Agents

Agent integration: AI agents connect to Mixpeek via MCP, SDK, or REST to access ingest, search, classify, and monitor capabilities

Mixpeek exposes its entire platform as agent-callable tools. Connect via MCP for zero-code setup, use the built-in Agent Runtime for stateful conversations, or wire retrievers into LangChain, OpenAI, or any framework via REST.

Connect your agent to your own data

Create a namespace to get your API key and MCP endpoint, then give your agent search, ingest, and classify tools over your own content.

MCP (Model Context Protocol)

The fastest way to connect an AI agent to Mixpeek. Four hosted servers expose different tool scopes:

Scope	URL	Tools
Full	`https://mcp.mixpeek.com/mcp`	48
Ingestion	`https://mcp.mixpeek.com/ingestion/mcp`	20
Retrieval	`https://mcp.mixpeek.com/retrieval/mcp`	11
Admin	`https://mcp.mixpeek.com/admin/mcp`	17

Claude Desktop
Claude Code
Cursor / Windsurf

{
  "mcpServers": {
    "mixpeek": {
      "url": "https://mcp.mixpeek.com/mcp",
      "headers": { "Authorization": "Bearer YOUR_API_KEY" }
    }
  }
}

claude mcp add mixpeek \
  --transport streamable-http \
  --url https://mcp.mixpeek.com/mcp \
  --header "Authorization: Bearer YOUR_API_KEY"

{
  "mcpServers": {
    "mixpeek": {
      "url": "https://mcp.mixpeek.com/mcp",
      "headers": { "Authorization": "Bearer YOUR_API_KEY" }
    }
  }
}

Per-Retriever Server

For a focused search agent, scope the MCP server to a single retriever. It reads your retriever’s input_schema and generates a typed search tool:

pip install mixpeek-mcp-retriever

mixpeek-mcp-retriever \
  --retriever-id ret_xxx \
  --namespace-id ns_xxx \
  --api-key YOUR_API_KEY

Exposes three tools: search (typed to your schema), describe (retriever metadata), and explain (pipeline walkthrough). Full MCP reference →

Agent Sessions

Mixpeek’s built-in agent runtime gives you stateful, multi-turn conversations backed by your data. Each session runs as a dedicated process with tool access, conversation memory, and SSE streaming.

# Create a session
curl -X POST "https://api.mixpeek.com/v1/agents/sessions" \
  -H "Authorization: Bearer $MIXPEEK_API_KEY" \
  -H "X-Namespace: $NAMESPACE_ID" \
  -H "Content-Type: application/json" \
  -d '{
    "agent_config": {
      "system_prompt": "You help users search and analyze video content.",
      "available_tools": ["execute_retriever", "list_collections", "get_taxonomy"]
    }
  }'

# Send a message (SSE streaming)
curl -N -X POST "https://api.mixpeek.com/v1/agents/sessions/$SESSION_ID/messages" \
  -H "Authorization: Bearer $MIXPEEK_API_KEY" \
  -H "X-Namespace: $NAMESPACE_ID" \
  -H "Content-Type: application/json" \
  -d '{ "content": "Find videos about machine learning", "stream": true }'

The agent reasons through a analyze → plan → execute → synthesize workflow, calling tools as needed and streaming events back:

Event	Description
`thinking`	Agent is analyzing or planning
`tool_call`	Agent is calling a tool
`tool_result`	Tool execution result
`message`	Response text chunk
`done`	Processing complete

Available Tools

Tool	Description
`execute_retriever`	Search documents via a retriever pipeline
`search_retrievers`	Find available retrievers
`get_retriever`	Get retriever configuration
`list_collections`	List collections in the namespace
`get_collection`	Get collection details
`list_taxonomies`	List taxonomies
`get_taxonomy`	Get taxonomy details
`list_clusters`	List cluster configurations
`get_object`	Get object metadata

Sessions persist for 7 days and automatically rehydrate after idle periods. Agent Sessions API →

LangChain

The langchain-mixpeek package provides a retriever, individual tools, and a full toolkit:

pip install langchain-mixpeek

from langchain_mixpeek import MixpeekToolkit
from langgraph.prebuilt import create_react_agent
from langchain_anthropic import ChatAnthropic

toolkit = MixpeekToolkit(
    api_key="mxp_...",
    namespace="my-namespace",
    bucket_id="bkt_...",
    collection_id="col_...",
    retriever_id="ret_...",
)

agent = create_react_agent(
    ChatAnthropic(model="claude-sonnet-4-20250514"),
    toolkit.get_tools(),
)

result = agent.invoke({
    "messages": [("user", "Find product demos and summarize what's shown")]
})

Toolkit Tools

Tool	What it does
`mixpeek_search`	Search video, images, audio, documents
`mixpeek_ingest`	Upload content (text, images, video, audio, PDFs)
`mixpeek_process`	Trigger feature extraction
`mixpeek_classify`	Run taxonomy classification
`mixpeek_cluster`	Group similar documents
`mixpeek_alert`	Set up monitoring (webhook, Slack, email)

Scope tools to what your agent needs with toolkit.get_tools(actions=["search", "ingest"]). Full LangChain guide →

OpenAI Function Calling

Define a Mixpeek retriever as an OpenAI function schema:

from openai import OpenAI
from mixpeek import Mixpeek

openai_client = OpenAI()
mixpeek_client = Mixpeek(api_key="mxp_...")

tools = [{
    "type": "function",
    "function": {
        "name": "search_mixpeek",
        "description": "Search video, image, and audio content",
        "parameters": {
            "type": "object",
            "properties": {
                "query": {"type": "string", "description": "Search query"},
                "limit": {"type": "integer", "description": "Max results"}
            },
            "required": ["query"]
        }
    }
}]

response = openai_client.chat.completions.create(
    model="gpt-4o", messages=messages, tools=tools
)

# Handle tool_calls by calling mixpeek_client.retrievers.execute()

Works with both the Chat Completions API and Assistants API. Full OpenAI guide →

Any Framework (REST)

The same pattern works with CrewAI, LlamaIndex, Haystack, Autogen, or plain HTTP — wrap the retriever execute endpoint as a tool:

import requests

def search(query: str, limit: int = 10) -> list:
    resp = requests.post(
        f"https://api.mixpeek.com/v1/retrievers/{RETRIEVER_ID}/execute",
        headers={
            "Authorization": f"Bearer {API_KEY}",
            "X-Namespace": NAMESPACE_ID,
            "Content-Type": "application/json",
        },
        json={"inputs": {"query_text": query}, "limit": limit},
    )
    return resp.json()["documents"]

Retriever Execute API →

Choosing an Integration

I want to…	Use
Connect Claude or Cursor with no code	MCP
Build a stateful conversational agent	Agent Sessions
Build a LangChain/LangGraph agent	LangChain
Add tools to GPT models	OpenAI Function Calling
Use any other framework	REST

Get started

Connect your data

Extract features

Build retrievers

Enrich & organize

Integrate & operate

Resources

Connect your agent to your own data

MCP (Model Context Protocol)

Per-Retriever Server

Agent Sessions

Available Tools

LangChain

Toolkit Tools

OpenAI Function Calling

Any Framework (REST)

Choosing an Integration

Connect your agent to your own data

​MCP (Model Context Protocol)

​Per-Retriever Server

​Agent Sessions

​Available Tools

​LangChain

​Toolkit Tools

​OpenAI Function Calling

​Any Framework (REST)

​Choosing an Integration

MCP (Model Context Protocol)

Per-Retriever Server

Agent Sessions

Available Tools

LangChain

Toolkit Tools

OpenAI Function Calling

Any Framework (REST)

Choosing an Integration