agentchanti 0.1.0

A multi-agent AI coding system with built-in RAG — local or cloud, CLI or API.

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                  ━━  A u t o n o m o u s   C o d e r  ━━

A multi-agent AI coding system with built-in RAG — local or cloud, CLI or API.
Plans. Codes. Reviews. Tests. Understands your codebase.

What is AgentChanti?

AgentChanti ships with a built-in RAG system. Before any agent writes code, it automatically retrieves the most relevant functions, classes, and docs from your codebase and injects them as context — so even a local 7B model running in Ollama understands your project structure and coding conventions. Teams can add internal docs, architecture decisions, and coding standards to the Global KB, and every agent on every run picks them up automatically.

AgentChanti is a command-line tool and Python library that takes a plain English description of a coding task and autonomously builds the software for you using a team of specialized AI agents:

Agent	Role
Planner	Breaks your task into numbered, actionable steps
Coder	Writes clean, idiomatic code for each step
Reviewer	Checks code for bugs, style issues, and correctness
Tester	Generates and runs unit tests to verify everything works

Supports local LLMs (Ollama, LM Studio) and cloud providers (OpenAI, Google Gemini, Anthropic Claude).

Beyond the CLI — Use It as a Service

AgentChanti ships as both a CLI and a Python library, so it can be embedded directly into any service:

# Inside a Flask endpoint — trigger the full agent pipeline on a PR event
from agentchanti import run_task

@app.route("/pr-review", methods=["POST"])
def on_pull_request():
    result = run_task(task="Generate unit tests for the changed files", auto=True)
    return {"status": result.status, "files": result.files_written}

The plugin system (StepPlugin base class) lets teams extend the pipeline with custom steps beyond code generation:

Example Plugin	What It Does
PR test generator	Auto-generates tests when a PR is opened
Image validator	Validates assets against design specs using a vision model
Deployment gate	Runs lint, security scan, or compliance checks before deploy
Custom linter	Enforces team-specific coding standards as a pipeline step

Plugins are discovered automatically from your config or via setuptools entry points — no changes to the core pipeline needed.

Built-in RAG — Any LLM Understands Your Codebase

AgentChanti includes a 4-phase RAG system that automatically indexes your project and injects relevant context into every agent prompt — so even a small local model running offline has deep awareness of your internal code and docs:

• Code graph — tree-sitter parses your codebase into a symbol graph (functions, classes, imports, call edges) across 11 languages
• Semantic search — every function and class is embedded into a local SQLite vector store; agents retrieve the most relevant symbols before writing any code
• Global KB — add your internal docs, ADRs, and coding standards; every agent picks them up automatically
• Error dictionary — maps known error patterns to fixes so agents self-correct without extra LLM calls

All storage is local SQLite — no cloud vector database required. Works fully offline with local LLMs.

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RAG Architecture

AgentChanti uses a 4-phase Retrieval-Augmented Generation (RAG) system to give every agent deep awareness of your codebase. Before any code is written, the system automatically indexes your project and injects the most relevant context into each LLM prompt.

┌──────────────────────────────────────────────────────────────┐
│                     Your Coding Task                         │
└──────────────────┬───────────────────────────────────────────┘
                   ▼
┌──────────────────────────────────────────────────────────────┐
│  Phase 1: Code Graph        Tree-sitter AST parsing          │
│  ───────────────────        Classes, functions, imports,      │
│                             call edges → NetworkX graph       │
├──────────────────────────────────────────────────────────────┤
│  Phase 2: Semantic KB       Embed symbols → SQLite vectors   │
│  ────────────────────       Cosine similarity search          │
│                             Graph-enriched results            │
├──────────────────────────────────────────────────────────────┤
│  Phase 3: Global KB         Error-fix dictionary (regex)     │
│  ──────────────────         Coding patterns, ADRs             │
│                             Behavioral instructions           │
├──────────────────────────────────────────────────────────────┤
│  Phase 4: Context Builder   Intent detection (error/review)  │
│  ────────────────────────   Retrieve → rank → budget (4000t) │
│                             Inject into agent prompt          │
└──────────────────┬───────────────────────────────────────────┘
                   ▼
┌──────────────────────────────────────────────────────────────┐
│              Planner / Coder / Reviewer / Tester             │
└──────────────────────────────────────────────────────────────┘

Phase	What It Does	Storage
Code Graph	Parses AST with tree-sitter (11 languages), builds a directed graph of symbols and call edges	graph.pkl + index.db
Semantic KB	Embeds functions/classes into vectors, enables natural-language search over your code	vectors.db (SQLite)
Global KB	Maps error patterns to fixes, stores coding best practices and behavioral rules	global_kb.db (SQLite)
Context Builder	Assembles retrieved context per-step with token budgeting and priority ranking	In-memory

Additional capabilities:

• Project Orientation -- auto-detects language, framework, test runner, and directory structure; injects as a grounding block in every prompt
• Runtime Watcher -- monitors file changes during execution and triggers incremental re-indexing in the background
• Smart Startup -- < 10ms for unchanged projects; incremental or full re-index only when needed

All storage is local SQLite -- no external vector database required. See documentation.md for the full technical deep-dive.

