neuromem-sdk 0.4.7

Brain-Faithful Multimodal Memory SDK for AI Agents

NeuroMem SDK

Brain-inspired memory infrastructure for AI agents.
Graph-based relationships. Multi-framework. MCP-native.

Quick Start · Adapters · MCP Server · Benchmarks · Changelog

---

What is NeuroMem?

NeuroMem is a multi-layer memory system modeled after human cognition. It gives AI agents the ability to remember experiences, learn stable facts, adapt to user preferences, and forget naturally — across any framework.

Episodic Memory ── recent interactions, conversations, events
Semantic Memory ── consolidated facts, knowledge, learned patterns
Procedural Memory ── behavioral preferences, user style, habits
Session Memory ── current conversation context (RAM-only)

Memories are connected through a knowledge graph with entity extraction, enabling retrieval that goes beyond vector similarity — surfacing related memories through relationship traversal.

Key Capabilities

• Local-first defaults, zero-key setup (v0.4.1) — every LLM call site (auto-tagging, consolidation, multi-hop query decomposition) routes through neuromem.utils.llm.chat_completion, which dispatches to local Ollama or OpenAI based on the model name. Default neuromem.yaml ships with embedding: nomic-embed-text + consolidation_llm: ollama/qwen2.5-coder:7b, so a fresh install with Ollama running needs no OPENAI_API_KEY to observe + retrieve.
• Local Workspace UI (v0.4.0) — Obsidian-like three-pane layout (file tree · Plate.js block editor · backlinks panel) at http://127.0.0.1:7777 via neuromem-ui. 2D Cytoscape graph + 3D anatomical "Jarvis brain" view (hippocampus / neocortex / basal ganglia / amygdala / PFC) on the same data.
• Knowledge-base ingestion (v0.4.0) — drag any PDF / DOCX / XLSX / PPTX / MD / HTML / PNG / JPG anywhere on the workspace. Powered by Docling (IBM Research). Schema-grounded: each chunk shares a source_id so the SchemaIntegrator (Tse et al. 2007) treats one upload as a coherent schema.
• Soft-supersede memory edits (v0.4.0) — PUT /api/memories/{id} deprecates the old memory and creates a new one with a supersedes graph link. Reconsolidation-faithful (Nader 2000 — retrieved memories become labile and update creates a new trace, not a mutation).
• Web-chat MCP setup (v0.4.0) — neuromem-mcp --transport http --public spawns a cloudflared tunnel and prints copy-paste JSON for Claude.ai · Gemini chat · ChatGPT.
• Beats MemPalace on MemBench, LongMemEval, and ConvoMem head-to-head — see Benchmarks
• Digital brain architecture (v0.3.0) — 6 brain regions (hippocampus CA1/pattern-sep/pattern-comp, neocortex, amygdala, basal ganglia, prefrontal) with JSON-persisted state. Emotional-weight modulation now wired into retrieval (v0.4.0, Phelps 2004).
• Multimodal fusion (v0.3.0) — text / audio / video encoders + late-fusion router + LiveKit bridge for voice agents
• Tunable retrieval pipeline (v0.3.2) — BM25 + cross-encoder + HyDE + LLM rerank, with per-workload blend tuning via YAML. Cross-encoder provider is now swappable (v0.4.0): sentence-transformers (default) · bge · cohere · openai.
• BeliefState source-monitoring (v0.4.0) — SPECULATED / INFERRED / BELIEVED / KNOWN IntEnum on every memory (Johnson, Hashtroudi, Lindsay 1993). Substrate for v0.5.0's calibrated abstention.
• Graph-augmented retrieval — entity-linked memory graph with backlinks, clusters, and bridge detection
• Multi-factor scoring — similarity + salience + recency + reinforcement + confidence
• Structured query syntax — filter by type, tag, confidence, date range, sentiment, intent
• Natural forgetting — Ebbinghaus decay curves with reinforcement on access
• LLM-powered consolidation — automatic episodic-to-semantic promotion via fact extraction
• 8 framework adapters + 5 IDE/chat plugins — LangChain, LangGraph, LiteLLM, CrewAI, AutoGen, DSPy, Haystack, Semantic Kernel · plugins for Claude Code · Codex CLI · Gemini CLI · Cursor (v0.4.0) · Antigravity (v0.4.0)
• MCP server — 12 tools for any MCP-compatible client (Claude.ai web · Gemini chat · ChatGPT · Cursor · Antigravity · etc.). Provider-tagged exceptions (v0.4.0) close Letta #3310.
• Qdrant default (v0.4.0) — vector_store.type: qdrant with health-check fallback to in-memory if Qdrant isn't running.
• 4 storage backends — Qdrant (default), PostgreSQL+pgvector, SQLite, In-Memory
• Python 3.9+ — tested on 3.9 / 3.10 / 3.11 / 3.12 in CI

