zymi-core 0.3.0

Event-sourced agent engine — CLI and Python bindings for auditable AI workflows

zymi-core

Declarative agent engine — dbt for AI workflows, with event sourcing built in.

_{Pronounced zoomi — like dog zoomies.}

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Why zymi-core?

Most agent frameworks are imperative Python: you write a script that makes LLM calls, maybe persists some messages, and leaves you guessing about the rest. Debugging a bad run means reading logs, hoping you logged enough.

zymi-core inverts that:

• Declarative, like dbt. Describe agents, tools, pipelines, and integrations in YAML. The engine loads them, validates them, runs them as a DAG. No orchestration code to write.
• Event-sourced. Every state change — inbound message, LLM call, tool result, approval, outbound reply — is an immutable event persisted to SQLite with a per-stream hash chain. Runs are replayable, resumable, and auditable without extra logging.
• Boundary-safe. Agents emit intentions ("I want to write this file", "I want to run this shell command") that pass through contracts and optional human approval before execution. The unsafe thing never happens until someone said yes.

The ergonomic target: you can bring a useful agent online in minutes without writing code, and a year later still answer exactly what this agent did on any past run.

---

Run a Telegram agent in two minutes

This is the primary demo — a real chat bot you talk to on your phone, wired declaratively.

pip install zymi-core

mkdir telegram-agent && cd telegram-agent
zymi init --example telegram

# 1. Create a bot via @BotFather in Telegram; copy the token.
# 2. Fill .env:
cp .env.example .env       # edit TELEGRAM_BOT_TOKEN + OPENAI_API_KEY
# 3. Open project.yml, replace "your_username_here" with your actual
#    Telegram username (no @). This keeps strangers out of the bot.

source .env
zymi serve chat

Message the bot — it replies within a couple of seconds. Every inbound message, LLM call, and outbound reply is persisted to .zymi/events.db; watch it live with zymi observe.

The entire wiring lives in project.yml:

llm:
  provider: openai
  model: gpt-4o-mini
  api_key: ${env.OPENAI_API_KEY}

connectors:
  - type: http_poll                   # long-polls getUpdates (no HTTPS needed)
    name: telegram
    url: "https://api.telegram.org/bot${env.TELEGRAM_BOT_TOKEN}/getUpdates"
    interval_secs: 2
    extract:
      items:     "$.result[*]"
      stream_id: "$.message.chat.id"
      content:   "$.message.text"
    cursor: { param: offset, from_item: "$.update_id", plus_one: true, persist: true }
    filter:
      "$.message.from.username":
        one_of: ["your_username_here"]
    pipeline: chat                    # every accepted message → `zymi serve chat`

outputs:
  - type: http_post                   # reply on every pipeline completion
    name: telegram_reply
    on: [ResponseReady]
    url: "https://api.telegram.org/bot${env.TELEGRAM_BOT_TOKEN}/sendMessage"
    headers: { Content-Type: "application/json" }
    body_template: '{"chat_id":"{{ event.stream_id }}","text":{{ event.content | tojson }}}'
    retry: { attempts: 3, backoff_secs: [1, 5, 30] }

That's it. No Rust. No Python. The scaffold ships with a small two-step DAG — respond (assistant with web_search / web_scrape tools) → polish (a brutally lazy reviewer that keeps the draft verbatim unless it's actually broken). Both steps are visible live in zymi observe.

Want a real search backend? Open tools/web_search.yml, uncomment a provider block (Brave, Tavily, SerpAPI, Google), set its key in .env. Out of the box the bot still works — the assistant just answers from its own knowledge.

Same primitives elsewhere

http_inbound / http_poll / http_post cover webhooks and REST for most SaaS. Pick http_inbound when the service can POST to you over HTTPS, http_poll when you're behind NAT or the service has no webhooks. Filter recipes for the common cases:

# GitHub — only react to PR opens, not pushes or issues
filter:
  "$.action":              { equals: "opened" }
  "$.pull_request.draft":  { equals: false }

# Slack Events API — one channel, skip bot echoes
filter:
  "$.event.channel":  { equals: "C0123456789" }
  "$.event.subtype":  { equals: null }

Out of the box filter: supports one_of / equals. 429 rate-limiting is handled automatically (Retry-After header + Telegram's body-level hint are both honoured on retries).

---

Highlights

Pipelines are DAGs

A pipeline is a list of steps with depends_on: edges. Independent steps run in parallel, dependencies stay explicit.

