C++ under src/examples/<lang>/long-agents/). All six languages support the native onMemoriesSearch / onSkillsSearch host callbacks, passed in the agent options at construction (Go/Java use native function values, Rust a agent_with_search_callbacks constructor, C++ a register_*_search helper); a static memory_search_results / skill_search_results config is also available.

Do Not Generate

• Do not assign the result of await recall(...) or await discover(...); both return void.
• Do not call recall() from the responder stage.
• Do not call discover({ skills }) from the responder stage.
• Do not loop recall() calls or wrap them in Promise.all(...).
• Do not loop discover() calls or wrap them in Promise.all(...).
• Do not assume child agents inherit onMemoriesSearch or onSkillsSearch.
• Do not pass onMemoriesSearch results via shared fields as a workaround; use recall(...).
• Do not assume inputs.memories persists across .forward() calls.
• Do not use onLoadedMemories / onLoadedSkills as proof that the actor relied on an item; use onUsedMemories / onUsedSkills for actual-use tracking.
• Do not write an onSkillsSearch / onMemoriesSearch callback that just scans a static array; pass the array as skillsCatalog / memoriesCatalog instead.
• Do not rely on the built-in catalog search for semantic matching over large stores; it is lexical token overlap — supply a host callback for embeddings/vector search.
• Do not confuse skills (always preloaded into the prompt) with skillsCatalog (searchable, loaded on demand).