strukto-ai--mirage/examples/python/agents/openhands

OpenHands + Mirage — agents that just use a shell

This example wires the OpenHands SDK to a Mirage Workspace and gives the agent one tool: terminal. No SaaS-specific tools, no MCP servers, no per-vendor schemas. Slack, S3, Gmail, GitHub, Linear — Mirage mounts each as a directory tree, and the agent treats them like a filesystem.

Run

./python/.venv/bin/python examples/python/agents/openhands/sandbox_agent.py

The task: "Find Slack messages containing 'hello' in #general." The agent finishes in 2 commands:

$ ls /slack/channels/
general__C04KEPWF6V7  random__C04JVGZM7UN  test__C0AS76ABXMK

$ grep -i hello /slack/channels/general__C04KEPWF6V7/*.jsonl
.../2026-04-16.jsonl:[zechengzhang97] hello
.../2026-04-04.jsonl:[demo app] Hello from MIRAGE Slack provider!

That's it. The agent never learned about Slack's API. It used ls and grep.

Why this matters: Mirage vs. the alternatives

The same task, three ways. Same answer; very different agent surface.

With Mirage (this example)

• Agent's tool list: terminal. One tool, one schema.
• Agent's vocabulary: every shell command it already knows — ls, cat, grep, head, wc, jq, find, pipes, redirection.
• What changed when we added Slack: mount it at /slack. No new tools, no new prompts, no new agent code.

With a Slack MCP server

• Agent's tool list: typically 6–12 Slack-specific tools — slack_search_messages, slack_list_channels, slack_get_channel_history, slack_get_user_info, slack_post_message, slack_add_reaction, …
• Agent's vocabulary per tool: every tool has its own JSON schema, parameter names, return shape. The model has to learn the API, then translate user intent into the right tool + the right params.
• Composition: want to filter messages with jq then count with wc? You can't — MCP tools are atomic; you get back what they return.
• Adding Discord: another MCP server with its own 6–12 tools. The agent's prompt now juggles two parallel APIs.

With the Slack CLI

• Agent's tool list: terminal (good — same as Mirage), but...
• Agent's vocabulary: slack search ..., slack chat send ..., slack auth login .... Vendor-specific subcommands, vendor-specific output formats, vendor-specific auth handling. The agent has to know the Slack CLI exists and how to invoke it.
• Composition: the CLI's stdout is its own format. Pipe it through jq if it happens to emit JSON, otherwise parse text.
• Adding Discord: install the Discord CLI. Now the agent needs to know two CLIs and pick correctly.

Side-by-side

	Mirage	Slack MCP	Slack CLI
Tools the agent sees	1 (terminal)	6–12 per backend	1 (terminal)
Vocabulary the agent must learn	shell + Mirage's filesystem layout	each tool's schema	each CLI's subcommand grammar
Composability (pipe / redirect / loop)	yes — real shell	no — atomic calls	partial — depends on CLI's stdout format
Adding a new backend	mount it; nothing else changes	new MCP server, new tool list, prompt churn	install new CLI; agent must learn it
Pushdown to native APIs (search, etc.)	automatic, in the builtin (Mirage rewrites grep over a Slack channel into one search.messages call)	only what the MCP exposes	none — text in, text out

What Mirage gives the agent

• One stable tool surface (terminal) regardless of how many backends are mounted.
• Pipes and composability because everything is a stream of bytes — cat /s3/data/2026-04.parquet | grep error | jq '.user' | sort | uniq -c.
• Format-aware reads — cat on .parquet / .feather / .orc returns a formatted table; head -n 5 on .jsonl returns the first 5 messages; grep on a Slack channel directory pushes down to search.messages automatically.
• One mental model for the agent: "the workspace is a filesystem; use shell."
• One mental model for you: "if I can mount it, the agent can use it."

Configure

The script loads .env.development from the repo root. Required:

Var	What it's for
LLM_API_KEY	OpenHands LLM (defaults to Anthropic — set to your ANTHROPIC_API_KEY)
AWS_S3_BUCKET, AWS_DEFAULT_REGION, AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY	/s3 mount
SLACK_BOT_TOKEN	/slack mount
SLACK_USER_TOKEN (recommended)	enables Slack's search.messages push-down so grep over /slack/channels/<channel>/*.jsonl runs in one API call instead of fanning out per day