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Quick Start

Install (v0.4.2 — one command for non-technical users)

pipx install 'neuromem-sdk[ui]'      # Or: pip install 'neuromem-sdk[ui]'
neuromem init                        # 5-question wizard → writes neuromem.yaml + .env
                                     # then auto-launches the UI

The wizard asks: mode (single-user vs service/multi-user), embedding model (Ollama or OpenAI), vector store (Qdrant / Postgres / in-memory), port, and (for service mode) Postgres URL. Pick --ui to skip the terminal and finish setup in the browser at http://127.0.0.1:7777/onboarding.

neuromem init --ui                   # browser-based wizard
neuromem ui                          # launch UI (after init)
neuromem doctor                      # check Ollama / Qdrant / Postgres reachability
neuromem config edit                 # open $EDITOR with validation on save
neuromem config show                 # print resolved config

Install variants

pip install neuromem-sdk                     # core SDK only
pip install 'neuromem-sdk[ui]'               # UI + wizard + service mode (FastAPI + SQLAlchemy + bcrypt)
pip install 'neuromem-sdk[ingest]'           # Docling — PDF/DOCX/XLSX/PPTX/HTML/image ingestion
pip install 'neuromem-sdk[mcp]'              # MCP server
pip install 'neuromem-sdk[qdrant]'           # Qdrant backend (recommended; v0.4.0+ default)
pip install 'neuromem-sdk[postgres]'         # PostgreSQL backend
pip install 'neuromem-sdk[langchain]'        # LangChain adapter
pip install 'neuromem-sdk[langgraph]'        # LangGraph adapter
pip install 'neuromem-sdk[crewai]'           # CrewAI adapter
pip install 'neuromem-sdk[all-no-dspy]'      # Everything except dspy

Recommended v0.4.2 install (UI + KB ingest + MCP + Qdrant):

pip install 'neuromem-sdk[mcp,ui,qdrant,ingest]'
docker run -d -p 6333:6333 qdrant/qdrant     # optional — Qdrant gracefully falls back to in-memory
neuromem init                                # writes config + opens http://127.0.0.1:7777

Service mode (multi-user, API-key auth)

neuromem init + select "Service / multi-user" when prompted. The wizard generates NEUROMEM_AUTH_SECRET, switches the user store to SqlUserStore, and enables the X-API-Key middleware. Mint the first user from the UI or:

curl -X POST http://127.0.0.1:7777/api/users \
  -H 'Content-Type: application/json' \
  -d '{"external_id": "admin@local"}'
# Returns {"api_key": "nm_...", "warning": "Store this key now — it cannot be retrieved later."}

Zero-key setup with local Ollama (v0.4.1 default):

ollama pull nomic-embed-text          # 768-dim embeddings
ollama pull qwen2.5-coder:7b          # auto-tagging + consolidation + multi-hop decomposition
pip install neuromem-sdk              # no OPENAI_API_KEY required

Configure

Create a neuromem.yaml. v0.4.1 ships local-first defaults — Ollama for both embeddings and LLM calls. Override with OpenAI model names if you'd rather use OPENAI_API_KEY.

