prismal-ai 3.10.0

Prism-inspired multi-agent orchestration framework built on LangGraph. Security-first, provider-agnostic, composable.

prismal-ai

Prismal AI Agent Framework — the core engine powering multi-agent orchestration, security guardrails, RAG, MCP integration, and observability.

This package is the agent framework layer extracted from the larger monorepo as a standalone, publishable PyPI package. It provides everything needed to build and run AI agents without the web server, dashboard, or CLI. It was published as lightagent-agents through v2.x and rebranded in v3.0.0: the distribution is published on PyPI as prismal-ai while the import namespace is prismal (lightagent.* → prismal.*). End-user backward compatibility is provided by the deprecated lightagent-agents distribution, which now depends on prismal-ai. The sibling lightagent app package historically shared this import namespace and is rebranded/coordinated in tandem.

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Project architecture (organization)

Prismal is split across eight repositories in the prismal-ai organization. prismal (this repo) is the core engine and the only one currently developed; everything else is a consumer or host that builds on top of it.

The macro architecture is a layered, client–server design where a single engine does the agent work and every other component talks to it through well-defined protocols:

• prismal — the core agent framework (published to PyPI as prismal-ai). It owns the LangGraph SUPERVISOR graph, the 26 specialist agents, the 8 RAG engines + VectorStorePort, the reasoning patterns and subgraphs, the 5-layer security stack, the hexagonal ports, and the opt-in layers (Multimodal, Kokoro, Skynet, A2A, Budget). Its full internal architecture is in Architecture below.
• prismal-server — the backend host. It composes the engine via build_runtime() / get_async_compiled_graph() and exposes it over REST, WebSocket, and SSE, plus inbound A2A (the Agent Card at /.well-known/agent-card.json) and session/auth handling. This is the one process that turns the library into a service.
• prismal-sdk — the client library that wraps the server's REST/WS/SSE endpoints so consumers don't hand-roll HTTP.
• prismal-dashboard, prismal-tui, prismal-webchat, prismal-chatbot — the consumer front-ends (web admin, terminal, web chat, and external bots such as Slack/Discord). They all reach the engine through the SDK → server path.
• prisma-notebooks — example and demo notebooks that import the prismal package directly rather than going through the server.

Communication flows top-to-bottom: front-ends call the SDK, the SDK speaks REST/WebSocket/SSE to prismal-server, and the server invokes the prismal engine in-process. The engine, in turn, reaches external systems — LLM providers through LiteLLM, MCP servers for tools, vector stores via VectorStorePort, datastores (SQLite/Postgres/Chroma) for checkpoints and memory, observability through OpenTelemetry/Langfuse, and other vendors' agents bidirectionally over A2A (outbound from the engine, inbound served by the server). In the diagram, the solid green block marks the developed core, while dashed outlines mark the planned/early-stage repositories.

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Features

• 26 specialized AI agents built on LangGraph — coder, researcher, planner, critic, data_analyst, rag_agent, codeact_agent, cua_agent, and more
• SUPERVISOR state machine — central supervisor routes each turn to the right specialist, then back to END
• Security-first (5-layer defense) — InputSanitizer → GuardrailsEngine (+ NeMo Guardrails L3) → ActionInterceptor → AuditLogger (hash-chained) + SecurePromptBuilder + PermissionManager
• Provider-agnostic — Anthropic Claude, OpenAI GPT, Google Gemini, Ollama via LiteLLM (isolated in prismal/providers/)
• 7 RAG engines — standard + CRAG, HyDE, RAG-Fusion (RRF), Hybrid (BM25 + semantic), Self-RAG, Parent-Child hierarchical, Multi-Vector, and Adaptive facade
• 7 agent reasoning patterns — Tree of Thoughts, Debate, Constitutional AI, LATS (MCTS), LLM-Compiler (parallel DAG), Mixture of Agents, Swarm/Handoff
• 5 domain subgraph pipelines — Customer Service, Document Generation, Data ETL, Code Review, Debate/Consensus — on top of the existing dev/ml/financial pipelines
• Multimodal layer (implemented, opt-in) — Vision / Audio / Video agents, modality router, multimodal fusion, multimodal subgraph, multimodal RAG engine with cross-modal embeddings, and MediaValidator security gate — gated by settings.multimodal_enabled (default False); see specs/multimodal-agents/
• Kokoro deliberation (implemented, opt-in) — three Markdown-authored persona souls (spirit 魂 / mind 知 / heart 情) deliberate toward agreement and a KokoroJudgeAgent renders the final, accountable verdict (optionally executing one ActionInterceptor-gated action) — gated by settings.kokoro_enabled (default False); see docs/kokoro.md and specs/kokoro-deliberation/
• Skynet swarm supervisor (implemented, opt-in) — a meta-supervisor that decomposes one order into N sub-orders, dispatches a dynamically-sized worker swarm via LangGraph Send fan-out (supervisor-sized, hard-capped, overflow deferred), reduces the results, and re-plans unmet work in a bounded loop — gated by settings.skynet_enabled (default False); see docs/skynet.md and specs/skynet-swarm/
• Extension surface (implemented, opt-in) — prismal.langgraph re-export, @prismal_node decorator (security/OTel/audit/retry middleware), PrismalStateGraphBuilder fluent API, plugin discovery via importlib.metadata entry points, LangChainRunnableAdapter, and formal Protocols for ports (checkpoint/audit/embeddings/tools) — see docs/extension.md and specs/extension-surface/
• MCP client with capability routing — Model Context Protocol with auto-discovery and per-agent capability-based tool filtering (config/mcp_servers.yaml)
• Process isolation — SandboxExecutor with docker/podman/nsjail/bwrap/firejail backends
• Human-in-the-Loop — hitl_gate() with LangGraph interrupt() support
• Composable primitives — reflection_loop() (generate → critique → refine) and make_parallel_dispatcher() (fan-out via Send())
• Cron engine — APScheduler + timezone-aware DateTimeService (single time source of truth)
• Long-term memory — PII-sanitized cross-session store (SQLite + ChromaDB; optional MongoDB)
• Observability — Langfuse traces, OpenTelemetry spans, structlog
• Deterministic intent routing — regex-based match_intent() ahead of LLM supervision
• Tool provider injection (implemented) — ToolProviderPort hexagonal port: the host composes MCP/Skills/stub providers and injects them (set_tool_provider or per-session via graph config); the agent core no longer imports prismal.mcp/prismal.skills — see docs/tool-providers.md and specs/tool-provider-injection/
• Runtime composition root (implemented) — build_runtime(settings, *, org_id=None) composes every core port (tool provider, vector store, embeddings, checkpointer, audit) into a RuntimeContext in a single call, with global/context modes (settings.runtime_mode), per-org_id collection isolation, coordinated aclose(), and build_test_runtime fakes — the one composition contract prismal-server/prismal-dashboard build on; see docs/composition-root.md and specs/composition-root/
• Config source injection (implemented) — ConfigSourcePort hexagonal port: the core stops reading .env/os.environ and consumes an injected source (EnvConfigSource default keeps byte-for-byte parity; MappingConfigSource/ChainedConfigSource compose secrets managers; per-tenant via build_settings(source)); set_config_source invalidates the get_settings cache, an AST guard forbids new config os.getenv in the core — see docs/configuration.md and specs/config-source-injection/
• Cost & Budget Governance (implemented, opt-in) — per-run/session/tenant token / cost(USD) / call / wall-clock budgets with soft (degrade) / hard (abort) circuit-breakers: a CostMeter accumulates real usage (priced via LiteLLM + a budget_pricing fallback table) and a BudgetGuard enforces it in react_loop, the expensive patterns (debate, ToT, LATS, MoA, reflection) and Skynet (unifying skynet_token_budget) — gated by settings.budget_enabled (default False); see docs/budget.md and specs/cost-budget-governance/
• Runtime Hardening (implemented, opt-in) — defense-in-depth for the 2026 OWASP LLM01/05/06/10 gaps: taint tracking of untrusted content, an IndirectInjectionDetector that scores tool/RAG/media results before re-injection, an OutputValidator (tool-arg schema + path/command/HTML escaping), an identity-agnostic ToolPolicyEngine (allow/deny/require-HITL/rate-limit via config/tool_policies.yaml), a RunawayGuard (step cap + stagnation), and PII-on-output — each with off/warn/enforce modes; gated by settings.hardening_enabled (default False, graph byte-for-byte unchanged when off); see docs/security/runtime-hardening.md and specs/runtime-hardening/
• Agent Evaluation & Reliability Harness (implemented) — prismal/eval/ runs eval-sets against the real compiled graph (closing the 2026 "scaffold gap"): EvalRunner over astream + build_test_runtime fakes, trajectory capture, composable assertions (exact/semantic/tool-usage/llm-judge/groundedness/security), LLM-as-judge, a regression gate, and an adversarial red-team suite proving security containment — the executable proof for Phase H. Fakes by default, live_api opt-in; python -m prismal.eval CLI. Imports only the public graph entry + ports (AST-guarded). See docs/eval.md and specs/agent-eval-harness/
• Agent Identity & Access Governance (implemented) — prismal/identity/ gives every agent a verifiable W3C DID (did:key offline + did:web for A2A), least-privilege scoped credentials from a pluggable vault (EnvVault via ConfigSourcePort / encrypted FileVault / FakeVault; secrets never reach state/logs), OAuth on-behalf-of delegation (scopes only narrow along the chain), and an identity-aware PolicyEngine that delegates (agent, tool, args) to the Phase H ToolPolicyEngine. Wired at the ActionInterceptor seam and composed per org_id in build_runtime. Opt-in: identity_enabled (graph byte-for-byte unchanged when off, snapshot-tested). See docs/identity.md and specs/agent-identity-governance/
• A2A (Agent2Agent) interop (implemented, opt-in) — prismal/a2a/ makes prismal a citizen of the multi-vendor agent ecosystem: inbound, an A2AServerHandler exposes the graph over JSON-RPC + SSE behind an Agent Card at /.well-known/agent-card.json (built from the capability registry, embeds the agent DID); outbound, A2AClient/A2AAgentNode delegate to remote agents as graph nodes and A2AToolProvider surfaces remote skills as tools (conforms to the Phase Y ToolProviderPort). Complements MCP (tools); every remote response is L1-sanitized + audited. Opt-in: a2a_enabled (default False, graph unchanged when off); [a2a] extra. See docs/a2a.md and specs/a2a-interop/
• Guardrails Modernization (implemented, opt-in) — closes the last gap in L3: config/nemo_rails/ finally ships the config NemoRailsLayer always expected, plus a reasoning-capable safety-classifier custom action (security/nemo_actions.py, gated by nemo_classifier_enabled) with its own independent timeout budget, settings-driven main-LLM resolution via providers/ (no hardcoded provider), and fail-open semantics. A new StructuredOutputGuard ([guardrails-ai] extra) adds bounded, Budget-metered automatic re-ask on schema violations and opt-in Guardrails Hub validators (PII/provenance/toxicity), composing with — never replacing — OutputValidator. Both flags default False (graph byte-for-byte unchanged when off). See docs/security/guardrails-modernization.md and specs/guardrails-modernization/
• Loop Hardening (implemented, opt-in) — closes two agentic-loop mechanics gaps: ContextCompactor (agents/context_compaction.py, context_compaction_enabled) trims/summarizes state["messages"] via RemoveMessage once a message-count or Budget-token threshold is exceeded, keeping the most recent messages verbatim; seeded per-turn in supervisor_node and optionally in react_loop(..., context_compactor=...) for a single node's local tool loop. resolve_phase() (agents/loop_phase.py) derives a deterministic, LLM-free task phase from task_plan/pending_tasks/completed_tasks; ToolProviderPort.get_tools() gains an optional phase keyword and CompositeToolProvider(phase_capability_map=...) narrows the tool catalogue per phase (tool_gating_enabled), falling open on a non-conforming provider. Both flags default False (graph and tool-resolution output byte-for-byte unchanged when off). See docs/loop-hardening.md and specs/loop-hardening/
• 120-tool global cap enforced by the official CompositeToolProvider (legacy constants kept in tool_registry.py)
• Graph visualization — to_mermaid() / visualize() / save_graph_image() (from prismal.langgraph) render any compiled graph or SubgraphDefinition; SubgraphDefinition.to_mermaid() and visualize_supervisor_graph() are one-line shortcuts (see examples/visualize_graphs.py)

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Installation

pip install prismal-ai
# or with uv:
uv pip install prismal-ai

The distribution is named prismal-ai, but the import namespace is prismal (e.g. from prismal.agents.graph import get_async_compiled_graph).

Optional extras

pip install "prismal-ai[postgres]"          # PostgreSQL checkpointing
pip install "prismal-ai[mongodb]"           # MongoDB long-term memory
pip install "prismal-ai[ollama]"            # Local LLMs via Ollama
pip install "prismal-ai[local-embeddings]"  # HuggingFace embeddings
pip install "prismal-ai[ml]"                # ML/AutoML pipeline
pip install "prismal-ai[ml-dl]"             # ML + PyTorch Lightning
pip install "prismal-ai[finance]"           # yfinance + pandas-ta
pip install "prismal-ai[analytics]"         # matplotlib + plotly
pip install "prismal-ai[datetime]"          # tzdata + NTP
pip install "prismal-ai[maintenance]"       # pip-audit
pip install "prismal-ai[multimodal]"         # Pillow + ffmpeg-python + imagehash (Phase F)
pip install "prismal-ai[multimodal-local]"   # faster-whisper (local STT)
pip install "prismal-ai[multimodal-premium]" # elevenlabs TTS
pip install "prismal-ai[multimodal-embed]"   # open-clip-torch (CLIP cross-modal embeddings)
pip install "prismal-ai[lancedb]"            # LanceDB embedded vector store (Phase Z)
pip install "prismal-ai[sqlite-vec]"         # sqlite-vec embedded vector store (Phase Z)
pip install "prismal-ai[qdrant]"             # Qdrant vector store, embedded or server (Phase Z)
pip install "prismal-ai[pgvector]"           # PostgreSQL + pgvector vector store (Phase Z)
pip install "prismal-ai[all]"                # Everything above

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Quick Start

from prismal.agents.graph import get_async_compiled_graph
from prismal.agents.state import create_initial_state
from prismal.core.config import get_settings

async def main():
    settings = get_settings()
    graph = await get_async_compiled_graph()   # async contexts MUST use this

    state = create_initial_state(
        session_id="my-session",
        user_message="Analyse the sales data in data/sales.csv",
    )

    result = await graph.ainvoke(
        state,
        config={"configurable": {"thread_id": "my-session"}},
    )
    print(result["messages"][-1].content)

A synchronous get_compiled_graph() entry point is also available for non-async callers.

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Advanced architectures

The package ships 19 composable architectures under specs/advanced-architectures/ (Phases A/B/C, ≥82% coverage per module, 0 bandit issues). Every component follows a callable-injection pattern — business logic accepts generate_fn, evaluate_fn, reward_fn, plan_fn, tool_executor, … so tests run without LLM backends. Defaults wire ProviderRegistry().get_llm() lazily.

RAG engines (prismal/rag/)

Engine	Module	Purpose
HyDE	hyde.py	Generates a hypothetical answer and searches by its embedding (recall boost on abstract queries)
RAG-Fusion	fusion.py	N query variants + reciprocal_rank_fusion() (RRF, k=60) over parallel searches
Hybrid Search	hybrid.py	BM25 (rank-bm25) + semantic linear fusion with configurable alpha
Self-RAG	self_rag.py	LLM decides whether to retrieve (RETRIEVE/NO_RETRIEVE) and self-assesses support (SUPPORTED/PARTIALLY_SUPPORTED/UNSUPPORTED) + utility score
Parent-Child	hierarchical.py	Indexes small child chunks (~100 tok) for precision but returns parent context (~500 tok) to the LLM
Multi-Vector	multi_vector.py	Indexes each chunk plus a summary and N hypothetical questions per chunk
Adaptive RAG	adaptive.py	Facade that classifies queries (FACTUAL_SIMPLE / ABSTRACT / AMBIGUOUS / MULTI_HOP / TECHNICAL / CONVERSATIONAL) and routes to the engine above

Agent reasoning patterns (prismal/agents/patterns/)

Pattern	Module	Purpose
Tree of Thoughts	tree_of_thoughts.py	Explores a tree of candidate thoughts with BFS / DFS / beam search
Debate	debate.py	N-agent multi-round debate with moderator / majority-vote / weighted synthesis and Jaccard agreement score
Constitutional AI	constitutional.py	Principle-driven self-critique + revision loop with audit log (3 default principles: no_harmful_content, factual_accuracy, no_pii_exposure)
LATS	lats.py	Monte Carlo Tree Search (UCB1) over the action space — real backtracking when a branch fails
LLM-Compiler	llm_compiler.py	Compiles a DAG of tasks, validates with Kahn topological sort, executes independent tasks in parallel waves
Mixture of Agents	mixture_of_agents.py	Parallel proposers across multiple providers + aggregator synthesis layers
Swarm/Handoff	swarm.py	Decentralised agent-to-agent handoff with HandoffRecord audit trail and allow-list validation

Domain subgraph pipelines (prismal/agents/subgraphs/)

Pipeline	Directory	Flow
Customer Service	customer_service/	classifier → faq_retrieval → escalation_gate → response | ticket_creator
Document Generation	document_generation/	planner → researcher → writer → editor → formatter (markdown/plain/html)
Data ETL	data_etl/	extractor → validator → (conditional gate) → transformer → loader → auditor
Code Review	code_review/	linter → security_scanner → logic_reviewer → suggester → report_generator
Debate/Consensus	debate_consensus/	proponent → opponent → moderator → consensus

Each subgraph exports both build_<name>_subgraph() (returns a SubgraphDefinition) and an idempotent register_<name>() mirroring the existing register_ml_pipeline. Wiring into the top-level supervisor is opt-in operational work — the primitives are ready to register.

MCP capability routing

config/mcp_servers.yaml declares each server's capabilities: list[str]. MCPClientManager.get_all_langchain_tools(capabilities=…) and get_tools_for_agent(agent, required_capabilities=…) filter the tool pool per agent. Servers tagged general are always included; omitting capabilities from a YAML entry defaults to ["general"] for backward compatibility. The capability set is extended in Phase F to include vision, audio, and video.

See specs/advanced-architectures/SPEC.md for the full interface contracts of Phases A/B/C/D/E.

Multimodal layer (Phase F — implemented, opt-in)

The multimodal expansion described in specs/multimodal-agents/ adds voice, image, and video to the existing text-only stack without modifying any existing agent. It is opt-in: gated by settings.multimodal_enabled (default False) and registered via register_multimodal_pipeline(registry) when the operator is ready.

Provider wrappers (prismal/providers/)

Wrapper	Module	Backends
STT	stt.py	OpenAI Whisper API, local (openai-whisper / faster-whisper)
TTS	tts.py	pyttsx3 (offline default), OpenAI, ElevenLabs — automatic cascade fallback
Vision LLM	vision.py	Any LiteLLM model with vision (Claude, GPT-4o, Gemini)
Multimodal LLM	multimodal.py	Gemini 2.x, GPT-4o, Sonnet 4.6 (native multimodal)
Cross-modal embeddings	cross_modal_embeddings.py	CLIP / open_clip_torch (opt-in extra)

Modal agents (prismal/agents/multimodal/)

Agent	Module	Purpose
VisionAgent	vision_agent.py	General-purpose image analysis: description, object detection, optional OCR
AudioAgent	audio_agent.py	Voice-to-voice pipeline: STT → LLM reasoning → optional TTS
VideoAgent	video_agent.py	FFmpeg frame extraction (via SandboxExecutor) + audio transcript + fusion summary
ModalityRouter	modality_router.py	Heuristic classifier (MIME + regex) with optional LLM fallback
MultimodalFusion	multimodal_fusion.py	Combines outputs from modal agents using moa, moderator, or concat strategies (reuses mixture_of_agents.py)

Multimodal subgraph (prismal/agents/subgraphs/multimodal_pipeline/)

router_node → [vision_node | audio_node | video_node | text passthrough] → fusion_node → output_formatter_node

Exports build_multimodal_subgraph() (returns SubgraphDefinition) and an idempotent register_multimodal_pipeline() matching the existing register_ml_pipeline pattern.

Multimodal RAG (prismal/rag/)

MultimodalRAGEngine indexes text + image captions + audio/video transcripts and exposes search(query, modalities=[...]) with metadata-based modality filtering. Without the [multimodal-embed] extra it falls back to textual captions; with it, vectors come from CLIP-style cross-modal embeddings. New loaders: loaders/image_loader.py, loaders/audio_loader.py, loaders/video_loader.py.

Security (prismal/security/)

MediaValidator enforces magic-byte verification + size/duration limits before any media reaches an agent. InputSanitizer.sanitize_media() strips EXIF; AuditLogger.log_media() records SHA-256 + modality (never content); ActionInterceptor.check_media_op() gates filesystem media operations; FFmpeg always runs inside SandboxExecutor.

See specs/multimodal-agents/SPEC.md for the full interface contracts of Phase F.

Extension surface (Phase X — implemented, opt-in)

The extension surface (user guide: docs/extension.md; contracts: specs/extension-surface/) exposes LangGraph as a first-class build target for users and third-party plugins, so you can write new patterns without forking prismal. All public symbols import from prismal.agents.extension. Five components:

prismal.langgraph — official re-export

from prismal.langgraph import StateGraph, START, END, Send, interrupt, add_messages, AgentState, VERSION

graph = StateGraph(AgentState)
graph.add_node("my_node", my_node)
graph.add_edge(START, "my_node")
graph.add_edge("my_node", END)
compiled = graph.compile()

Importing from prismal.langgraph (rather than langgraph.* directly) guarantees the LangGraph version prismal was tested against, exposed as VERSION.

@prismal_node decorator

from prismal.agents.extension import prismal_node

@prismal_node(name="my_classifier", capabilities=["general"], security="standard", audit=True)
async def my_classifier(state):
    last = state["messages"][-1].content
    label = await classify(last)
    return {"metadata": {"my_classifier": {"label": label}}}

Wraps any async (state) → state_update with a middleware chain: InputSanitizer + SecurePromptBuilder + ActionInterceptor → OTel span → structured logger bind → retry/backoff → timeout → user function → audit log → error mapping. Side effect: registers the node's capabilities in tool_registry.DEFAULT_CAPABILITY_MAP.

PrismalStateGraphBuilder — fluent API

from prismal.agents.extension import PrismalStateGraphBuilder

builder = PrismalStateGraphBuilder("my_pipeline")
builder.add_node("classify", classify_fn)        # auto-wraps with @prismal_node if missing
builder.add_node("respond", respond_fn)
builder.add_edge("classify", "respond")
builder.set_entry_point("classify")
subgraph = builder.compile()                      # returns SubgraphDefinition

Plugin discovery via entry points

# prismal-x-healthcare/pyproject.toml
[project.entry-points."prismal.subgraphs"]
healthcare_triage = "prismal_x_healthcare:register_healthcare_pipeline"

After pip install prismal-x-healthcare, discover_plugins() auto-registers the subgraph. Allowlist/denylist via settings.plugins_allowlist / plugins_denylist. CLI: python -m prismal.plugins list | info <name> | doctor. Each plugin loads in isolation — individual failures do not abort startup.

LangChainRunnableAdapter — bridge for existing LangChain code

from prismal.agents.extension import LangChainRunnableAdapter

adapter = LangChainRunnableAdapter(my_agent_executor)
node = adapter.as_node(name="legacy_research", capabilities=["research"])

Automatically maps state["messages"] ↔ the Runnable's input/output. Supports Runnable, RunnableSequence, RunnableLambda, AgentExecutor.

Formal ports (hexagonal)

prismal/agents/extension/ports.py declares CheckpointPort, AuditPort, EmbeddingsPort, ToolPort, ToolProviderPort as Protocols. Existing implementations (AsyncSqliteSaver, AuditLogger, ChromaDB embeddings, BaseTool, the Phase Y tool providers) conform structurally; users substitute their own (Redis checkpointer, Splunk audit, custom tool source, etc.) without modifying the core.

See specs/extension-surface/SPEC.md for the full interface contracts of Phase X.

Tool provider injection (Phase Y — implemented)

Tool resolution is a hexagonal port (user guide: docs/tool-providers.md; contracts: specs/tool-provider-injection/): the agent core asks an injected ToolProviderPort for tools and never imports prismal.mcp / prismal.skills (enforced by an architecture test). The host composes the providers and injects them at startup:

from prismal.agents.extension import build_default_tool_provider
from prismal.agents.tool_registry import set_tool_provider

async def on_startup() -> None:                       # FastAPI lifespan or equivalent
    set_tool_provider(await build_default_tool_provider())   # MCP + Skills + stubs

• Providers (prismal.agents.extension): McpToolProvider, SkillToolProvider, StubToolProvider, CompositeToolProvider (merge with MCP→Skills→stubs priority, name dedupe, 60/120 tool caps, fixed-tool-agent exemption — exact parity with the historical registry) and FakeToolProvider for tests.
• Variante A (global) — set_tool_provider() once per process; nodes keep calling get_tools_for_agent(name) unchanged.
• Variante B (multi-tenant) — get_async_compiled_graph(tool_provider=provider) with tool_provider_mode="context" binds a per-session provider; nodes resolve via get_tools_for_agent_ctx(name, config). No shared global state.
• No provider? The registry degrades to static stubs with a warning (tool_provider_strict=True raises ToolProviderNotConfigured instead).
• Legacy shims — init_mcp() / get_mcp_tools() / get_skill_tools() still work, emit DeprecationWarning, and will be removed in the next minor.