# pipelines/research.yml
steps:
  - id: search_web
    agent: researcher
    task: "Find articles on: ${inputs.topic}"

  - id: search_deep
    agent: researcher
    task: "Find technical details on: ${inputs.topic}"

  - id: analyze
    agent: researcher
    task: "Cross-reference findings, store a structured summary in memory."
    depends_on: [search_web, search_deep]    # runs after both finish

  - id: write_report
    agent: writer
    task: "Read memory, write ./output/report.md."
    depends_on: [analyze]

Try it: zymi init --example research. The scaffold pre-configures a two-agent pipeline with parallel search.

Replay, resume, observe

Because everything is an event, you don't just log runs — you keep them.

zymi runs                                   # all pipeline runs
zymi events --stream pipeline-research-abc  # every event in one run
zymi verify --stream pipeline-research-abc  # hash-chain integrity check
zymi observe                                # 3-panel TUI: runs / DAG / events live

# Fork-resume an earlier run from a chosen step. Upstream steps are frozen;
# the fork step + DAG-descendants re-run against current configs on disk.
zymi resume pipeline-research-abc --from-step write_report
zymi resume pipeline-research-abc --from-step write_report --dry-run

Fork-resume is useful when you're iterating on a prompt or tool config: you don't have to re-burn the expensive early steps every time you tweak the later ones. See ADR-0018.

MCP servers — N tools per YAML entry

Declarative tools cover HTTP and shell. For anything heavier — filesystem sandbox, git client, search index, proprietary protocol — drop a Model Context Protocol server into project.yml and zymi-core handles the subprocess, handshake, restarts, and tool-catalog wiring.

mcp_servers:
  - name: fs
    command: [npx, -y, "@modelcontextprotocol/server-filesystem", ./sandbox]
    allow: [read_text_file, write_file, list_directory]
    restart: { max_restarts: 2, backoff_secs: [1, 5] }

Then in the agent:

tools:
  - mcp__fs__read_text_file
  - mcp__fs__write_file
  - mcp__fs__list_directory

No per-tool schemas to author — they come from the server's tools/list at startup. Every call is audited as a normal tool event. Probe new servers before wiring:

zymi mcp probe fs -- npx -y @modelcontextprotocol/server-filesystem /tmp
zymi mcp probe gh --env GITHUB_PERSONAL_ACCESS_TOKEN=ghp_... \
                  -- npx -y @modelcontextprotocol/server-github

End-to-end demo: zymi init --example mcp. Full posture (PATH forwarding, env-isolation, restart policy) in ADR-0023.

Declarative custom tools

HTTP and shell tools live in tools/*.yml. No rebuild, no Rust.

# tools/slack_post.yml
name: slack_post
description: "Post a message to a Slack channel"
parameters:
  type: object
  properties:
    channel: { type: string }
    text:    { type: string }
  required: [channel, text]
implementation:
  kind: http
  method: POST
  url: "https://slack.com/api/chat.postMessage"
  headers:
    Authorization: "Bearer ${env.SLACK_TOKEN}"
    Content-Type:  "application/json"
  body_template: '{"channel": "${args.channel}", "text": "${args.text}"}'

${env.*} resolves at parse time; ${args.*} resolves at call time from LLM arguments. Collisions with built-in tools are a hard error.

Automatic context management

The agent's working context is reconstructed from the event log each iteration, not accumulated in a growing buffer. Older tool observations are masked in-place (~2× cost reduction, no extra LLM calls). When the token budget still gets tight, hybrid compaction summarises the oldest masked batch with a fast LLM call. Both caps are tunable:

runtime:
  context:
    observation_window: 10         # recent turns kept verbatim
    soft_cap_chars: 400000         # triggers LLM summarisation
    hard_cap_chars: 600000         # fatal if exceeded after compaction
    min_tail_turns: 4

Design and trade-offs in ADR-0016.

Safer side effects

Agents don't take actions directly — they emit intentions (ExecuteShellCommand, WriteFile, ReadFile, WebSearch, SpawnSubAgent, CallCustomTool, …). Intentions pass through:

1. Policy engine — shell command allow/deny patterns.
2. Contracts — file-write boundaries, rate limits, tool-specific rules.
3. Approval — terminal prompt, webhook, or fail-closed on deny.

Nothing with side effects runs until the intention is approved. zymi run --approval=webhook --callback-url=... wires an HTTP-backed handler; --approval=none runs fail-closed.

