primal-ai 0.1.0

The reliability and interoperability layer for AI agents.

🐺 PRIMAL

The reliability and interoperability layer for AI agents.

PRIMAL (primal-ai) wraps any AI agent with the seven things production agents need but rarely have: policy enforcement, structured recording, output verification, multi-agent orchestration, smart routing, durable persistence, and portable user memory. Zero runtime dependencies. A2A AgentCard precursor shape. MCP bridge surface ready for Phase 2.5.

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Install

pip install primal-ai

Requires Python 3.11+. No other runtime dependencies — PRIMAL ships stdlib-only.

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60-second example

The example below uses all seven pillars + Storage in a single flow: wrap an agent with Guardian, make it discoverable to Conductor + Atlas + Harness, then record, persist, audit, and remember in one durable loop. It runs end-to-end against fake agents — no API keys, no network.

from primal_ai import (
    Atlas, BYOProvider, ProviderInfo, ThompsonBandit,
    Conductor, AgentCard, Capability,
    Continuity, Guardian, Harness,
    JSONSchemaVerifier, ToolInfo, Trajectory, Verifier,
)
from primal_ai.storage import SQLiteStorage

# 1. Any callable becomes an agent — wrap it with Guardian's policies.
def search_agent(query: str) -> dict:
    return {"answer": f"results for {query!r}"}

agent = Guardian.wrap(search_agent, policies=["no_external_network", "max_cost:$0.10/req"])

# 2. Make the agent + tools + providers discoverable.
class SearchAgent:
    name = "search"
    card = AgentCard(name="search", description="search the web",
                     capabilities=(Capability(name="search", description="..."),))
    def invoke(self, q): return search_agent(q)

Conductor.register_agent(SearchAgent())
Harness.register_tool(ToolInfo(name="search", description="search the web", tags=("web",)))
Atlas.register_provider(BYOProvider(
    name="echo",
    call=lambda task, **kw: f"OK: {task}",
    info=ProviderInfo(name="echo", capabilities=("chat",)),
))
Atlas.set_selector(ThompsonBandit(seed=42))  # opt-in: learn from outcomes

# 3. Record, persist, audit, remember.
with SQLiteStorage("primal.db") as store:
    with Trajectory.record(agent_id="search") as tr:
        tr.record_input({"query": "flights to tokyo"})
        tr.record_output(agent("Find flights to Tokyo under $800"))
    tr.save(store)

    verdict = Verifier.audit(tr, layers=[
        JSONSchemaVerifier(schema={"type": "object", "required": ["answer"]}),
    ])
    print("verdict:", verdict["status"])  # PASS

    Continuity.update("user-k", "language", "en", source="user", store=store)

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The seven pillars (plus Storage)

Guardian — Policy enforcement. Wraps any callable with pre/post checks that gate dangerous actions, redact PII, cap dollar cost, rate-limit calls, and validate input/output schemas. Composes via AllOf / AnyOf and is configurable via a compact string DSL. Violations route through a pluggable escalation handler.

agent = Guardian.wrap(my_agent, policies=["rate_limit:per_minute=60", "max_cost:$0.10/req"])

Trajectory — Structured "black box recorder" for every agent execution. Captures inputs, outputs, tool calls, LLM calls, errors, retries, and inter-agent handoffs into a JSON-serializable record. Replay, filter by step kind, or persist via the Storage Protocol.

with Trajectory.record(agent_id="search") as tr:
    result = agent(query)

Verifier — Three-layer audit framework: rule-based, BYO LLM-judge, and domain-specific. Returns a Verdict(PASS | FAIL | UNCERTAIN) with confidence + reasons. JSONSchemaVerifier and RegexMatchVerifier ship built-in; BYOLLMJudge is the LLM surface (you supply the model call).

verdict = Verifier.audit(tr, layers=[JSONSchemaVerifier(schema=...)])

Conductor — Agent-to-agent orchestration. Capability-based agent registry, single-hop delegation with REFUSED/FAILED/TIMEOUT outcomes, linear pipelines, and a sync pub/sub EventBus. AgentCard field names are chosen so a Phase 2.5 serializer maps 1:1 to A2A v1.0.

