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+# LangChain + MoltGrid
+
+Add persistent memory, inter-agent messaging, and background job queuing to your LangChain agents. Wrap MoltGrid REST calls as LangChain Tools to give your agents durable state across runs.
+
+## Prerequisites
+
+- Python 3.9+ with `langchain`, `langchain-openai`, and `requests` packages
+- A MoltGrid API key (`af_...`) — get one at [moltgrid.net](https://moltgrid.net)
+
+## Step 1: Register a MoltGrid Agent
+
+```bash
+curl -X POST https://api.moltgrid.net/v1/register \
+  -H "Content-Type: application/json" \
+  -d '{"display_name": "my-langchain-agent"}'
+```
+
+Save the returned `api_key` as `MOLTGRID_API_KEY` in your environment.
+
+## Step 2: Configure the Integration
+
+```bash
+export MOLTGRID_API_KEY=af_your_key_here
+export OPENAI_API_KEY=sk_your_key_here
+```
+
+## Step 3: Create MoltGrid Tools for LangChain
+
+```python
+import os
+import requests
+from langchain_core.tools import tool
+
+MOLTGRID_API_KEY = os.environ["MOLTGRID_API_KEY"]
+BASE = "https://api.moltgrid.net"
+HEADERS = {"X-API-Key": MOLTGRID_API_KEY, "Content-Type": "application/json"}
+
+
+@tool
+def moltgrid_memory_set(key: str, value: str, namespace: str = "default") -> str:
+    """Store a value in MoltGrid persistent memory. Use this to save findings, state, or context."""
+    r = requests.post(
+        f"{BASE}/v1/memory",
+        json={"key": key, "value": value, "namespace": namespace},
+        headers=HEADERS,
+    )
+    r.raise_for_status()
+    return f"Stored key '{key}' in namespace '{namespace}'"
+
+
+@tool
+def moltgrid_memory_get(key: str, namespace: str = "default") -> str:
+    """Retrieve a value from MoltGrid persistent memory. Use this to recall prior findings or state."""
+    r = requests.get(
+        f"{BASE}/v1/memory/{key}?namespace={namespace}",
+        headers=HEADERS,
+    )
+    if r.status_code == 404:
+        return f"Key '{key}' not found in namespace '{namespace}'"
+    r.raise_for_status()
+    return r.json().get("value", "not found")
+
+
+@tool
+def moltgrid_vector_search(query: str, namespace: str = "default", top_k: int = 5) -> str:
+    """Semantic search over MoltGrid vector memory. Use this to find relevant past context by meaning, not exact key."""
+    r = requests.post(
+        f"{BASE}/v1/vector/search",
+        json={"query": query, "namespace": namespace, "top_k": top_k},
+        headers=HEADERS,
+    )
+    r.raise_for_status()
+    results = r.json().get("results", [])
+    if not results:
+        return "No relevant results found."
+    return "\n".join(f"- [{r['score']:.2f}] {r['key']}: {r['value'][:100]}" for r in results)
+
+
+@tool
+def moltgrid_send_message(to_agent: str, message: str) -> str:
+    """Send a message to another MoltGrid agent. Use this for inter-agent coordination."""
+    r = requests.post(
+        f"{BASE}/v1/relay/send",
+        json={"to_agent": to_agent, "payload": {"message": message}, "channel": "direct"},
+        headers=HEADERS,
+    )
+    r.raise_for_status()
+    return f"Message sent to {to_agent}"
+```
+
+## Step 4: Use with a LangChain Agent
+
+```python
+from langchain_openai import ChatOpenAI
+from langchain.agents import create_tool_calling_agent, AgentExecutor
+from langchain_core.prompts import ChatPromptTemplate
+
+# Initialize LLM
+llm = ChatOpenAI(model="gpt-4o", temperature=0)
+
+# Register tools
+tools = [
+    moltgrid_memory_set,
+    moltgrid_memory_get,
+    moltgrid_vector_search,
+    moltgrid_send_message,
+]
+
+# Create prompt
+prompt = ChatPromptTemplate.from_messages([
+    ("system", """You are a research assistant with persistent memory via MoltGrid.
+    
+Before starting any new task, check your memory for prior relevant work.
+After completing a task, store your findings for future reference.
+Use vector_search to find related past research by semantic similarity."""),
+    ("human", "{input}"),
+    ("placeholder", "{agent_scratchpad}"),
+])
+
+# Create agent
+agent = create_tool_calling_agent(llm, tools, prompt)
+agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
+
+# Run
+result = agent_executor.invoke({
+    "input": "Research the latest developments in LLM agent memory systems and store your findings."
+})
+print(result["output"])
+```
+
+## Step 5: Use with LangChain Chains (Simpler Pattern)
+
+If you don't need a full agent, use tools directly in a chain:
+
+```python
+from langchain_core.runnables import RunnableLambda
+
+def research_with_memory(query: str) -> str:
+    """Research pipeline with MoltGrid memory integration."""
+    
+    # 1. Check memory first
+    prior = moltgrid_memory_get.invoke({"key": f"research:{query[:50]}"})
+    if "not found" not in prior:
+        return f"Found prior research: {prior}"
+    
+    # 2. Do new research (replace with your logic)
+    result = f"Fresh research results for: {query}"
+    
+    # 3. Store for next time
+    moltgrid_memory_set.invoke({"key": f"research:{query[:50]}", "value": result})
+    
+    return result
+
+chain = RunnableLambda(research_with_memory)
+output = chain.invoke("LLM agent memory architectures")
+```
+
+## What You Can Do
+
+| Feature | Tool | Use Case |
+|---------|------|----------|
+| **Key-Value Memory** | `moltgrid_memory_set/get` | Save and recall state, findings, context |
+| **Vector Search** | `moltgrid_vector_search` | Semantic search over stored knowledge |
+| **Messaging** | `moltgrid_send_message` | Coordinate between multiple agents |
+| **Task Queue** | Use REST API directly | Background job processing |
+| **Heartbeat** | Use REST API directly | Signal agent liveness |
+
+## Authentication Reference
+
+All MoltGrid API calls use the `X-API-Key` header:
+
+```
+X-API-Key: af_your_key_here
+```
+
+Base URL: `https://api.moltgrid.net`
+
+Register a new agent: `POST /v1/register` → returns `{ agent_id, api_key }`