ajolopy 0.1.3

The Python framework for building AI-native applications in production.

Ajolopy

The Python framework for building AI-native applications in production.

Documentation · Quickstart · Tutorial · Reference · Recipes

What Rails was for database-backed web apps and what NestJS is for enterprise Node services: the default choice when what you're building has LLMs, agents, tools, prompts, evals, streaming, and MCP as core ingredients — not as an addon.

Ajolopy does not compete with FastAPI or LangChain. It competes for the complete AI-native application framework category, which was empty in Python before v0.1.

Install

pip install ajolopy           # or: uv pip install ajolopy

Requires Python 3.14+. Optional extras documented under Install: otel, mcp, redis, postgres, mongo, qdrant, pgvector.

Why Python 3.14+

• PEP 649 — deferred evaluation of annotations is native, so from __future__ import annotations is forbidden in this codebase (it would hide real typing errors at runtime).
• Pyright strict pays off — runtime introspection of type hints (inspect.get_annotations) works without the lazy-eval workarounds older Pythons need.
• DI by type-hints — Ajolopy resolves @Injectable providers via real annotation objects; PEP 649 makes that cheap and correct.
• Modern stdlib — asyncio.TaskGroup, structural pattern matching, and the new error-message machinery are assumed everywhere.

The killer demo

Twelve real lines (plus imports). Each token has a job — see the Step 1 tutorial for the line-by-line breakdown.

from typing import Annotated

from pydantic import BaseModel

from ajolopy import Agent, Stream, Tool
from ajolopy.http import Body


class ChatRequest(BaseModel):
    message: str


@Agent(
    model="claude-opus-4-7",
    system="You are Acme Support. Be concise, friendly, and accurate.",
    fallback="claude-haiku-4-5",
)
class Support:
    @Tool
    async def lookup_order(self, order_id: str) -> dict[str, str]:
        """Look up an order's current state by id."""
        return {"order_id": order_id, "status": "in_transit", "eta": "tomorrow"}

    @Stream("/chat")
    async def respond(self, body: Annotated[ChatRequest, Body()]):
        async for chunk in self.stream(body.message):
            yield chunk

Run it:

ajolopy new acme-support
cd acme-support
uv sync
ajolopy dev

curl -N -X POST http://127.0.0.1:8000/chat \
  -H "Content-Type: application/json" \
  -d '{"message": "Where is order 4392? Use the tool."}'

Token-by-token streaming response, function-calling loop, env-var validation at boot, fallback to claude-haiku-4-5 on retriable failure, OpenTelemetry spans for free. Twelve lines.

Why Ajolopy

The framework targets the seven production pains the wedge AI Engineer hits in week one:

1. Env vars — ajolopy dev refuses to boot with missing required variables. No more 2 a.m. discovery in staging.
2. Cost — every span carries gen_ai.cost_usd. Sum by user / tenant / agent in one SQL query against your trace backend.
3. Evals as a CI gate — ajolopy eval --ci blocks the PR when a metric regresses. The model upgrade that quietly broke safety is visible before a customer reports it.
4. SSE streaming with backpressure — @Stream handles disconnects, heartbeats, and cancellation. Not 30 lines of Starlette glue.
5. Cross-provider fallback — fallback="claude-haiku-4-5" cuts in when Anthropic returns a retriable error. Switching providers is a kwarg, not a rewrite.
6. Diagnostics — ajolopy doctor runs twelve health checks: Python version, env vars, deps, provider connectivity, etc. Lighthouse for AI apps.
7. Project layout — ajolopy new scaffolds a NestJS-style module tree. Any senior backend dev reads it on first sight.

Read the long form for the full framing.

The 10 primitives

AI (7)	Framework (3)
@Agent, @Tool, @Stream, @Eval, @Metric, @Workflow, @MCP	@Module, @Injectable, @Controller

Every primitive ships with a magical default (covers the 90% case with zero ceremony) and an escape hatch (subclass or override) for the 10% that needs full control. The surface is locked at ten — anything that would require an eleventh decorator goes to v0.2+.

