vincio 1.1.0

Vincio: the context engineering platform for AI applications. Compiles prompts, memory, retrieval, tools, schemas, and policies into optimized, validated, observable model-ready context packets.

The scarce resource is not the model. It is the context you feed it.

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Vincio is a Python platform for building context-engineered AI applications. It compiles prompts, memory, retrieval, tools, schemas, and policies into optimized, testable, observable, provider-neutral context packets — then validates and evaluates every output.

Most LLM frameworks help you call a model. Vincio governs the boundary between your application state and the model: what evidence is selected, how it is scored and budgeted, how it is rendered for cache reuse, and how the result is validated, measured, and traced. Named for Leonardo da Vinci — engineering and craft in equal measure.

Raw Input → Normalization → Objective Detection → Memory Selection
→ Retrieval Planning → Evidence Retrieval → Ranking + Distillation
→ Tool Planning → Context Compilation → Model Execution
→ Parsing + Validation → Evaluation + Guardrails → Trace + Learning Loop

Contents

Why Vincio · Install · 60-second quickstart · Features · Benchmarks · Comparison · Use cases · Examples · CLI · Architecture · Roadmap · Documentation

Why Vincio

Teams ship a prompt, watch it work, then spend months fighting everything around it: context that overflows the window, retrieved chunks that contradict each other, outputs that fail to parse, silent quality regressions, untraceable costs, and prompt-injection risk. These are not model problems — they are context problems.

Vincio treats context as a compiled artifact with a clear contract:

• Deterministic where it matters. Security, permissions, and validation are enforced in code — never gated on model output. The same input compiles to the same packet.
• Measured, not asserted. Every run is traced and costed; every change can be gated by an eval suite before it ships.
• Provider-neutral. OpenAI, Anthropic, Google, Mistral, any OpenAI-compatible endpoint, or a deterministic offline mock — behind one interface.
• One coherent model from input to output, instead of a bag of loosely-coupled utilities.

Install

pip install vincio                  # core — runs fully offline with the mock provider
pip install "vincio[openai]"        # + OpenAI provider
pip install "vincio[anthropic]"     # + Anthropic provider
pip install "vincio[chroma]"        # + a vector store (also: pinecone, lancedb, postgres)
pip install "vincio[langchain]"     # + LangChain interop export (also: llamaindex)
pip install "vincio[all]"           # every optional integration

Python 3.11+. Core dependencies are just pydantic, httpx, pyyaml, and typing-extensions; every heavy integration (vector stores, OCR, server, OpenTelemetry, …) is an opt-in extra.

60-second quickstart

from vincio import ContextApp

app = ContextApp(name="docs_qa")
app.add_source("docs", path="./docs", retrieval="hybrid")
app.set_policy("answer_only_from_sources", True)

result = app.run("How do I configure SSO?")
print(result.output)      # the grounded answer
print(result.citations)   # evidence the answer actually cited
print(result.trace_id)    # every run produces a full trace
print(result.cost_usd)    # …and a cost

No API key? It runs offline out of the box on a deterministic mock provider that emits schema-valid output — so your whole pipeline (retrieval, validation, evals, traces) runs for real in CI.

Typed output

from pydantic import BaseModel
from vincio import ContextApp

class TicketClassification(BaseModel):
    label: str
    confidence: float
    reason: str

app = ContextApp(name="triage", output_schema=TicketClassification)
result = app.run("The dashboard crashes after login")

result.output.label        # → a validated TicketClassification instance

Agents with tools and memory

app = ContextApp(name="support_refunds", output_schema=RefundDecision)
app.add_memory(scope="user", strategy="semantic")
app.add_tool("billing_lookup", permissions=["billing:read"])
app.add_tool("refund_create", permissions=["billing:write"], approval_required=True)

agent = app.agent(max_steps=6)
result = agent.run("Customer asks for a refund on invoice INV-123.")

