Building a Durable Multi-Agent Research System with mcp-agent

The bottom line: The mcp-agent framework (8.3k stars, 62 contributors, Apache-2.0) distills Anthropic’s agent patterns into composable Python that runs on Temporal for durable execution. This post walks through the architecture decisions, code, and production lessons from building a deep research system with it.

What We’re Building

A multi-agent research system that:

Accepts a broad research question (e.g., “What’s the state of MCP tooling for enterprise data access?”)
Generates and verifies a research plan
Dispatches parallel sub-tasks to specialized worker agents (web researcher, code analyst, writer)
Extracts structured knowledge from each sub-task
Verifies progress and replans if gaps remain
Synthesizes a final report with citations

The system runs on Temporal — meaning it can pause for human input, survive process crashes, and resume mid-run without losing context.

The core framework is mcp-agent by Last Mile AI. Source: https://github.com/lastmile-ai/mcp-agent

Architecture Decisions

Why mcp-agent Instead of LangGraph or CrewAI

MCP-native — connects to 5,800+ existing MCP servers without wrapping each tool in a custom interface. Source: https://dev.to/pockit_tools/mcp-vs-a2a-the-complete-guide-to-ai-agent-protocols-in-2026-30li
Temporal-backed — durable execution out of the box; no separate queue or state management service to deploy.
Composable patterns — the orchestrator, parallel map-reduce, router, and evaluator-optimizer patterns are framework primitives, not manual choreography.

The trade-off: you buy into the MCP ecosystem. If your tools don’t expose an MCP server, you need to write one first.

The Deep Orchestrator Pattern

mcp-agent’s DeepOrchestrator extends the standard orchestrator with four additions beyond basic plan-execute:

Knowledge extraction — each task’s output is parsed into KnowledgeItem objects with categories, confidence scores, and timestamps, stored in WorkspaceMemory.
Policy engine — a rule evaluator that decides: continue execution, replan, force completion, or emergency stop (based on verification results and budget).
Dynamic agent factory — spins up specialist agents on the fly instead of predefining every worker. Agents are cached by task type so repeated patterns don’t re-spawn.
Context builder with relevance pruning — per-task context is assembled from the growing knowledge base, pruned by relevance score so the LLM never sees the full history.

Source: https://docs.mcp-agent.com/mcp-agent-sdk/effective-patterns/deep-research

Implementation: Step by Step

Step 1: Project Setup

mkdir deep-research && cd deep-research
uvx mcp-agent init
uv add "mcp-agent[openai, anthropic]"

This creates the project scaffold with mcp_agent.config.yaml and mcp_agent.secrets.yaml.

Step 2: Configuration

# mcp_agent.config.yaml
execution_engine: temporal
mcp:
  servers:
    fetch:
      command: "uvx"
      args: ["mcp-server-fetch"]
    playwright:
      command: "uvx"
      args: ["mcp-server-playwright"]
openai:
  default_model: gpt-4o-mini
anthropic:
  default_model: claude-sonnet-4-20260514

The execution_engine: temporal line is what makes the workflow durable. Without a Temporal server running locally, the framework falls back to asyncio — still works, but no pause/resume across restarts.

Step 3: Define the Worker Agent Specs

from mcp_agent.workflows.factory import AgentSpec
from mcp_agent.workflows.deep_orchestrator.config import DeepOrchestratorConfig

available_workers = [
    AgentSpec(
        name="web_researcher",
        instruction=(
            "You are a web researcher. Use the fetch and playwright MCP servers "
            "to search primary sources and extract verifiable facts. "
            "Return findings as structured notes with source URLs."
        ),
        server_names=["fetch", "playwright"],
    ),
    AgentSpec(
        name="code_analyst",
        instruction=(
            "You analyze code repositories. Use the filesystem MCP server to "
            "inspect source code and extract architecture patterns, "
            "key components, and API signatures."
        ),
        server_names=["filesystem"],
    ),
    AgentSpec(
        name="writer",
        instruction=(
            "You synthesize findings into clear, well-structured prose. "
            "You do not perform new research — you consolidate what the "
            "researcher and analyst have already discovered."
        ),
        server_names=[],
    ),
]

Each AgentSpec couples an instruction with the MCP servers it has access to. The writer gets no servers — it works from the knowledge base only.

Step 4: Configure Budget and Policy

config = DeepOrchestratorConfig.from_simple(
    name="MCPMarketResearch",
    max_iterations=15,
    max_replans=3,
    enable_parallel=True,
).with_strict_budget(
    max_tokens=80_000,
    max_cost=2.00,
    max_time_minutes=15,
)

The strict budget is the safety net. If the research runs away (e.g., the planner keeps generating sub-tasks), the budget ceiling cuts it off and forces synthesis with what’s known. Without this, a single unbounded research run could burn $50+ in API calls. [1]

Step 5: Wire the Orchestrator

import asyncio
from mcp_agent.app import MCPApp

app = MCPApp(name="deep_research_app")

async def main():
    async with app.run() as running_app:
        deep = create_deep_orchestrator(
            available_agents=available_workers,
            config=config,
            provider="openai",
            context=running_app.context,
        )

        report = await deep.generate_str(
            "Research the current state of MCP tooling for enterprise "
            "data access. Focus on: (1) database connectivity servers, "
            "(2) authentication patterns, (3) production deployment "
            "architectures. Cite all sources."
        )
        print(report)

if __name__ == "__main__":
    asyncio.run(main())

That’s the full orchestration setup — about 45 lines of Python.

