Amplifier Anthropic Provider Module

Claude model integration for Amplifier via Anthropic API.

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 Anthropic's Claude models (Claude 4 series: Sonnet, Opus, Haiku) as an LLM provider for Amplifier.

Contract

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

Supported Models

• claude-sonnet-4-5 - Claude Sonnet 4.5 (recommended, default)
• claude-opus-4-1 - Claude Opus 4.1 (most capable)
• claude-haiku-4-5 - Claude Haiku 4.5 (fastest, cheapest)

Configuration

[[providers]]
module = "provider-anthropic"
name = "anthropic"
config = {
    default_model = "claude-sonnet-4-5",
    max_tokens = 8192,
    temperature = 1.0,
    debug = false,      # Enable standard debug events
    raw_debug = false   # Enable ultra-verbose raw API I/O logging
}

Debug Configuration

Standard Debug (debug: true):

• Emits llm:request:debug and llm:response:debug events
• Contains request/response summaries with message counts, model info, usage stats
• Moderate log volume, suitable for development

Raw Debug (debug: true, raw_debug: true):

• Emits llm:request:raw and llm:response:raw events
• Contains complete, unmodified request params and response objects
• Extreme log volume, use only for deep provider integration debugging
• Captures the exact data sent to/from Anthropic API before any processing

Example:

providers:
  - module: provider-anthropic
    config:
      debug: true      # Enable debug events
      raw_debug: true  # Enable raw API I/O capture
      default_model: claude-sonnet-4-5

Beta Headers

Anthropic provides experimental features through beta headers. Enable these features by adding the beta_headers configuration field.

Configuration

Single beta header:

providers:
  - module: provider-anthropic
    config:
      default_model: claude-sonnet-4-5
      beta_headers: "context-1m-2025-08-07"  # Enable 1M token context window

Multiple beta headers:

providers:
  - module: provider-anthropic
    config:
      default_model: claude-sonnet-4-5
      beta_headers:
        - "context-1m-2025-08-07"
        - "future-feature-header"

1M Token Context Window

Claude Sonnet 4.5 supports a 1M token context window when the context-1m-2025-08-07 beta header is enabled:

providers:
  - module: provider-anthropic
    config:
      default_model: claude-sonnet-4-5
      beta_headers: "context-1m-2025-08-07"
      max_tokens: 8192  # Output tokens remain separate from context window

With this configuration:

• Context window: Up to 1M tokens of input (messages, tools, system prompt)
• Output tokens: Controlled by max_tokens (separate from context window)
• Use case: Process large codebases, extensive documentation, or long conversation histories

Notes

• Beta features are experimental and subject to change
• Check Anthropic's documentation for available beta headers
• Beta headers are optional - existing configurations work unchanged
• Invalid beta headers will cause API errors (fail fast)
• Beta header usage is logged at initialization for observability

Environment Variables

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

Usage

# In amplifier configuration
[provider]
name = "anthropic"
default_model = "claude-sonnet-4-5"

Features

• Streaming support
• Tool use (function calling)
• Vision capabilities (on supported models)
• Token counting and management
• Message validation before API calls (defense in depth)

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 Anthropic 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 validation - 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
6. Validate remaining - After repair, strict validation catches any remaining inconsistencies

Example:

# Anthropic format (after _convert_messages)
messages = [
    {
        "role": "assistant",
        "content": [
            {"type": "tool_use", "id": "toolu_123", "name": "get_weather", "input": {...}}
        ]
    },
    # MISSING: {"role": "user", "content": [{"type": "tool_result", "tool_use_id": "toolu_123", ...}]}
    {"role": "user", "content": "Thanks"}
]

# Provider repairs by injecting synthetic result:
# Either appends to existing user message or inserts new one
{
    "role": "user",
    "content": [{
        "type": "tool_result",
        "tool_use_id": "toolu_123",
        "content": "[SYSTEM ERROR: Tool result missing]\n\nTool: get_weather\n..."
    }]
}

Observability: Repairs are logged as warnings and emit provider:tool_sequence_repaired events for monitoring.

Philosophy: This is graceful degradation following kernel philosophy - errors in other modules (context management) don't crash the provider or kill the user's session

Dependencies

• amplifier-core>=1.0.0
• anthropic>=0.25.0

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.

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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

This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft's Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies.