Skip to content

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

pip install ace-framework[langchain]

Quick Start

from ace_next 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
ace_llm LLMClientLike None Pre-built LLM 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

  1. INJECT — Skillbook strategies are added to the chain input
  2. EXECUTE — LangChain runs the chain normally
  3. Extract trace — ACE extracts intermediate steps, tool calls, and reasoning
  4. 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",
)