memwire 0.1.1

Enterprise-grade memory infrastructure for AI agents

MemWire

Enterprise-grade, self-hosted AI memory infrastructure layer. Deploy persistent AI memory on-premise or in any cloud with your own LLM and database.

What is MemWire?

MemWire is an open source & enterprise-ready AI memory infrastructure layer. MemWire gives your AI applications persistent, auditable memory with structured, updatable facts, fastest semantic retrieval across conversations and knowledge using graph-based memory.

• Fully customizable — adapt schemas, memory types, and pipelines to your use case
• Self-hosted — run entirely on your local machine, on-premise or in your own cloud
• Multi-tenant — isolate applications, users, and workspaces securely
• Bring your own database — PostgreSQL pgvector, Qdrant, Pinecone, ChromaDB, Weawiate or your preferred stack
• Bring your own LLM — OpenAI, Anthropic, Gemini, Ollama, or any provider
• Deploy anywhere — edge, private cloud, public cloud, air-gapped environments
• Knowledge ingestion — ingest documents (PDF, Excel, CSV, etc.) alongside conversation memory; recalled together at query time
• Auditable — every memory is traceable, categorized (fact, preference, instruction, event, entity), and inspectable
• Feedback loop — reinforce memory paths that led to good responses; unused edges decay over time

---

Quickstart

Python SDK

Install

pip install memwire

---

Embedded mode

Data is stored on disk in ./memwire_data/.

from memwire import MemWire, MemWireConfig

config = MemWireConfig(
    qdrant_path="./memwire_data",  # local vector store
    qdrant_collection_prefix="app_",
)
memory = MemWire(config=config)

USER_ID = "alice"

# Add messages to memory
records = memory.add(
    user_id=USER_ID,
    messages=[{"role": "user", "content": "I prefer dark mode and short answers."}],
)
for r in records:
    print(f"[stored] ({r.category}) {r.content}")

# Recall relevant context for a query
result = memory.recall("How should I format my answers?", user_id=USER_ID)
if result.formatted:
    print(result.formatted)
    # → "alice prefers dark mode and short answers."

# Inject recalled context into your LLM prompt
messages = [
    {"role": "system", "content": "You are a helpful assistant."},
]
if result.formatted:
    messages.append(
        {"role": "system", "content": f"Memory context:\n{result.formatted}"}
    )
messages.append({"role": "user", "content": "How should I format my answers?"})

# After you get the LLM response, reinforce the memory paths that were used
memory.feedback(assistant_response="<assistant response here>", user_id=USER_ID)

# Search memories by keyword / semantic similarity
hits = memory.search("dark mode", user_id=USER_ID, top_k=5)
for record, score in hits:
    print(f"[{score:.2f}] ({record.category}) {record.content}")

# Inspect stats
stats = memory.get_stats(user_id=USER_ID)
print(stats)  # {"memories": 1, "nodes": ..., "edges": ..., "knowledge_bases": 0}

# Always close to flush background writes
memory.close()

---

With a local Qdrant server

docker run -p 6333:6333 qdrant/qdrant

config = MemWireConfig(
    qdrant_url="http://localhost:6333",
    qdrant_collection_prefix="app_",
)
memory = MemWire(config=config)

---

REST API

The api/ folder provides a self-hosted REST API backed by FastAPI and Qdrant.

Start the server

cd api
docker compose up --build   # Qdrant + MemWire API on :8000

---

Store memory

curl -X POST http://localhost:8000/v1/memories \
  -H "Content-Type: application/json" \
  -d '{
    "user_id": "alice",
    "app_id": "app_a",
    "workspace_id": "team_1",
    "messages": [
      { "role": "user", "content": "I prefer dark mode and short answers." }
    ]
  }'

[
  {
    "memory_id": "mem_3f7a1c2d9e4b",
    "user_id": "alice",
    "content": "I prefer dark mode and short answers.",
    "role": "user",
    "category": "preference",
    "strength": 1.0
  }
]

---

Recall context

curl -X POST http://localhost:8000/v1/memories/recall \
  -H "Content-Type: application/json" \
  -d '{
    "user_id": "alice",
    "app_id": "app_a",
    "workspace_id": "team_1",
    "query": "How should I format my answers?"
  }'

{
  "query": "How should I format my answers?",
  "supporting": [{ "tokens": ["dark", "mode"], "score": 0.87, "memories": [...] }],
  "conflicting": [],
  "knowledge": [],
  "formatted": "alice prefers dark mode and short answers.",
  "has_conflicts": false
}

---

Search memories

curl -X POST http://localhost:8000/v1/memories/search \
  -H "Content-Type: application/json" \
  -d '{
    "user_id": "alice",
    "app_id": "app_a",
    "workspace_id": "team_1",
    "query": "dark mode",
    "limit": 10
  }'

[
  {
    "memory": {
      "memory_id": "mem_3f7a1c2d9e4b",
      "content": "I prefer dark mode and short answers.",
      "category": "preference"
    },
    "score": 0.94
  }
]

See API Reference for configuration options and local development setup.

Customization

All MemWire behaviour is controlled through MemWireConfig. Choose your vector store, embedding model, and LLM provider, then tune recall and graph settings to fit your use case. Learn more.

Supported databases

Storage	Type	Status	Notes
Qdrant	Vector store	✅ Supported	Embedded, local server, or Qdrant Cloud

Supported LLMs

MemWire is model-agnostic. Memory operations like storage, recall, and search work with any language model or provider.

Provider	Example
OpenAI	examples/openai/
Azure OpenAI	examples/azure-openai/
Anthropic, Gemini, Ollama, or any other	Pass the recalled context into any LLM

Roadmap

See ROADMAP.md for the full plan.

Contributing

PRs and issues are welcome. See CONTRIBUTING.md and GOVERNANCE.md.

License

Apache License 2.0