Multi-agent crews and durable graphs

from vincio.agents import interrupt

crew = app.crew(members=[
    {"name": "researcher", "goal": "gather the numbers", "keywords": ["find"]},
    {"name": "writer", "goal": "draft the recommendation"},
])
result = crew.run("Explain the Q3 refund trend")   # bounded, traced, blackboard-shared

graph = app.graph("review")                        # checkpointed in your own store
graph.add_node("analyze", analyze)
graph.add_node("approve", lambda s: {"ok": interrupt(s, "proceed?")})
graph.add_edge("analyze", "approve")
flow = graph.compile()
paused = flow.invoke({"doc": "msa.pdf"})           # pauses at the human gate
done = flow.resume(paused.thread_id, value=True)   # later — even after a restart

Reliability as a guarantee

from vincio import Signature, InputField, OutputField

class Triage(Signature):
    """Classify a support ticket."""
    ticket: str = InputField(desc="the raw ticket text")
    label: str = OutputField(desc="bug | billing | feature | other")
    confidence: float = OutputField()

result = app.predictor(Triage)(ticket="The export button 500s")  # typed + validated

app.add_rail(name="no_leaks", kind="safety", detectors=["pii", "secrets"], action="redact")
app.add_rail(name="on_topic", kind="topic", direction="input", blocked_topics=["legal advice"])
app.enable_self_correction(max_cycles=2, max_cost_usd=0.05)      # facts never invented
app.add_output_schema(BugReport, keywords=["bug", "crash"])       # multi-schema routing

async for event in app.astream("Extract the invoice"):
    if event.type == "partial_output" and event.valid_prefix is False:
        break   # streaming validation: stop paying for a doomed answer

Evaluation as a gate

from vincio.evals import Dataset, EvalRunner

dataset = Dataset.load("golden/support_triage.jsonl")
report = EvalRunner(app).run(dataset)
report.print_summary()     # groundedness, citation accuracy, schema validity, cost — with CI exit codes

Interoperate: MCP, A2A, Skills

# Consume an MCP server — its tools run through the permissioned, sandboxed,
# audited runtime; its resources become cited evidence.
app.add_mcp_server("weather", command=["python", "weather_server.py"])

# Load portable SKILL.md procedural knowledge (progressive disclosure).
app.add_skill("skills/pdf-invoice")

# Expose your app over the protocols — one ContextApp, both consumer and provider.
mcp_server = app.serve_mcp()                       # serve tools/resources/prompts
a2a_server = app.serve_a2a(crew, name="research")  # Agent Card + task lifecycle

# One portable reasoning knob across providers (thinking tokens are billed).
from vincio.core.types import RunConfig
app.run("Plan the migration", config=RunConfig(reasoning_effort="high"))

Features

Vincio is organized into composable subsystems. Use the high-level ContextApp runtime, or reach for any engine directly.