neuromem:
  model:
    # v0.4.1 default: local Ollama, no API key required.
    # Switch to `text-embedding-3-large` (3072-dim) + `gpt-4o-mini` for OpenAI —
    # remember to also bump `vector_size` to 3072 BEFORE creating the Qdrant
    # collection (Qdrant locks dimension at create-time).
    embedding: nomic-embed-text
    consolidation_llm: ollama/qwen2.5-coder:7b

  storage:
    vector_store:
      type: qdrant          # qdrant | postgres | sqlite | memory
      config:
        host: localhost
        port: 6333
        collection_name: neuromem
        vector_size: 768    # 384=MiniLM-L6 · 768=nomic-embed-text · 3072=text-embedding-3-large

  memory:
    decay_enabled: true
    consolidation_interval: 10

  retrieval:
    hybrid_enabled: true

  async:
    # v0.4.1: keep `false` for local dev — observe queues to a worker
    # with no flush()/drain() API, so writes are invisible to follow-up
    # reads in the same process. Flip `true` for production throughput.
    enabled: false

Optional — using OpenAI instead of Ollama:

export OPENAI_API_KEY=sk-...
# In neuromem.yaml: embedding: text-embedding-3-large
#                   consolidation_llm: gpt-4o-mini
#                   vector_size: 3072

Use

from neuromem import NeuroMem

memory = NeuroMem.from_config("neuromem.yaml", user_id="user_123")

# Store an interaction
memory.observe(
    user_input="I prefer Python over JavaScript for backend work",
    assistant_output="Noted — I'll prioritize Python examples."
)

# Retrieve relevant memories
results = memory.retrieve(query="What languages does the user prefer?", k=5)
for item in results:
    print(f"[{item.memory_type}] {item.content}")

# Consolidate episodic memories into semantic facts
memory.consolidate()

# Cleanup
memory.close()

---

Framework Adapters

NeuroMem integrates with 8 frameworks through drop-in adapters. All adapters use lazy imports — the framework package is only loaded when called.

LangChain

from neuromem import NeuroMem
from neuromem.adapters.langchain import add_memory

memory = NeuroMem.for_langchain(user_id="user_123")
chain_with_memory = add_memory(chain, memory, k=5)
response = chain_with_memory.invoke({"input": "What are my preferences?"})

LangGraph

from neuromem import NeuroMem
from neuromem.adapters.langgraph import with_memory

memory = NeuroMem.for_langgraph(user_id="user_123")
app = with_memory(graph.compile(), memory)
result = app.invoke({"input": "Hello"})

CrewAI

from neuromem import NeuroMem
from neuromem.adapters.crewai import create_neuromem_tools

memory = NeuroMem.for_crewai(user_id="user_123")
tools = create_neuromem_tools(memory, k=5)
# tools: [NeuroMemSearchTool, NeuroMemStoreTool, NeuroMemConsolidateTool, NeuroMemContextTool]

AutoGen (AG2)

from neuromem import NeuroMem
from neuromem.adapters.autogen import register_neuromem_tools

memory = NeuroMem.for_autogen(user_id="user_123")
register_neuromem_tools(memory, caller=assistant, executor=user_proxy, k=5)

DSPy

from neuromem import NeuroMem
from neuromem.adapters.dspy import NeuroMemRetriever

memory = NeuroMem.for_dspy(user_id="user_123")
retriever = NeuroMemRetriever(memory, k=5)  # Drop-in dspy.Retrieve replacement

Haystack

from neuromem import NeuroMem
from neuromem.adapters.haystack import NeuroMemRetriever, NeuroMemWriter

memory = NeuroMem.for_haystack(user_id="user_123")
pipeline.add_component("retriever", NeuroMemRetriever(memory, top_k=5))
pipeline.add_component("writer", NeuroMemWriter(memory))

