pica-langchain

Original：🇺🇸 English

Translated

Integrate PICA into a LangChain/LangGraph Python application via MCP. Use when adding PICA tools to a LangChain agent, setting up PICA MCP with LangChain, or when the user mentions PICA with LangChain or LangGraph.

31installs

Sourcepicahq/skills

Added on2026-03-09

NPX Install

npx skill4agent add picahq/skills pica-langchain

SKILL.md Content

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PICA MCP Integration with LangChain

PICA provides a unified API platform that connects AI agents to third-party services (CRMs, email, calendars, databases, etc.) through MCP tool calling.

PICA MCP Server

PICA exposes its capabilities through an MCP server distributed as

@picahq/mcp

. It uses stdio transport — it runs as a local subprocess via

npx

MCP Configuration

json

{
  "mcpServers": {
    "pica": {
      "command": "npx",
      "args": ["@picahq/mcp"],
      "env": {
        "PICA_SECRET": "your-pica-secret-key"
      }
    }
  }
}

Package:
```
@picahq/mcp
```
(run via
```
npx
```
, no install needed)
Auth:
```
PICA_SECRET
```
environment variable (obtain from the PICA dashboard https://app.picaos.com/settings/api-keys)
Transport: stdio (standard input/output)

Environment Variable

Always store the PICA secret in an environment variable, never hardcode it:

PICA_SECRET=sk_test_...

Add it to

.env

and load with

python-dotenv

Using PICA with LangChain

LangChain provides MCP client support via the

langchain-mcp-adapters

package. Always refer to the latest docs before implementing. See langchain-mcp-reference.md.

Required packages

bash

pip install langchain-mcp-adapters langgraph langchain-anthropic mcp python-dotenv

Before implementing: look up the latest docs

The

langchain-mcp-adapters

API has changed across versions (e.g.,

MultiServerMCPClient

is no longer a context manager as of v0.1.0). Always check the latest docs before writing code. See langchain-mcp-reference.md.

Integration pattern

Create an MCP client using
```
MultiServerMCPClient
```
with stdio transport pointed at
```
npx @picahq/mcp
```
Get tools from the client via
```
await client.get_tools()
```

Create a ReAct agent using

create_react_agent(model, tools)

from

langgraph.prebuilt

Stream or invoke the agent with your messages
Pass environment variables (
```
PICA_SECRET
```
,
```
PATH
```
,
```
HOME
```
) to the MCP client's
```
env
```
config

Minimal example

python

from langchain_anthropic import ChatAnthropic
from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent

model = ChatAnthropic(model="claude-haiku-4-5-20251001", streaming=True)

client = MultiServerMCPClient({
    "pica": {
        "command": "npx",
        "args": ["@picahq/mcp"],
        "transport": "stdio",
        "env": {
            "PICA_SECRET": os.environ.get("PICA_SECRET", ""),
            "PATH": os.environ.get("PATH", ""),
            "HOME": os.environ.get("HOME", ""),
        },
    },
})
tools = await client.get_tools()
agent = create_react_agent(model, tools)

# Invoke
result = await agent.ainvoke({"messages": [{"role": "user", "content": "..."}]})

# Or stream events
async for event in agent.astream_events({"messages": messages}, version="v2"):
    kind = event["event"]
    if kind == "on_chat_model_stream":
        content = event["data"]["chunk"].content
        # content may be a list of content blocks (Anthropic models) or a string

Important: Anthropic content blocks

When streaming with

astream_events(version="v2")

, Anthropic models return

chunk.content

as a list of content blocks, not plain strings. Always handle both: