hermes-agent/toolset_distributions.py
Teknium c6575df927
feat(moa): expose MoA presets as selectable virtual models (#46081)
* feat(moa): expose MoA presets as selectable virtual models

Reconstructed onto current main (PR #46081's base had diverged with no common
ancestor, marking the PR dirty so CI never dispatched). MoA is now a virtual
provider: each named preset is a selectable model under provider 'moa', and the
preset's aggregator is the acting model that answers and calls tools.

Reference models fan out in parallel via a bounded ThreadPoolExecutor (the same
batch pattern delegate_task uses) — all references dispatched at once, collected
when every one finishes, then handed to the aggregator. Output order is
preserved, failures and the MoA-recursion guard stay isolated per reference.

- Removed the old mixture_of_agents model tool and moa toolset.
- Added moa as a virtual provider in the provider/model inventory.
- /moa is shortcut behavior over model selection (default preset / named preset
  / one-shot prompt).
- Dashboard + Desktop manage named presets; presets appear in model pickers.
- Parallel reference fan-out in agent/moa_loop.py with regression test.

* fix(moa): thread moa_config through _run_agent to _run_agent_inner

The reconstructed gateway MoA wiring declared moa_config on _run_agent (the
profile-scoping wrapper) and used it inside _run_agent_inner, but the wrapper
never forwarded it — _run_agent_inner had no such parameter, so the runtime hit
NameError: name 'moa_config' is not defined on the compression-failure session
sync path. Add moa_config to _run_agent_inner's signature and forward it from
both wrapper call sites (multiplex and non-multiplex). Caught by
tests/gateway/test_compression_failure_session_sync.py on CI shard test(4).

* fix(moa): classify moa as a virtual provider in the catalog

The moa virtual provider has no PROVIDER_REGISTRY/ProviderProfile entry, so
provider_catalog() fell through to the default auth_type="api_key" with no
env vars — tripping two catalog invariants:
  - test_provider_catalog: api_key providers must expose a credential env var
  - test_provider_parity: every hermes-model provider must be desktop-configurable

moa already declares auth_type="virtual" in HERMES_OVERLAYS; consult that
overlay as an auth_type fallback so the catalog reports moa as virtual (no real
credential, no network endpoint). Exempt virtual providers from the desktop
parity union check the same way 'custom' is exempt — derived from the catalog,
not a hardcoded slug, so future virtual providers are covered too.
2026-06-25 13:52:06 -07:00

