LangChain Integration

The LangChain runner wraps any LangChain Runnable (chains, AgentExecutor, LangGraph graphs) with ACE learning. The runner extracts execution traces and learns strategies from them.

Installation

uv add ace-framework[langchain]

Quick Start

from ace import LangChain

runner = LangChain.from_model(your_chain, ace_model="gpt-4o-mini")

results = runner.run([
    {"input": "Summarize this document"},
    {"input": "Extract key entities"},
])

runner.save("chain_expert.json")

Parameters

from_model()

Parameter	Type	Default	Description
`runnable`	`Any`	—	LangChain Runnable (chain, AgentExecutor, graph)
`ace_model`	`str`	`"gpt-4o-mini"`	Model for Reflector + SkillManager
`ace_max_tokens`	`int`	`2048`	Max tokens for ACE LLM responses
`ace_temperature`	`float`	`0.0`	Sampling temperature for ACE roles

from_roles()

Parameter	Type	Default	Description
`runnable`	`Any`	—	LangChain Runnable
`reflector`	`ReflectorLike`	—	Reflector instance
`skill_manager`	`SkillManagerLike`	—	SkillManager instance
`skillbook_path`	`str`	`None`	Load saved skillbook
`output_parser`	`Callable`	`None`	Custom output extraction
`dedup_config`	`DeduplicationConfig`	`None`	Deduplication config
`checkpoint_dir`	`str`	`None`	Checkpoint directory

Methods

results = runner.run(inputs, epochs=1)      # Run with learning
results = runner.invoke(single_input)       # Single input convenience
runner.save("path.json")                    # Save skillbook
runner.wait_for_background()                # Wait for async learning

How It Works

INJECT — Skillbook strategies are added to the chain input
EXECUTE — LangChain runs the chain normally
Extract trace — ACE extracts intermediate steps, tool calls, and reasoning
LEARN — Reflector analyzes the trace, SkillManager updates the skillbook

The runner handles simple chains, AgentExecutor (with intermediate_steps), and LangGraph graphs automatically.

Input Types

The runner accepts any input your chain expects:

# String input
runner.run(["What is ACE?"])

# Dict input
runner.run([{"input": "query", "context": "..."}])

# Message list
runner.run([[HumanMessage(content="Hello")]])

Resuming from a Saved Skillbook

runner = LangChain.from_model(
    your_chain,
    ace_model="gpt-4o-mini",
    skillbook_path="chain_expert.json",
)