personakit 0.1.6

A declarative framework for building role-based LLM agents — code reviewers, compliance officers, clinical triage, support triage. Not a personality classifier; an agent builder. Persona, frameworks, probes, red flags, and themes as data; you get a typed, cited, safety-aware agent.

personakit

A declarative framework for building role-based LLM agents. Describe a specialist — persona, frameworks, probes, red flags, recommendation themes — as a single data object, and get a typed, cited, safety-aware agent. No chain wiring, no graph building, no orchestration code.

Created by Majidul Islam.

pip install personakit

---

What personakit is — and what it is not

personakit IS a declarative framework for building role-based LLM agents: code reviewers, compliance officers, clinical triage, support triage, contract reviewers, scrum masters, and similar domain specialists. You describe the role as data; personakit produces the runnable agent backed by OpenAI / Anthropic / your local model.

personakit is NOT:

• ❌ Not a personality classifier (not MBTI, not Big Five, not trait inference). It has nothing to do with pypersonality, persai, or similar trained classifiers.
• ❌ Not an ML training library. No feature extraction, no fitted models, no datasets. It wraps LLMs you already have access to.
• ❌ Not a RAG framework. Bring your own vector store via the optional @tool system — we don't ship embeddings or retrieval.
• ❌ Not a chain-orchestration engine. Composable alongside LangChain, LangGraph, and CrewAI — personakit owns the specialist layer, they own the pipeline layer.

---

30-second quickstart

import asyncio
from personakit import Agent, Specialist, Framework, Probe, RedFlag, Severity, Theme

code_reviewer = Specialist(
    name="code_reviewer",
    persona="Senior staff engineer. Correctness, security, perf — in that order.",
    frameworks=[Framework(name="OWASP Top 10"), Framework(name="SOLID")],
    probes=[Probe(question="Does the change include tests?", value_type="boolean")],
    red_flags=[
        RedFlag(
            trigger="Hard-coded secret",
            severity=Severity.CRITICAL,
            action="BLOCK merge. Rotate the secret immediately.",
            patterns=[r"sk-[A-Za-z0-9]{20,}", r"AKIA[0-9A-Z]{16}"],
        ),
    ],
    themes=[Theme(name="Correctness"), Theme(name="Security"), Theme(name="Performance")],
)

agent = Agent(specialist=code_reviewer, model="gpt-4o-mini")
result = asyncio.run(agent.analyze("--- a.py\n+api_key = 'sk-proj-abc123456789012345678'"))

print(result.pretty())              # full structured summary
print(result.red_flags_triggered)   # [TriggeredRedFlag(..., severity=CRITICAL, evidence='sk-proj-...')]
print(result.has_urgent)            # True

That's the whole agent. No chain wiring. One Specialist dataclass. Typed, cited, safety-aware output.

---

Bundled specialists — 7 domains, zero boilerplate

Import any of these directly, or read the source as a template:

Specialist	Domain	What it does
CODE_REVIEWER	engineering.software.review	PR reviewer — OWASP, SOLID, 12-Factor, secret detection
FINTECH_TRANSACTION_REVIEWER	finance.fintech.aml	AML/fraud triage — OFAC, FATF typologies, SAR filing
CUSTOMER_SUPPORT_TRIAGE	support.saas.b2c	SaaS B2C support — refund policy, chargeback, GDPR routing
SCRUM_MASTER	engineering.delivery.agile	Sprint health — scope creep, WIP limits, blockers
CONTRACT_REVIEWER	legal.contracts.m_and_a	M&A redlining — English common law, UCC, GDPR Art. 28
FALLS_PREVENTION_NURSE	healthcare.clinical.falls	Post-fall clinical triage — NICE NG161, CG176, Morse
MATH_TUTOR	education.secondary	Socratic GCSE tutor — minimal persona-only specialist

from personakit import Agent
from personakit.examples import FINTECH_TRANSACTION_REVIEWER

agent = Agent(specialist=FINTECH_TRANSACTION_REVIEWER, model="gpt-4o-mini")
result = asyncio.run(agent.analyze(transaction_details))

---

FAQ

Q: Is this a personality classifier (MBTI, Big Five, etc.)? No. personakit is an agent builder. It has no trained models, no feature extraction, and no personality taxonomy. If you need MBTI or Big Five, look at pypersonality or persai — completely different category of library.