358 lines
12 KiB
Python

#!/usr/bin/env python3
"""
Toolset Distributions Module
This module defines distributions of toolsets for data generation runs.
Each distribution specifies which toolsets should be used and their probability
of being selected for any given prompt during the batch processing.
A distribution is a dictionary mapping toolset names to their selection probability (%).
Probabilities should sum to 100, but the system will normalize if they don't.
Usage:
from toolset_distributions import get_distribution, list_distributions
# Get a specific distribution
dist = get_distribution("image_gen")
# List all available distributions
all_dists = list_distributions()
"""
from typing import Dict, List, Optional
import random
from toolsets import validate_toolset
# Distribution definitions
# Each key is a distribution name, and the value is a dict of toolset_name: probability_percentage
DISTRIBUTIONS = {
# Default: All tools available 100% of the time
"default": {
"description": "All available tools, all the time",
"toolsets": {
"web": 100,
"vision": 100,
"image_gen": 100,
"terminal": 100,
"file": 100,
"browser": 100
}
},
# Image generation focused distribution
"image_gen": {
"description": "Heavy focus on image generation with vision and web support",
"toolsets": {
"image_gen": 90, # 80% chance of image generation tools
"vision": 90, # 60% chance of vision tools
"web": 55, # 40% chance of web tools
"terminal": 45
}
},
# Research-focused distribution
"research": {
"description": "Web research with vision analysis and reasoning",
"toolsets": {
"web": 90, # 90% chance of web tools
"browser": 70, # 70% chance of browser tools for deep research
"vision": 50, # 50% chance of vision tools
"terminal": 10 # 10% chance of terminal tools
}
},
# Scientific problem solving focused distribution
"science": {
"description": "Scientific research with web, terminal, file, and browser capabilities",
"toolsets": {
"web": 94, # 94% chance of web tools
"terminal": 94, # 94% chance of terminal tools
"file": 94, # 94% chance of file tools
"vision": 65, # 65% chance of vision tools
"browser": 50, # 50% chance of browser for accessing papers/databases
"image_gen": 15 # 15% chance of image generation tools
}
},
# Development-focused distribution
"development": {
"description": "Terminal, file tools, and reasoning with occasional web lookup",
"toolsets": {
"terminal": 80, # 80% chance of terminal tools
"file": 80, # 80% chance of file tools (read, write, patch, search)
"web": 30, # 30% chance of web tools
"vision": 10 # 10% chance of vision tools
}
},
# Safe mode (no terminal)
"safe": {
"description": "All tools except terminal for safety",
"toolsets": {
"web": 80,
"browser": 70, # Browser is safe (no local filesystem access)
"vision": 60,
"image_gen": 60
}
},
# Balanced distribution
"balanced": {
"description": "Equal probability of all toolsets",
"toolsets": {
"web": 50,
"vision": 50,
"image_gen": 50,
"terminal": 50,
"file": 50,
"browser": 50
}
},
# Minimal (web only)
"minimal": {
"description": "Only web tools for basic research",
"toolsets": {
"web": 100
}
},
# Terminal only
"terminal_only": {
"description": "Terminal and file tools for code execution tasks",
"toolsets": {
"terminal": 100,
"file": 100
}
},
# Terminal + web (common for coding tasks that need docs)
"terminal_web": {
"description": "Terminal and file tools with web search for documentation lookup",
"toolsets": {
"terminal": 100,
"file": 100,
"web": 100
}
},
# Creative (vision + image generation)
"creative": {
"description": "Image generation and vision analysis focus",
"toolsets": {
"image_gen": 90,
"vision": 90,
"web": 30
}
},
# Reasoning heavy
"reasoning": {
"description": "Heavy research/reasoning distribution with minimal other tools",
"toolsets": {
"web": 90,
"file": 60,
"terminal": 20
}
},
# Browser-based web interaction
"browser_use": {
"description": "Full browser-based web interaction with search, vision, and page control",
"toolsets": {
"browser": 100, # All browser tools always available
"web": 80, # Web search for finding URLs and quick lookups
"vision": 70 # Vision analysis for images found on pages
}
},
# Browser only (no other tools)
"browser_only": {
"description": "Only browser automation tools for pure web interaction tasks",
"toolsets": {
"browser": 100
}
},
# Browser-focused tasks distribution (for browser-use-tasks.jsonl)
"browser_tasks": {
"description": "Browser-focused distribution (browser toolset includes web_search for finding URLs since Google blocks direct browser searches)",
"toolsets": {
"browser": 97, # 97% - browser tools (includes web_search) almost always available
"vision": 12, # 12% - vision analysis occasionally
"terminal": 15 # 15% - terminal occasionally for local operations
}
},
# Terminal-focused tasks distribution (for nous-terminal-tasks.jsonl)
"terminal_tasks": {