Subsystem	What it does
Prompt compiler	Typed prompt ASTs with ${variables}, lint rules, cache-aware stable-prefix layout, versioning, hashing, diffing, variant generation.
Context compiler	Scores every candidate (relevance, novelty, authority, freshness, provenance, token cost, leakage risk), deduplicates, resolves conflicts, compresses, and packs to a token budget — with an excluded-context report explaining every omission.
Retrieval (RAG)	BM25 + dense + learned-sparse (SPLADE-style) + late-interaction (ColBERT-style MaxSim with PLAID-style compression) fused in one weighted RRF; query understanding (HyDE, multi-query, decomposition, step-back); sentence-window, parent-document/auto-merging, and contextual chunking; GraphRAG with community summaries and global/local routing; live indexes (upsert/TTL/migrations); entity-graph, multi-hop, and reasoning retrieval; citations.
Memory	Layered (session → episodic → semantic → tenant → graph) with a guarded write pipeline, confidence decay, contradiction resolution, and privacy scoping; remember/recall personalization over user/agent/session scopes, hybrid vector+graph recall, episodic→semantic consolidation with provenance, TTL + importance-weighted retention, audited GDPR-style edit/forget/export/erase, and a CI-gated memory eval harness.
Tools	Permissioned registry (RBAC scopes + ABAC rules), schema derivation from type hints, a resource-limited sandbox (timeout, output caps, scrubbed env, POSIX CPU/memory/fd setrlimit), reliability scoring, idempotent write-action guardrails with approval callbacks.
Agents	Bounded DAG execution with planners (direct / static / dynamic / ReAct / plan-and-execute), critics, validators, human gates, and hard budget enforcement.
Orchestration	Multi-agent crews — roles, delegation, and a shared versioned blackboard — with per-agent budget shares and guaranteed termination; durable stateful graphs with checkpoints on your storage, resume, edit-and-resume, and time-travel forks; first-class human-in-the-loop interrupts; a declarative compose/pipe API with streaming node events; runtime backends exporting to LangGraph and the OpenAI Agents SDK.
Workflows	Deterministic DAGs with retries, branching, parallelism, compensation, and approval gates that pause the run and resume without re-executing finished steps.
Structured output	Pydantic output contracts, provider-native constrained decoding with strict schema sanitization (robust-parser fallback everywhere else), streaming validation with mid-stream early abort, DSPy-style typed signatures (Signature / Predict) that feed the optimizer, bounded self-correcting loops with cost ceilings, multi-schema routing by task or content, and principled repair that fixes structure only — never invents facts.
Evaluation	Golden JSONL datasets, 25+ task / grounding / quality / safety / conversational / retrieval / operational metrics (faithfulness, answer relevance, hallucination with strict number checks, toxicity, bias, summarization, knowledge retention), deterministic / model / G-Eval judges with calibration, synthetic dataset generation with provenance, red-teaming judged by the security detectors, experiment tracking with statistical significance, regression gates, and baseline-diff reports — plus a pytest plugin (assert_eval / assert_grounded, packet/trace snapshots).
Optimization	Prompt / context / routing / cache search driven by an eval-fitness function, with safety-gated promotion that blocks any candidate regressing schema validity or safety.
The closed loop	One continuous, reproducible cycle — trace → dataset → eval → optimize → promote (ImprovementLoop / vincio loop run): production traces become datasets, the gated optimizer searches, and the winner lands in the prompt registry tagged, eval-linked, applied live, and audited. Plus: grounded auto-memory from runs, eval-driven retrieval feedback (gated fusion/reranker tuning, chunking recommendations), cost/quality Pareto frontiers with knee-point selection, learned per-task budget allocation, and hill-climb/annealing search strategies — every signal flowing through one packet, ledger, and trace.
Observability	Every run yields a full trace span tree with sessions, threaded runs, user feedback, and eval scores on spans; JSONL and OpenTelemetry exporters (GenAI semantic conventions); a local viewer (TUI + self-contained static HTML export + visual trace diff); traces become eval datasets in one command; a versioned prompt registry with tags, diffs, rollback, and eval links; per-run cost tracking.
Security	Deterministic PII / secret detection and redaction, prompt-injection defense, programmable input/output rails (topic / format / safety / custom) in the deterministic policy engine, RBAC / ABAC, tenant isolation, and a hash-chained audit log with offline tamper verification (vincio audit verify) — all documented in a threat model and shipped with SBOM + SLSA provenance attestations.
Storage	Pluggable metadata (in-memory / SQLite / Postgres), blob, analytics (DuckDB), vector (Qdrant / pgvector / Chroma / Pinecone / LanceDB behind one build_vector_index factory), and graph (Neo4j) backends.
Providers	OpenAI (Chat Completions + Responses API), Anthropic, Google, Mistral, any OpenAI-compatible endpoint (with hosted-gateway presets: groq, together, fireworks, openrouter, deepseek, perplexity, xai, nvidia), and a deterministic offline mock — all async-first with sync wrappers, pooled transport, retries, failover, and in-flight request coalescing. Unified reasoning control (reasoning_effort / thinking budget) maps across OpenAI/Anthropic/Gemini, with thinking tokens recorded and billed.
Protocols & interoperability	Speaks the standards in-process: MCP client and server (stdio / Streamable HTTP / in-process) — MCP tools run through the permissioned, sandboxed, audited, budgeted runtime; resources become cited evidence. A2A agent-to-agent — expose a crew/graph as an Agent Card + task lifecycle, and reach remote agents as bounded, traced crew delegates. Agent Skills — SKILL.md with progressive disclosure, bundled scripts as sandboxed tools. All via app.add_mcp_server / serve_mcp / serve_a2a / add_skill (experimental, 1.1).
Performance	End-to-end streaming (astream + SSE) with incremental partial-JSON output, concurrent retrieval/memory/tool fan-out with cancellation propagation and hard latency deadlines, content-addressed compile/chunk/embedding caches, zero-copy (slim) context packets, and CI-gated VincioBench performance budgets.
Connectors	Pluggable data connectors — web, GitHub, SQL, S3, GCS, Notion, Confluence, Slack, plus custom via register_connector — feeding the document engine with full provenance: app.add_source("kb", connector=connect("github", repo="acme/handbook")).
Integrations & DX	LangChain + LlamaIndex interop (vincio.interop) for tools, retrievers, loaders, and embeddings — both directions, duck-typed from_* (no heavy import); hosted rerankers/embedders (Cohere/Jina/Voyage, httpx-only) behind build_reranker/build_embedder; opt-in domain packs (support, engineering, finance, legal) via app.use_pack(...); vincio init templates (rag/agent/eval) with a typed vincio.yaml JSON Schema for editor completion; notebook reprs (enable_rich_reprs) and an interactive vincio tui inspector.
Stability & guarantees	Semantic Versioning on a frozen public surface (vincio.__all__) with a mechanical deprecation policy (@deprecated / @experimental / stability_of); published performance & quality SLOs held by at-least-as-strict VincioBench budgets; CycloneDX SBOM + SLSA build-provenance attestations on every release.

