fast-langgraph 0.1.13

High-performance Rust implementation of LangGraph components

Fast-LangGraph

High-performance Rust accelerators for LangGraph applications. Drop-in components that provide up to 700x speedups for checkpoint operations and 10-50x speedups for state management.

Why Fast-LangGraph?

LangGraph is great for building AI agents, but production workloads often hit performance bottlenecks:

• Checkpoint serialization - Python's deepcopy is slow for complex state
• State management at scale - High-frequency updates accumulate overhead
• Repeated LLM calls - Identical prompts waste API costs

Fast-LangGraph solves these by reimplementing critical paths in Rust while maintaining full API compatibility.

Install

pip install fast-langgraph

or

uv add fast-langgraph

Acceleration Modes

Fast-LangGraph offers two types of acceleration:

Automatic Acceleration (via Shim)

Enable transparent acceleration with a single environment variable or function call. No code changes required to your existing LangGraph application.

# Option 1: Environment variable (recommended for production)
export FAST_LANGGRAPH_AUTO_PATCH=1
python your_app.py

# Option 2: Explicit patching at startup
import fast_langgraph
fast_langgraph.shim.patch_langgraph()

# Your existing LangGraph code runs faster automatically

What gets accelerated automatically:

Component	Speedup	Description
Executor Caching	2.3x	Reuses ThreadPoolExecutor across invocations
apply_writes	1.2x	Rust-based channel batch updates

Combined automatic speedup: ~2.8x for typical graph invocations.

Check acceleration status:

import fast_langgraph
fast_langgraph.shim.print_status()

Manual Acceleration (Explicit Usage)

For maximum performance, use Rust components directly. These require small code changes but provide the largest speedups.

from fast_langgraph import (
    RustSQLiteCheckpointer,  # 5-6x faster checkpointing
    cached,                   # LLM response caching
    langgraph_state_update,   # Fast state merging
)

Component	Speedup	When to Use
RustSQLiteCheckpointer	5-6x	State persistence
@cached decorator	10x+	Repeated LLM calls (with 90% hit rate)
langgraph_state_update	13-46x	High-frequency state updates

Quick Start

1. Automatic Acceleration (Easiest)

# At the top of your application
import fast_langgraph
fast_langgraph.shim.patch_langgraph()

# Rest of your code unchanged - runs 2-3x faster
from langgraph.graph import StateGraph
# ...

2. Fast Checkpointing (Biggest Impact)

Drop-in replacement for LangGraph's SQLite checkpointer:

from fast_langgraph import RustSQLiteCheckpointer

# 5-6x faster than the default checkpointer
checkpointer = RustSQLiteCheckpointer("state.db")
graph = graph.compile(checkpointer=checkpointer)

3. LLM Response Caching

Cache LLM responses to avoid redundant API calls:

from fast_langgraph import cached

@cached(max_size=1000)
def call_llm(prompt):
    return llm.invoke(prompt)

# First call: hits the API (~500ms)
response = call_llm("What is LangGraph?")

# Second identical call: returns from cache (~0.01ms)
response = call_llm("What is LangGraph?")

# Check cache statistics
print(call_llm.cache_stats())
# {'hits': 1, 'misses': 1, 'size': 1}

4. Optimized State Updates

Efficient state merging for high-frequency updates:

from fast_langgraph import langgraph_state_update

new_state = langgraph_state_update(
    current_state,
    {"messages": [new_message]},
    append_keys=["messages"]
)

5. Performance Profiling

Find bottlenecks with minimal overhead:

from fast_langgraph.profiler import GraphProfiler

profiler = GraphProfiler()

with profiler.profile_run():
    result = graph.invoke(input_data)

profiler.print_report()

Performance

Rust's Key Strengths

These are the operations where Rust provides the most dramatic improvements:

Operation	Speedup	Best Use Case
Checkpoint Serialization	43-737x	State persistence (scales with state size)
Sustained State Updates	13-46x	Long-running graphs with many steps
E2E Graph Execution	2-3x	Production workloads with checkpointing

All Features

Feature	Performance	Use Case
Complex Checkpoint (250KB)	737x faster than deepcopy	Large agent state
Complex Checkpoint (35KB)	178x faster	Medium state
LLM Response Caching	10x speedup (90% hit rate)	Repeated prompts, RAG
Function Caching	1.6x speedup	Expensive computations
In-Memory Checkpoint	1.4 us/op	Fast state snapshots
LangGraph State Update	1.4 us/op	High-frequency updates

Note: Rust excels at complex state operations. For simple dict operations, Python's built-in dict (implemented in C) is already highly optimized. See BENCHMARK.md for detailed results.

Requirements

• Python 3.9+
• Works with any LangGraph version

Documentation

Authoritative docs live under documentation/docs/ and power the MkDocs site.

• Getting Started - Installation + first run
• User Guide - Automatic/manual acceleration details
• API Reference - Python + Rust surface area
• Architecture - Internal design + trade-offs
• Contributing - Tooling, tests, and release flow

Project Metadata

• Author: Dipankar Sarkar ([e-mail])
• Organization: Neul Labs
• Repository: https://github.com/neul-labs/fast-langgraph
• License: MIT

Examples

See the examples/ directory for complete working examples:

• function_cache_example.py - Caching patterns
• profiler_example.py - Performance analysis
• state_merge_example.py - State manipulation

Contributing

Contributions welcome! See documentation/docs/development/contributing.md for setup instructions.

License

MIT