Q: Can it fetch external knowledge (RAG, vector store, real-time APIs)? Yes — via the opt-in @tool system. personakit is bring-your-own-retrieval: wrap your vector store (Pinecone, pgvector, Chroma, Qdrant, Weaviate) or any API in a @tool function, and the agent uses it. We don't ship a vector store because every team already has one they prefer.

Q: Why not just use LangChain or LangGraph? Different problems. LangChain/LangGraph describe what the agent does (imperative chains, graphs). personakit describes who the agent is (declarative role). For role-based agents — compliance, code review, support triage, clinical — the declarative approach is ~10× less code and lets non-engineers author specialists in YAML. Use personakit inside a LangChain chain or LangGraph node when you need the chain / graph for orchestration.

Q: What does it depend on? Just two runtime dependencies: pydantic and httpx. Providers (openai, anthropic, pyyaml) ship as optional extras — install only what you need. Total transitive footprint on a fresh venv: ~12 packages.

Q: Is it production-ready? It's v0.1.3 — alpha. API may evolve before v1.0. 36 tests pass; mypy --strict clean; no unreleased breaking changes. Used in the wild but you should pin the minor version in production.

---

Why personakit?

Building a specialist LLM agent shouldn't take 200 lines of chain wiring.

Every specialist — regardless of domain — has the same anatomy:

• A role the agent plays
• Knowledge bodies it draws from and cites
• Diagnostic questions it asks of any input
• Safety triggers demanding immediate action
• Output categories organizing what it recommends

That anatomy holds whether your specialist reviews legal documents, evaluates clinical cases, audits financial transactions, scores research papers, drafts user stories, scopes engineering work, supports customers, moderates content, qualifies sales leads, grades coursework, or any of a thousand other roles. personakit captures the anatomy. You bring the role.

You describe WHO the specialist is, as data. The library produces a typed, cited, safety-aware agent that runs on any LLM provider — without chain wiring, graph building, or orchestration code.

The discipline is the role description. Everything else — JSON schema generation, red-flag matching (deterministic + semantic), citation enforcement, provider routing, structured-output validation — is automatic.

If a domain expert can articulate what they look for, what they ask, and what they recommend, you can build them an agent in 30 lines.

The primitives

Concept	What it gives you
Specialist	Frozen dataclass — the entire agent definition, authorable in YAML
Framework	Body of knowledge with a citation key, cited in output
Probe	Diagnostic question; becomes a typed field in the structured response
RedFlag	Trigger → severity → action → citation, matched deterministically AND semantically
Theme	User-selectable recommendation category
Priority	Always-on checks reported as met / unmet / unknown
Tool (optional)	@tool decorator — opt-in for external memory, DB, APIs

Core has just two runtime deps: pydantic and httpx.

---

Real agent types you can build in one file

You want an agent that...	Define these concepts
Reviews pull requests for security and correctness	Framework(OWASP), RedFlag(sql_injection, hardcoded_secret), Theme(Security, Performance)
Screens fintech transactions for AML / sanctions	Framework(BSA/AML, OFAC), RedFlag(sanctioned_counterparty, structuring), typology Themes
Triages customer support messages	Probe(order_id, sentiment), RedFlag(chargeback_language), Theme(Resolution, Escalation)
Coaches a sprint team as a scrum master	Framework(Scrum Guide, DORA), RedFlag(scope_creep, external_blocker), Theme(At-risk stories, Retro candidates)
Reviews M&A contracts for legal risk	Framework(English common law, UCC), RedFlag(unlimited_liability), Theme(Liability, IP)
Runs a post-fall clinical assessment	Framework(NICE NG161, NICE CG176), RedFlag(LOC, head_contact_on_anticoagulant), clinical probes
Writes product specs against JTBD	Framework(Jobs-to-be-Done, RICE), RedFlag(no_success_metric), Theme(Edge cases, Open questions)
Tutors a student without giving away answers	Theme(Concept check, Next hint, Common pitfall), Constraint(no direct answer first)
Does equity research on a public SaaS name	Framework(DCF, Rule of 40), RedFlag(NDR_below_100, negative_fcf_plus_decel), Theme(Thesis, Risks)