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Getting Started

Prerequisites

• Python 3.10+ (python.org)

• Git (git-scm.com)

• A local LLM server or a cloud API key

Installation

Option 1: pipx (recommended for end users)

pipx installs CLI tools into isolated environments and puts them on your PATH — no virtualenv to activate, no pip install polluting your global site-packages.

pipx install agentchanti
agentchanti --help

To upgrade later: pipx upgrade agentchanti. To install pre-release builds straight from main: pipx install git+https://github.com/udaykanth/agentchanti.git.

Note: The package will be on PyPI once the first tagged release is cut. Until then, use Option 2 or 3 below.

Option 2: Clone and install (for contributors / latest code)

git clone https://github.com/udaykanth/agentchanti.git
cd agentchanti

python3 -m venv .venv
source .venv/bin/activate        # Linux/macOS
.venv\Scripts\activate           # Windows

python -m pip install -e ".[dev]"   # includes pytest + ruff for tests
agentchanti --help

Option 3: Convenience scripts

If you'd rather not type the venv steps yourself, the repo ships helper scripts that do the same thing:

git clone https://github.com/udaykanth/agentchanti.git
cd agentchanti

# Linux / macOS
chmod +x install.sh && ./install.sh

# Windows
./install.bat

source .venv/bin/activate        # Linux/macOS
.venv\Scripts\activate           # Windows

Why no curl | bash installer? AgentChanti is a Python package, not a single static binary. The standard Python tooling (pipx, pip) already handles isolated installs, version pinning, and upgrades — wrapping that in a shell script downloaded over HTTPS would add an attack surface without adding any value.

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Usage

agentchanti "<task description>" [options]

Quick Examples

# With Ollama
agentchanti "Create a Flask REST API with CRUD" --provider ollama --model deepseek-coder-v2:16b

# With OpenAI
OPENAI_API_KEY="sk-..." agentchanti "Build a CLI tool" --provider openai --model gpt-4o-mini

# With Gemini
GEMINI_API_KEY="..." agentchanti "Build a REST API" --provider gemini --model gemini-2.5-flash

# With Claude
ANTHROPIC_API_KEY="sk-ant-..." agentchanti "Build a CLI tool" --provider anthropic --model claude-sonnet-4

# Non-interactive (CI/scripts)
agentchanti "Generate unit tests" --auto --no-git --no-report

All Options

Flag	Description	Default
"task"	The coding task to perform (required)	—
--prompt-from-file	Read task description from a file	—
--provider	ollama, lm_studio, openai, gemini, anthropic	lm_studio
--model	Model name	deepseek-coder-v2-lite-instruct
--embed-model	Embedding model name	nomic-embed-text
--language	Override auto-detected language	auto-detect
--config	Path to .agentchanti.yaml config file	auto-discover
--auto	Non-interactive mode (auto-approve plan)	off
--no-embeddings	Disable semantic embeddings	off
--no-stream	Disable streaming responses	off
--no-git	Disable git checkpoint/rollback	off
--no-diff	Disable diff preview before writing	off
--no-cache	Disable step-level caching	off
--clear-cache	Clear step cache before running	off
--no-knowledge	Disable project knowledge base	off
--no-search	Disable web search agent	off
--no-kb	Disable KB context injection	off
--report / --no-report	Enable/disable HTML report	on
--resume	Force resume from checkpoint	off
--fresh	Ignore checkpoint, start fresh	off
--generate-yaml	Generate .agentchanti.yaml and exit	off

Knowledge Base Commands

Manage the project knowledge base via agentchanti kb:

agentchanti kb embed                 # Embed symbols into the vector store
agentchanti kb search "query"        # Semantic search over KB
agentchanti kb query find-callers X  # Find all callers of a function
agentchanti kb error-lookup "msg"    # Look up error fixes
agentchanti kb health                # Show KB health report
agentchanti kb update                # Pull global KB updates

See documentation.md for the full list of KB commands.

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Documentation

For full documentation including architecture details, configuration reference, library API, plugin system, and troubleshooting, see documentation.md.

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Contributing

Contributions are welcome! Feel free to open issues or submit pull requests.

1. Fork the repository
2. Create a feature branch (git checkout -b feature/my-feature)
3. Run the tests: python -m pytest tests/ -v
4. Commit and push
5. Open a pull request

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License

MIT License. See LICENSE for details.

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Disclaimer

This is a personal project by Uday Kanth. It is not affiliated with, endorsed by, sponsored by, or in any way officially connected with my current or past employer(s), or any of their subsidiaries, clients, or affiliates. All opinions, code, and design decisions in this project are my own and do not represent the views or intellectual property of any organization I am or have been associated with. This project was built entirely on my own time using my own resources.