@vbch/total-agent-memory-client

TypeScript / JavaScript client for total-agent-memory — the local-first MCP memory layer for AI coding agents.

New in 0.2.0 (server v8.0+ required): structured decisions with per-criterion scoring, an L1-L4 task state machine (classifyTask / taskCreate / phaseTransition), progressive-disclosure retrieval (mode: "index" + memoryGet) that cuts typical recall tokens by 80-90%, intent capture, phase-scoped rules, LLM-compressed sessionEnd, and inline <private>…</private> redaction reporting.

Why use this? If you're building in Node.js, Bun, Deno, or browsers and you want persistent, graph-aware memory that learns how you work, not just what you said — this is your client. 100% local by default. Temporal facts. Procedural workflows. Cross-project analogy. See the engine feature matrix vs mem0 / Letta / Zep / Supermemory.

Install

npm i @vbch/total-agent-memory-client
# peerless — bundles @modelcontextprotocol/sdk

You also need the server running. One-line install:

curl -fsSL https://raw.githubusercontent.com/vbcherepanov/total-agent-memory/main/install.sh | bash

Quick start (stdio — local MCP subprocess)

import { connectStdio } from "@vbch/total-agent-memory-client";

const memory = await connectStdio();

// resume where you left off
const { summary, next_steps } = await memory.sessionInit("my-project");

// remember a decision
await memory.save({
  type: "decision",
  content: "Chose pgvector over ChromaDB for multi-tenant RLS.",
  context: "Why: single DB, per-tenant row-level security.",
  project: "my-project",
  tags: ["database"],
});

// recall it later (or from a different process, or next month)
const hits = await memory.recallFlat({
  query: "vector store choice",
  project: "my-project",
  limit: 5,
});

Quick start (HTTP — remote gateway)

Works against the dashboard HTTP gateway (total-agent-memory v7.1+):

import { connectHttp } from "@vbch/total-agent-memory-client";

const memory = connectHttp({
  baseUrl: "http://127.0.0.1:37737",
  token: process.env.MEMORY_TOKEN, // optional
});

const stats = await memory.stats();
console.log(stats.knowledge.active, "records across", Object.keys(stats.by_project).length, "projects");

What you get over mem0 / Zep / Supermemory SDKs

| Capability | mem0 JS | Zep JS | Supermemory JS | this | |---|:-:|:-:|:-:|:-:| | Local-first (no network) | ❌ | ❌ | ❌ | ✅ stdio | | Semantic + BM25 + graph hybrid | 🟡 | ✅ | ✅ | ✅ 6-tier | | Temporal facts (kg_at) | ❌ | ✅ | ❌ | ✅ | | Procedural memory (workflow_predict) | ❌ | ❌ | ❌ | ✅ | | Cross-project analogy (analogize) | ❌ | ❌ | ❌ | ✅ | | Pre-edit risk warnings (file_context) | ❌ | ❌ | ❌ | ✅ | | AST codebase ingest (ingest_codebase) | ❌ | ❌ | ❌ | ✅ | | Strict TypeScript types for all tools | 🟡 | 🟡 | 🟡 | ✅ |

API surface

Session lifecycle

• sessionInit(project) → resume previous session's summary + next steps
• sessionEnd({ summary, next_steps, pitfalls })

Core memory

• save({ type, content, context?, project?, tags?, branch? })
• recall({ query, project?, limit?, rerank?, diverse?, … })
• recallFlat(input) — flattened, RRF-sorted list

v7 tools

• fileContext(path) — risk score + past-error warnings for a file
• learnError({ file, error, root_cause, fix, pattern }) — auto-consolidates into rules at N≥3
• analogize({ query, exclude_project? }) — cross-project pattern match
• kgAddFact({ subject, predicate, object, valid_from? }) — append-only temporal KG
• kgAt(timestamp) — point-in-time fact snapshot
• workflowPredict(task_description) — procedural prediction with confidence
• workflowTrack(workflow_id, outcome) — close the loop after task completion
• ingestCodebase({ path, languages? }) — AST indexing via tree-sitter (9 langs)

v8 tools (requires server 8.0+)

• classifyTask(description, project?) — L1-L4 classifier, returns suggested phases + confidence
• taskCreate(taskId, description, level?) — open a task in the state machine
• phaseTransition(taskId, newPhase, artifacts?, notes?) — step through van → plan → creative → build → reflect → archive
• taskPhasesList(taskId) — full phase timeline for a task
• saveDecision({ title, options, criteria_matrix, selected, rationale }) — structured decisions with per-criterion scoring, indexed for recall
• memoryRecall({ mode: "index" }) + memoryGet(ids, detail?) — progressive-disclosure retrieval: cheap index scan → targeted fetch. ~80-90% token savings on typical 20-hit queries vs. mode: "search", detail: "full".
• saveIntent / listIntents / searchIntents — captured user prompts
• ruleSetPhase(ruleId, phase) — scope a rule to a task phase (or null for global)
• Extended sessionEnd({ auto_compress, transcript }) — LLM-generated summary/next-steps from a raw transcript
• Extended MemorySaveResult.privacy_redacted_sections — count of inline <private>…</private> sections stripped before storage

Progressive disclosure in practice

// Layer 1: cheap index scan (id + title + preview per hit, ~50 tokens each)
const idx = await memory.memoryRecall({
  query: "database choice",
  mode: "index",
  limit: 20,
});

// Layer 2: agent picks what's worth the full body
const keepIds = (idx.index_results ?? []).slice(0, 3).map((r) => r.id);
const full = await memory.memoryGet(keepIds, "full");

Introspection

• stats() — active knowledge, projects, storage, queues
• benchmark() — reproducible R@k + latency eval

Integrations

• LangChain JS — examples/langchain-adapter.ts
• Vercel AI SDK — wire memory_save as a tool in your agent loop
• LlamaIndex TS — implement BaseMemory around save/recallFlat
• CrewAI / AutoGen (Python peers) — use the Python client directly

Privacy

This client spawns a local subprocess (stdio) or calls a locally-bound HTTP port. No data is sent to any cloud service unless you configure the server to use one (the default nomic-embed-text via Ollama is also fully local).

License

MIT. See LICENSE.