Every extension point — providers, metrics, chunkers, rerankers, judges, validators, tools — accepts your own implementation via a registry.

Benchmarks

VincioBench ships in benchmarks/ and runs fully offline (deterministic provider + deterministic metrics) so results are reproducible. Each family compares the Vincio pipeline against a naive baseline. Representative results on the bundled reference corpus:

Family	Metric	Vincio	Naive baseline
Context compression	evidence tokens for the same task	216	1,175 (stuff-everything)
	→ token reduction	−81.6%	—
Output recovery	malformed model outputs successfully parsed	5 / 5	3 / 5 (json.loads)
Security	prompt-injection detection rate	100%	—
	injection false-positive rate	0%	—
	PII coverage	100%	—
Retrieval	recall@3 / MRR (known-answer corpus)	1.00 / 1.00	—
	per-mode recall@3 (sparse · late-interaction · PLAID · hybrid_full)	1.00 each	—
Memory	preference recall · contradiction supersede · tenant isolation	pass	—
Tools	runtime overhead, p50	0.02 ms	—
Agents	adversarial infinite-loop model	bounded (budget)	unbounded
Orchestration	crew over-budget termination · delegation recorded	pass	—
	graph interrupt→resume and fork-replay vs straight run	identical state	—
Evals	metric agreement on labeled examples	100%	—
	red-team detector coverage · guarded attack success	100% · 0%	naive target: 85% attacks succeed
	A/B significance (real shift detected / null ignored)	pass	—
Reliability	invalid output detected mid-stream → tokens saved	98%	0% (validate at end)
	self-correction recovery rate (bounded cycles)	3 / 3	—
	rail catch rate · false positives on clean text	100% · 0	—
	schema routing / classification accuracy	100%	—
Closed loop	loop promotion fires · deterministic · gates block regressions	pass	—
	grounded facts written · ungrounded excluded	pass	—
	retrieval feedback (noisy index corrected · healthy index untouched)	pass	—
	Pareto front excludes dominated · knee balanced · learned budgets promote	pass	—
Protocols	MCP tool schema fidelity · resource provenance · round-trip	1.00 · pass · pass	thin adapter
	A2A budget-bounded delegation terminates	pass	—
	Agent-Skill progressive-disclosure token savings (off-topic)	100%	—

Honest by design. These numbers come from a small, synthetic offline corpus and are meant to demonstrate the mechanisms, not to be quoted as universal gains. The context-compression hypothesis (a 20–40% reduction target) is measured per run, and VincioBench reports whether it was met on your data. Run python benchmarks/vinciobench.py against your own corpus — and trust only what that prints. See benchmarks/README.md.

How Vincio compares

Each ecosystem below is broad and capable in its own focus area. The table reflects built-in, in-library capabilities — not what is reachable by bolting on a separate product or SaaS.

Capability	Vincio	LangChain	LlamaIndex	DSPy	Ragas
Scored, budgeted context compiler	✅	➖	➖	❌	❌
Typed prompt AST + lint + cache layout	✅	❌	❌	➖	❌
Hybrid (BM25 + dense) RAG	✅	✅	✅	❌	❌
Sparse + late-interaction + GraphRAG in one fusion	✅	➖	➖	❌	❌
Layered memory (decay, conflicts, scopes)	✅	➖	➖	❌	❌
Permissioned tool registry (RBAC/ABAC)	✅	❌	❌	❌	❌
Bounded agents + deterministic workflows	✅	✅	➖	➖	❌
Durable graphs (checkpoint / resume / time-travel) + bounded crews	✅	➖	❌	❌	❌
Structured output + structure-only repair	✅	➖	➖	✅	❌
Built-in evals + CI gates	✅	➖	➖	➖	✅
pytest assertions + red-teaming + synthetic data	✅	❌	❌	❌	➖
Eval-driven optimization (gated promotion)	✅	❌	❌	✅	❌
Native tracing + cost, no account needed	✅	➖	➖	❌	❌
Sessions, feedback, prompt registry, trace viewer in-process	✅	➖	❌	❌	❌
Deterministic security (PII / injection / audit)	✅	❌	❌	❌	❌
MCP client and server + A2A + Agent Skills	✅	➖	➖	➖	❌

