Agentic-AI-Pipeline

Agentic Multi‑Stage Bot - A Research & Outreach Agent 🤖

Python FastAPI Uvicorn LangGraph LangChain OpenAI Anthropic ChromaDB SQLite DuckDuckGo SQLAlchemy Pydantic Pytest Ruff SSE Makefile GitHub Actions Code Quality Open Source Shell AWS Docker Ansible

A full‑fledged, multi‑stage, agentic chatbot that plans → discovers → reasons → uses tools → learns. The reference task baked into this repo is a Research & Outreach Agent (“DossierOutreachAgent”): given a topic/company, it builds a compact, cited briefing and (optionally) drafts an outreach email, saving artifacts to disk.

Table of Contents

Key Features

7-Layer Architecture

This system is designed with a 7-layer architecture to separate concerns and allow for easy extension. Each layer has a specific role, and they communicate through well-defined interfaces. The core agent logic is implemented in LangGraph, which orchestrates the reasoning and tool use.

[!NOTE] Agent ≠ LLM. This system implements a layered design that’s easy to extend and operate.

Experience (UI/API)
   └─ FastAPI + SSE → web UI, CLI, REST
Discovery
   └─ WebSearch, WebFetch, KB search (RAG)
Agent Composition
   └─ Agent profile (persona, objective, capabilities)
Reasoning & Planning
   └─ LangGraph plan→decide→act→reflect→finalize
Tool & API
   └─ Calculator, FileWrite, Emailer, Web tools, KB add/search
Memory & Feedback
   └─ SQLite (turns, feedback), ChromaDB (vector KB)
Infrastructure
   └─ Logging, rate limiting, config, packaging

Directory mapping

Layer Where
Experience src/agentic_ai/app.py, web/
Discovery src/agentic_ai/tools/webtools.py, tools/knowledge.py
Agent Composition src/agentic_ai/layers/composition.py
Reasoning & Planning src/agentic_ai/layers/reasoning.py, graph.py
Tools & API src/agentic_ai/tools/*, layers/tools.py
Memory & Feedback src/agentic_ai/memory/*, layers/memory.py
Infrastructure src/agentic_ai/infra/*, config.py, pyproject.toml

By separating these layers, we ensure that each component can evolve independently while maintaining a clear contract with the others. This modularity allows for easy testing, debugging, and extension of the system.

7-Layer Architecture

Credit: Anil Inamdar for the original 7-layer architecture diagram and for the inspiration behind the agentic design principles.

Agent Graph (LangGraph)

The agent runs a typed state machine driving the LLM and tools. It uses LangGraph to orchestrate the reasoning and tool use, with a focus on single atomic actions per step. This design ensures clarity in execution and allows for easy debugging and extensibility.

Agentic AI Graph

Dynamic Mermaid graph of the agent’s architecture:

flowchart LR

%% =========================
%% EXPERIENCE LAYER
%% =========================
subgraph EXP[Experience Layer FastAPI SSE]
direction LR
IN[User prompt]
RL{Rate limit check}
STREAM[Token stream SSE]
FB[Feedback API]
IN --> RL
STREAM --> FB
end

%% =========================
%% MEMORY LAYER
%% =========================
subgraph MEM[Memory and Feedback]
direction TB
SQL[(SQLite messages)]
VEC[(Chroma vector KB)]
CIT[Citations store]
end

RL -->|ok| START
RL -->|429| HALT[Backoff and retry]

%% =========================
%% REASONING AND PLANNING
%% =========================
subgraph GRAPH[Reasoning and Planning LangGraph]
direction LR
START((START))
PLAN[PLAN create plan]
DECIDE{DECIDE next action}
ACT[ACT bind tool emit call]
TOOLS((ToolNode executes call))
REFLECT[REFLECT briefing or next]
FINALIZE([FINALIZE result])

START --> PLAN
PLAN --> DECIDE

DECIDE -->|search| ACT
DECIDE -->|fetch| ACT
DECIDE -->|kb_search| ACT
DECIDE -->|calculate| ACT
DECIDE -->|write_file| ACT
DECIDE -->|draft_email| ACT
DECIDE -->|finalize| FINALIZE
DECIDE -->|otherwise| REFLECT