YAML authoring — hand off to a domain expert

name: code_reviewer
persona: Senior staff engineer reviewing PRs. Correctness, security, perf, in that order.
frameworks: [OWASP Top 10, SOLID, 12-Factor App]
probes:
  - question: Does the change include tests?
    key: has_tests
    value_type: boolean
    weight: high
red_flags:
  - trigger: Hard-coded secret or API key
    severity: critical
    action: BLOCK merge. Rotate the secret. Move to a secret manager.
    match: both
    patterns: ['sk-[A-Za-z0-9]{20,}', 'AKIA[0-9A-Z]{16}']
themes: [Correctness, Security, Performance, Maintainability, Tests]

from personakit import Specialist, Agent

spec = Specialist.from_yaml("code_reviewer.yaml")
agent = Agent(specialist=spec, model="claude-sonnet-4-6")

Red flags — the feature no-one else has

Every RedFlag is a trigger → severity → action → citation contract, matched in two phases:

1. Deterministic pre-match — regex / keywords on raw input. Fast, offline, quotable.
2. Semantic post-match — the LLM evaluates whether the trigger applies in context. Catches paraphrase, synonyms, implicit meaning.

Results merge, with deterministic evidence winning on ties:

RedFlag(
    trigger="Hard-coded secret, token, or credential",
    severity=Severity.CRITICAL,
    action="BLOCK merge. Rotate secret. Move to secret manager.",
    citation="OWASP A02:2021",
    match=MatchMode.BOTH,
    patterns=[r"sk-[A-Za-z0-9]{20,}", r"AKIA[0-9A-Z]{16}"],
)

Structured output — derived from the Specialist

You never write a JSON schema by hand. The probes, red flags, and themes are the schema:

result = await agent.analyze(input_text)

result.summary                # narrative summary
result.probes_answered        # {probe_key: typed_value_or_null}
result.probes_unanswered      # list[Probe] — next-round questions
result.red_flags_triggered    # list[TriggeredRedFlag] with evidence + source
result.recommendations        # themed list with citations
result.citations_used         # framework citation keys referenced
result.priorities_status      # per-priority met / unmet / unknown
result.has_urgent             # convenience boolean

Tools — opt-in, for external memory & APIs

Core has zero coupling to tool calling. When you want tools, decorate functions and attach:

from personakit.tools import tool

@tool
def lookup_order(order_id: str) -> dict:
    """Fetch an order from the order database."""
    return order_db.get(order_id)

@tool
def search_knowledge_base(query: str, top_k: int = 5) -> list[str]:
    """Semantic search against the internal KB — your vector store."""
    return kb.search(query, k=top_k)

support_agent = Agent(specialist=CUSTOMER_SUPPORT_TRIAGE, model="gpt-4o-mini")
support_agent = support_agent.with_tools([lookup_order, search_knowledge_base])

OpenAI and Anthropic tool-calling happen through the same interface. Schemas are auto-built from your function signatures and docstrings.

