Honcho uses a flexible configuration system that supports both TOML files and environment variables. Configuration values are loaded in the following priority order (highest to lowest):
  1. Environment variables (always take precedence)
  2. .env file (for local development)
  3. config.toml file (base configuration)
  4. Default values

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Option 1: Environment Variables Only (Production)

  • Use environment variables for all configuration
  • No config files needed
  • Ideal for containerized deployments (Docker, Kubernetes)
  • Secrets managed by your deployment platform

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Option 2: config.toml (Development/Simple Deployments)

  • Use config.toml for base configuration
  • Override sensitive values with environment variables
  • Good for development and simple deployments

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Option 3: Hybrid Approach

  • Use config.toml for non-sensitive base settings
  • Use .env file for sensitive values (API keys, secrets)
  • Good for development teams

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Option 4: .env Only (Local Development)

  • Use .env file for all configuration
  • Simple for local development
  • Never commit .env files to version control

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Configuration Methods

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Using config.toml

Copy the example configuration file to get started: 
cp config.toml.example config.toml
Then modify the values as needed. The TOML file is organized into sections:
  • [app] - Application-level settings (log level, host, port, embedding settings)
  • [db] - Database connection and pool settings
  • [auth] - Authentication configuration
  • [llm] - LLM provider API keys and general settings
  • [dialectic] - Dialectic API configuration (provider, model, search settings)
  • [deriver] - Background worker settings and theory of mind configuration
  • [summary] - Session summarization settings
  • [sentry] - Error tracking and monitoring settings

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Using Environment Variables

All configuration values can be overridden using environment variables. The environment variable names follow this pattern:
  • {SECTION}_{KEY} for nested settings
  • Just {KEY} for app-level settings
Examples:
  • DB_CONNECTION_URI → [db].CONNECTION_URI
  • DB_POOL_SIZE → [db].POOL_SIZE
  • AUTH_JWT_SECRET → [auth].JWT_SECRET
  • DIALECTIC_MODEL → [dialectic].MODEL
  • LOG_LEVEL (no section) → [app].LOG_LEVEL

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Configuration Priority

When a configuration value is set in multiple places, Honcho uses this priority:
  1. Environment variables - Always take precedence
  2. .env file - Loaded for local development
  3. config.toml - Base configuration
  4. Default values - Built-in defaults
This allows you to:
  • Use config.toml for base configuration
  • Override specific values with environment variables in production
  • Use .env files for local development without modifying config.toml

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Example

If you have this in config.toml: 
[db]
CONNECTION_URI = "postgresql://localhost/honcho_dev"
POOL_SIZE = 10
You can override just the connection URI in production: 
export DB_CONNECTION_URI="postgresql://prod-server/honcho_prod"
The application will use the production connection URI while keeping the pool size from config.toml.

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Core Configuration

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Application Settings

Basic Application Configuration: 
# Logging and server settings
LOG_LEVEL=INFO  # DEBUG, INFO, WARNING, ERROR
SESSION_PEERS_LIMIT=10
GET_CONTEXT_MAX_TOKENS=100000

# Embedding settings (optional)
EMBED_MESSAGES=false
MAX_EMBEDDING_TOKENS=8192
MAX_EMBEDDING_TOKENS_PER_REQUEST=300000
Environment-specific settings: 
# Development
LOG_LEVEL=DEBUG
FASTAPI_HOST=127.0.0.1

# Production
LOG_LEVEL=WARNING
FASTAPI_HOST=0.0.0.0

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Database Configuration

Required Database Settings: 
# PostgreSQL connection string (required)
DB_CONNECTION_URI=postgresql+psycopg://username:password@host:port/database

# Example for local development
DB_CONNECTION_URI=postgresql+psycopg://postgres:postgres@localhost:5432/honcho

# Example for production
DB_CONNECTION_URI=postgresql+psycopg://honcho_user:secure_password@db.example.com:5432/honcho_prod
Database Pool Settings: 
# Connection pool configuration
DB_SCHEMA=public
DB_POOL_SIZE=10
DB_MAX_OVERFLOW=20
DB_POOL_TIMEOUT=30
DB_POOL_RECYCLE=300
DB_POOL_PRE_PING=true
DB_SQL_DEBUG=false
DB_TRACING=false
Docker Compose for PostgreSQL: 
# docker-compose.yml
version: '3.8'
services:
  database:
    image: pgvector/pgvector:pg15
    environment:
      POSTGRES_USER: postgres
      POSTGRES_PASSWORD: postgres
      POSTGRES_DB: honcho
    ports:
      - "5432:5432"
    volumes:
      - postgres_data:/var/lib/postgresql/data
      - ./init.sql:/docker-entrypoint-initdb.d/init.sql

volumes:
  postgres_data:

