Amplifier Gemini Provider Module

Google Gemini model integration for Amplifier via Google AI API.

Quick Start

Option 1: Install as Module (Recommended)

The simplest way to use Gemini with Amplifier. Once installed, Amplifier will automatically discover it.

1. Set your API key:

   export GOOGLE_API_KEY="your-api-key-here"

   Get your API key from Google AI Studio.

2. Add the module:

   amplifier module add provider-gemini --source git+https://github.com/microsoft/amplifier-module-provider-gemini@main --global

3. Use Gemini as your provider:

   amplifier provider use gemini --global

4. Start using it:

   amplifier run "Hello from Gemini!"

That's it! The module is now available for all your projects. Use --project instead of --global to install for just the current project.

Option 2: Via Bundle

For more control over configuration or to compose with other capabilities, use a bundle:

1. Set your API key:

   export GOOGLE_API_KEY="your-api-key-here"

   Get your API key from Google AI Studio.

2. Create a bundle in your project or home directory (e.g., gemini-bundle/bundle.md):

   ---
   bundle:
     name: gemini-dev
     version: 1.0.0
     description: Gemini provider with full 1M context
   
   includes:
     - bundle: foundation
   
   session:
     context:
       config:
         max_tokens: 1048576  # Full 1M input context
   
   providers:
     - module: provider-gemini
       source: git+https://github.com/microsoft/amplifier-module-provider-gemini@main
       config:
         default_model: gemini-3.7-flash
         max_output_tokens: 65536  # Full 65K output capacity
         temperature: 0.7
         priority: 50  # Lower number = higher priority (beats default 100)
   ---
   
   # Gemini Development Bundle
   
   This bundle configures Gemini with full context windows and includes foundation capabilities.
   
   ## Available Models
   
   - **Gemini 3.7 Flash** - `gemini-3.7-flash` - Current flagship Flash model, best price-performance (default)
   - **Gemini 3.5 Flash / Flash-Lite** - `gemini-3.5-flash` / `gemini-3.5-flash-lite` - Legacy Flash generation
   - **Gemini 2.5 Flash / Flash-Lite / Pro** - `gemini-2.5-flash` / `gemini-2.5-flash-lite` / `gemini-2.5-pro` - Two generations back; still served

3. Use it:

   amplifier run --bundle ./gemini-bundle "Hello from Gemini!"

Prerequisites

• Python 3.11+
• UV - Fast Python package manager

Installing UV

# macOS/Linux/WSL
curl -LsSf https://astral.sh/uv/install.sh | sh

# Windows
powershell -c "irm https://astral.sh/uv/install.ps1 | iex"

Purpose

Provides access to Google's Gemini models as an LLM provider for Amplifier with 1M token context windows and extended thinking capabilities.

Contract

Module Type: Provider Mount Point: providers Entry Point: amplifier_module_provider_gemini:mount

Supported Models

Current support: Text generation, tool calling, and thinking. Multimodal capabilities (images, video, audio) are not yet implemented.

Model availability and naming change frequently -- this list reflects what was verified live against list_models() on 2026-08-29. Always prefer amplifier provider models gemini (or your account's actual list_models() result) over this table for what your key can currently use.

Gemini 3.x (Current generation)

• gemini-3.7-flash - Current flagship Flash model ("the latest and most capable" per ai.google.dev). Default model for this provider. Uses thinking_level (low/medium/high -- no minimal).
• gemini-3.5-flash / gemini-3.5-flash-lite - Documented by Google as legacy relative to 3.7. Uses thinking_level (full minimal/low/medium/high range).
• Other gemini-3.* preview/dated ids (e.g. gemini-3.1-flash-lite-preview, gemini-3-pro-image-preview) come and go -- this provider assumes any gemini-3.* id supports thinking_level with the full range unless proven otherwise by a live 400.