_{✅ first-class in-library · ➖ partial or via a separate add-on/SaaS · ❌ not a focus. Reflects mid-2026; ecosystems evolve. Vincio is built to interoperate — it speaks MCP (client and server), A2A, and Agent Skills in-process, vincio.interop brings LangChain and LlamaIndex tools, retrievers, loaders, and embeddings in (and hands Vincio's back), and you can point at any OpenAI-compatible model and the vector store you already run. See the migration guides, the integrations guide, and the in-depth write-ups in docs/comparisons/.}

Use cases

You want to…	Reach for	Example
Classify and route support tickets into typed labels	typed output	01_support_triage.py
Answer questions over your docs with real citations	hybrid RAG + grounding policy	02_document_qa.py
Review contracts clause-by-clause	end-to-end context app	03_contract_review.py
Extract structured fields from invoices	structured extraction + F1 eval	04_invoice_extraction.py
Build a research agent with bounded budgets	ReAct agent + tools	05_research_agent.py
Automate a CRM agent with approval-gated writes	memory + permissioned tools	06_crm_agent.py
Ask questions over a codebase	code-aware chunking + import graph	07_codebase_qa.py
Analyze spreadsheets with schema awareness	table chunking + quality checks	08_spreadsheet_analysis.py
Gate quality in CI	datasets, gates, baseline diff	09_eval_pipeline.py
Tune prompts/context against an eval suite	optimization + gated promotion	10_optimization_run.py
Stream answers token-by-token through the full pipeline	astream + partial-JSON + compile caches	11_streaming_performance.py
Push retrieval quality with the full retrieval toolkit	sparse+late-interaction fusion, HyDE, auto-merge, GraphRAG, connectors	12_advanced_rag.py
Personalize an app with governed memory	scoped remember/recall, consolidation, hygiene, memory evals	13_memory_personalization.py
Evaluate, test, and observe without a platform	quality metrics, synthetic data, red-teaming, experiments, prompt registry, sessions + trace viewer	14_evaluation_observability.py
Run a multi-agent team with roles and delegation	crews + shared blackboard + budget guarantees	15_multi_agent_crew.py
Build an interruptible, auditable, resumable process	durable graphs + human gates + time-travel	16_durable_graph.py
Guarantee output shape and guard every generation	signatures, constrained decoding, streaming validation, rails, self-correction, schema routing	17_reliable_structured_output.py
Improve the app from its own production traffic	the closed loop: traces→dataset→eval→optimize→promote, auto-memory, retrieval feedback, Pareto, learned budgets	18_closed_loop.py
Reuse LangChain/LlamaIndex assets and any OpenAI-compatible model	framework interop + provider/vector-store breadth	19_framework_interop.py
Configure an app for a domain in one line	opt-in domain packs (support/engineering/finance/legal)	20_domain_pack.py
Govern PII, injection, access, and audit integrity	deterministic security primitives + tamper-evident audit	21_security_governance.py
Use MCP servers as tools/resources, or expose your app as one	MCP client + server	22_mcp_tools_and_resources.py
Expose a crew over A2A and delegate across vendor agents	A2A agent card + task lifecycle + remote delegate	23_a2a_delegation.py
Drop in portable SKILL.md knowledge with budgeting	Agent Skills + progressive disclosure	24_agent_skills.py
Control reasoning effort across providers with honest cost	unified reasoning control + Responses API	25_reasoning_control.py

More examples

All twenty-five examples in examples/ run fully offline with no API keys. Point them at a real model with environment variables:

export VINCIO_PROVIDER=openai VINCIO_MODEL=gpt-5.2-mini OPENAI_API_KEY=sk-...
cd examples && python 02_document_qa.py