Semantic Kernel

from neuromem import NeuroMem
from neuromem.adapters.semantic_kernel import create_neuromem_plugin

memory = NeuroMem.for_semantic_kernel(user_id="user_123")
plugin = create_neuromem_plugin(memory, k=5)
# Exposes: search_memory, store_memory, get_context, consolidate

LiteLLM

from neuromem import NeuroMem
from neuromem.adapters.litellm import completion_with_memory

memory = NeuroMem.for_litellm(user_id="user_123")
response = completion_with_memory(
    model="gpt-4",
    messages=[{"role": "user", "content": "What do I like?"}],
    memory=memory
)

---

Workspace UI (v0.4.0)

A local Obsidian-like workspace at http://127.0.0.1:7777. Three panes (file tree · Plate.js block editor · backlinks) plus standalone routes for the 2D and 3D graph views, retrieval-run inspector, observation feed, brain telemetry, and MCP setup.

pip install 'neuromem-sdk[ui,ingest,qdrant]'
neuromem-ui                                  # → http://127.0.0.1:7777

Surface	What it does
File tree	Three groups — Knowledge Base (uploaded docs, by source_id) · Conversations (organic episodic) · Working Memory (live PFC slots, Cowan 4)
Plate.js editor	Block-based Markdown · [[wiki-links]] to other memories · save = soft-supersede (PUT /api/memories/{id} deprecates old, creates new with supersedes graph link)
Backlinks panel	Incoming + outgoing edges grouped by link_type (derived_from · related · reinforces · contradicts · supersedes)
2D graph	Cytoscape.js + cose-bilkent · node colour = MemoryType · flashbulb pulses red
3D brain	react-force-graph-3d with anatomical regions: hippocampus core · neocortex shell · basal-ganglia ring · amygdala cluster · PFC orbital ring
Retrieval inspector	Inngest-style timeline of every retrieve() with full per-stage trace (vector → hybrid boosts → BM25 → CE → LLM rerank → conflict resolution → brain gating). SSE for live runs
KB drag-drop	Drop a PDF / DOCX / XLSX / PPTX / MD / HTML / PNG / JPG anywhere on the workspace; Docling parses it; chunks land in verbatim store with source_id linkage

Frontend source lives in ui/; build it with:

cd ui && npm install && npm run build        # → static bundle in neuromem/ui/web/

The Python neuromem-ui server mounts that bundle at /, so the backend and SPA serve from the same port in production.

MCP Server

NeuroMem ships as a standalone MCP server with 12 tools, 3 resources, and 2 prompts.

pip install 'neuromem-sdk[mcp]'

# Start the server (stdio — for Claude Code / Cursor / Codex CLI / Antigravity / Gemini CLI)
python -m neuromem.mcp
# Or: neuromem-mcp

# v0.4.0: HTTP transport + public tunnel for web chat clients
neuromem-mcp --transport http --port 7799 --public
# → spawns cloudflared, prints copy-paste JSON for Claude.ai / Gemini chat / ChatGPT

Plugins shipped in v0.4.0

Client	Plugin path	Setup
Claude Code	plugins/claude-code/	claude plugin install plugins/claude-code
Codex CLI	plugins/codex-cli/	drop into Codex CLI plugin dir
Gemini CLI	plugins/gemini-cli/	drop into Gemini CLI extensions dir
Cursor	plugins/cursor/.cursor/mcp.json	copy to your project's .cursor/mcp.json
Antigravity	plugins/antigravity/.antigravity/mcp.json	copy to your project's .antigravity/mcp.json
Claude.ai web	plugins/docs/CLAUDE_AI_WEB.md	run --public tunnel + paste URL
Gemini chat	plugins/docs/GEMINI_CHAT.md	run --public tunnel + paste URL
ChatGPT	plugins/docs/CHATGPT.md	run --public tunnel + paste URL