Registry — one app, many specialists

from personakit import SpecialistRegistry

registry = SpecialistRegistry.from_directory("personas/")

# Route by domain
engineering_agents = registry.by_domain("engineering")
finance_agents = registry.by_domain("finance")

# Route by name
support = registry.get("support_triage")

Providers

Extra	Install	What you get
personakit[openai]	openai>=1.0	Native OpenAI SDK — gpt-4o-mini default
personakit[anthropic]	anthropic>=0.20	Native Anthropic SDK — claude-sonnet-4-6
personakit[litellm]	litellm>=1.40	100+ providers via one extra (see below)
personakit[yaml]	pyyaml>=6.0	YAML specialist authoring
personakit[all]	all of the above	Everything

100+ providers via LiteLLM

LiteLLM normalises the APIs of 100+ providers — OpenAI, Anthropic, Azure OpenAI, AWS Bedrock, Google Vertex AI, Cohere, Mistral, Hugging Face, Ollama, DeepSeek, Together AI, Groq, Fireworks, Anyscale, and any OpenAI-compatible endpoint — into a single unified call. LiteLLMProvider plugs that into personakit so switching providers is a one-line change:

from personakit import Agent
from personakit.providers import LiteLLMProvider
from personakit.examples import FINTECH_TRANSACTION_REVIEWER

# Same Specialist, any provider LiteLLM supports — change the model string only.
provider = LiteLLMProvider(default_model="bedrock/anthropic.claude-v2")
# or:  LiteLLMProvider(default_model="azure/my-gpt-4-deployment",
#                       api_key=..., api_version="2024-06-01")
# or:  LiteLLMProvider(default_model="vertex_ai/gemini-pro")
# or:  LiteLLMProvider(default_model="ollama/llama3",
#                       api_base="http://localhost:11434")
# or:  LiteLLMProvider(default_model="groq/mixtral-8x7b-32768")

agent = Agent(specialist=FINTECH_TRANSACTION_REVIEWER, provider=provider)
result = await agent.analyze(transaction_details)

Install: pip install 'personakit[litellm]'. You can also use LiteLLM's proxy mode with OpenAIProvider(base_url="http://localhost:4000") if you prefer running LiteLLM as a separate gateway.

MockProvider ships in the core for offline testing — no API key needed:

from personakit.testing import MockProvider
provider = MockProvider(responses={"summary": "...", "recommendations": [...]})

Testing helpers

from personakit.testing import assert_triggered, assert_cited

result = await agent.analyze("Customer: 'I want to chargeback this transaction.'")
assert_triggered(result, "legal_or_chargeback_language_attorney_lawsuit_chargeback_small_claims_bbb")

Design principles

1. Specialist is pure data. No behaviour, no side effects, serializable.
2. Schema is derived. Probes, red flags, themes are the output contract.
3. Deterministic where possible, semantic where needed. Red flags run both.
4. Tools are opt-in. Core has zero coupling to tool calling.
5. Minimal dependencies. pydantic + httpx. Everything else is an extra.
6. Domain-neutral. Engineering, support, fintech, legal, clinical, education, delivery, product — one library.
7. Provider-agnostic. Native OpenAI + Anthropic adapters, plus 100+ providers via the LiteLLM extra (Azure, Bedrock, Vertex AI, Cohere, Mistral, Ollama, Groq, and any OpenAI-compatible endpoint). Same Specialist, any model.

Works alongside the rest of the LLM toolchain

personakit focuses on declarative specialist definition. It intentionally does not try to be:

• a chain-composition library — use LangChain when you need to wire up complex multi-step LLM pipelines
• a multi-agent orchestration framework — use CrewAI when you need a team of agents collaborating on a shared goal
• a branching control-flow engine — use LangGraph when you need conditional routing and loops across nodes

They compose nicely. A LangChain chain can invoke a personakit Agent as one of its steps. A CrewAI crew member can be a personakit Specialist. A LangGraph node can call agent.analyze() and route on result.has_urgent.

Use personakit for what it's best at — the declarative specialist layer — and reach for the others when the problem actually needs chains, crews, or graphs.

Status

Alpha — API may evolve. See CHANGELOG.md.

Author

Majidul Islam — @Majidul17068

personakit is an independent open-source project. Contributions welcome.

License

MIT © 2026 Majidul Islam.