Documentation Index
Fetch the complete documentation index at: https://docs.mixpeek.com/docs/llms.txt
Use this file to discover all available pages before exploring further.
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.
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
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 }'
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 |
| 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 |
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")]
})
| 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) |
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()
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()["results"]
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 |