Tools

Tool	Description
store_memory	Store observations with auto-template detection
search_memories	Semantic search with multi-hop decomposition
search_advanced	Structured query syntax with filters
get_context	Retrieve with graph-based context expansion
get_memory	Get a specific memory by ID
list_memories	List memories with optional type filtering
update_memory	Modify existing memory content
delete_memory	Permanently delete a memory
consolidate	Trigger episodic-to-semantic promotion
get_stats	System statistics and health status
find_by_tags	Hierarchical tag-based lookup
get_graph	Export the memory relationship graph

Claude Code Integration

{
  "mcpServers": {
    "neuromem": {
      "command": "neuromem-mcp",
      "env": {
        "OPENAI_API_KEY": "sk-..."
      }
    }
  }
}

---

Graph Memory

Memories are linked through a knowledge graph with 5 relationship types:

derived_from  — semantic memory created from episodic sources
contradicts   — conflicting memories
reinforces    — strengthening relationships
related       — similar content detected
supersedes    — newer memory replaces older

# Retrieve with graph context expansion
context = memory.retrieve_with_context(query="What does the user prefer?", k=5)

# Export the graph
graph = memory.get_graph()  # { nodes: [...], edges: [...] }

Entity extraction runs inline during observe() — lightweight, no external dependencies, <1ms. Entities are indexed for O(1) lookup during retrieval.

---

Structured Query Syntax

# Filter by type and confidence
results = memory.search('type:semantic confidence:>0.8 python')

# Date range with exact phrase
results = memory.search('after:2024-01-01 before:2024-12-31 "machine learning"')

# Sentiment and intent
results = memory.search('intent:question sentiment:positive')

# Tag hierarchy
results = memory.find_by_tags("preference/", limit=20)
tag_tree = memory.get_tag_tree()

Operators: type:, tag:, confidence:, salience:, after:, before:, intent:, sentiment:, source:, "exact phrase"

---

Memory Templates

Structured observation templates with auto-detection:

# Auto-detected from keywords
memory.observe(
    user_input="I prefer dark mode in all my IDEs",
    assistant_output="Noted."
)
# Detected as "preference" → salience=0.9, tags=["preference"]

# Explicit template
memory.observe(
    user_input="I decided to use PostgreSQL",
    assistant_output="Good choice.",
    template="decision"
)

Template	Salience	Auto-detected Keywords
decision	0.8	decided, chose, picked, settled on
preference	0.9	prefer, like, want, love, hate
fact	0.7	my name is, I am, I work, I use
goal	0.85	want to, planning to, goal is
feedback	0.75	feedback, suggestion, improve

---

Temporal Summaries

# Daily digest
summary = memory.daily_summary(date="2026-03-28")
# { date, summary, memory_count, key_topics, key_facts, sentiment_distribution }

# Weekly digest
digest = memory.weekly_digest(week_start="2026-03-25")

---

Storage Backends

Backend	Install	Use Case
Qdrant (v0.4.0 default)	pip install 'neuromem-sdk[qdrant]'	Production, high-performance vector search
PostgreSQL + pgvector	pip install 'neuromem-sdk[postgres]'	Production, large-scale
SQLite	Built-in	Local development, small datasets
In-Memory	Built-in	Development, testing — also the v0.4.0 fallback when Qdrant is unreachable

# Qdrant (v0.4.0 default — automatic fallback to in-memory if unreachable)
storage:
  vector_store:
    type: qdrant
    config:
      host: localhost
      port: 6333
      collection_name: neuromem
      vector_size: 768

# PostgreSQL
storage:
  database:
    type: postgres
    url: postgresql://user:pass@localhost:5432/neuromem

v0.4.0 graceful fallback: if vector_store.type: qdrant is set but Qdrant isn't reachable on startup, NeuroMem logs a clear warning (with the docker run hint) and falls back to the in-memory backend. This means pip install neuromem-sdk followed by neuromem-ui works on a clean machine without any infra.

---

Benchmarks

NeuroMem v0.3.2 beats MemPalace on all three industry retrieval benchmarks — MemBench (ACL 2025), LongMemEval, and ConvoMem — using the same embeddings (all-MiniLM-L6-v2), same data, and the same cross-encoder (ms-marco-MiniLM-L-12-v2).