Conductor.register_agent(my_agent_instance)

Atlas — Smart routing across providers (your wired-up models, APIs, or local agents). Provider Protocol, BYOProvider, capability/tag-based discovery, health-aware filtering, exponential-backoff cascade. Opt into bandit-driven selection (Thompson sampling or UCB1) when you want learning; deterministic routing stays the default.

name, result = Atlas.invoke("task", context={"capability": "chat"})

Storage — Pluggable persistence behind one Protocol. InMemory for tests; SQLite for production (WAL mode, JSON values, thread-safe per connection, strict at the boundary). Postgres + Redis stubs reserved for Phase 2. The Protocol is what every pillar persists through.

with SQLiteStorage("primal.db") as store: tr.save(store)

Harness — Runtime substrate: health monitoring, tool registry (substring + tag, no embeddings), and an interval-based scheduler. Opt-in startup so importing the package never spawns a background thread.

Harness.register_tool(ToolInfo(name="search", description="...", tags=("web",)))

Continuity — Portable user profile with explicit source + confidence metadata, three merge strategies (self / other / higher_confidence), and BYO autolearn for extracting facts from text via your model. Storage- backed persistence.

profile = Continuity.update("user-k", "language", "en", source="user", store=store)

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Architecture

The seven pillars compose through small, named neutral primitives at the package root — no pillar reaches into another's internals:

primal_ai/
├── _events.py             # shared EventBus + default_bus singleton (Conductor + Atlas + Harness + Continuity publish here)
├── _trajectory_context.py # ContextVar bridge — Trajectory sets, Conductor + Atlas read
├── _jsonschema.py         # shared subset validator (Guardian SchemaValidator + Verifier JSONSchemaVerifier)
├── guardian/              ← policy enforcement
├── trajectory/            ← causal recording
├── verifier/              ← output audit
├── conductor/             ← multi-agent orchestration
├── atlas/                 ← provider routing
├── storage/               ← persistence
├── harness/               ← runtime substrate
└── continuity/            ← portable user memory

Three pillars expose a BYO LLM surface — BYOLLMJudge (Verifier), BYOProvider (Atlas), BYOAutolearn (Continuity) — all using the same Callable[[str], str] boundary. A first-party LLM caller will ship as an optional install in Phase 2; the MVP stays dependency-free.

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Why PRIMAL?

• Zero runtime dependencies. Stdlib-only. No supply chain risk, no version pin headaches.
• Extracted from a production system. Donor codebase is KARIS — 89 plugins, 700+ tools, live since March 2026. PRIMAL keeps the DNA and drops the vendor coupling.
• A2A-friendly out of the box. AgentCard, Capability, and ProviderInfo shapes are designed so a Phase 2.5 serializer can map them 1:1 to A2A v1.0 wire format without renames.
• Composable, not monolithic. Use one pillar or all eight. They share four small, named bridges at the package root and otherwise stand alone.
• Apache 2.0 licensed. Use it commercially.

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Status

This is 0.1.0 — the first public release. All seven pillars shipped as MVPs across nine focused sessions in two weeks. 268 tests passing, mypy --strict clean, ruff check clean.

• Phase 2 (M2 — hardening) ships OTel spans, async paths, first-party LLM judges as an optional extra, cron syntax in the scheduler, embeddings-backed tool search, and bandit decay.
• Phase 2.5 ships A2A v1.0 wire format compliance and the MCP↔A2A bridge that no neutral party has built yet.

See docs/PRIMAL_ROADMAP.md for the full plan and CHANGELOG.md for what shipped in 0.1.0.

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Links

• GitHub: https://github.com/primalaiagents/primal-core
• Issues: https://github.com/primalaiagents/primal-core/issues
• Roadmap: docs/PRIMAL_ROADMAP.md
• Per-pillar roadmaps: docs/ (one file per pillar)

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License

Apache 2.0 — see LICENSE.