ACT --> TOOLS
TOOLS --> REFLECT

REFLECT -->|done| FINALIZE
REFLECT -->|not done| DECIDE

PLAN -.-> STREAM
DECIDE -.-> STREAM
REFLECT -.-> STREAM
FINALIZE -.-> STREAM
end

PLAN -. use kb hits .-> VEC

%% =========================
%% TOOL AND API LAYER
%% =========================
subgraph TOOLS_LAYER[Tool and API Layer]
direction TB
TSEARCH[web search]
TFETCH[web fetch]
TKBS[kb search]
TKBADD[kb add]
TCALC[calculator]
TWRITE[file write]
TEMAIL[emailer]
end

TOOLS -->|query| TSEARCH
TOOLS -->|url| TFETCH
TOOLS -->|semantic| TKBS
TOOLS -->|ingest| TKBADD
TOOLS -->|expr| TCALC
TOOLS -->|path content| TWRITE
TOOLS -->|to subject body| TEMAIL

TKBS -->|results| PLAN
TKBADD --> VEC
TSEARCH --> TFETCH
TFETCH -->|text| PLAN
TFETCH --> CIT
TWRITE -->|artifact path| PLAN
TEMAIL -->|eml path| PLAN

IN -->|save user| SQL
FINALIZE -->|save assistant| SQL
FB -->|save feedback| SQL

The graph consists of the following key nodes:

This design ensures that each step is clear and focused, allowing for easy debugging and understanding of the agent’s behavior. The use of LangGraph allows for powerful state management and tool orchestration, making it a robust solution for building agentic systems.

Repository Layout

Agentic-RAG-Pipeline/     # Bonus: full agentic RAG pipeline in addition to this bot
Makefile                  # Common tasks (setup, ingest, run, test)
requirements.txt          # Python dependencies
src/
  agentic_ai/
    app.py                # FastAPI app (Experience)
    graph.py              # LangGraph runner
    cli.py                # CLI utilities (ingest/demo)
    config.py             # Pydantic settings
    infra/
      logging.py          # Rotating logs
      rate_limit.py       # Token bucket
    memory/
      sql_store.py        # SQLite conversation store
      vector_store.py     # ChromaDB KB
    layers/
      composition.py      # Agent profile
      reasoning.py        # Plan→Decide→Act→Reflect graph
      memory.py           # Memory orchestrator helpers
      tools.py            # Tool registry
    tools/
      webtools.py         # DuckDuckGo search + URL fetch & extract
      ops.py              # Calculator, FileWrite, Emailer (mock)
      knowledge.py        # KB add/search tools
web/
  index.html styles.css app.js
data/
  seed/                   # Non-empty RAG seeds
  agent_output/          # FileWrite artifacts
  emails/                # Emailer artifacts
tests/
  ... (unit & smoke)

[!IMPORTANT] This layout separates concerns clearly, with src/agentic_ai containing the core logic, web/ for the UI, and data/ for input/output artifacts. The tests/ directory holds unit and smoke tests to ensure code quality.

Bonus: Agentic RAG

Looking for a full agentic RAG pipeline (multi-step plan → retrieve → reflect → verify → answer) with memory, tool use, and quality checks? Check out the companion project Agentic-RAG-Pipeline for a complete, production-grade implementation and deeper docs.

[!TIP] See Agentic-RAG-Pipeline Directory for more info and end-to-end code.

flowchart TD
  U[User] --> IR[Intent Router - Flash]
  IR --> PL[Planner - Pro]
  PL --> RP[Retrieval Planner - Pro]
  RP --> R1[VectorRetriever - FAISS]
  RP --> R2[WebRetriever - Google CSE + Fetch]
  R1 --> W[Writer - Pro]
  R2 --> W
  W --> C[Critic - Pro]
  C -->|follow-ups| RP
  W --> G[Guardrails]
  G --> A[Answer + Evidence]
  PL -. session .-> M[(File-backed Memory)]

This is a full-fledged, multi-stage, agentic chatbot that plans → discovers → reasons → uses tools → learns. The reference task baked into this repo is a Research & Outreach Agent (“DossierOutreachAgent”): given a topic/company, it builds a compact, cited briefing and (optionally) drafts an outreach email, saving artifacts to disk.

Quickstart

To quickly get started with the Agentic AI bot, follow these steps:

# 1) Create venv & install
python -m venv .venv
. .venv/bin/activate
pip install -U pip
pip install -r requirements.txt

# 2) Configure model keys
cp .env.example .env
# Edit .env with your OPENAI_API_KEY (or ANTHROPIC_API_KEY) and preferences

# 3) Ingest seed knowledge into vector store
make ingest

# 4) Run the server
make run
# Open http://localhost:8000 and ask:
# "Build a competitive briefing on ACME Robotics and draft a short outreach email."