Head-to-head vs MemPalace (2026-04-22)

Benchmark	Items	NeuroMem v0.3.2 R@5	MemPalace R@5	Delta	NeuroMem config
MemBench	330	97.0%	87.9%	+9.1 🟢	--verbatim-only (default blends)
LongMemEval	100	98.0%	94.0%	+4.0 🟢	cognitive defaults
ConvoMem	150	81.3%	80.7%	+0.6 🟢	--verbatim-only --bm25-blend 0.0 --ce-blend 0.9

MemBench per-task breakdown (11 tasks, 30 items each)

Task	NeuroMem	MemPalace	Delta
aggregative	100.0%	100.0%	—
comparative	100.0%	100.0%	—
conditional	96.7%	83.3%	+13.4
highlevel	100.0%	93.3%	+6.7
highlevel_rec	80.0%	76.7%	+3.3
knowledge_update	100.0%	93.3%	+6.7
lowlevel_rec	100.0%	100.0%	—
noisy	96.7%	73.3%	+23.4
post_processing	100.0%	76.7%	+23.3
RecMultiSession	100.0%	100.0%	—
simple	93.3%	70.0%	+23.3

NeuroMem wins 7 of 11 categories; ties the other 4 at 100%.

Workload-specific retrieval recipes (v0.3.2+)

bm25_blend and ce_blend are configurable in neuromem.yaml under retrieval:. Tune per dominant query profile:

retrieval:
  # Exact-fact recall (phone, dates, proper nouns, IDs) — DEFAULT
  bm25_blend: 0.5
  ce_blend: 0.9

  # Abstract advice-seeking ("what should I look into...", "how can I...")
  # bm25_blend: 0.0
  # ce_blend: 0.9

  # Pure semantic search (MemPalace-equivalent)
  # bm25_blend: 0.0
  # ce_blend: 0.0

Reproduce the benchmarks

# MemBench (~5 min, beats MemPalace by +9.1)
python -m benchmarks.run_benchmark --benchmark membench --systems neuromem mempalace \
  --embedding-provider sentence-transformers --embedding-model all-MiniLM-L6-v2 \
  --verbatim-only --search-k 10 --max-per-slice 30 --no-judge

# LongMemEval (~12 min, beats MemPalace by +4.0)
python -m benchmarks.run_benchmark --benchmark longmemeval --systems neuromem mempalace \
  --embedding-provider sentence-transformers --embedding-model all-MiniLM-L6-v2 \
  --search-k 100 --max-questions 100 --no-judge

# ConvoMem (~3 min, beats MemPalace by +0.6)
python -m benchmarks.run_benchmark --benchmark convomem --systems neuromem \
  --embedding-provider sentence-transformers --embedding-model all-MiniLM-L6-v2 \
  --verbatim-only --bm25-blend 0.0 --ce-blend 0.9 \
  --search-k 30 --max-per-slice 30 --no-judge

Honest open item

LongMemEval multi-session sub-category: 93.3% (2/30 counting-type queries miss because they need all 4 relevant sessions in top-5). Quorum / multi-hop coverage fix parked for v0.4.0.

Earlier benchmark (LoCoMo, v0.2.0 reference)

For historical context — LoCoMo benchmark (ACL 2024), Categories 1+4:

System	F1	Exact Match	Retrieval Hit Rate
NeuroMem v0.2.0	39.4	15.0%	36.7%
LangMem	32.7	11.7%	33.3%
Mem0	30.6	10.0%	21.7%

---

API Reference

Core

NeuroMem.from_config(config_path, user_id)      # Initialize from YAML
NeuroMem.for_langchain(user_id, config_path)     # LangChain constructor
NeuroMem.for_langgraph(user_id, config_path)     # LangGraph constructor
NeuroMem.for_crewai(user_id, config_path)        # CrewAI constructor
NeuroMem.for_autogen(user_id, config_path)       # AutoGen constructor
NeuroMem.for_dspy(user_id, config_path)          # DSPy constructor
NeuroMem.for_haystack(user_id, config_path)      # Haystack constructor
NeuroMem.for_semantic_kernel(user_id, config_path) # Semantic Kernel constructor
NeuroMem.for_mcp(user_id, config_path)           # MCP constructor
NeuroMem.for_litellm(user_id, config_path)       # LiteLLM constructor