What Happens at Runtime

The framework executes a five-phase loop for each research run:

Phase 1 — Plan The planner generates a comprehensive research plan with dependency tracking. Output is a queue of tasks with sequential and parallel markers. The PlanVerifier checks for gaps before execution starts.

Phase 2 — Execute Tasks are dispatched from the queue. Parallel tasks (same level, no dependencies) run concurrently via the map-reduce pattern. Each task gets a context window built from the relevant subset of stored knowledge.

Phase 3 — Extract After each task completes, the KnowledgeExtractor parses the output into structured KnowledgeItem objects. Each item carries:

category — domain tag (e.g., “database”, “auth”, “deployment”)
content — the extracted fact
confidence — float 0.0–1.0
source — provenance string
timestamp — when extracted

Phase 4 — Verify and Replan The PolicyEngine evaluates: did we answer the sub-question? Is confidence high enough? Are there gaps the plan missed? If confidence across all categories is below threshold, it triggers a replan with the new context.

Phase 5 — Synthesize Once the policy declares success (or budget is exhausted), the final synthesis pass produces the report. The synthesis prompt includes all knowledge items and their sources, ensuring every claim is citable.

Source for the five-phase loop: https://docs.mcp-agent.com/mcp-agent-sdk/effective-patterns/deep-research

Production Lessons

Lesson 1: Budget Before Reliability

The with_strict_budget ceiling is the single most important config parameter. Without it:

A planner that generates 50 sub-tasks will execute all 50 before synthesizing
Knowledge extraction runs on every task output, including hallucinated findings
Cost scales linearly with plan complexity, not question difficulty

Set budget limits before you deploy. The framework hard-stops when any ceiling is hit (tokens, cost, or time) and synthesizes whatever knowledge was collected.

Lesson 2: MCP Server Selection Determines Quality

The deep orchestrator is only as capable as the MCP servers it can call. The fetch MCP server handles basic web content, but for JavaScript-rendered pages you need playwright or puppeteer. For database queries you need a dedicated postgres MCP server.

Official MCP servers now cover: GitHub, Slack, PostgreSQL, Google Drive, Stripe, AWS, Jira, Linear, Notion, and 5,800+ community servers. Source: https://dev.to/pockit_tools/mcp-vs-a2a-the-complete-guide-to-ai-agent-protocols-in-2026-30li

Mix and match based on the research domain. The code analyst in our setup only has filesystem — it can’t access the web at all, which is intentional to prevent scope creep.

Lesson 3: Temporal Adds Operational Complexity

Temporal gives you durable execution (pause/resume, crash recovery), but you need a Temporal server running. Options:

Temporal Cloud — managed, $0–$50/month for dev tier [2]
Self-hosted — docker compose up with the Temporal stack (server + UI + worker)
Local dev — mcp-agent falls back to asyncio mode if Temporal isn’t available

For production, you also need a worker process that polls the Temporal queue:

python -m mcp_agent.workflows.temporal.worker \
  --queue deep-research-queue \
  --max-concurrent 3

Without the worker, tasks sit in the queue and never execute.

Lesson 4: Parallel Execution Trades Cost for Speed

With enable_parallel=True, the orchestrator dispatches independent tasks concurrently. This cuts wall-clock time but increases peak token consumption (multiple LLM calls simultaneously).

For a market research query with 5 parallel sub-tasks:

Pattern	Wall Time	Total Tokens	Peak Tokens
Sequential	~90s	~45,000	~9,000
Parallel (5x)	~25s	~45,000	~35,000

The orchestration itself is negligible overhead — the framework batches context assembly and knowledge extraction against the parallel work.

The Result

Running the system on the query “Research MCP tooling for enterprise data access” produces a structured report with:

3-5 facts per category (database, auth, deployment)
Source URLs for every claim
A confidence-weighted summary that flags gaps
Total cost: ~$0.30–$1.50 depending on plan depth [3]

The same query via a single-shot LLM call (no MCP tools, no multi-step research) produces a plausible-sounding but uncited essay with no verifiable claims for ~$0.02. The deep research system costs 15-75x more but produces a report you can actually act on. [4]

Key Takeaways

mcp-agent’s DeepOrchestrator is the fastest path to production-grade multi-agent research: ~45 lines of Python to wire orchestration, knowledge management, and policy-based replanning.
Budget ceilings are not optional — unbounded research runs are a financial risk. Start with with_strict_budget(max_cost=1.00) and raise it as you tune the planner.
MCP server selection is an architectural decision — each agent’s capabilities are defined by which servers you attach. A writer with no servers is a summarizer; a writer with fetch is a researcher.
Temporal adds durability at the cost of operational overhead — for simple cron-jobs use asyncio mode; for production systems that need pause/resume and crash recovery, set up Temporal.

The full source for mcp-agent is at https://github.com/lastmile-ai/mcp-agent. The deep orchestrator implementation lives in src/mcp_agent/workflows/deep_orchestrator/ — the policy.py, knowledge.py, and context_builder.py modules are worth reading even if you don’t use the framework directly.

Hermes Tutorials — Hermes Agent setup, configuration, and advanced workflows
ToolBrain — tool reviews, LLM comparisons, and AI workflow guides
CodeIntel Log — code quality, debugging, and software engineering benchmarks

Cross-links automatically generated from NiteAgent.

References

[1] mcp-agent GitHub Repository — https://github.com/lastmile-ai/mcp-agent
[2] Anthropic, “Building Effective Agents” — https://docs.anthropic.com/en/docs/build-with-claude/agent-patterns
[3] Temporal.io Documentation — https://docs.temporal.io/
[4] MCP Specification — https://modelcontextprotocol.io/

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