The Problem
Production LangChain agents lose context between turns ('amnesia') and pick wrong tools when 8+ tools are attached. Either failure breaks the agent loop.
The Scavio Solution
LangGraph state + checkpointer for cross-turn memory. Scavio MCP for semantically-named tools (search, reddit_search, youtube_search) so routing decisions are unambiguous by construction. Postgres for persistent state.
Before
LangChain agent with 12 raw tools + no state = 48% task success on 5-step research.
After
LangGraph + Scavio MCP (4-6 named tools per intent) + Postgres state = ~94% task success in the post's reported benchmark.
Who It Is For
Production LangChain agent maintainers, agent platform teams, devs shipping multi-turn research agents.
Key Benefits
- Unambiguous tool routing via semantic names
- Cross-turn memory via LangGraph checkpointer
- One MCP attachment vs 5+ wired tools
- Stack cost ~$35-45/mo
- Production-ready architecture
Python Example
from langgraph.checkpoint.postgres import PostgresSaver
from langchain_scavio import ScavioSearchTool, ScavioRedditTool
checkpointer = PostgresSaver.from_conn_string(POSTGRES_URL)
tools = [ScavioSearchTool(name='web_search'), ScavioRedditTool(name='reddit_search')]
# Build LangGraph state machine with checkpointer + toolsJavaScript Example
// LangGraph is Python-first. JS-side: LangChain.js with manual state in Postgres or KV.
import { ChatAnthropic } from '@langchain/anthropic';
// Build graph with manual state schema, attach Scavio fetch as tools.Platforms Used
Web search with knowledge graph, PAA, and AI overviews
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