gittielabs-agentflow 0.7.3

Context engineering framework for multi-agent systems

AgentFlow

Context engineering framework for multi-agent systems.

AgentFlow is a framework-agnostic toolkit for building multi-agent workflows using plain Markdown and YAML configuration files. Define agents, workflows, routing rules, and context -- all in version-controllable .md files.

Full Documentation | PyPI | GitHub

Key Features

• Markdown + YAML config files -- Agents, workflows, and routing defined in .prompt.md, .workflow.md, .context.md files
• Pluggable LLM providers -- Anthropic Claude, OpenAI GPT, Google Gemini, or any OpenAI-compatible API
• Hybrid routing -- YAML rule matching with LLM fallback, plus hierarchical domain routing
• DAG-based workflows -- Sync, parallel, and async node execution with input mapping
• Session management -- Scratchpads, multi-user history, and artifact storage
• Memory system -- File-based and vector search (embedding-agnostic)
• Tool registry -- Local and HTTP tool dispatchers
• Event-driven observability -- EventBus with Langfuse telemetry integration

Install

pip install gittielabs-agentflow

# With a specific LLM provider
pip install "gittielabs-agentflow[anthropic]"    # Claude
pip install "gittielabs-agentflow[google]"       # Gemini
pip install "gittielabs-agentflow[openai]"       # OpenAI / compatible

# Everything
pip install "gittielabs-agentflow[all]"

Quick Start

1. Define an agent (context/agents/researcher.prompt.md)

---
name: researcher
provider: anthropic
model: claude-sonnet-4-6
temperature: 0.7
max_tokens: 4096
tools: [web_search, summarize]
context_files: [shared/guidelines.context.md]
---

You are a research agent. Given a topic, search for relevant information
and provide a comprehensive summary with sources.

2. Define a workflow (context/workflows/research.workflow.md)

---
name: research_pipeline
trigger: api
nodes:
  - id: research
    agent: researcher
    next: format
  - id: format
    agent: formatter
    inputs:
      message: "research.text"
---

Research pipeline: search, then format results.

3. Define routing rules (context/router.prompt.md)

---
name: main_router
routing_rules:
  - if: "'research' in message or 'find' in message"
    routeTo: research_pipeline
  - if: "'analyze' in message"
    routeTo: analyzer
fallback: general_assistant
llmFallback: true
---

Route incoming messages to the appropriate agent or workflow.

4. Run

from agentflow import (
    ConfigLoader, RouterEngine, WorkflowExecutor, AgentExecutor,
    ToolRegistry, SessionManager, EventBus,
    FileSystemStorage, AnthropicProvider,
)

# Load configs
loader = ConfigLoader("./context")
loader.load()

# Set up infrastructure
storage = FileSystemStorage("./data")
events = EventBus()
provider = AnthropicProvider()
tools = ToolRegistry()
sessions = SessionManager(storage)

# Route a message
router = RouterEngine(loader, provider, events)
result = await router.route("Research the latest AI safety papers")

# Execute workflow
if result.target == "research_pipeline":
    executor = WorkflowExecutor(loader, provider, tools, sessions, storage, events)
    outputs = await executor.run("Research the latest AI safety papers", session_id="s1")

See the Quick Start guide for a complete walkthrough.

Architecture

agentflow/
  agent/          # AgentExecutor, ContextAssembler, PromptTemplate
  config/         # ConfigLoader, schemas, parser, ContextResolver
  router/         # RouterEngine, DomainRouter, RuleEvaluator
  workflow/       # WorkflowExecutor, WorkflowDAG, NodeRunner
  session/        # SessionManager, Scratchpad, ArtifactStore
  memory/         # MemoryManager, FileMemory, VectorMemory
  tools/          # ToolRegistry, LocalToolDispatcher, HTTPToolDispatcher
  providers/      # Anthropic, OpenAI-compat, Google GenAI, Mock
  storage/        # FileSystem, InMemory, S3
  orchestration/  # DAGExecutor, ComplexityClassifier, Plan
  telemetry/      # LangfuseEventHandler
  events.py       # EventBus pub/sub system
  types.py        # Canonical data types (Message, AgentResponse, etc.)
  protocols.py    # Structural typing interfaces (LLMProvider, StorageBackend, etc.)

Learn more in the Architecture docs.

Context File Types

Extension	Purpose	Example
*.prompt.md	Agent config + system prompt	agents/planner.prompt.md
*.workflow.md	DAG workflow definition	workflows/analysis.workflow.md
*.context.md	Shared context / conditional profiles	shared/schema.context.md
*.memory.md	Memory retention config	agents/researcher.memory.md
*.domain.md	Domain routing boundary	domains/content.domain.md

See Context Files for full schema reference.

Documentation

Full documentation is available at gittielabs.github.io/agentflow, including:

• Installation & setup
• Context file reference
• Provider configuration
• Routing & domain routing
• Workflow execution
• Multi-agent pipeline guide
• Changelog

Contributing

git clone https://github.com/GittieLabs/agentflow.git
cd agentflow
pip install -e ".[dev]"
pytest

License

MIT