Gemini 2.5 (Two generations back; still served)

• gemini-2.5-flash - Best price-performance for large-scale processing (1M context, 65K max output)
• gemini-2.5-pro - State-of-the-art thinking model for complex reasoning (1M context, 65K max output)
• gemini-2.5-flash-lite - Fastest model optimized for cost-efficiency (1M context, 65K max output)

Verified live (2026-08-29): these three REJECT thinking_level outright ("Thinking level is not supported for this model") -- this provider automatically falls back to the legacy thinking_budget control for them. See Thinking/Reasoning below.

Gemini 2.0

Shut down by Google -- no longer served. Do not configure gemini-2.0-flash / gemini-2.0-flash-lite as your model.

Note: Image/video/audio models not listed as the provider doesn't support multimodal capabilities yet.

Configuration

[[providers]]
module = "provider-gemini"
name = "gemini"
config = {
    default_model = "gemini-3.7-flash",
    max_output_tokens = 8192,
    temperature = 0.7,
}

Configuration Options

Every key below corresponds either to a real parameter in Google's google.genai.types.GenerateContentConfig (linked as "API: field_name") or is Amplifier-only glue with no Google equivalent (marked "Amplifier-only").

Parameter	Type	Default	API param / origin	Description
api_key	string	env: GOOGLE_API_KEY or GEMINI_API_KEY	Amplifier-only	Google AI API key. Env vars match the official SDK's own resolution (GOOGLE_API_KEY wins if both are set).
default_model	string	gemini-3.7-flash	Amplifier-only	Default model to use when a request doesn't override it.
max_output_tokens	int	8192	API: max_output_tokens	Maximum output tokens. Renamed from max_tokens to match Google's own parameter name -- max_tokens still works as a deprecated alias (one-shot warning; max_output_tokens always wins if both are set).
max_tokens	int	--	(deprecated alias)	Old name for max_output_tokens. Prefer the new name in new configs.
temperature	float	0.7	API: temperature	Sampling temperature (0.0-2.0 per Google's docs; this provider does not clamp the range itself).
timeout	float	600.0	Amplifier-only	API call timeout in seconds, enforced client-side via asyncio.wait_for.
priority	int	100	Amplifier-only	Provider selection priority (lower = preferred). Read by the orchestrator's provider-selection logic, not by this module's own request-building code.
raw	bool	false	Amplifier-only	Enable raw API request/response capture on the llm:request / llm:response events (non-streaming path only).
use_streaming	bool	true	Amplifier-only	Use generate_content_stream instead of a single blocking generate_content call. Per-request override: request.metadata["stream"] = False.
max_retries	int	5	Amplifier-only	Max retry attempts on transient failures (5xx, timeouts, rate limits, Cloudflare/CDN challenges).
min_retry_delay	float	1.0	Amplifier-only	Initial retry backoff delay, in seconds.
max_retry_delay	float	60.0	Amplifier-only	Maximum retry backoff delay, in seconds.
retry_jitter	bool	true	Amplifier-only	Add random jitter to retry backoff delays.
max_concurrent_requests	int	5	Amplifier-only	Process-wide concurrency limit shared across all provider instances (parent + delegated sessions). 0 disables the limit.
extra_request_params	dict	{}	(passthrough)	Settings-only escape hatch -- see extra_request_params below. Never an interactive config field; owner-beware.

Boolean and numeric values above tolerate string input ("true"/"false", "600", etc. -- as written by the app-cli wizard or hand-edited YAML) and warn-and-default rather than crash on anything unparseable. Unrecognized config keys log a warning at mount time (with a "did you mean" suggestion for likely typos); they never silently do nothing without a signal.