Command line

vincio init my-project --template rag  # scaffold config + app + golden set (minimal|rag|agent|eval)
vincio config schema --output vincio.schema.json  # typed JSON Schema for editor completion
vincio packs list                # opt-in domain packs (support/engineering/finance/legal)
vincio tui                       # interactive inspector for runs, traces, and memory
vincio run app.py --input "..."  # run an app
vincio eval run golden.jsonl     # run an eval suite (with CI gates and baseline compare)
vincio eval dataset golden.jsonl --min-feedback 0.5  # curate traces into a dataset
vincio prompt lint prompts/      # lint prompt specs
vincio prompt push prompts/support.yaml --tag production  # version a prompt
vincio trace view trace_123      # TUI trace tree with scores + feedback
vincio trace export trace_123    # self-contained static HTML (also --session)
vincio trace diff a b --html diff.html  # visual side-by-side diff
vincio trace sessions            # list sessions with aggregates
vincio trace feedback trace_123 --score 1.0
vincio optimize run --target groundedness
vincio loop run --app app.py --min-feedback 0.5 --gate groundedness=">= 0.8"  # one closed-loop cycle
vincio index build ./docs        # build a retrieval index
vincio memory inspect --user u1  # inspect a user's memory
vincio memory recall "answer style" --user u1  # scored hybrid recall
vincio audit verify              # verify the audit-log hash chain offline
vincio mcp tools --command "python server.py"  # inspect an MCP server's tools
vincio mcp serve app.py          # expose an app as an MCP server (stdio)

A FastAPI server (API-key + JWT auth, real-token SSE streaming) is available via from vincio.server import create_app — see docs/reference/api.md.

Architecture

                         ┌──────────────────────────────────────────────┐
   user input  ─────────▶│  Input engine   normalize · classify · scope  │
                         └───────────────┬──────────────────────────────┘
                                         ▼
        ┌──────────────┐        ┌────────────────┐        ┌──────────────┐
        │   Memory     │───────▶│    CONTEXT     │◀───────│  Retrieval   │
        │  L0…L5       │        │   COMPILER     │        │  hybrid RAG  │
        └──────────────┘        │ score·dedupe·  │        └──────────────┘
        ┌──────────────┐        │ conflict·      │        ┌──────────────┐
        │    Tools     │───────▶│ compress·budget│◀───────│   Prompt     │
        │ permissioned │        └───────┬────────┘        │  compiler    │
        └──────────────┘                ▼                 └──────────────┘
                              ┌────────────────────┐
                              │   Model execution  │   provider-neutral
                              └─────────┬──────────┘
                                        ▼
                    ┌─────────────────────────────────────────┐
                    │ Output validation · Evals · Security ·   │
                    │ Trace + cost · Memory write-back         │
                    └─────────────────────────────────────────┘

See AGENTS.md for the package layout and docs/concepts/ for a tour of each engine.

Roadmap

Every subsystem above is implemented, tested offline, documented, and demonstrated by a runnable example. The public API is frozen under Semantic Versioning with a mechanical deprecation policy; performance and quality targets are published as SLOs and gated by VincioBench; the threat model is documented with offline audit-chain verification and a resource-limited tool sandbox; and releases ship a CycloneDX SBOM with SLSA provenance attestations.

New capabilities are added without breaking working code: each one sits behind a new entry point or an opt-in extra, and unproven surface is marked @experimental. Vincio adopts the ecosystem's standards — including the MCP, A2A, and Agent Skills protocols — in your process; it never becomes a hosted service to do so.

See ROADMAP.md for what ships today, what's planned, and what's intentionally out of scope.

Vincio is, and stays, a library. The building blocks for production operation (audit chain, retention, tenant isolation, RBAC/ABAC, a server) ship in the package for you to deploy on your own infrastructure. Hosted services and managed control planes are not part of this project.

Documentation

• Getting started — install, your first app, offline development
• Concepts — context packets · prompt compiler · memory · retrieval · agents & workflows · evaluation
• Guides — build a RAG app · connect data sources · structured output · reliability & guardrails · add tools · orchestrate multi-agent systems · run evals · optimize · close the loop · performance & streaming · integrations
• Protocols & interoperability (1.1) — MCP client + server · A2A agent-to-agent · Agent Skills · reasoning control & Responses API
• Migrating — coming from LangChain · LlamaIndex · Ragas · Mem0
• Reference — API · CLI · config · API stability & deprecation policy · performance & quality SLOs
• Security — threat model · security policy · reliability & guardrails guide
• Comparisons — LangChain · LlamaIndex · RAGatouille · Mem0 · CrewAI · OpenAI Agents SDK · DSPy · Pydantic AI · Guardrails AI · NeMo Guardrails · Ragas

Contributing

Contributions are welcome. The test suite runs fully offline and must stay green:

pip install -e ".[dev]"
python -m pytest tests/ -q     # 698 tests, no network or API keys required
ruff check vincio/ tests/

See AGENTS.md for the codebase layout and engineering conventions.

License

Apache License 2.0 © Vincio Contributors.