🤖 Agents

LocalAI includes a built-in agent platform powered by LocalAGI. Agents are autonomous AI entities that can reason, use tools, maintain memory, and interact with external services — all running locally as part of the LocalAI process.

Overview

The agent system provides:

• Autonomous agents with configurable goals, personalities, and capabilities
• Tool/Action support — agents can execute actions (web search, code execution, API calls, etc.)
• Knowledge base (RAG) — per-agent collections with document upload, chunking, and semantic search
• Skills system — reusable skill definitions that agents can leverage, with git-based skill repositories
• SSE streaming — real-time chat with agents via Server-Sent Events
• Import/Export — share agent configurations as JSON files
• Agent Hub — browse and download ready-made agents from agenthub.localai.io
• Web UI — full management interface for creating, editing, chatting with, and monitoring agents

Getting Started

Agents are enabled by default. To disable them, set:

LOCALAI_DISABLE_AGENTS=true

Creating an Agent

1. Navigate to the Agents page in the web UI
2. Click Create Agent or import one from the Agent Hub
3. Configure the agent’s name, model, system prompt, and actions
4. Save and start chatting

Importing an Agent

You can import agent configurations from JSON files:

1. Download an agent configuration from the Agent Hub or export one from another LocalAI instance
2. On the Agents page, click Import
3. Select the JSON file — you’ll be taken to the edit form to review and adjust the configuration before saving
4. Click Create Agent to finalize the import

Configuration

Environment Variables

All agent-related settings can be configured via environment variables:

Knowledge Base Storage

By default, the knowledge base uses chromem — an in-process vector store that requires no external dependencies. For production deployments with larger knowledge bases, you can switch to PostgreSQL with pgvector support:

LOCALAI_AGENT_POOL_VECTOR_ENGINE=postgres
LOCALAI_AGENT_POOL_DATABASE_URL=postgresql://localrecall:localrecall@postgres:5432/localrecall?sslmode=disable

The PostgreSQL image quay.io/mudler/localrecall:v0.5.2-postgresql is pre-configured with pgvector and ready to use.

Docker Compose Example

Basic setup with in-memory vector store:

services:
  localai:
    image: localai/localai:latest
    ports:
      - 8080:8080
    environment:
      - MODELS_PATH=/models
      - LOCALAI_AGENT_POOL_DEFAULT_MODEL=hermes-3-llama3.1-8b
      - LOCALAI_AGENT_POOL_EMBEDDING_MODEL=granite-embedding-107m-multilingual
      - LOCALAI_AGENT_POOL_ENABLE_SKILLS=true
      - LOCALAI_AGENT_POOL_ENABLE_LOGS=true
    volumes:
      - models:/models
      - localai_config:/etc/localai
volumes:
  models:
  localai_config:

Setup with PostgreSQL for persistent knowledge base:

services:
  localai:
    image: localai/localai:latest
    depends_on:
      postgres:
        condition: service_healthy
    ports:
      - 8080:8080
    environment:
      - MODELS_PATH=/models
      - LOCALAI_AGENT_POOL_DEFAULT_MODEL=hermes-3-llama3.1-8b
      - LOCALAI_AGENT_POOL_EMBEDDING_MODEL=granite-embedding-107m-multilingual
      - LOCALAI_AGENT_POOL_ENABLE_SKILLS=true
      - LOCALAI_AGENT_POOL_ENABLE_LOGS=true
      # PostgreSQL-backed knowledge base
      - LOCALAI_AGENT_POOL_VECTOR_ENGINE=postgres
      - LOCALAI_AGENT_POOL_DATABASE_URL=postgresql://localrecall:localrecall@postgres:5432/localrecall?sslmode=disable
    volumes:
      - models:/models
      - localai_config:/etc/localai

  postgres:
    image: quay.io/mudler/localrecall:v0.5.2-postgresql
    environment:
      - POSTGRES_DB=localrecall
      - POSTGRES_USER=localrecall
      - POSTGRES_PASSWORD=localrecall
    volumes:
      - postgres_data:/var/lib/postgresql/data
    healthcheck:
      test: ["CMD-SHELL", "pg_isready -U localrecall"]
      interval: 10s
      timeout: 5s
      retries: 5

volumes:
  models:
  localai_config:
  postgres_data:

Agent Configuration

Each agent has its own configuration that controls its behavior. Key settings include:

• Name — unique identifier for the agent
• Model — the LLM model the agent uses for reasoning
• System Prompt — defines the agent’s personality and instructions
• Actions — tools the agent can use (web search, code execution, etc.)
• Connectors — external integrations (Slack, Discord, etc.)
• Knowledge Base — collections of documents for RAG
• MCP Servers — Model Context Protocol servers for additional tool access

The pool-level defaults (API URL, API key, models) can be set via environment variables. Individual agents can further override these in their configuration, allowing them to use different LLM providers (OpenAI, other LocalAI instances, etc.) on a per-agent basis.

API Endpoints

All agent endpoints are grouped under /api/agents/:

Agent Management

Skills

Collections (Knowledge Base)

Actions

Using Agents via the Responses API

Agents can be used programmatically via the standard /v1/responses endpoint (OpenAI Responses API). Simply use the agent name as the model field:

curl -X POST http://localhost:8080/v1/responses \
  -H "Content-Type: application/json" \
  -d '{
    "model": "my-agent",
    "input": "What is the weather today?"
  }'

This returns a standard Responses API response:

{
  "id": "resp_...",
  "object": "response",
  "status": "completed",
  "model": "my-agent",
  "output": [
    {
      "type": "message",
      "role": "assistant",
      "content": [
        {
          "type": "output_text",
          "text": "The agent's response..."
        }
      ]
    }
  ]
}

You can also send structured message arrays as input:

curl -X POST http://localhost:8080/v1/responses \
  -H "Content-Type: application/json" \
  -d '{
    "model": "my-agent",
    "input": [
      {"role": "user", "content": "Summarize the latest news about AI"}
    ]
  }'

When the model name matches an agent, the request is routed to the agent pool. If no agent matches, it falls through to the normal model-based inference pipeline.

Chat with SSE Streaming

For real-time streaming responses, use the chat endpoint with SSE:

Send a message to an agent:

curl -X POST http://localhost:8080/api/agents/my-agent/chat \
  -H "Content-Type: application/json" \
  -d '{"message": "What is the weather today?"}'

Listen to real-time events via SSE:

curl -N http://localhost:8080/api/agents/my-agent/sse

The SSE stream emits the following event types:

• json_message — agent/user messages
• json_message_status — processing status updates (processing / completed)
• status — system messages (reasoning steps, action results)
• json_error — error notifications

Architecture

Agents run in-process within LocalAI. By default, each agent calls back into LocalAI’s own API (http://127.0.0.1:<port>/v1/chat/completions) for LLM inference. This means:

• No external dependencies — everything runs in a single binary
• Agents use the same models loaded in LocalAI
• Per-agent overrides allow pointing individual agents to external providers
• Agent state is persisted to disk and restored on restart

User → POST /api/agents/:name/chat → LocalAI
  → AgentPool → Agent reasoning loop
    → POST /v1/chat/completions (self-referencing)
      → LocalAI model inference → response
        → SSE events → GET /api/agents/:name/sse → UI