Runnable examples: examples/tool_provider_host.py, examples/tool_provider_custom.py.

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Config source injection (Phase W — implemented)

Configuration is a hexagonal port (user guide: docs/configuration.md; contracts: specs/config-source-injection/): the core stops reading .env/os.environ and instead consumes an injected ConfigSourcePort that supplies raw values. Settings keeps its schema and only validates. Additive and opt-in — with no source injected the default EnvConfigSource reproduces today's behaviour byte-for-byte, so the ~151 get_settings() call sites are untouched.

from prismal.core.config_source import ChainedConfigSource, EnvConfigSource, set_config_source

# Front a secrets manager, fall back to the environment (first-wins).
set_config_source(ChainedConfigSource([VaultConfigSource(), EnvConfigSource()]))

• Sources (prismal.core.config_source): EnvConfigSource (the only core reader of os.environ/.env; folds the legacy LIGHTAGENT_ mirror into its mapping, no global mutation), MappingConfigSource, ChainedConfigSource (first-wins, sub-error skipped), FakeConfigSource for tests.
• Global — set_config_source(source) once per process; invalidates the get_settings cache so the next read rebuilds Settings.
• Per-tenant — build_settings(source) is a pure constructor (no global state, ContextVar-isolated); composition-root threads it via apply_org_overrides(*, source=...).
• Strict — config_source_strict=True makes build_settings raise ConfigSourceError when no source is available instead of falling back.
• Guardrail — an AST guard (tests/unit/core/test_no_env_reads.py) forbids new direct config os.getenv/os.environ reads in prismal/** (exempt: EnvConfigSource, the LiteLLM write-bridge).

Runnable examples: examples/config_source_env.py, examples/config_source_custom.py.

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Cost & Budget Governance (Phase C — implemented, opt-in)

The enforcement layer atop the monitoring stack (user guide: docs/budget.md; contracts: specs/cost-budget-governance/): meter real per-run usage, compare it to a Budget, and cut off when exceeded. Additive and opt-in — gated by settings.budget_enabled (default False); with the flag off there is zero extra state and the compiled supervisor graph is byte-for-byte unchanged (snapshot-tested).

# settings (env: PRISMAL_BUDGET_*)
budget_enabled       = True
budget_scope         = "turn"     # turn | session | tenant
budget_max_tokens    = 200_000    # 0 = unlimited on that dimension
budget_max_cost_usd  = 5.0
budget_soft_ratio    = 0.8        # warn/degrade at 80% of any cap
budget_hard_cap      = True       # raise BudgetExceeded on a hard breach

• Value objects (prismal/budget/types.py): Budget (per-dimension ceiling; 0 = unlimited), BudgetScope (turn|session|tenant), Usage (cumulative, summable), BudgetStatus, Degradation.
• Meter (prismal/budget/meter.py): CostMeter accumulates Usage per run (O(1), no hot-path I/O), prices each call via providers/cost.py (LiteLLM native map → budget_pricing table → zero-cost none), emits OTel counters tagged agent/pattern/model/tenant, and optionally persists to a CostTracker (FinOps history).
• Guard (prismal/budget/guard.py): BudgetGuard.check() is a pure within/soft/hard verdict; enforce() audits (hash-first) and raises BudgetExceeded on a hard cap; make_budget_guard_fn() adapts it to the budget_guard_fn the patterns consume.
• Per-run wiring (prismal/budget/resolve.py): seed_budget_run installs the {meter, guard} in an in-process registry keyed by session_id (never checkpointed — only a serializable marker lands in state["metadata"]["budget"]), idempotent per user-turn so usage resets between turns.
• Enforcement sites: react_loop (record after, check before, hard → partial answer + break) and the expensive patterns debate / tree_of_thoughts / lats / mixture_of_agents / reflection_loop; Skynet builds Budget(max_tokens=skynet_token_budget) over a shared meter and raises SkynetBudgetExceeded(BudgetExceeded, SkynetError).

Runnable example: examples/budget_governance.py.

---

Roadmap — features to build

Already implemented: extension surface (Phase X), tool provider injection (Phase Y), advanced architectures (Phase A/B/C), multimodal (Phase F), Kokoro (Phase K), Skynet (Phase S), the vector store port (Phase Z), the runtime composition root (Phase R), the config source injection (Phase W), the cost & budget governance (Phase C), the runtime hardening (Phase H), the agent evaluation & reliability harness (Phase V), the agent identity & access governance (Phase IDN), the A2A interop (Phase I), the guardrails modernization (Phase GRD), the loop hardening (Phase LH), the node I/O type-safety (Phase NTS), the observability integration (Phase OBS), and the dependency remediation (18/18 alerts in a terminal state).

New pending phase from the 2026-07 gap analysis (docs/gap-analysis-loops-harness-guardrails-2026-07.md), not yet started: the out-of-repo reference host bootstrap (prismal-server, specs/reference-host-bootstrap/).

What remains, ordered from fast-and-necessary → complex-and-less-necessary. Each feature has its SDD contract in specs/. Status: spec ready = ready to build (PLAN/ARCHITECTURE/SPEC/TASKS); PRD seed = PRD only, needs expansion before building.

