Code Execution (Programmatic Tool Calling)

The execute_code tool lets the agent write Python scripts that call Hermes tools programmatically, collapsing multi-step workflows into a single LLM turn. The script runs in a child process on the agent host, communicating with Hermes over a Unix domain socket RPC.

How It Works

1. The agent writes a Python script using from hermes_tools import ...
2. Hermes generates a hermes_tools.py stub module with RPC functions
3. Hermes opens a Unix domain socket and starts an RPC listener thread
4. The script runs in a child process — tool calls travel over the socket back to Hermes
5. Only the script's print() output is returned to the LLM; intermediate tool results never enter the context window

# The agent can write scripts like:
from hermes_tools import web_search, web_extract

results = web_search("Python 3.13 features", limit=5)
for r in results["data"]["web"]:
    content = web_extract([r["url"]])
    # ... filter and process ...
print(summary)

Available tools inside scripts: web_search, web_extract, read_file, write_file, search_files, patch, terminal (foreground only).

When the Agent Uses This

The agent uses execute_code when there are:

• 3+ tool calls with processing logic between them
• Bulk data filtering or conditional branching
• Loops over results

The key benefit: intermediate tool results never enter the context window — only the final print() output comes back, dramatically reducing token usage.

Practical Examples

Data Processing Pipeline

from hermes_tools import search_files, read_file
import json

# Find all config files and extract database settings
matches = search_files("database", path=".", file_glob="*.yaml", limit=20)
configs = []
for match in matches.get("matches", []):
    content = read_file(match["path"])
    configs.append({"file": match["path"], "preview": content["content"][:200]})

print(json.dumps(configs, indent=2))

Multi-Step Web Research

from hermes_tools import web_search, web_extract
import json

# Search, extract, and summarize in one turn
results = web_search("Rust async runtime comparison 2025", limit=5)
summaries = []
for r in results["data"]["web"]:
    page = web_extract([r["url"]])
    for p in page.get("results", []):
        if p.get("content"):
            summaries.append({
                "title": r["title"],
                "url": r["url"],
                "excerpt": p["content"][:500]
            })

print(json.dumps(summaries, indent=2))

Bulk File Refactoring

from hermes_tools import search_files, read_file, patch

# Find all Python files using deprecated API and fix them
matches = search_files("old_api_call", path="src/", file_glob="*.py")
fixed = 0
for match in matches.get("matches", []):
    result = patch(
        path=match["path"],
        old_string="old_api_call(",
        new_string="new_api_call(",
        replace_all=True
    )
    if "error" not in str(result):
        fixed += 1

print(f"Fixed {fixed} files out of {len(matches.get('matches', []))} matches")

Build and Test Pipeline

from hermes_tools import terminal, read_file
import json

# Run tests, parse results, and report
result = terminal("cd /project && python -m pytest --tb=short -q 2>&1", timeout=120)
output = result.get("output", "")

# Parse test output
passed = output.count(" passed")
failed = output.count(" failed")
errors = output.count(" error")

report = {
    "passed": passed,
    "failed": failed,
    "errors": errors,
    "exit_code": result.get("exit_code", -1),
    "summary": output[-500:] if len(output) > 500 else output
}

print(json.dumps(report, indent=2))

Execution Mode

execute_code has two execution modes controlled by code_execution.mode in ~/.hermes/config.yaml:

Mode	Working directory	Python interpreter
project (default)	The session's working directory (same as terminal())	Active VIRTUAL_ENV / CONDA_PREFIX python, falling back to Hermes's own python
strict	A temp staging directory isolated from the user's project	sys.executable (Hermes's own python)

When to leave it on project: you want import pandas, from my_project import foo, or relative paths like open(".env") to work the same way they do in terminal(). This is almost always what you want.

When to flip to strict: you need maximum reproducibility — you want the same interpreter every session regardless of which venv the user activated, and you want scripts quarantined from the project tree (no risk of accidentally reading project files through a relative path).