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Authentication Configuration

JWT Authentication: 
# Enable/disable authentication
AUTH_USE_AUTH=false  # Set to true for production

# JWT settings (required if AUTH_USE_AUTH is true)
AUTH_JWT_SECRET=your-super-secret-jwt-key
Generate JWT Secret: 
# Generate a secure JWT secret
python scripts/generate_jwt_secret.py

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LLM Provider Configuration

Honcho supports multiple LLM providers for different tasks. API keys are configured in the [llm] section, while specific features use their own configuration sections.

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API Keys

All provider API keys use the LLM_ prefix: 
# Provider API Keys
LLM_ANTHROPIC_API_KEY=your-anthropic-api-key
LLM_OPENAI_API_KEY=your-openai-api-key
LLM_GEMINI_API_KEY=your-gemini-api-key
LLM_GROQ_API_KEY=your-groq-api-key

# OpenAI-compatible endpoints
LLM_OPENAI_COMPATIBLE_API_KEY=your-api-key
LLM_OPENAI_COMPATIBLE_BASE_URL=https://your-openai-compatible-endpoint.com

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General LLM Settings

# Default settings for all LLM calls
LLM_DEFAULT_MAX_TOKENS=2500

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Feature-Specific Model Configuration

Different features can use different providers and models: Dialectic API: 
# Main dialectic model (default: Anthropic)
DIALECTIC_PROVIDER=anthropic
DIALECTIC_MODEL=claude-sonnet-4-20250514
DIALECTIC_MAX_OUTPUT_TOKENS=2500
DIALECTIC_THINKING_BUDGET_TOKENS=1024

# Query generation for dialectic (default: Groq)
DIALECTIC_QUERY_GENERATION_PROVIDER=groq
DIALECTIC_QUERY_GENERATION_MODEL=llama-3.1-8b-instant

# Semantic search settings
DIALECTIC_SEMANTIC_SEARCH_TOP_K=10
DIALECTIC_SEMANTIC_SEARCH_MAX_DISTANCE=0.85
Deriver: 
# Deriver model (default: Google)
DERIVER_PROVIDER=google
DERIVER_MODEL=gemini-2.0-flash-lite

# Worker settings
DERIVER_WORKERS=1
DERIVER_STALE_SESSION_TIMEOUT_MINUTES=5
DERIVER_POLLING_SLEEP_INTERVAL_SECONDS=1.0

# Peer card settings
DERIVER_PEER_CARD_PROVIDER=openai
DERIVER_PEER_CARD_MODEL=gpt-5-nano-2025-08-07
DERIVER_PEER_CARD_MAX_OUTPUT_TOKENS=2000

# Context token limit for get_context method
DERIVER_CONTEXT_TOKEN_LIMIT=30000

# Maximum number of observations to store in working representation
# This is applied to both explicit and deductive observations
DERIVER_WORKING_REPRESENTATION_MAX_OBSERVATIONS=100
Summary Generation: 
# Summary model (default: Google)
SUMMARY_PROVIDER=google
SUMMARY_MODEL=gemini-1.5-flash-latest
SUMMARY_MAX_TOKENS_SHORT=1000
SUMMARY_MAX_TOKENS_LONG=2000
SUMMARY_THINKING_BUDGET_TOKENS=512

# Summary frequency
SUMMARY_MESSAGES_PER_SHORT_SUMMARY=20
SUMMARY_MESSAGES_PER_LONG_SUMMARY=60

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Default Provider Usage

By default, Honcho uses:
  • Anthropic for dialectic API responses
  • Groq for query generation
  • Google for deriving theory of mind and summarization
  • OpenAI for embeddings (if EMBED_MESSAGES=true)
You only need to set the API keys for the providers you plan to use.