"description": "Terminal-focused distribution with high terminal/file availability, occasional other tools",
"toolsets": {
"terminal": 97, # 97% - terminal almost always available
"file": 97, # 97% - file tools almost always available
"web": 97, # 15% - web search/scrape for documentation
"browser": 75, # 10% - browser occasionally for web interaction
"vision": 50, # 8% - vision analysis rarely
"image_gen": 10 # 3% - image generation very rarely
}
},
# Mixed browser+terminal tasks distribution (for mixed-browser-terminal-tasks.jsonl)
"mixed_tasks": {
"description": "Mixed distribution with high browser, terminal, and file availability for complex tasks",
"toolsets": {
"browser": 92, # 92% - browser tools highly available
"terminal": 92, # 92% - terminal highly available
"file": 92, # 92% - file tools highly available
"web": 35, # 35% - web search/scrape fairly common
"vision": 15, # 15% - vision analysis occasionally
"image_gen": 15 # 15% - image generation occasionally
}
}
}
def get_distribution(name: str) -> Optional[Dict[str, any]]:
"""
Get a toolset distribution by name.
Args:
name (str): Name of the distribution
Returns:
Dict: Distribution definition with description and toolsets
None: If distribution not found
"""
return DISTRIBUTIONS.get(name)
def list_distributions() -> Dict[str, Dict]:
"""
List all available distributions.
Returns:
Dict: All distribution definitions
"""
return DISTRIBUTIONS.copy()
def sample_toolsets_from_distribution(distribution_name: str) -> List[str]:
"""
Sample toolsets based on a distribution's probabilities.
Each toolset in the distribution has a % chance of being included.
This allows multiple toolsets to be active simultaneously.
Args:
distribution_name (str): Name of the distribution to sample from
Returns:
List[str]: List of sampled toolset names
Raises:
ValueError: If distribution name is not found
"""
dist = get_distribution(distribution_name)
if not dist:
raise ValueError(f"Unknown distribution: {distribution_name}")
# Sample each toolset independently based on its probability
selected_toolsets = []
for toolset_name, probability in dist["toolsets"].items():
# Validate toolset exists
if not validate_toolset(toolset_name):
print(f"⚠️ Warning: Toolset '{toolset_name}' in distribution '{distribution_name}' is not valid")
continue
# Roll the dice - if random value is less than probability, include this toolset
if random.random() * 100 < probability:
selected_toolsets.append(toolset_name)
# If no toolsets were selected (can happen with low probabilities),
# ensure at least one toolset is selected by picking the highest probability one
if not selected_toolsets and dist["toolsets"]:
# Find toolset with highest probability
highest_prob_toolset = max(dist["toolsets"].items(), key=lambda x: x[1])[0]
if validate_toolset(highest_prob_toolset):
selected_toolsets.append(highest_prob_toolset)
return selected_toolsets
def validate_distribution(distribution_name: str) -> bool:
"""
Check if a distribution name is valid.
Args:
distribution_name (str): Distribution name to validate
Returns:
bool: True if valid, False otherwise
"""
return distribution_name in DISTRIBUTIONS
def print_distribution_info(distribution_name: str) -> None:
"""
Print detailed information about a distribution.
Args:
distribution_name (str): Distribution name
"""
dist = get_distribution(distribution_name)
if not dist:
print(f"❌ Unknown distribution: {distribution_name}")
return
print(f"\n📊 Distribution: {distribution_name}")
print(f" Description: {dist['description']}")
print(" Toolsets:")
for toolset, prob in sorted(dist["toolsets"].items(), key=lambda x: x[1], reverse=True):
print(f"{toolset:15} : {prob:3}% chance")
if __name__ == "__main__":
"""
Demo and testing of the distributions system
"""
print("📊 Toolset Distributions Demo")
print("=" * 60)
# List all distributions
print("\n📋 Available Distributions:")
print("-" * 40)
for name, dist in list_distributions().items():
print(f"\n {name}:")
print(f" {dist['description']}")
toolset_list = ", ".join([f"{ts}({p}%)" for ts, p in dist["toolsets"].items()])
print(f" Toolsets: {toolset_list}")
# Demo sampling
print("\n\n🎲 Sampling Examples:")
print("-" * 40)
test_distributions = ["image_gen", "research", "balanced", "default"]
for dist_name in test_distributions:
print(f"\n{dist_name}:")
# Sample 5 times to show variability
samples = []
for _ in range(5):
sampled = sample_toolsets_from_distribution(dist_name)
samples.append(sorted(sampled))
print(f" Sample 1: {samples[0]}")
print(f" Sample 2: {samples[1]}")
print(f" Sample 3: {samples[2]}")
print(f" Sample 4: {samples[3]}")
print(f" Sample 5: {samples[4]}")
# Show detailed info
print("\n\n📊 Detailed Distribution Info:")
print("-" * 40)
print_distribution_info("image_gen")
print_distribution_info("research")