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MCP verified MIT Self-run

Reasonflow

mcp-kosminus-reasonflow · by kosminus

SDK-first agent orchestration framework. Build LLM pipelines as DAGs with >> chaining, parallel() branches, and 4 node types (LLM, Code, Decision, MCP). Auto cost/token tracking, budget enforcement, retries, JSON traces, and CLI. Supports OpenAI, Anthropic, Google, and Ollama.

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Install

$ agentstack add mcp-kosminus-reasonflow

✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.

Security review

✓ Passed

No issues found. Passed automated security review. · v0.1.0 How review works →

  • Prompt-injection patterns
  • Secret / credential exfiltration
  • Dangerous shell & filesystem operations
  • Untrusted network calls
  • Known-malicious package signatures

What it can access

  • Network access No
  • Filesystem access No
  • Shell / process execution No
  • Environment & secrets Used
  • Dynamic code execution No

From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.

View the full security report →

Verified badge

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Reliability & compatibility

Security review passed
0 installs to date
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3mo ago

Declared compatibility

Claude CodeClaude DesktopCursorWindsurf

Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.

Preview Execution monitoring

We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.

How agent discovery & health will work →
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About

ReasonFlow

SDK-first, MCP-native agent orchestration framework.

from reasonflow import DAG, LLMNode, CodeNode, DecisionNode, parallel

@CodeNode
def step1(state):
    return {"greeting": f"Hello, {state['name']}!"}

@CodeNode
def step2(state):
    return {"upper": state["greeting"].upper()}

dag = DAG("hello")
dag.connect(step1 >> step2)
result = dag.run(name="World")
print(result["upper"])  # HELLO, WORLD!

Install

pip install reasonflow

Setup

Copy .env.example to .env and add your API keys:

cp .env.example .env

Supports cloud providers (OpenAI, Anthropic, Google) and local models via Ollama — use model names like ollama/llama3 in @LLMNode(model=...).

Features

  • 4 node types: LLMNode, MCPNode, CodeNode, DecisionNode
  • >> operator for DAG construction
  • parallel() for concurrent branches
  • Automatic cost/token tracking per node
  • Built-in retries with error-type awareness
  • MCP-native tool integration
  • JSON execution traces
  • CLI: reasonflow run, reasonflow trace
  • Debug mode: --debug flag, DAG(debug=True), or per-node .debug()

Examples

# Research pipeline — parallel branches + LLM synthesis
reasonflow run examples/research_pipeline.py -v topic="AI safety"

# Multi-model debate — 3 LLMs argue in parallel, judge synthesizes
reasonflow run examples/multi_model_debate.py -v question="Should we adopt microservices?"

# Email drafter — bullets → LLM draft → LLM review → save
reasonflow run examples/email_drafter.py -v recipient="Engineering" -v subject="Q1 Update"

# Code reviewer — git diff → LLM code review → saved report
reasonflow run examples/code_reviewer.py -v repo_path="."

# CSV analyzer — parse CSV + compute stats → LLM insights
reasonflow run examples/csv_analyzer.py -v csv_path="data.csv"

# Git changelog — git log → LLM-generated release notes
reasonflow run examples/git_changelog.py -v repo_path="."

# Web scraper — fetch URL → strip HTML → LLM summary
reasonflow run examples/web_scraper_summarizer.py -v url="https://example.com"

# Log analyzer — read logs → grep errors → LLM categorize
reasonflow run examples/log_analyzer.py -v log_path="/var/log/system.log"

# Health check — parallel system checks → DecisionNode → alert/report
reasonflow run examples/health_check.py

# Test runner — pytest → DecisionNode → LLM failure analysis
reasonflow run examples/test_runner.py -v test_path="tests/"

# Process manager — ps/grep/kill via CodeNodes
reasonflow run examples/process_manager.py

# Run & analyze — execute a Python script → LLM analyzes output
reasonflow run examples/run_and_analyze.py

# Docker cleanup — parallel inventory → decide → prune (dry-run default)
reasonflow run examples/docker_cleanup.py

# Debug mode — see each node's output
reasonflow run examples/process_manager.py --debug

Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

Install and usage instructions live in the source repository linked above.

Reviews

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Versions

  • v0.1.0 Imported from the upstream source.