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Monitoring Configuration

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Sentry Error Tracking

Sentry Settings: 
# Enable/disable Sentry
SENTRY_ENABLED=false

# Sentry configuration
SENTRY_DSN=https://your-sentry-dsn@sentry.io/project-id
SENTRY_TRACES_SAMPLE_RATE=0.1
SENTRY_PROFILES_SAMPLE_RATE=0.1

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Environment-Specific Examples

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Development Configuration

config.toml for development: 
[app]
LOG_LEVEL = "DEBUG"
SESSION_PEERS_LIMIT = 10
EMBED_MESSAGES = false

[db]
CONNECTION_URI = "postgresql+psycopg://postgres:postgres@localhost:5432/honcho_dev"
POOL_SIZE = 5

[auth]
USE_AUTH = false

[dialectic]
PROVIDER = "anthropic"
MODEL = "claude-sonnet-4-20250514"
QUERY_GENERATION_PROVIDER = "groq"
QUERY_GENERATION_MODEL = "llama-3.1-8b-instant"
MAX_OUTPUT_TOKENS = 2500

[summary]
PROVIDER = "google"
MODEL = "gemini-1.5-flash-latest"
MAX_TOKENS_SHORT = 1000
MAX_TOKENS_LONG = 2000

[deriver]
WORKERS = 1

[sentry]
ENABLED = false
Environment variables for development: 
# .env.development
LOG_LEVEL=DEBUG
DB_CONNECTION_URI=postgresql+psycopg://postgres:postgres@localhost:5432/honcho_dev
AUTH_USE_AUTH=false
ANTHROPIC_API_KEY=your-dev-anthropic-key

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Production Configuration

config.toml for production: 
[app]
LOG_LEVEL = "WARNING"
SESSION_PEERS_LIMIT = 10
EMBED_MESSAGES = true

[db]
CONNECTION_URI = "postgresql+psycopg://honcho_user:secure_password@prod-db:5432/honcho_prod"
POOL_SIZE = 20
MAX_OVERFLOW = 40

[auth]
USE_AUTH = true

[dialectic]
PROVIDER = "anthropic"
MODEL = "claude-sonnet-4-20250514"
QUERY_GENERATION_PROVIDER = "groq"
QUERY_GENERATION_MODEL = "llama-3.1-8b-instant"
MAX_OUTPUT_TOKENS = 2500

[summary]
PROVIDER = "google"
MODEL = "gemini-1.5-flash-latest"
MAX_TOKENS_SHORT = 1000
MAX_TOKENS_LONG = 2000

[deriver]
WORKERS = 4
PROVIDER = "google"
MODEL = "gemini-2.0-flash-lite"

[sentry]
ENABLED = true
TRACES_SAMPLE_RATE = 0.1
Environment variables for production: 
# .env.production
LOG_LEVEL=WARNING
DB_CONNECTION_URI=postgresql+psycopg://honcho_user:secure_password@prod-db:5432/honcho_prod
AUTH_USE_AUTH=true
AUTH_JWT_SECRET=your-super-secret-jwt-key
ANTHROPIC_API_KEY=your-prod-anthropic-key
GEMINI_API_KEY=your-prod-gemini-key
SENTRY_DSN=https://your-sentry-dsn@sentry.io/project-id

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Migration Management

Running Database Migrations: 
# Check current migration status
uv run alembic current

# Upgrade to latest
uv run alembic upgrade head

# Downgrade to specific revision
uv run alembic downgrade revision_id

# Create new migration
uv run alembic revision --autogenerate -m "Description of changes"

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Troubleshooting

Common Configuration Issues:
  1. Database Connection Errors
    • Ensure DB_CONNECTION_URI uses postgresql+psycopg:// prefix
    • Verify database is running and accessible
    • Check pgvector extension is installed
  2. Authentication Issues
    • Set AUTH_USE_AUTH=true for production
    • Generate and set AUTH_JWT_SECRET if authentication is enabled
    • Use python scripts/generate_jwt_secret.py to create a secure secret
  3. LLM Provider Issues
    • Verify API keys are set correctly
    • Check model names match provider specifications
    • Ensure provider is enabled in configuration
  4. Deriver Issues
    • Increase DERIVER_WORKERS for better performance
    • Check DERIVER_STALE_SESSION_TIMEOUT_MINUTES for session cleanup
    • Monitor background processing logs
This configuration guide covers all the settings available in Honcho. Always use environment-specific configuration files and never commit sensitive values like API keys or JWT secrets to version control.