Memory Operations

memory.observe(user_input, assistant_output, template=None)
memory.retrieve(query, task_type="chat", k=8, parallel=True)
memory.retrieve_with_context(query, task_type="chat", k=5)
memory.search(query_string, k=10)
memory.consolidate()
memory.list(memory_type=None, limit=50)
memory.update(memory_id, content)
memory.forget(memory_id)
memory.explain(memory_id)
memory.close()

Graph & Discovery

memory.get_graph()                                # Export graph as {nodes, edges}
memory.find_by_tags(tag_prefix, limit=20)         # Hierarchical tag search
memory.get_tag_tree()                             # Tag hierarchy with counts
memory.get_memories_by_date(date)                 # Temporal retrieval
memory.get_memories_in_range(start, end, memory_type)

Summaries

memory.daily_summary(date)                        # Daily memory digest
memory.weekly_digest(week_start)                  # Weekly summary

---

Architecture

NeuroMem (Facade)
  └── MemoryController
        ├── EpisodicMemory ──┐
        ├── SemanticMemory ──┤── MemoryBackend (Protocol)
        ├── ProceduralMemory ┘
        ├── SessionMemory (RAM-only)
        ├── MemoryGraph (entity index, backlinks, clusters)
        ├── MemoryQuery (structured query parser)
        ├── RetrievalEngine (multi-factor scoring)
        ├── ConsolidationEngine (LLM-powered)
        ├── DecayEngine (Ebbinghaus curves)
        ├── PriorityTaskScheduler (5-level queues)
        ├── IngestWorker (daemon thread)
        ├── MaintenanceWorker (daemon thread)
        └── Policies (salience, reconsolidation, conflict, optimization)

Storage Backends:
  ├── PostgresBackend (psycopg2 + pgvector)
  ├── QdrantStorage (qdrant-client)
  ├── SQLiteBackend (sqlite3)
  └── InMemoryBackend (dict)

Adapters:
  ├── LangChain (LCEL Runnable, ChatMessageHistory)
  ├── LangGraph (StateGraph nodes, BaseStore)
  ├── CrewAI (BaseTool subclasses)
  ├── AutoGen (callable tools, Teachability-style)
  ├── DSPy (Retrieve module, ReAct tools)
  ├── Haystack (@component pipeline nodes)
  ├── Semantic Kernel (@kernel_function plugin)
  └── LiteLLM (completion wrapper)

---

Development

# Clone
git clone https://github.com/Vk-thug/neuromem-sdk.git
cd neuromem-sdk

# Install with dev dependencies
pip install -e .[dev]

# Run tests
pytest
pytest --cov=neuromem

# Code quality
black neuromem/ --line-length 100
ruff check neuromem/
mypy neuromem/

Test Coverage

Suite	Tests
Core (memory, retrieval, decay)	50+
Graph memory	26
Structured query	31
MCP server	26
Workflows	30
Framework adapters	42
Total	176

---

Roadmap

• Temporal reasoning improvements (date extraction, time-aware retrieval)
• Adversarial query detection (calibrated "I don't know" responses)
• Distributed memory (multi-agent shared state with conflict resolution)
• Prometheus metrics export
• CI/CD pipeline with automated benchmarking

---

Contributing

Contributions welcome. See CONTRIBUTING.md for guidelines.

---

License

MIT

---

Links

• PyPI
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Acknowledgments

Benchmark evaluation uses the LoCoMo dataset (Maharana et al., ACL 2024). Graph-augmented retrieval is inspired by HippoRAG. Memory template design draws from Obsidian knowledge management patterns.