Fastal LangGraph Toolkit

A toolkit born from experience

The Fastal LangGraph Toolkit was originally developed internally by the Fastal Group to support enterprise-grade agentic application implementations across multiple client projects. After proving its effectiveness in production environments, we’ve open-sourced this toolkit to contribute to the broader LangGraph community.

pip install fastal-langgraph-toolkit

View package on PyPI

Why this toolkit

Building production LangGraph agents involves solving common challenges in advanced research and development projects:

• Multi-provider Management — Support for multiple LLM/embedding/speech providers with seamless switching
• Context Management — Intelligent conversation summarization for long-running sessions
• Memory Optimization — Token-efficient context handling for cost control
• Speech Processing — Enterprise-grade speech-to-text transcription capabilities
• Type Safety — Proper state management with TypedDict integration
• Configuration Injection — Clean separation between business logic and framework concerns

This toolkit provides battle-tested solutions for these challenges, extracted from real enterprise implementations.

Main features

Multi-Provider Model Factory

The current version of the model factory supports the following providers:

LLM Support: OpenAI (including GPT-5 models), Anthropic, Ollama, AWS Bedrock

Embeddings Support: OpenAI, Ollama, AWS Bedrock

Speech-to-Text Support: OpenAI Whisper (more providers coming soon)

Features:

• Full GPT-5 Support: GPT-5, GPT-5-mini, and GPT-5-nano with automatic parameter mapping
• Configuration Injection: Clean provider abstraction
• Provider Health Checks: Availability validation
• Seamless Switching: Change providers without code changes

Enterprise Speech Processing

Production-ready speech-to-text processing with enterprise-grade reliability and performance.

• Multi-Format Support: MP3, WAV, M4A, and other common audio formats
• Language Detection: Automatic language identification and custom language hints
• Async Processing: Full async/await support for non-blocking operations
• Segment Information: Detailed timestamp and confidence data when available
• Error Handling: Robust error management with detailed logging
• Lazy Loading: Efficient resource management with provider lazy loading

Intelligent Conversation Summarization

The LangChain/LangGraph framework provides good support for managing both short-term and long-term memory in agents through the LangMem module. However, we found that automated summarization based solely on token counting is not a sufficient approach for most real and complex agents.

The solution included in this kit offers an alternative and more sophisticated method, based on the structure of the conversation and a focus on the object and content of the discussions.

• Ready-to-Use LangGraph Node: summary_node() method provides instant integration
• Conversation Pair Counting: Smart Human+AI message pair detection
• ReAct Tool Filtering: Automatic exclusion of tool calls from summaries
• Configurable Thresholds: Customizable trigger points for summarization
• Context Preservation: Keep recent conversations for continuity
• Custom Prompts: Domain-specific summarization templates
• State Auto-Injection: Seamless integration with existing states

Memory Management

• SummarizableState: Type-safe base class for summary-enabled states
• Automatic State Management: No manual field initialization required
• LangGraph Integration: Native compatibility with LangGraph checkpointing
• Clean Architecture: Separation of concerns between summary and business logic