Removed in this revision: debug, raw_debug, and debug_truncate_length were documented here in earlier README revisions but were never actually implemented by this provider (no code path reads them). If your config still sets them, they are now flagged with a specific "this key is inert" warning at mount time instead of being silently ignored. Use raw: true for this provider's actual raw-I/O capture on llm:request/llm:response events.

extra_request_params (advanced)

extra_request_params merges arbitrary fields directly into the GenerateContentConfig this provider builds for every request -- reaching Google API parameters this provider doesn't otherwise expose as a first-class config key: safety_settings, top_p, top_k, seed, stop_sequences, presence_penalty, frequency_penalty, response_mime_type, labels, and anything else google.genai.types.GenerateContentConfig defines.

providers:
  - module: provider-gemini
    config:
      default_model: gemini-3.7-flash
      extra_request_params:
        top_p: 0.95
        safety_settings:
          - category: HARM_CATEGORY_DANGEROUS_CONTENT
            threshold: BLOCK_ONLY_HIGH

Contract:

• Merged LAST, after every value this provider computes itself (temperature, max_output_tokens, thinking_config, tools). Your extra_request_params value always wins.
• Overriding a value this provider had already set logs a warning naming the field, this provider's computed value, and your override -- a silent production override never happens.
• An unrecognized field name (not a real GenerateContentConfig field) logs a warning and is skipped -- it never crashes the provider mount.
• Settings-only, deliberately not a ConfigField: it will never appear in the interactive amplifier init / amplifier provider use wizard. Set it directly in settings.yaml or a bundle's config block. If your tooling round-trips provider config (e.g. re-serializing settings), this key passes through unchanged like any other dict value -- there is no special handling on this provider's side beyond the merge described above.
• Safety filters default OFF on Gemini 2.5/3.x models (verified against ai.google.dev) -- this provider never injects a safety_settings default of its own. If you want filtering, set it explicitly via extra_request_params.safety_settings.

Environment Variables

The provider supports both environment variables (matching the official Google GenAI SDK):

# Either of these works (GOOGLE_API_KEY takes precedence if both are set)
export GEMINI_API_KEY="your-api-key-here"
# or
export GOOGLE_API_KEY="your-api-key-here"

Get your API key from Google AI Studio.

Usage

# In amplifier configuration
[provider]
name = "gemini"
default_model = "gemini-3.7-flash"

Example Bundle Configurations

For advanced configuration, add Gemini to any bundle. These examples show the YAML configuration section (the frontmatter between --- markers in your bundle.md file):

Basic Configuration:

providers:
  - module: provider-gemini
    source: git+https://github.com/microsoft/amplifier-module-provider-gemini@main
    config:
      default_model: gemini-3.7-flash
      max_output_tokens: 65536  # Use full 65K output capacity
      temperature: 0.7
      priority: 50  # IMPORTANT: Lower number = higher priority (beats default 100)

Balanced (1M context, cost-effective):

providers:
  - module: provider-gemini
    source: git+https://github.com/microsoft/amplifier-module-provider-gemini@main
    config:
      default_model: gemini-3.7-flash
      max_output_tokens: 65536  # Full 65K output capacity
      priority: 50  # Lower number = higher priority

Thinking (complex reasoning with full 1M context):

session:
  context:
    config:
      max_tokens: 1048576  # Full 1M input context

  orchestrator:
    module: loop-streaming
    source: git+https://github.com/microsoft/amplifier-module-loop-streaming@main
    config:
      extended_thinking: true  # Show thinking content

providers:
  - module: provider-gemini
    source: git+https://github.com/microsoft/amplifier-module-provider-gemini@main
    config:
      default_model: gemini-3.7-flash
      max_output_tokens: 65536  # Full 65K output capacity
      temperature: 1.0
      priority: 50  # Lower number = higher priority

Fast (simple queries, low cost):

providers:
  - module: provider-gemini
    source: git+https://github.com/microsoft/amplifier-module-provider-gemini@main
    config:
      default_model: gemini-3.5-flash-lite
      max_output_tokens: 65536  # Full 65K output capacity
      temperature: 0.5
      priority: 50  # Lower number = higher priority

Features

Core Capabilities

• Text Generation - Single and multi-turn conversations
• Tool/Function Calling - OpenAPI schema format
• Extended Thinking - Reasoning via thinking_level (current models) or thinking_budget (legacy models)
• Streaming Support - Incremental response generation
• 1M Token Context - Process extremely large inputs (Flash models)
• Message Validation - Defense-in-depth error checking

Thinking/Reasoning

Google's thinking control surface changed generations: thinking_budget (an approximate output-token budget) is the legacy control; thinking_level (an enum: minimal/low/medium/high) is the current control -- and, as of this revision, the only control some models accept at all. Sending both on one request is rejected by the API with a 400.