# ~/.hermes/config.yaml
code_execution:
  mode: project   # or "strict"

Fallback behavior in project mode: if VIRTUAL_ENV / CONDA_PREFIX is unset, broken, or points at a Python older than 3.8, the resolver falls back cleanly to sys.executable — it never leaves the agent without a working interpreter.

Security-critical invariants are identical across both modes:

• environment scrubbing (API keys, tokens, credentials stripped)
• tool whitelist (scripts cannot call execute_code recursively, delegate_task, or MCP tools)
• resource limits (timeout, stdout cap, tool-call cap)

Switching mode changes where scripts run and which interpreter runs them, not what credentials they can see or which tools they can call.

Resource Limits

Resource	Limit	Notes
Timeout	5 minutes (300s)	Script is killed with SIGTERM, then SIGKILL after 5s grace
Stdout	50 KB	Shown head-and-tail inline; the full output is saved to ~/.hermes/cache/exec/ and the path is included in the result
Stderr	10 KB	Included in output on non-zero exit for debugging
Tool calls	50 per execution	Error returned when limit reached

All limits are configurable via config.yaml:

# In ~/.hermes/config.yaml
code_execution:
  mode: project      # project (default) | strict
  timeout: 300       # Max seconds per script (default: 300)
  max_tool_calls: 50 # Max tool calls per execution (default: 50)

State Between Calls (the session kernel)

On the local terminal backend, execute_code does not start a fresh interpreter for every call. Each session owns a persistent Python kernel, so variables, imports, and loaded data from one call are available in the next. The agent can load a dataset once and query it across several turns instead of re-reading it every time. Subagents get their own kernel; kernels are never shared across sessions.

What ends a kernel:

• Timeout or interrupt. A cell that hits the timeout (or is interrupted) kills the kernel process and its state is lost on purpose; the result says so and the next call starts a fresh kernel.
• reset=true. The agent can pass reset: true to discard the kernel's state and start clean. This is also the way to pick up environment changes: a kernel's environment is frozen when it spawns, so a newly allowlisted passthrough variable is invisible until the kernel is reset.
• Idle timeout and eviction. Kernels die with the session, after code_execution.kernel_idle_timeout idle seconds (default 1800), or when more than code_execution.max_session_kernels (default 4) are alive and the oldest is evicted.

The security envelope is the same as a one-shot script: environment scrubbing, the tool whitelist, and the per-call tool budget all apply to every cell, and tool-call authority (approvals, session, allow-list) is rebound on each cell.

# ~/.hermes/config.yaml
code_execution:
  kernel_idle_timeout: 1800   # seconds a kernel may sit idle before it is reaped
  max_session_kernels: 4      # kernels kept alive at once; oldest is evicted past this

Remote backends (Docker, SSH, Modal) run a remote session kernel with the same contract. If the kernel cannot be spawned on the backend, Hermes falls back to running each call as a standalone script and says so in the result.

Large output. Stdout over 50 KB is shown head-and-tail inline, and the full text is saved under ~/.hermes/cache/exec/ with the path included in the result, so the agent can page through it with read_file instead of re-running the script.

How Tool Calls Work Inside Scripts

When your script calls a function like web_search("query"):

1. The call is serialized to JSON and sent over a Unix domain socket to the parent process
2. The parent dispatches through the standard handle_function_call handler
3. The result is sent back over the socket
4. The function returns the parsed result

This means tool calls inside scripts behave identically to normal tool calls — same rate limits, same error handling, same capabilities. The only restriction is that terminal() is foreground-only (no background or pty parameters).

Error Handling

When a script fails, the agent receives structured error information:

• Non-zero exit code: stderr is included in the output so the agent sees the full traceback
• Timeout: Script is killed and the agent sees "Script timed out after 300s and was killed."
• Interruption: If the user sends a new message during execution, the script is terminated and the agent sees [execution interrupted — user sent a new message]
• Tool call limit: When the 50-call limit is hit, subsequent tool calls return an error message

The response always includes status (success/error/timeout/interrupted), output, tool_calls_made, and duration_seconds.

