> ## Documentation Index
> Fetch the complete documentation index at: https://docs.litellm-agent-platform.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Deep Agents

> Run agents using the Deep Agents runtime via Docker Compose.

[Deep Agents](https://docs.langchain.com/oss/python/deepagents/overview) is a multi-step agent runtime with deep reasoning and planning capabilities. The Docker Compose profile starts Deep Agents alongside the LiteLLM Agent Platform and registers `local-deepagents` automatically.

This page covers the LangChain Deep Agents Compose runtime. For the Pydantic
Deep Agents bridge template, see [Pydantic Deep Agents](/runtimes/pydantic-deepagents).

## Prerequisites

* Docker Desktop installed and running
* LiteLLM Agent Platform repo cloned

## 1. Start the stack

```bash theme={null}
docker compose --profile deepagents up
```

This starts:

* The LiteLLM Agent Platform web/API service
* A Postgres database
* The Deep Agents runtime harness
* Registers `local-deepagents` in the UI automatically

Open [http://localhost:4000](http://localhost:4000) and sign in with your master key (`sk-local` by default).

## 2. Add model provider credentials

In **Settings → Credentials**, add a model provider API key. Deep Agents routes all model calls through your LiteLLM gateway.

## 3. Create an agent

In the UI, click **New Agent**, choose `local-deepagents` as the runtime, select a model, and set a system prompt describing the agent's role.

Or via the API:

```bash theme={null}
curl -X POST http://localhost:4000/api/agents \
  -H "Authorization: Bearer sk-local" \
  -H "Content-Type: application/json" \
  -d '{
    "name": "my-deepagent",
    "owner_id": "local-user",
    "runtime": "local-deepagents",
    "model": "claude-opus-4-5",
    "system": "You are an autonomous research agent. Plan carefully before acting."
  }'
```

## 4. Run your agent

Select your agent in the UI and start a session.

Or via the API:

```bash theme={null}
# Start and run a session
SESSION=$(curl -s -X POST http://localhost:4000/session \
  -H "Authorization: Bearer sk-local" \
  -H "Content-Type: application/json" \
  -d '{
    "runtime": "local-deepagents",
    "agent_id": "<agent-id>",
    "prompt": "Research the top 5 open-source vector databases and compare them on latency and scalability."
  }' | jq -r .id)

# Stream the response
curl -N "http://localhost:4000/v1/sessions/$SESSION/events/stream" \
  -H "Authorization: Bearer sk-local"
```

## Stop the stack

```bash theme={null}
docker compose --profile deepagents down
```

## Run alongside other runtimes

```bash theme={null}
docker compose --profile deepagents --profile opencode up
```

## CRON schedules

Deep Agents works well with scheduled runs. In the UI, open your agent, go to **Schedules**, and add a CRON expression:

```
0 9 * * 1-5
```

This runs the agent every weekday at 9 AM and creates a new session automatically.