1. Finish Tool Provider Injection (Phase Y) — fast · necessary · in progress — specs/tool-provider-injection/. The Y1–Y5 code has already landed; what's left is closing Y6–Y8 (settings/observability, docs/examples, parity tests) and marking the spec IMPLEMENTED.
2. Vector Store Port (Phase Z) — moderate · necessary · ✅ implemented — specs/vector-store-port/. Removes the ChromaDB lock-in behind a VectorStorePort with adapters (Chroma default + LanceDB, sqlite-vec, Qdrant, pgvector), selectable via settings.vector_store_backend. Reduces the security surface and opens up embedded backends. See docs/vector-stores.md.
3. Runtime Composition Root (Phase R) — moderate · necessary · ✅ implemented — specs/composition-root/. build_runtime() composes and injects every port (tools, vector store, embeddings, checkpoint, audit) into a RuntimeContext in a single call, with global/context modes and per-org_id collection isolation; unblocks prismal-server / prismal-dashboard. See docs/composition-root.md.
4. Cost & Budget Governance (Phase C) — fast-to-moderate · useful · ✅ implemented — specs/cost-budget-governance/. Per-run/session/tenant budgets + cost/token/call/wall-clock circuit-breakers (soft = degrade, hard = abort) in react_loop, the expensive patterns (debate, ToT, LATS, MoA, reflection) and Skynet. A cheap insurance policy against runaway spend. See docs/budget.md.
5. A2A / Agent Cards interop (Phase I) — complex · necessary (ecosystem) · ✅ implemented (v3.5.0) — specs/a2a-interop/. Bidirectional agent-to-agent interop: expose prismal as an A2A agent (Agent Card at /.well-known/agent-card.json, JSON-RPC + SSE) and consume remote agents as nodes/tools. Complements MCP; closes the gap with MS Agent Framework / Google ADK. See docs/a2a.md.
6. Agent Identity & Access Governance (Phase IDN) — complex · necessary (enterprise) · ✅ implemented (v3.4.0) — specs/agent-identity-governance/. Per-agent identity (W3C DID), scoped credentials, OAuth-on-behalf, and an identity-aware PolicyEngine (delegates to the Phase H tool policy). The trust foundation that A2A consumes. See docs/identity.md. (ID6-02 PermissionManager-DID deferred.)
7. Agent Evaluation & Reliability Harness (Phase V) — moderate-to-complex · useful (reliability) · ✅ implemented (v3.3.0) — specs/agent-eval-harness/. System-level evaluation of the graph (trajectories, tool usage, RAG groundedness), regression with a CI gate, and an adversarial red-team suite. Closes the "scaffold gap". See docs/eval.md.
8. Polish — variable · less urgent — per-node type safety (Phase NTS) ✅ implemented (v3.8.0) — specs/node-io-typesafety/: an opt-in Pydantic input_model/output_model contract per @prismal_node, validated at the node boundary (off | warn | enforce), byte-for-byte unchanged when disabled (docs/node-typesafety.md). Observability integration (Phase OBS) ✅ implemented (v3.9.0) — specs/observability-integration/: an opt-in ObservabilityPort (queryable RunSummary, LangSmith/Langfuse naming/scoring/dataset-export parity) wrapping the existing OTel/Langfuse emission, byte-for-byte unchanged when disabled (docs/observability-integration.md). The framework ships the port; a first-party observability UI remains the planned prismal-dashboard's job.
9. Guardrails Modernization (Phase GRD) — fast-to-moderate · necessary (security) · ✅ implemented (v3.6.0) — specs/guardrails-modernization/. Ships the NeMo config NemoRailsLayer always expected plus a reasoning-capable safety-classifier rail, and a new StructuredOutputGuard (bounded, Budget-metered re-ask + opt-in Hub validators) composing with OutputValidator. See docs/security/guardrails-modernization.md.
10. Loop Hardening (Phase LH) — moderate · necessary (agentic-loop mechanics) · spec ready — specs/loop-hardening/. Context compaction (AgentState.messages grows unbounded today) and dynamic tool gating by task phase.

Framework or host? (where each feature lives)

Rule: contract/logic → framework (prismal/); serving HTTP, authenticating, rendering, persisting config → host (prismal-server / prismal-dashboard). That's why A2A and Identity are split across both.

#	Feature	Framework (prismal/)	Host (prismal-server / dashboard)
1	Tool Provider (Phase Y)	✅ ports/providers (agents/extension)	composes and injects at startup
2	Vector Store Port (Phase Z)	✅ rag/stores/ + VectorStorePort	picks the backend via config
3	Composition Root (Phase R)	✅ prismal/composition/ · build_runtime() / RuntimeContext	calls it in the lifespan
3b	Config Source Injection (Phase W)	✅ core/config_source.py · ConfigSourcePort / build_settings(source)	owns secrets/.env, injects per-tenant sources
4	Cost & Budget Governance (Phase C)	✅ prismal/budget/ · meter + guard in react_loop/patterns/Skynet	per-tenant quotas
5	A2A / Agent Cards (Phase I)	✅ types · card · client · A2AToolProvider · handler	HTTP endpoint (/a2a, /.well-known/agent-card.json) + auth
6	Agent Identity & Governance	✅ PolicyEngine + identity port (security/)	IdP/OAuth + credential vault + DID issuance/rotation
7	Agent Eval Harness (Phase V)	✅ prismal/eval/ (runner/assertions/judges/regression/redteam/report/CLI)	runs as a dev/CI tool
8	Polish	per-node type safety (AgentState) · ✅ ObservabilityPort (prismal/monitoring/)	observability UI (prismal-dashboard) consumes the port

The framework defines the ports and logic; the host composes and exposes them. Details in docs/competitive-analysis.md.

A full analysis and comparison with 2026 frameworks is in docs/competitive-analysis.md.

---

Development

Python 3.13+ is required. uv is the recommended package manager.

# Install with dev tools
uv pip install -e ".[dev]"
# or with dev + extras:
uv pip install -e ".[dev,all]"

# Run the test suite (pytest-asyncio auto-mode, filterwarnings="error")
uv run pytest                                      # full suite
uv run pytest tests/unit                           # one tier
uv run pytest -m unit                              # by marker (unit|integration|security|slow|live_api)
uv run pytest tests/unit/security/test_sanitizer.py::TestSanitizer::test_strip_controls  # single test
uv run pytest -n auto                              # parallel (pytest-xdist)
uv run pytest --cov=prismal --cov-report=term-missing   # coverage (fail_under = 80)

# Lint + format (ruff, line-length=100, target py313)
uv run ruff check .
uv run ruff check --fix .
uv run ruff format .

# Strict type-check (mypy strict mode, namespace_packages=true)
uv run mypy prismal

# Security linting
uv run bandit -r prismal -c pyproject.toml

# Build the distribution
uv run python -m build

live_api tests call real LLM APIs and require provider keys; skip them locally with -m "not live_api". Integration tests under tests/integration/ expect running services (sandbox backends, databases).

---

Architecture

The core is a LangGraph StateGraph[AgentState] assembled in prismal/agents/graph.py following the SUPERVISOR pattern: a central supervisor_node routes each turn to one of 26 specialist agent nodes, each of which returns control to the supervisor; the supervisor routes to END when the task is complete. Checkpointing is handled by AsyncSqliteSaver (or PostgreSQL via the [postgres] extra).

Diagram walkthrough

The diagram above reads top-to-bottom as a request flows through the framework, organized into eight layers plus a detail panel for A2A.

1 — Entry + Security. Every request enters through get_async_compiled_graph() and immediately crosses the five-layer defense-in-depth stack before it can touch any state: InputSanitizer (L1) strips control characters and normalizes unicode, GuardrailsEngine + NeMo Guardrails (L2/L3) apply regex and risk scoring, SecurePromptBuilder isolates user input with canary tokens, ActionInterceptor (L4) runs pre-tool permission checks, and AuditLogger (L5) writes a hash-chained JSONL trail alongside the PermissionManager and filesystem guard. No user text is ever f-stringed into a prompt — it always passes through SecurePromptBuilder first.

2 — Core supervisor. The heart of the framework is the LangGraph StateGraph[AgentState]. A deterministic regex intent_router.match_intent() runs ahead of LLM supervision; the SUPERVISOR node then routes each turn to the right specialist (or to END). All turn state lives in AgentState, a TypedDict whose messages field uses the add_messages reducer, and progress is persisted by the checkpointer (SQLite by default, PostgreSQL optional).

3 — Specialist agents. The supervisor dispatches to one of 26 specialist nodes — coder, researcher, rag_agent, data_analyst, planner, critic, codeact_agent, cua_agent, file_manager, skill_manager, cron_manager, parallel_research, meta_learner, skill_creator, domain_supervisor, network_supervisor, and more. Each node does its work and hands control back to the supervisor, which can also route into the reusable patterns and subgraphs below.

4 — Agent patterns. Composable reasoning strategies live in prismal/agents/patterns/: Tree-of-Thoughts, N-agent Debate, Constitutional self-revision, LATS (MCTS with UCB1), LLM-Compiler (parallel task DAG), Mixture-of-Agents, Swarm handoff, and the reflection_loop.

5 — Subgraphs / pipelines. Multi-node pipelines assembled by SubgraphFactory and held in SubgraphRegistry: dev_pipeline, ml_pipeline, financial, customer_service, document_generation, data_etl, code_review, plus the analysis/engineering/research orchestrators.

6 — RAG + vector store. Eight retrieval engines (HyDE, RAG-Fusion, Hybrid BM25+semantic, Self-RAG, Hierarchical, Multi-Vector, the Adaptive router, and CRAG) sit on top of the hexagonal VectorStorePort. A factory selects the backend — Chroma is the default, with LanceDB, sqlite-vec, Qdrant, and pgvector available via extras — and embeddings plus federated search round out the layer.

7 — Opt-in layers. Capabilities gated by settings flags that, when off, leave the compiled graph byte-for-byte identical: Multimodal (vision/audio/video), Kokoro deliberation (Spirit/Mind/Heart souls + a Judge), Skynet swarm map-reduce (dynamic worker fan-out via Send), A2A interop, and Cost/Budget governance (CostMeter + BudgetGuard).

8 — Hexagonal ports, providers & infrastructure. Tool resolution is inverted behind ToolProviderPort (composing MCP, Skills, and Souls); configuration behind ConfigSourcePort; and build_runtime() is the single composition root that wires every port together. All LLM calls are isolated behind the LiteLLM-based providers layer (Anthropic, OpenAI, Gemini, Ollama). Supporting infrastructure includes short/long-term memory, the scheduler (APScheduler/Prefect), the sandbox (docker/nsjail/bwrap), monitoring (OpenTelemetry/Langfuse/structlog), and the public Extension API (@prismal_node, plugins).

A2A detail panel. Expanding layer 7's A2A box: build_agent_card() publishes an AgentCard (one AgentSkill per capability, identified by a did:web). Inbound traffic is served by A2AServerHandler over JSON-RPC + SSE (message/send, tasks/get, tasks/cancel); outbound, A2AClient + A2AConnectionManager reach remote agents behind an fnmatch allowlist with bearer/oauth2 auth. A2AAgentNode.as_node() turns a remote agent into a graph node, and A2AToolProvider (conforming to ToolProviderPort) surfaces remote skills as a2a__agent__skill tools. A2A complements MCP (tools vs. agents), and the trust boundary holds: every remote response is L1-sanitized and audited by hash, never by content.

