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What is Omnigent?

Most AI agent frameworks give you wrappers around LLM APIs. Omnigent gives you the entire brain.

It's the domain-agnostic architecture of a production autonomous agent — the ReAct loop, multi-provider LLM routing, structured memory, hierarchical planning, reasoning graphs, error recovery, reflection, and plugin system. Everything you need to build a real agent, not a chatbot with tools.

You bring the domain. Omnigent brings the intelligence.

┌──────────────────────────────────────────────────────┐
│                   Agent Loop (ReAct)                 │
│         Reason → Act → Observe → Reflect             │
├──────────────┬────────────┬────────────┬─────────────┤
│   Router     │  Planner   │  Context   │   Graph     │
│  4 Providers │  Phases    │  Smart Trim│  Reasoning  │
├──────────────┴────────────┴────────────┴─────────────┤
│            Post-Processing Pipeline                  │
│    Extractors → Reflection → Error Recovery          │
├──────────────────────────────────────────────────────┤
│            Tool Registry + Plugin System             │
├──────────────────────────────────────────────────────┤
│  State │ DomainProfile │ Session │ Cost │ Knowledge  │
├──────────────────────────────────────────────────────┤
│          Config │ Logging │ MCP Integration          │
└──────────────────────────────────────────────────────┘

Why Omnigent?

Problem	Omnigent Solution
Agents that loop forever	Circuit breaker + loop detection (hash-based, blocks on first repeat) + rate limiting (per-iteration and total caps)
Context window overflow	3-level smart trimming preserving atomic message groups + semantic compression via LLM
"Just a tool caller"	Reasoning Graph chains findings into multi-step escalation paths
No methodology	Hierarchical Planner with phase-based execution, LLM refinement, skip conditions, and macro-reflection at phase end
Blind tool execution	Extractors auto-parse results → structured memory → async reflection
Failures crash the agent	Error recovery patterns with retry strategies and graceful degradation
Vendor lock-in	4 LLM providers with task-based routing and automatic fallback + extensible provider ABC
No human oversight	Human-in-the-loop approval steps for sensitive tool calls
Lost progress on crash	Checkpoint/replay mid-execution with session resume
Untrusted plugins	Plugin strict checksum mode with SHA-256 verification
Starting from zero	Production-proven — extracted from a real agent, not built in a weekend

Quick Start

Install

pip install -e .

Set up an API key

export DEEPSEEK_API_KEY="sk-..."  # Cheapest option (~$0.001 per analysis)
# or
export ANTHROPIC_API_KEY="sk-ant-..."
# or
export OPENAI_API_KEY="sk-..."

Run the example agent

# CodeLens — code quality analyzer (included example)
python -m examples.codelens.main /path/to/any/project

Build your own agent in 4 steps

Step 1: Define your domain memory

from dataclasses import dataclass, field
from omnigent.domain_profile import DomainProfile

@dataclass
class MyProfile(DomainProfile):
    items_analyzed: list[str] = field(default_factory=list)
    risk_score: float = 0.0

Step 2: Register your tools

from omnigent.tools import ToolRegistry

registry = ToolRegistry()
registry.register(
    name="my_scanner",
    schema={"description": "Scan a target", "parameters": {
        "type": "object",
        "properties": {"target": {"type": "string"}},
        "required": ["target"],
    }},
    handler=my_scanner_function,
)

Step 3: Populate registries (plan templates, chains, extractors, reflectors, error patterns)

from omnigent.registry import DomainRegistry
from omnigent.chains import ChainStep

registry = DomainRegistry(
    plan_templates={
        "my_domain": [
            {"name": "Discovery", "objective": "Map the target", "steps": [
                ("Initial scan", "my_scanner"),
            ]},
        ],
    },
    chains={
        "high_risk": [
            ChainStep("Deep dive on flagged items", "deep_scanner"),
            ChainStep("Generate remediation plan", ""),
        ],
    },
    extractors={
        "my_scanner": lambda profile, result, args: setattr(
            profile, 'risk_score', 0.8
        ),
    },
)

Step 4: Wire it up and run

import asyncio
from omnigent.agent import Agent
from omnigent.router import LLMRouter, Provider

async def main():
    agent = Agent(
        router=LLMRouter(primary=Provider.DEEPSEEK),
        tools=tool_registry,
        registry=registry,  # DomainRegistry with all domain-specific behavior
    )
    async for event in agent.run("Analyze this target"):
        if event.type == "text":
            print(event.content, end="")
        elif event.type == "finding":
            print(f"\n[{event.finding.severity}] {event.finding.title}")

asyncio.run(main())

What Can You Build?

Omnigent is domain-agnostic — it provides the intelligence architecture, you provide the domain knowledge:

Domain	What You Add	What Omnigent Provides
Security	Nmap, SQLMap, Burp tools + vuln knowledge	ReAct loop, attack chain reasoning, session persistence
Code Quality	AST parsers, complexity tools + refactoring patterns	Planning, structured findings, escalation chains
DevOps	K8s, Terraform, monitoring tools + runbooks	Error recovery, multi-step incident chains, cost tracking
Compliance	Document scanners, policy tools + regulation KB	Hypothesis tracking, evidence collection, reporting
Research	Search, scraping, DB tools + domain ontology	Context management, iterative refinement, reflection

See examples/codelens/ for a complete working implementation.