This provider maps Amplifier's portable reasoning_effort request field to thinking_level automatically, per-model, clamped against a small maintained support table:

Model	Supported thinking_level values	Notes
gemini-3.7-flash	low, medium, high	Rejects minimal (verified live: "Thinking level MINIMAL is not supported for this model"). Google's own default (when omitted) is medium.
gemini-3.5-flash, gemini-3.5-flash-lite	minimal, low, medium, high	Full range.
Other gemini-3.* ids	minimal, low, medium, high (assumed)	Not individually verified -- assumed full range until a live 400 proves narrower.
gemini-2.5-flash, gemini-2.5-pro, gemini-2.5-flash-lite	none	Rejects thinking_level entirely (verified live: "Thinking level is not supported for this model"). Falls back to the legacy thinking_budget mapping below.
gemini-2.0-*	none (shut down)	Not servable at all.

reasoning_effort -> thinking_level mapping (for models that support it):

reasoning_effort	Target level	If not supported by the model
none	(no level sent)	Uses minimal if the model supports it, otherwise the model's own default (Gemini 3.x cannot disable thinking at all -- verified live)
minimal	minimal	Clamped up to the nearest supported level (e.g. low on gemini-3.7-flash), logged at INFO
low	low	Clamped as above
medium	medium	Clamped as above
high, xhigh, max	high	Gemini has no level above high

For models with no thinking_level support (the gemini-2.5-* family), reasoning_effort instead maps to the legacy numeric thinking_budget: none -> 0 (disabled), minimal/low -> 4096, medium/high/xhigh/max -> -1 (dynamic, model decides).

An explicit thinking_budget passed via request.metadata["thinking_budget"] or a provider **kwargs override always wins outright and is sent alone -- never combined with thinking_level on the same request.

To display thinking output, configure your orchestrator (not the provider):

session:
  orchestrator:
    module: loop-streaming     # Required for thinking display
    source: git+https://github.com/microsoft/amplifier-module-loop-streaming@main
    config:
      extended_thinking: true  # Show thinking content to user

Note: The provider captures thinking from the API automatically (include_thoughts defaults to true). The orchestrator's extended_thinking: true config controls whether it's displayed. Without this config, thinking still happens but isn't shown to the user.

Thought signatures: Gemini 2.5+ models attach an opaque thought_signature to parts that follow a thinking burst. Because this provider is stateless full-resend (the entire conversation is rebuilt from stored history and resent on every turn, with no server-side session), these signatures must be captured and replayed unmodified or the API returns FinishReason MISSING_THOUGHT_SIGNATURE. This provider captures and echoes signatures on text, thinking, and tool-call parts alike, encoded as base64 so the value survives any JSON serialization the conversation history passes through (e.g. session persistence, event logging).

Tool Calling

Functions are declared using OpenAPI schema format:

tools = [{
    "name": "get_weather",
    "description": "Get weather for a location",
    "parameters": {
        "type": "object",
        "properties": {
            "location": {"type": "string", "description": "City name"}
        },
        "required": ["location"]
    }
}]

The provider handles tool call marshaling and response integration automatically.

Graceful Error Recovery

The provider implements automatic repair for incomplete tool call sequences:

The Problem: If tool results are missing from conversation history (due to context compaction bugs, parsing errors, or state corruption), the Gemini API rejects the entire request, breaking the user's session.

