gittielabs-agentflow 0.2.1

Context engineering framework for multi-agent systems

AgentFlow

Context engineering framework for multi-agent systems.

A framework-agnostic toolkit for building multi-agent workflows with:

• Markdown + YAML front-matter config files (.prompt.md, .workflow.md, .context.md)
• Pluggable LLM providers (Anthropic Claude, OpenAI GPT, Google Gemini, any OpenAI-compatible API)
• Hybrid routing (YAML rules + LLM fallback)
• DAG-based workflow execution (sync, parallel, async nodes)
• Session scratchpads and long-term memory (file-based + vector search)
• Tool registry with local and HTTP dispatchers

Install

# Core only
pip install -e .

# With Claude support
pip install -e ".[anthropic]"

# With Google Gemini support
pip install -e ".[google]"

# Everything
pip install -e ".[all]"

# Development
pip install -e ".[dev]"

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")

Architecture

agentflow/
  agent/       # AgentExecutor, ContextAssembler, PromptTemplate
  config/      # ConfigLoader, schemas, parser, ContextResolver
  router/      # RouterEngine, 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
  events.py    # EventBus pub/sub system
  types.py     # Canonical data types (Message, AgentResponse, etc.)
  protocols.py # Structural typing interfaces (LLMProvider, StorageBackend, etc.)

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

Testing

pip install -e ".[dev]"
pytest

License

MIT