langchain-mesh-cognition
Give any LangChain agent distributed memory across devices. What one agent learns, all agents can recall.
Install
pip install langchain-mesh-cognition
Prerequisite: the mesh service must be running on each device:
npm install -g mesh-cognition-service
mesh-cognition start --daemon
Quick Start
Use tools directly (no LLM needed)
The tools work standalone — no API keys, no LLM costs:
from langchain_mesh_cognition import MeshMemoryWrite, MeshMemorySearch
# Write a memory — broadcasts to all mesh peers
MeshMemoryWrite().invoke({"content": "Team standup moved to 10am Monday", "tags": "meeting,standup"})
# Search from any device on the mesh
print(MeshMemorySearch().invoke({"query": "standup"}))
# → [Hongweis-MacBook-Air.local] Team standup moved to 10am Monday (tags: meeting, standup)
Use with a LangChain agent (optional)
Add mesh memory to a conversational agent that decides when to read/write:
from langchain_mesh_cognition import MeshCognitionToolkit
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent
agent = create_react_agent(ChatOpenAI(model="gpt-4o"), MeshCognitionToolkit().get_tools())
agent.invoke({"messages": [("user", "What meetings do I have?")]})
# → Agent calls mesh_memory_search, finds meetings stored by any device on the mesh
Tools
| Tool | Description |
|---|---|
mesh_memory_write |
Store a memory and broadcast to all mesh peers |
mesh_memory_search |
Search memories across all devices on the mesh |
mesh_status |
Check mesh health — peer count, Kuramoto r(t), uptime |
mesh_context |
Get context from aligned peers for enriched reasoning |
Use Individual Tools
from langchain_mesh_cognition import MeshMemoryWrite, MeshMemorySearch
# Add specific tools to any agent
tools = [MeshMemoryWrite(), MeshMemorySearch()]
Custom Service URL
toolkit = MeshCognitionToolkit(base_url="http://192.168.1.100:18790")
Multi-Agent with LangGraph
from langchain_mesh_cognition import MeshCognitionToolkit
from langgraph.prebuilt import create_react_agent
# Two agents sharing the same mesh
research_agent = create_react_agent(llm, MeshCognitionToolkit().get_tools())
writing_agent = create_react_agent(llm, MeshCognitionToolkit().get_tools())
# Research agent stores findings → writing agent recalls them
research_agent.invoke({"messages": [("user", "Research quantum computing trends")]})
writing_agent.invoke({"messages": [("user", "Write a summary using mesh memory")]})
For cross-device mesh, start mesh-cognition on each machine. Agents on different devices share memory automatically via Bonjour/mDNS discovery.
How It Works
LangChain Agent
↓ uses tools
MeshCognitionToolkit
↓ HTTP calls
mesh-cognition-service (localhost:18790)
↓ TCP + Bonjour
Other mesh nodes on the network
The agent doesn't need to know about networking, discovery, or coupling. It just reads and writes memories — the mesh handles distribution.
Links
SYM.BOT Ltd · Apache 2.0
Metadata
Release files for langchain-mesh-cognition 0.1.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| langchain_mesh_cognition-0.1.0.tar.gz | 6.2 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| langchain_mesh_cognition-0.1.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 13.4 kB
Release files / langchain_mesh_cognition-0.1.0.tar.gz
| Download URL | langchain_mesh_cognition-0.1.0.tar.gz |
|---|---|
| Size | 6.2 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
77b83092ea270a6d2fd5a523665c0d0a3164e60a89d557b93f58bcc4579ae6b9
|
|
BLAKE2b-256 checksum How to use checksums |
762bc071fa4e5e86c433c6153d2d082ecf90b1755fb55affe8d8256393e8f3a5
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.2.0 CPython/3.9.6
|
Release files / langchain_mesh_cognition-0.1.0-py3-none-any.whl
| Download URL | langchain_mesh_cognition-0.1.0-py3-none-any.whl |
|---|---|
| Size | 7.2 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
3bdefd54a1eff9fb73d68ffc290c29efb28977f74d0782cc33094ce12a46bf07
|
|
BLAKE2b-256 checksum How to use checksums |
8cb123ac7fa16f9c39e8032327a5a38bd3c7e951446758e38f29e1da154f10ce
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.2.0 CPython/3.9.6
|