Components

Module	Purpose	How to Customize
agent.py	ReAct loop, circuit breaker, loop detection, rate limiting, approval	Subclass Agent, override step methods and hooks
registry.py	Centralised DomainRegistry dataclass for all domain-specific registries	Pass DomainRegistry(...) to Agent
router.py	Multi-provider LLM routing with LLMProvider ABC and extended thinking	Subclass LLMProvider for new providers
reasoning_graph.py	Directed graph for multi-step reasoning chains	Subclass ReasoningGraph
planner.py	Hierarchical task planning with skip conditions and macro-reflection	Populate plan_templates in DomainRegistry
context.py	Smart context trimming + LLM-based semantic compression	Works as-is
domain_profile.py	Structured memory with bounded hypothesis tracking	Subclass DomainProfile
state.py	Agent state with Pydantic-validated findings	Set enrich_fn hook
extractors.py	Auto-parse tool results into DomainProfile	Populate extractors in DomainRegistry
reflection.py	Async strategic insight after each tool call	Populate reflectors in DomainRegistry
error_recovery.py	Pattern-matched recovery guidance	Populate error_patterns in DomainRegistry
chains.py	Escalation chains for confirmed findings	Populate chains in DomainRegistry
knowledge_loader.py	Section-level knowledge retrieval with budgets	Populate knowledge_map in DomainRegistry
few_shot_examples.py	Tool usage examples for improved accuracy	Populate examples in DomainRegistry
plugins.py	Filesystem plugin discovery with strict checksum mode	Drop into ~/.omnigent/plugins/
session.py	Session persistence, resume, export, checkpoint/replay	Works as-is
cost_tracker.py	Per-provider, per-task cost tracking	Works as-is
config.py	YAML + .env + ENV config loading	Works as-is
tools/	Tool registry with scope checking and schema caching	Register domain tools

Key Design Patterns

Data-Driven Registries (Zero Domain Code in Core)

All domain-specific behavior lives in a single injectable DomainRegistry dataclass. Your agent populates it at startup:

from omnigent.registry import DomainRegistry

registry = DomainRegistry(
    plan_templates={...},   # Task plan templates
    chains={...},           # Escalation chains
    extractors={...},       # Tool result parsers
    reflectors={...},       # Post-tool strategic analysis
    error_patterns={...},   # Failure recovery patterns
    knowledge_map={...},    # Knowledge file routing
    examples={...},         # Few-shot tool examples
    tool_timeouts={...},    # Per-tool timeouts
)

agent = Agent(registry=registry)

Multiple agents can run with independent registries — no global state leaks. For backward compatibility, DomainRegistry.default() reads the module-level dicts.

Subclass for Complex Domains

For behavior that can't be expressed as data, override methods:

class MyAgent(Agent):
    # Domain hooks
    def _is_failure(self, tool_name, result): ...
    def _extract_finding(self, text): ...
    def _build_dynamic_system_prompt(self): ...

    # Overridable step methods (decomposed agent loop)
    def _do_context_management(self): ...
    async def _do_llm_call(self, system_prompt): ...
    async def _do_tool_execution(self, tool_calls): ...
    async def _do_post_tool_processing(self, tc, result): ...
    def _check_termination(self, text_buffer): ...

class MyGraph(ReasoningGraph):
    def _build_default_graph(self): ...

class MyProfile(DomainProfile):
    def to_prompt_summary(self): ...

The Reasoning Graph (The Differentiator)

What separates Omnigent from simple tool-callers: when a finding is confirmed, the reasoning graph activates downstream escalation paths. The agent doesn't just find issues — it chains them into multi-step reasoning.

Security:    SQLi → DB Dump → Credential Extraction → Admin Access → RCE
Code:        God Object → High Coupling → Low Testability → Regression Risk
Incident:    Alert → Log Correlation → Root Cause → Blast Radius
Compliance:  Gap Found → Control Missing → Risk Assessment → Remediation

Testing

# Run all tests
pytest

# Run with coverage
pytest --cov=omnigent --cov-report=term-missing

# Run specific module tests
pytest tests/test_reasoning_graph.py -v

# Run only unit tests
pytest -m unit

325 tests covering all core components. Every test runs without LLM calls or network access.

Project Structure

omnigent/
├── src/omnigent/         # Core framework
│   ├── agent.py           # The ReAct loop (1024 lines)
│   ├── registry.py        # DomainRegistry dataclass (97 lines)
│   ├── router.py          # Multi-provider LLM routing + LLMProvider ABC (700 lines)
│   ├── reasoning_graph.py # Chain reasoning engine (389 lines)
│   ├── planner.py         # Hierarchical task planner + macro-reflection (544 lines)
│   ├── context.py         # Smart context + semantic compression (358 lines)
│   ├── state.py           # State + Pydantic findings
│   ├── domain_profile.py  # Structured agent memory (bounded summaries)
│   ├── extractors.py      # Result parsing pipeline
│   ├── reflection.py      # Async post-tool strategic analysis
│   ├── error_recovery.py  # Failure recovery engine
│   ├── chains.py          # Escalation chain registry
│   ├── knowledge_loader.py # Knowledge base retrieval
│   ├── few_shot_examples.py # Tool usage examples
│   ├── plugins.py         # Plugin system + strict checksum mode (556 lines)
│   ├── session.py         # Session persistence + checkpoint/replay (546 lines)
│   ├── cost_tracker.py    # Cost tracking
│   ├── config.py          # Configuration management
│   ├── logging_config.py  # Structured JSON logging
│   ├── prompts/system.md  # Base system prompt
│   └── tools/             # Tool registry + schema caching (309 lines)
├── examples/codelens/     # Complete working example agent
├── tests/                 # 325 tests
├── ARCHITECTURE.md        # Deep technical architecture doc
├── CONTRIBUTING.md        # Contribution guide
└── CHANGELOG.md           # Version history

Origin

Omnigent was extracted from NumaSec, a production autonomous security agent (17,878 LOC, 320 tests).

Built by Francesco Stabile

License

MIT — use it for anything.

Contributing

See CONTRIBUTING.md. We welcome domain implementations, bug fixes, and documentation improvements.