prismal/                ← PEP 420 namespace package (NO __init__.py at root)
├── agents/                ← LangGraph state machine + 26 agent nodes
│   ├── graph.py           ← get_compiled_graph() / get_async_compiled_graph()
│   ├── supervisor.py      ← Central router
│   ├── state.py           ← AgentState (TypedDict; messages uses add_messages reducer)
│   ├── intent_router.py   ← Deterministic regex routing
│   ├── tool_registry.py   ← stable facade: delegates to the injected ToolProviderPort (Phase Y)
│   ├── patterns/
│   │   ├── reflection.py           ← reflection_loop()
│   │   ├── parallel.py             ← make_parallel_dispatcher() via Send()
│   │   ├── tree_of_thoughts.py     ← ToT with BFS/DFS/beam
│   │   ├── debate.py               ← N-agent multi-round debate + Jaccard
│   │   ├── constitutional.py       ← principle-driven self-revision + audit
│   │   ├── lats.py                 ← MCTS with UCB1
│   │   ├── llm_compiler.py         ← DAG compilation + Kahn validation + parallel waves
│   │   ├── mixture_of_agents.py    ← multi-provider proposers + aggregator
│   │   └── swarm.py                ← decentralised handoff with audit
│   ├── multimodal/                  ← (Phase F) vision / audio / video agents + router + fusion
│   │   ├── vision_agent.py
│   │   ├── audio_agent.py
│   │   ├── video_agent.py
│   │   ├── modality_router.py
│   │   └── multimodal_fusion.py
│   └── subgraphs/
│       ├── factory.py              ← SubgraphFactory
│       ├── registry.py             ← SubgraphRegistry
│       ├── gates.py                ← hitl_gate() with interrupt()
│       ├── dev_pipeline/           ← PO → Architect → Developer → Tests → QA → Reviewer
│       ├── ml_pipeline/            ← Ingester → EDA → Features → Trainer → Evaluator → Exporter
│       ├── financial/              ← Collector → Technical → Fundamental → Risk → Report
│       ├── customer_service/       ← classifier → faq_retrieval → gate → response | ticket
│       ├── document_generation/    ← planner → researcher → writer → editor → formatter
│       ├── data_etl/               ← extractor → validator → gate → transformer → loader → auditor
│       ├── code_review/            ← linter → security_scanner → logic_reviewer → suggester → report
│       ├── debate_consensus/       ← proponent → opponent → moderator → consensus
│       ├── multimodal_pipeline/    ← (Phase F) router → vision|audio|video → fusion → output_formatter
│       ├── analysis_orchestrator/
│       ├── engineering_orchestrator/
│       └── research_orchestrator/
├── core/                  ← Pydantic Settings, logging, exceptions, DB, user model
├── budget/                ← (Phase C) Cost & Budget Governance — opt-in (settings.budget_enabled)
│   ├── types.py           ← Budget / BudgetScope / Usage / BudgetStatus / Degradation
│   ├── usage.py           ← extract_token_usage() from LangChain message metadata
│   ├── meter.py           ← CostMeter (accumulate Usage + OTel + optional CostTracker bridge)
│   ├── guard.py           ← BudgetGuard (check/enforce/degradation) + make_budget_guard_fn()
│   └── resolve.py         ← resolve_budget() + per-run seeding (session-keyed registry)
├── providers/             ← LiteLLM wrapper (ONLY location for provider-specific imports;
│                             Phase F adds stt/tts/vision/multimodal/cross_modal_embeddings;
│                             Phase C adds cost.py — per-call USD pricing)
├── memory/                ← Short-term history + long-term PII-sanitized store
├── mcp/                   ← MCP client, adapter, connection manager, capability routing
├── security/              ← 5-layer defense-in-depth (see below) + (Phase F) media_validator.py
├── rag/                   ← 7 retrieval engines:
│   ├── engine.py          ← standard RAGEngine
│   ├── crag.py            ← CRAG pipeline
│   ├── hyde.py            ← Hypothetical Document Embeddings
│   ├── fusion.py          ← RAG-Fusion (RRF)
│   ├── hybrid.py          ← BM25 + semantic hybrid search
│   ├── self_rag.py        ← Self-RAG (conditional retrieval + self-assessment)
│   ├── hierarchical.py    ← Parent-Child chunking
│   ├── multi_vector.py    ← chunk + summary + N hypothetical questions
│   ├── adaptive.py        ← facade routing by query type
│   ├── federated.py       ← federated search
│   ├── multimodal.py      ← (Phase F) MultimodalRAGEngine — text + image captions + audio/video transcripts
│   ├── loaders/           ← (Phase F) document/image/audio/video loaders
│   ├── stores/            ← (Phase Z) VectorStorePort adapters: chroma (default), lancedb, sqlite_vec, qdrant, pgvector
│   ├── vector_store_factory.py ← (Phase Z) VectorStoreFactory + FakeVectorStore
│   └── vector_store.py    ← (Phase Z) backward-compatible shim re-exporting ChromaVectorStore from stores/chroma.py
├── skills/                ← available/ (source) · active/ (gitignored) · custom/ (gitignored)
├── scheduler/             ← APScheduler CronExecutor, DateTimeService, Prefect flows
├── monitoring/            ← Langfuse, OpenTelemetry, structlog
├── data/                  ← DuckDB + Polars utilities
├── sandbox/               ← SandboxExecutor process isolation
├── utils/                 ← Shared utilities
└── events/                ← Event bus

Namespace package

prismal/ has no __init__.py — it is a PEP 420 implicit namespace package (renamed from lightagent/ in v3.0.0). Both prismal and the sibling lightagent app package contribute modules into the same prismal.* namespace. Do not add prismal/__init__.py; it would break the sibling package.

Security stack (5 layers)

Layer	Component	Purpose
L1	InputSanitizer	Strip control chars, normalize unicode, enforce MAX_INPUT_LENGTH
L2	GuardrailsEngine	Regex pattern matching + risk scoring
L3	nemo_rails.py	NVIDIA NeMo Guardrails integration
L4	ActionInterceptor	LangChain callback, pre-tool permission checks
L5	AuditLogger	Append-only JSONL audit log with xxhash chaining
Support	SecurePromptBuilder	User-input isolation with canary tokens
Support	PermissionManager	TTL-based SQLite permission grants
Support	filesystem_guard.py	Path confinement via resolve().is_relative_to()

---

Critical rules

1. Never concatenate user input into prompts — use SecurePromptBuilder. This applies to STT transcripts, OCR text, and image captions as well — they are user-controlled content.
2. Never bypass GuardrailsEngine / ActionInterceptor.
3. Always use get_async_compiled_graph() in async contexts (the sync variant wires a non-async SQLite saver).
4. Never add provider-specific imports (anthropic, openai, google.generativeai, ollama, whisper, pyttsx3, elevenlabs, open_clip_torch, …) outside prismal/providers/.
5. Always call ActionInterceptor.check() before tool calls that write files or execute code; call ActionInterceptor.check_media_op() before media filesystem operations (Phase F).
6. Always validate incoming media with MediaValidator.validate() before passing to a multimodal agent (Phase F); FFmpeg always runs inside SandboxExecutor.
7. Never add __init__.py to prismal/ — it must remain a PEP 420 namespace package.

See CLAUDE.md for the full working guide (commands, testing notes, architectural context for contributors and AI assistants).

---

Versioning

This package follows Semantic Versioning. Tag format for releases: prismal/vMAJOR.MINOR.PATCH

git tag prismal/v3.1.5
git push --tags

See CHANGELOG.md for release history.

---

Releasing (maintainers)

Run only after the full suite, linters and type/security checks are green.

# 0) Verify on the release branch
cd prismal && git switch main

# 1) Quality gates (must all pass)
uv pip install -e ".[dev,all]"
uv run pytest -m "not live_api"
uv run ruff check . && uv run mypy prismal && uv run bandit -r prismal -c pyproject.toml

# 2) Build + validate the prismal-ai distribution
rm -rf dist/ && python -m build && twine check dist/*
twine upload --repository testpypi dist/*          # validate on TestPyPI first

# 3) Push history and publish prismal-ai
git push origin main
twine upload dist/*                                 # publish to PyPI
git tag prismal/v3.1.5 && git push --tags         # tag format: prismal/vMAJOR.MINOR.PATCH

# 4) Publish the deprecated compatibility bridge (lightagent-agents -> prismal-ai)
cd compat/lightagent-agents
rm -rf dist/ && python -m build && twine check dist/*
twine upload dist/*                                 # publishes lightagent-agents 2.9.0

Post-release follow-ups: configure DNS/site for prismal.dev, coordinate the sibling lightagent app package (the namespace rename breaks the shared PEP 420 namespace), and regenerate the branded binary assets (PDF/PPTX/HTML).

---

License

MIT © Ernesto Crespo