CLI demo (streams to stdout)

. .venv/bin/activate
python -m agentic_ai.cli demo "Summarize top AMR vendors for grocery DCs with citations"

This will start the agent, which will plan, search, and reflect on the topic, providing a compact briefing with citations. You can also use the CLI to ingest additional documents into the vector knowledge base.

Configuration

The app uses Pydantic Settings and .env.

MODEL_PROVIDER=openai          # openai | anthropic
OPENAI_API_KEY=sk-...          # required if MODEL_PROVIDER=openai
OPENAI_MODEL_CHAT=gpt-4o-mini
OPENAI_MODEL_EMBED=text-embedding-3-small

ANTHROPIC_API_KEY=sk-ant-...   # required if MODEL_PROVIDER=anthropic
ANTHROPIC_MODEL_CHAT=claude-3-5-sonnet-latest

CHROMA_DIR=.chroma
SQLITE_PATH=.sqlite/agent.db

APP_HOST=0.0.0.0
APP_PORT=8000

You can switch providers at runtime by changing MODEL_PROVIDER and the corresponding keys/models.

Running

We recommend using a virtual environment to isolate dependencies. The provided Makefile simplifies setup and common tasks:

Makefile targets

Run the server

. .venv/bin/activate
uvicorn src.agentic_ai.app:app --host

This will start the FastAPI server on http://localhost:8000, where you can interact with the agent via the web UI or API.

CLI Utilities

This repository also provides a CLI for quick interactions and ingestion of knowledge:

# Ingest any folder recursively (adds text up to 10k chars per file)
python -m agentic_ai.cli ingest ./data/seed

# Run a single prompt through the graph (streams responses)
python -m agentic_ai.cli demo "Give me a briefing on collaborative robots with citations"

The CLI allows you to quickly test the agent’s capabilities without needing to set up the web UI. It streams responses directly to the console, making it easy to see how the agent processes prompts and uses tools.

HTTP API

A simple HTTP API is provided for programmatic access to the agent’s capabilities. It supports both synchronous and streaming interactions:

GET /api/new_chat

Returns a new chat_id.

POST /api/chat (SSE stream)

Request body

{ "chat_id": "uuid-string", "message": "Your prompt" }

Stream events

POST /api/ingest

Add an internal KB document.

{ "id": "doc-123", "text": "content...", "metadata": { "source": "..." } }

POST /api/feedback

Attach a rating/comment to a chat/message.

{ "chat_id": "uuid", "message_id": null, "rating": 1, "comment": "đź‘Ť" }

This API allows you to integrate the agent into your applications, enabling programmatic access to its reasoning and tool capabilities. The SSE stream provides real-time updates, making it suitable for interactive applications. Feedback can be used to improve the agent’s performance over time.

Tools & Capabilities

The agent uses a set of atomic tools to perform actions. Each tool is designed for a specific purpose, and they can be chained together to achieve complex tasks. The tools are registered in the tools.py layer and can be invoked by the agent during reasoning.

Tool Name Purpose Input Output
web_search WebSearch General discovery NL query JSON list of results
web_fetch WebFetch Extract readable text from URL URL Clean text
kb_search KbSearch Vector KB semantic search NL query JSON list of {id,text,metadata}
kb_add KbAdd Add doc to KB JSON {id,text,metadata} "ok"
calculator Calculator Safe math via math module Expression Result string
file_write FileWrite Persist artifacts under data/agent_output/ JSON {path,content} Absolute path
emailer Emailer (mock) Draft/queue email as .eml JSON {to,subject,body} .eml path

Tool‑chaining is managed by the graph: DECIDE picks one atomic action per step, ACT binds tools and forces one structured call, TOOLS executes, REFLECT synthesizes or continues.

This design ensures that each tool is used in a controlled manner, allowing for clear reasoning and traceability of actions. The agent can also reflect on its actions and decide whether to finalize or continue with further steps.