What's in the box (v0.1)

• Multi-provider LLM — Anthropic, OpenAI, Gemini, and a universal OpenAI-compatible client that covers Ollama / Together / Groq / Mistral / DeepSeek / OpenRouter, plus any other OpenAI-compatible endpoint via the base_urls= constructor escape hatch.
• ajolopy deploy <target> — generates fly.toml, railway.json, render.yaml, vercel.json (with warnings), or a universal Dockerfile.prod for k8s / VPS / ECS.
• Memory — five chat-history backends behind one Memory ABC: in-memory, Redis, Postgres, Mongo, SQLite.
• RAG — Retriever ABC with Qdrant and pgvector backends.
• MCP — @MCP to consume external Model Context Protocol servers, @MCPServer to publish your own @Tool methods as one.
• Observability — OpenTelemetry on by default; five recipes for Langfuse, Sentry, Grafana, Honeycomb, and Datadog.
• Eval framework — @Eval + @Metric with built-in metrics (exact_match, json_match, llm_judge, contains, intent_match, tool_called) and regression detection.
• CLI — new, dev, doctor, env:show/validate/diff, generate, eval, deploy.

Examples

• examples/support-agent/ — runnable companion to the 3-step killer demo tutorial. Fork it, set ANTHROPIC_API_KEY, run uv sync && ajolopy dev, and watch every primitive from @Agent through @Workflow and @MCP answer real HTTP requests.
• examples/web-research/ — the canonical "wire an external HTTP API as a @Tool" reference. A Researcher agent that calls the Tavily search API through one @Tool, composes a Markdown-cited answer through a second pure-Python @Tool, and streams the result over SSE. Ships with @Eval + LLM-judge regression suite and pre-built Dockerfile.prod + fly.toml.
• examples/oncall-agent/ — focused demo of the @MCP primitive against a real external server. Spawns the canonical @modelcontextprotocol/server-github over stdio so the on-call agent can search issues, PRs, and commits while triaging incidents. Pairs a local @Tool with the MCP tools to show how Ajolopy resolves name collisions in favour of local code.
• examples/local-ollama/ — the same @Agent + @Tool + @Stream shape running on a local Ollama server through Ajolopy's universal OpenAI-compatible provider. No API key required — install Ollama, ollama pull llama3.3, uv sync && ajolopy dev, and the streaming code reviewer answers POST /chat entirely on your laptop.
• examples/memory-assistant/ — persistent task tracker showing @Agent(memory="redis://...") end-to-end. A Memory escape-hatch wrapper partitions chat history by a request-scoped session_id, so two users hitting the same /chat endpoint see independent transcripts in Redis. Ships with a docker-compose.yml (app + Redis) and a 5-row eval suite whose memory_isolation metric catches cross-session leaks.
• dogfood/docsbot/ — Ajolopy's own docs bot, the first dogfood app. Answers questions about the framework using an in-memory Retriever subclass over the project's own docs/ tree. Exercises @Agent + @Tool + @Stream + @Eval end-to-end and ships pre-generated Dockerfile.prod + fly.toml so it can be deployed in one fly deploy.
• examples/contextual-rag/ — production-grade RAG agent with contextual chunking, hybrid retrieval, and citation-enforcing evals over a synthetic handbook. Builds on dogfood/docsbot with header-based chunks, a 0.4 keyword
  • 0.6 semantic-hash hybrid scorer, and three eval metrics (one LLM-as-judge, two deterministic) that fail when citations are missing or point at the wrong section. Documents the upgrade path to QdrantRetriever / PgvectorRetriever for real embeddings.

Where to go next

• Quickstart — five minutes from pip install to a streaming agent.
• Tutorial — the three-step killer demo arc (hello → evals → multi-agent + MCP) in ~55 lines.
• Reference — one page per primitive with every kwarg and the escape hatch.
• Observability recipes — Langfuse / Sentry / Grafana / Honeycomb / Datadog wired in via the standard OTel exporter.

Status

v0.1 — first public release. APIs are stable for the 10 primitives; expect additive changes in v0.1.x. Pin exact versions if you depend on the framework today.

The roadmap and current work are tracked in board.json, validated against .board-schema.json. Prose specs live in specs/.

Contributing

Work is tracked on a PM-style board. AGENTS.md documents the claim / branch / transition protocol for both human and AI contributors. The contributor release guide covers how to cut a new version.

uv run python tools/board.py list      # see what's open
uv run python tools/board.py next      # see what to pick up next

File issues or open discussions at github.com/jcocano/Ajolopy.

License

MIT — see LICENSE.

The axolotl regenerates. So does Ajolopy.