Multi-Agent Local Coding: What Search Adds
Pi Coding Agent plus llama-swap local multi-agent setup lacks live docs. Search MCP adds current documentation, package versions, and API references.
Local multi-agent coding setups like Pi Coding Agent with llama-swap give you model routing (small model for simple edits, large model for architecture decisions) but lack real-time context. Adding a search layer via MCP gives the local agent access to live documentation, current package versions, and API references -- the same information that makes cloud coding assistants useful, without sending your code to external servers.
The local multi-agent setup
llama-swap is a model proxy that lets you hot-swap between local models based on task complexity. Pi Coding Agent (or similar local agent frameworks) sends requests through llama-swap, which routes to the appropriate model: Qwen 2.5 Coder 7B for autocomplete and simple edits, Llama 3.1 70B or DeepSeek Coder V2 for complex reasoning and architecture.
This works well for code generation and refactoring. Where it fails: any task requiring knowledge of the current state of the world. Package versions change weekly. API endpoints deprecate. Framework best practices evolve. A local model trained on data from six months ago generates code using outdated APIs.
What search adds to local coding
- Live documentation: the model can look up current docs instead of generating from stale training data
- Package versions: check what the latest stable release actually is before adding it to requirements.txt
- API reference: verify endpoint URLs, required headers, and response schemas before writing integration code
- Error resolution: search for the exact error message to find current solutions, not outdated Stack Overflow answers from training data
- Dependency compatibility: check if two packages actually work together in their current versions
MCP integration for local agents
{
"mcpServers": {
"scavio": {
"type": "url",
"url": "https://mcp.scavio.dev/mcp",
"headers": {
"x-api-key": "your-scavio-api-key"
}
}
}
}MCP (Model Context Protocol) provides a standard way for agents to call external tools. When the local agent needs current information, it calls the search MCP server, gets structured results, and incorporates them into its context before generating code.
Practical example: building an integration
import requests, os
SCAVIO_KEY = os.environ["SCAVIO_API_KEY"]
def search_docs(query, count=5):
"""Search for current documentation and API references."""
resp = requests.post(
"https://api.scavio.dev/api/v1/search",
headers={"x-api-key": SCAVIO_KEY},
json={"query": query, "num_results": count}
)
return resp.json()["results"]
# Scenario: local agent needs to write a Stripe integration
# Without search: uses training data (possibly outdated API version)
# With search: gets current docs
docs = search_docs("Stripe API python create payment intent 2026")
for d in docs[:3]:
print(f"{d['title']}")
print(f" {d['url']}")
print(f" {d['description'][:120]}")
# The agent now has current endpoint info, not 6-month-old training dataError resolution with search grounding
def resolve_error(error_message, framework=""):
"""Search for current solutions to an error."""
query = f"{framework} {error_message} fix solution 2026"
results = search_docs(query, count=5)
solutions = []
for r in results:
solutions.append({
"source": r["url"],
"title": r["title"],
"fix_hint": r["description"]
})
return solutions
# Local agent hits a dependency conflict
error = "ModuleNotFoundError: No module named 'pydantic.v1'"
fixes = resolve_error(error, "FastAPI")
for fix in fixes:
print(f"Possible fix from: {fix['source']}")
print(f" {fix['fix_hint'][:150]}")
# 1 credit for current error solutions vs hallucinated fixesVersion checking before code generation
def check_current_version(package_name):
"""Verify the current version of a package before using it."""
results = search_docs(
f"{package_name} latest version release 2026", count=3
)
print(f"Current info for {package_name}:")
for r in results:
print(f" {r['title']}: {r['description'][:100]}")
return results
def check_compatibility(package_a, package_b):
"""Check if two packages work together in current versions."""
results = search_docs(
f"{package_a} {package_b} compatibility version conflict", count=3
)
print(f"\nCompatibility: {package_a} + {package_b}")
for r in results:
print(f" {r['title']}: {r['description'][:100]}")
return results
# Before generating requirements.txt:
check_current_version("langchain")
check_compatibility("langchain", "pydantic")
# 2 credits = $0.01 to avoid version hellThe architecture with search
The complete local coding stack: llama-swap handles model routing (small model for speed, large model for complexity). The agent framework (Pi Coding Agent, Continue, or custom) manages the conversation loop and tool execution. The search MCP server provides real-time web access. ChromaDB or similar stores project-specific context from your codebase.
# Architecture summary
local_stack = {
"model_router": "llama-swap",
"models": {
"fast": "qwen2.5-coder:7b", # autocomplete, simple edits
"strong": "llama3.1:70b", # architecture, complex reasoning
},
"tools": {
"search": "mcp.scavio.dev/mcp", # live docs, versions, errors
"filesystem": "local", # read/write project files
"terminal": "local", # run tests, install packages
},
"context": {
"project": "chromadb (local)", # indexed codebase
"web": "search API (on demand)", # current documentation
}
}
# Cost per coding session (estimated):
# - Models: free (local hardware)
# - Search: 10-20 queries per session = $0.05-0.10
# - Storage: free (local disk)
# Total: under $0.10 per session for grounded local codingThe search layer is the cheapest upgrade to a local coding setup with the highest impact. A local model generating code from stale training data produces code that looks correct but uses deprecated APIs. The same model with 10 search queries per session produces code that actually runs against current APIs. At $0.05-0.10 per session on Scavio, there is no reason to code blind when current documentation is one API call away.