Memory & Feedback

The agent has two memory systems to persist conversation history and knowledge:

DB schema (simplified)

CREATE TABLE IF NOT EXISTS chats (
  id TEXT PRIMARY KEY,
  created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
  title TEXT
);
CREATE TABLE IF NOT EXISTS messages (
  id INTEGER PRIMARY KEY AUTOINCREMENT,
  chat_id TEXT,
  role TEXT,
  content TEXT,
  tool_call TEXT,
  created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);
CREATE TABLE IF NOT EXISTS feedback (
  id INTEGER PRIMARY KEY AUTOINCREMENT,
  chat_id TEXT,
  message_id INTEGER,
  rating INTEGER,
  comment TEXT,
  created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);

Feel free to extend the memory layer with additional tables or fields as needed. The agent can use this memory to maintain context across interactions, allowing for more coherent and informed responses. More details on the memory layer can be found in src/agentic_ai/memory/.

Extending the System

To extend the system, you can add new tools, nodes, or modify the agent profile. Here are some guidelines:

Add a new Tool

  1. Create src/agentic_ai/tools/<your_tool>.py implementing a BaseTool with _run.
  2. Register it in src/agentic_ai/layers/tools.py::registry().
  3. Teach the decision policy by adding the token to DECIDE’s prompt and mapping in ACT.

Add a new Node (LangGraph)

Change the Agent Profile

Use Another Vector DB / Embeddings

The system is designed to be modular and extensible, allowing you to adapt it to your specific use cases. The LangGraph framework provides a powerful way to manage the agent’s reasoning and tool use, making it easy to add new capabilities without disrupting existing functionality.

[!TIP] Feel free to experiment with different tools, nodes, and configurations to tailor the agent to your needs. The modular design allows for easy testing and iteration, making it a flexible platform for building agentic systems!

Testing & Quality

This repository includes a comprehensive suite of tests to ensure code quality and functionality. The tests cover unit tests for individual components, as well as smoke tests for end-to-end scenarios.

Run all tests

. .venv/bin/activate
pytest -q

What’s covered

Lint & Format

make format
make lint

GitHub Actions

This repository uses GitHub Actions for continuous integration (CI) to ensure code quality and functionality. The CI pipeline includes:

You can view the CI pipeline in the .github/workflows/ directory. This should provide you with a solid foundation for maintaining code quality and ensuring that your changes do not break existing functionality, and feel free to extend the CI pipeline with additional checks or steps as needed.

Security & Ops

We also incorporate DevSecOps best practices to ensure the system is secure and production-ready:

[!CAUTION] For production, consider:

This repository is designed to be a solid foundation for building agentic systems, with security and operational best practices in mind. However, always review and adapt the security measures based on your specific deployment environment and requirements. However, in production, make sure your agent is running behind a reverse proxy (like nginx or Caddy) to handle HTTPS, rate limiting, and security headers, and comply with standards like GDPR or CCPA if handling personal data.

Troubleshooting

If you encounter issues while running the agent, here are some common troubleshooting steps:

If you still have issues, feel free to open an issue on GitHub with details about your setup and the error messages you’re encountering. The community is here to help!

Roadmap Ideas

We still have a lot of ideas for future improvements and features. Here are some potential roadmap items:

If you have ideas or contributions, feel free to open an issue or PR! We welcome community input to make this agentic system even better.

Credits

This project leverages several powerful libraries and frameworks to provide a robust agentic system:

What makes this opinionated?

This system is designed with a few key principles in mind:

This opinionated design allows for a clear and consistent development experience, making it easier to build and maintain agentic systems. The modular architecture ensures that you can adapt the system to your specific needs while still benefiting from the core features and best practices.

License

This project is licensed under the Apache License. You are free to use, modify, and distribute this code as long as you include the original license and copyright notice.

[!NOTE] Regardless of use, make sure you credit the original authors and contributors when using this code in your projects. Contributions are welcome, and we encourage you to share your improvements or extensions to the system.

Conclusion

This repository provides a solid foundation for building agentic systems with multi‑stage reasoning, autonomous tool use, and a focus on security and extensibility. The modular architecture allows for easy extension and customization, making it suitable for a wide range of applications.

We hope this project serves as a useful resource for your agentic AI projects. Whether you’re building research agents, outreach bots, or any other type of agentic system, the principles and patterns demonstrated here can help you get started quickly and effectively.

If you have any questions, suggestions, or contributions, please feel free to open an issue or pull request on GitHub. We welcome community input and are excited to see how you use this system in your own projects!

Happy building! We hope this repository serves as a useful resource for your agentic AI projects. If you have any questions or suggestions, feel free to reach out or contribute!

This site is open source. Improve this page.