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Configuration Guide
Aden Hive is a Python-based agent framework. Configuration is handled through environment variables and agent-level config files. There is no centralized config.yaml or Docker Compose setup.
Configuration Overview
Environment variables (API keys, runtime flags)
Agent config.py (per-agent settings: model, tools, storage)
pyproject.toml (package metadata and dependencies)
.mcp.json (MCP server connections)
Environment Variables
LLM Providers (at least one required for real execution)
# Anthropic (primary provider)
export ANTHROPIC_API_KEY="sk-ant-..."
# OpenAI (optional, for GPT models via LiteLLM)
export OPENAI_API_KEY="sk-..."
# Cerebras (optional, used by output cleaner and some nodes)
export CEREBRAS_API_KEY="..."
# Groq (optional, fast inference)
export GROQ_API_KEY="..."
The framework supports 100+ LLM providers through LiteLLM. Set the corresponding environment variable for your provider.
Search & Tools (optional)
# Web search for agents (Brave Search)
export BRAVE_SEARCH_API_KEY="..."
# Exa Search (alternative web search)
export EXA_API_KEY="..."
Runtime Flags
# Run agents without LLM calls (structure-only validation)
export MOCK_MODE=1
# Custom credentials storage path (default: ~/.aden/credentials)
export ADEN_CREDENTIALS_PATH="/custom/path"
# Custom agent storage path (default: /tmp)
export AGENT_STORAGE_PATH="/custom/storage"
Agent Configuration
Each agent package in exports/ contains its own config.py:
# exports/my_agent/config.py
CONFIG = {
"model": "claude-haiku-4-5-20251001", # Default LLM model
"max_tokens": 4096,
"temperature": 0.7,
"tools": ["web_search", "pdf_read"], # MCP tools to enable
"storage_path": "/tmp/my_agent", # Runtime data location
}
Agent Graph Specification
Agent behavior is defined in agent.json (or constructed in agent.py):
{
"id": "my_agent",
"name": "My Agent",
"goal": {
"success_criteria": [...],
"constraints": [...]
},
"nodes": [...],
"edges": [...]
}
See the Getting Started Guide for building agents.
MCP Server Configuration
MCP (Model Context Protocol) servers are configured in .mcp.json at the project root:
{
"mcpServers": {
"agent-builder": {
"command": "core/.venv/bin/python",
"args": ["-m", "framework.mcp.agent_builder_server"],
"cwd": "."
},
"tools": {
"command": "tools/.venv/bin/python",
"args": ["-m", "aden_tools.mcp_server", "--stdio"],
"cwd": "."
}
}
}
The tools MCP server exposes tools including web search, PDF reading, CSV processing, and file system operations.
Storage
Aden Hive uses file-based persistence (no database required):
{storage_path}/
runs/{run_id}.json # Complete execution traces
indexes/
by_goal/{goal_id}.json # Runs indexed by goal
by_status/{status}.json # Runs indexed by status
by_node/{node_id}.json # Runs indexed by node
summaries/{run_id}.json # Quick-load run summaries
Storage is managed by framework.storage.FileStorage. No external database setup is needed.
IDE Setup
VS Code
Add to .vscode/settings.json:
{
"python.analysis.extraPaths": [
"${workspaceFolder}/core",
"${workspaceFolder}/exports"
]
}
PyCharm
- Open Project Settings > Project Structure
- Mark
coreas Sources Root - Mark
exportsas Sources Root
Security Best Practices
- Never commit API keys - Use environment variables or
.envfiles .envis git-ignored - Copy.env.exampleto.envat the project root and fill in your values- Use real provider keys in non-production environments - validate configuration with low-risk inputs before production rollout
- Credential isolation - Each tool validates its own credentials at runtime
Troubleshooting
"ModuleNotFoundError: No module named 'framework'"
Install the core package:
cd core && uv pip install -e .
API key not found
Ensure the environment variable is set in your current shell session:
echo $ANTHROPIC_API_KEY # Should print your key
On Windows PowerShell:
$env:ANTHROPIC_API_KEY = "sk-ant-..."
Agent not found
Run from the project root with PYTHONPATH:
PYTHONPATH=exports uv run python -m my_agent validate
See Environment Setup for detailed installation instructions.