Providers

Agent.md supports five LLM providers: Google, OpenAI, Anthropic, Ollama, and Local (self-hosted). Each provider is an optional dependency—install only what you need.

Supported Providers

Provider	Package	Default Model
`google`	`langchain-google-genai`	`gemini-2.5-flash`
`openai`	`langchain-openai`	`gpt-4o`
`anthropic`	`langchain-anthropic`	`claude-sonnet-4`
`ollama`	`langchain-ollama`	User-defined
`local`	`langchain-openai`	User-defined

Installation

Core Installation

pip install agentmd
# or with uv:
uv pip install agentmd

Provider-Specific Installation

# OpenAI
pip install agentmd[openai]

# Anthropic
pip install agentmd[anthropic]

# Ollama
pip install agentmd[ollama]

# All providers
pip install agentmd[all]

Note: The local provider uses the same package as OpenAI (langchain-openai).

API Key Setup

Create a .env file in your project root:

GOOGLE_API_KEY=your-key-here
OPENAI_API_KEY=your-key-here
ANTHROPIC_API_KEY=your-key-here

Agent.md automatically loads .env at startup via python-dotenv.

Get Your API Keys

Google: Google AI Studio
OpenAI: OpenAI Platform
Anthropic: Anthropic Console

Ollama and Local providers don't require API keys.

Google

Fast, cost-effective, multimodal—best for high-frequency tasks.

Installation:

pip install agentmd
# Google is included by default

Configuration:

model:
  provider: google
  name: gemini-2.5-flash

Available models: - gemini-2.5-flash — Fast, multimodal (recommended) - gemini-2.5-pro — More capable, larger context - gemini-2.0-flash-exp — Experimental

Example:

---
name: quick-summarizer
model:
  provider: google
  name: gemini-2.5-flash
trigger:
  type: manual
settings:
  temperature: 0.3
---

Summarize the file at `input.txt` and save to `summary.txt`.

OpenAI

Versatile, excellent reasoning—industry standard for complex tasks.

Installation:

pip install agentmd[openai]

Configuration:

model:
  provider: openai
  name: gpt-4o

Available models: - gpt-4o — Latest flagship - gpt-4o-mini — Faster, cheaper - gpt-4-turbo — Previous generation - o1 / o1-mini — Advanced reasoning

Example:

---
name: research-assistant
model:
  provider: openai
  name: gpt-4o
trigger:
  type: manual
settings:
  temperature: 0.7
  max_tokens: 8192
---

Research the topic in `query.txt` and write a comprehensive report.

Anthropic

Long context (200K+ tokens), excellent for detailed analysis.

Installation:

pip install agentmd[anthropic]

Configuration:

model:
  provider: anthropic
  name: claude-sonnet-4

Available models: - claude-sonnet-4 — Balanced performance - claude-opus-4 — Highest capability - claude-haiku-4 — Fast and efficient

Example:

---
name: document-analyzer
model:
  provider: anthropic
  name: claude-sonnet-4
trigger:
  type: manual
settings:
  temperature: 0.5
  max_tokens: 4096
---

Analyze the legal document in `contract.txt` and extract key terms.

Ollama

Local, offline, privacy-first. No API key required.

Setup:

Install Ollama: ollama.com/download
Pull a model: ollama pull llama3.2
Ollama runs at http://localhost:11434 by default

Installation:

pip install agentmd[ollama]

Configuration:

model:
  provider: ollama
  name: llama3.2

Popular models: - llama3.2 — Meta's Llama (3B, 7B, 70B variants) - mistral — Mistral 7B - phi3 — Microsoft Phi-3 (small) - gemma2 — Google Gemma 2

Browse all: ollama.com/library

Example:

---
name: private-summarizer
model:
  provider: ollama
  name: llama3.2
trigger:
  type: manual
settings:
  temperature: 0.7
---

Summarize confidential files without sending data to external APIs.

Local (OpenAI-Compatible)

Self-hosted or third-party OpenAI-compatible endpoints (vLLM, LM Studio, LocalAI).

Installation:

pip install agentmd[openai]

Configuration:

model:
  provider: local
  name: your-model-name
  base_url: http://localhost:8000

The base_url is automatically normalized (e.g., http://localhost:8000 becomes http://localhost:8000/v1).

Common endpoints: - vLLM: http://localhost:8000 - LM Studio: http://localhost:1234 - LocalAI: http://localhost:8080

Example:

---
name: custom-model-agent
model:
  provider: local
  name: my-fine-tuned-model
  base_url: http://localhost:8000
trigger:
  type: manual
---

Use your custom fine-tuned model hosted on vLLM.

Common Settings

All providers support:

settings:
  temperature: 0.7      # 0.0 = deterministic, 1.0 = creative
  max_tokens: 4096      # Maximum output tokens
  timeout: 300          # Execution timeout in seconds

Temperature Guide

0.0 — Deterministic (data extraction, structured outputs)
0.3 — Focused (summaries, factual tasks)
0.7 — Balanced (general-purpose, default)
0.9 — Creative (writing, brainstorming)

Switching Providers

Change providers by editing the agent file:

# Before
model:
  provider: openai
  name: gpt-4o

# After
model:
  provider: google
  name: gemini-2.5-flash

Save and run—the next execution uses the new provider.

Troubleshooting

Provider Package Not Installed

ImportError: Provider 'openai' requires langchain-openai.

Solution:

pip install agentmd[openai]

API Key Not Found

Error: OPENAI_API_KEY not found

Solution: 1. Create .env in project root

Add: OPENAI_API_KEY=your-key
Restart the runtime

Ollama Connection Error

Error: Could not connect to Ollama at http://localhost:11434

Solution: 1. Install Ollama: ollama.com/download

Start Ollama: ollama serve
Pull a model: ollama pull llama3.2

Local Provider 404 Error

Error: 404 Not Found at http://localhost:8000/v1/chat/completions

Solution: - Verify your server is running

Check the correct port (vLLM: 8000, LM Studio: 1234)

Token Limit Exceeded

Error: maximum context length exceeded

Solution: - Reduce max_tokens in settings

Use a model with a larger context window
Split large inputs into smaller chunks