> ## 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. # Agents > Give AI agents tools to search, ingest, classify, and monitor multimodal content 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. 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 | Use it for | | - | - | - | | **Full** | `https://mcp.mixpeek.com/mcp` | Everything below in one server | | **Ingestion** | `https://mcp.mixpeek.com/ingestion/mcp` | Buckets, uploads, collections, batches | | **Retrieval** | `https://mcp.mixpeek.com/retrieval/mcp` | Retrievers, search, agent sessions | | **Admin** | `https://mcp.mixpeek.com/admin/mcp` | Namespaces, keys, taxonomies, clusters | ```json theme={null} { "mcpServers": { "mixpeek": { "url": "https://mcp.mixpeek.com/mcp", "headers": { "Authorization": "Bearer YOUR_API_KEY" } } } } ``` ```bash theme={null} claude mcp add mixpeek \ --transport streamable-http \ --url https://mcp.mixpeek.com/mcp \ --header "Authorization: Bearer YOUR_API_KEY" ``` ```json theme={null} { "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: ```bash theme={null} 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 →](/docs/agent-integrations/mcp) ## 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. ```bash theme={null} # Create a session curl -X POST "https://api.mixpeek.com/v1/agents/sessions" \ -H "Authorization: Bearer $MIXPEEK_API_KEY" \ -H "X-Namespace: $NAMESPACE_ID" \ -H "Content-Type: application/json" \ -d '{ "agent_config": { "system_prompt": "You help users search and analyze video content.", "available_tools": ["execute_retriever", "list_collections", "get_taxonomy"] } }' ``` ```bash theme={null} # Send a message (SSE streaming) curl -N -X POST "https://api.mixpeek.com/v1/agents/sessions/$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 →](/docs/api-reference/agent-sessions/create-session) ## LangChain The `langchain-mixpeek` package provides a retriever, individual tools, and a full toolkit: ```bash theme={null} pip install langchain-mixpeek ``` ```python theme={null} 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 →](/docs/agent-integrations/langchain) ## OpenAI Function Calling Define a Mixpeek retriever as an OpenAI function schema: ```python theme={null} 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 →](/docs/agent-integrations/openai-function-calling) ## Any Framework (REST) The same pattern works with CrewAI, LlamaIndex, Haystack, Autogen, or plain HTTP — wrap the retriever execute endpoint as a tool: ```python theme={null} 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 →](/docs/api-reference/retrievers/execute-retriever-auto-optimized) ## Choosing an Integration | I want to... | Use | | - | - | | Connect Claude or Cursor with no code | [MCP](#mcp-model-context-protocol) | | Build a stateful conversational agent | [Agent Sessions](#agent-sessions) | | Build a LangChain/LangGraph agent | [LangChain](#langchain) | | Add tools to GPT models | [OpenAI Function Calling](#openai-function-calling) | | Use any other framework | [REST](#any-framework-rest) | This documentation is built and hosted on [Mintlify](https://mintlify.com), a developer documentation platform.