The Solution: The provider automatically detects and repairs missing tool_results by injecting synthetic results:

1. Repair before API call - Detects missing tool_results and injects synthetic ones
2. Make failures visible - Synthetic results contain [SYSTEM ERROR: Tool result missing] messages
3. Maintain conversation validity - API accepts repaired messages, session continues
4. Enable recovery - LLM acknowledges error and can ask user to retry
5. Provide observability - Emits provider:tool_sequence_repaired event with repair details

Example:

# Conversation with missing tool result
messages = [
    {
        "role": "assistant",
        "content": [
            {"type": "tool_call", "id": "gemini_call_abc123", "name": "get_weather", "input": {...}}
        ]
    },
    # MISSING: {"role": "tool", "tool_call_id": "gemini_call_abc123", "content": "..."}
    {"role": "user", "content": "Thanks"}
]

# Provider repairs by injecting synthetic result:
{
    "role": "tool",
    "tool_call_id": "gemini_call_abc123",
    "name": "get_weather",
    "content": "[SYSTEM ERROR: Tool result missing from conversation history]\n\nTool: get_weather\nCall ID: gemini_call_abc123\n\nThis indicates the tool result was lost after execution.\nLikely causes: context compaction bug, message parsing error, or state corruption.\n\nThe tool may have executed successfully, but the result was lost.\nPlease acknowledge this error and offer to retry the operation."
}

This is a defense-in-depth safety net. The orchestrator should handle tool execution errors at runtime, so this repair only triggers when results go missing due to bugs in context management.

Known Limitations

Synthetic Tool Call IDs

The Gemini API does not provide tool call IDs (unlike Anthropic and OpenAI). The provider generates synthetic IDs using the format gemini_call_{uuid} to maintain compatibility with Amplifier's tool protocol.

Impact: Tool call IDs are unique and functional but not provided by the API itself. This is transparent to users but documented for debugging purposes.

Text-Only in Current Version

The provider implements text generation, tool calling, and thinking support. Multimodal capabilities (images, video, audio) are not yet supported.

Gemini 3.x Thinking Cannot Be Disabled

Verified live: thinking_budget=0 on gemini-3.7-flash still produced thinking tokens, and there is no "off" thinking_level. Thinking is mandatory for Gemini 3.x models regardless of what this provider sends. reasoning_effort="none" on these models falls back to the model's own default thinking amount rather than actually disabling it -- this is a vendor limitation, not something this provider can work around.

Dependencies

• google-genai>=1.56.0 - Official Google AI Python SDK. 1.56.0 is the floor because it's the first release whose ThinkingConfig exposes the full thinking_level enum (minimal/low/medium/high) this provider needs -- verified by probing the SDK's own installed types directly: 1.46.0 has no thinking_level field at all; 1.51.0 adds it with only LOW/HIGH; 1.56.0 completes the four-level enum.

Development

Local Testing with Installed Amplifier CLI

Test your local provider changes with the installed amplifier CLI:

# From the provider repository root
cd amplifier-module-provider-gemini

# Add your local provider (use --local for development, not --global)
amplifier module add provider-gemini --source file://. --local

# Set your API key
export GOOGLE_API_KEY="your-api-key-here"

# Now you can use amplifier init and see your local provider in the menu
amplifier init

# Or configure it directly
amplifier provider use gemini --local

# Test your local changes
amplifier run "Hello, testing local Gemini provider!"

# List modules to verify your local provider is registered
amplifier module list -t provider

When you're done testing:

# Remove the local provider registration
amplifier module remove provider-gemini --local

Why --local instead of --global?

• --local registers the provider only for the current working directory
• --global would affect all your projects (not ideal during development)
• --project works if you want to share with your team

Unit Testing (Fastest)

For rapid iteration without the CLI:

cd amplifier-module-provider-gemini

# Install dependencies
uv sync --dev

# Run validation tests (protocol compliance)
uv run pytest

# Test with coverage
uv run pytest --cov

These tests validate the provider implements the required protocol without needing the full CLI.

Contributing

Note

This project is not currently accepting external contributions, but we're actively working toward opening this up. We value community input and look forward to collaborating in the future. For now, feel free to fork and experiment!

Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit Contributor License Agreements.

When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.

Trademarks

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