Security

Security Model

The child process runs with a minimal environment. API keys, tokens, and credentials are stripped by default. The script accesses tools exclusively via the RPC channel — it cannot read secrets from environment variables unless explicitly allowed.

Environment variables containing KEY, TOKEN, SECRET, PASSWORD, CREDENTIAL, PASSWD, or AUTH in their names are excluded. Only safe system variables (PATH, HOME, LANG, SHELL, PYTHONPATH, VIRTUAL_ENV, etc.) are passed through.

Skill Environment Variable Passthrough

When a skill declares required_environment_variables in its frontmatter, those variables are automatically passed through to both execute_code and terminal child processes after the skill is loaded. This lets skills use their declared API keys without weakening the security posture for arbitrary code.

For non-skill use cases, you can explicitly allowlist variables in config.yaml:

terminal:
  env_passthrough:
    - MY_CUSTOM_KEY
    - ANOTHER_TOKEN

See the Security guide for full details.

HERMES_* variables in the child

The child process receives only a small, fixed set of operational HERMES_* variables by exact name:

• HERMES_HOME
• HERMES_PROFILE
• HERMES_CONFIG
• HERMES_ENV

(plus HERMES_RPC_DIR / HERMES_RPC_SOCKET / TZ / HOME, which Hermes injects explicitly so the RPC channel works).

:::note Behavior change Earlier versions passed any variable whose name began with HERMES_ through to the child. That broad prefix was removed for security hardening: it could leak HERMES_*-named configuration that doesn't match a secret substring (for example HERMES_BASE_URL, HERMES_KANBAN_DB, or a HERMES_*_WEBHOOK endpoint) into arbitrary sandboxed code.

If an execute_code script — or a repo/plugin module it imports at import time — relied on a HERMES_* variable outside the four operational names above, it will now find that variable unset in the child. The drop is intentional, not a bug. :::

Workaround — opt the variable back in explicitly. Both routes pass the variable through execute_code and terminal children, and neither weakens the secret-stripping guarantee (Hermes-managed provider credentials can never be re-allowed this way):

1. Per-machine, in config.yaml — add the exact variable name to the passthrough allowlist:

   terminal:
     env_passthrough:
       - HERMES_KANBAN_DB
       - HERMES_BASE_URL

2. Per-skill, in the skill's frontmatter — declare it so it is registered automatically whenever that skill is loaded:

   required_environment_variables:
     - HERMES_KANBAN_DB

Diagnosing it. When the child drops one or more non-allowlisted HERMES_* variables, Hermes emits a one-line debug log naming them and pointing at the env_passthrough escape hatch. Run with debug logging (hermes logs --level DEBUG, or check ~/.hermes/logs/agent.log) and look for execute_code: dropped N non-allowlisted HERMES_* var(s) if a script behaves as though a HERMES_* variable is missing.

Hermes always writes the script and the auto-generated hermes_tools.py RPC stub into a temp staging directory that is cleaned up after execution. In strict mode the script also runs there; in project mode it runs in the session's working directory (the staging directory stays on PYTHONPATH so imports still resolve). The child process runs in its own process group so it can be cleanly killed on timeout or interruption.

execute_code vs terminal

Use Case	execute_code	terminal
Multi-step workflows with tool calls between	✅	❌
Simple shell command	❌	✅
Filtering/processing large tool outputs	✅	❌
Running a build or test suite	❌	✅
Looping over search results	✅	❌
Interactive/background processes	❌	✅
Needs API keys in environment	⚠️ Only via passthrough	✅ (most pass through)

Rule of thumb: Use execute_code when you need to call Hermes tools programmatically with logic between calls. Use terminal for running shell commands, builds, and processes.

Platform Support

Code execution is available on Linux, macOS, and Windows. On Linux and macOS the RPC channel uses a Unix domain socket; on Windows, where AF_UNIX is unreliable, Hermes automatically falls back to a loopback TCP socket for the sandbox RPC transport. Remote terminal backends (Docker/SSH/Modal/etc.) use a file-based RPC transport instead and additionally require Python 3 inside the backend.