JSON Schemas for configs

IDE autocomplete and LLM-assisted config generation come free:

zymi schema project       # draft-07 JSON Schema for project.yml
zymi schema --all

---

Python bindings

The same pip install zymi-core that gives you the CLI also exposes Runtime, Event, EventBus, EventStore, Subscription, ToolRegistry.

from zymi_core import Runtime

rt = Runtime.for_project(".", approval="terminal")
result = rt.run_pipeline("research", {"topic": "rust event sourcing"})
print(result.success, result.final_output)

rt.bus() and rt.store() hand out wrappers over the same Arcs the Rust runtime uses — a Python subscriber sees exactly what the handler publishes, no second bus over the same SQLite file.

Cross-process events (Django, Celery, scripts)

The SQLite store is the source of truth: events written from one process are visible to every other process that opens the same file, and zymi serve picks them up via a polling tail watcher (ADR-0012).

Canonical pattern — a web app publishes PipelineRequested, the long-running zymi serve runs the pipeline, the result comes back as PipelineCompleted with the same correlation_id.

# Terminal A: zymi serve research
# Terminal B: any Python process — Django view, Celery task, script
import uuid
from zymi_core import Event, EventBus, EventStore

store = EventStore(".zymi/events.db")
bus   = EventBus(store)

correlation_id = str(uuid.uuid4())
sub = bus.subscribe_correlation(correlation_id)

event = Event(
    stream_id=f"web-{correlation_id}",
    kind={"type": "PipelineRequested", "data": {
        "pipeline": "research",
        "inputs":   {"topic": "rust event sourcing"},
    }},
    source="django",
)
event.with_correlation(correlation_id)
bus.publish(event)

result = sub.recv(timeout_secs=300)
print(result.kind)

Inside zymi serve the PipelineRequested → RunPipeline translation is done by EventCommandRouter — re-exported from zymi_core::runtime, so your own scheduler or bot adapter can drive a Runtime without copying cli/serve.rs.

---

Rust crate

[dependencies]
zymi-core = "0.2"

use std::sync::Arc;
use zymi_core::{open_store, Event, EventBus, EventKind, Message, StoreBackend};

let store = open_store(StoreBackend::Sqlite { path: "events.db".into() })?;
let bus   = EventBus::new(store.clone());
let mut rx = bus.subscribe().await;

let event = Event::new(
    "conversation-1".into(),
    EventKind::UserMessageReceived {
        content:   Message::User("Hello".into()),
        connector: "cli".into(),
    },
    "cli".into(),
);
bus.publish(event).await?;

let received   = rx.recv().await.unwrap();
let verified   = store.verify_chain("conversation-1").await?;

Feature flags:

Feature	Description
python	PyO3 bindings for the _zymi_core extension module
cli	The zymi CLI binary (implies connectors)
runtime	Async runtime and HTTP dependencies
webhook	HTTP approval handler (axum)
services	Event-bus services (LangFuse)
connectors	Declarative connectors: / outputs: (http_inbound / http_poll / http_post, ADR-0021)

---

CLI reference

zymi init                              # minimal scaffold
zymi init --example telegram           # declarative chat bot
zymi init --example research           # parallel-search + writer pipeline
zymi init --example mcp                # agent wired to an MCP server

zymi run <pipeline> -i key=value ...   # one-shot run
zymi serve <pipeline>                  # long-running: react to PipelineRequested

zymi pipelines                         # list pipelines in the project
zymi runs [--pipeline NAME] [--limit N]
zymi events [--stream ID] [--kind TAG] [--raw]
zymi verify [--stream ID]              # hash-chain integrity check
zymi observe [--run ID]                # 3-panel TUI
zymi resume <run-id> --from-step <id> [--dry-run]

zymi mcp probe <name> -- <cmd> [args...]
zymi schema project|agent|pipeline|tool
zymi schema --all

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How it works

1. Every state change becomes an event. The SQLite event store with per-stream hash chain is the source of truth.
2. Agents emit intentions, not side effects. Intentions are evaluated against policy + contracts + approvals before execution.
3. Pipelines are DAGs. Independent steps run in parallel; dependencies stay explicit.
4. Context is event-sourced. The working context is reassembled from the log each iteration — older observations masked, hybrid compaction when the budget gets tight.
5. Runs stay replayable. Inspect with zymi events, verify with zymi verify, fork-resume from any step.

More detail lives in adr/ — each architectural decision is a short markdown file.

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Contributing

Bug reports, examples, and PRs welcome. See CONTRIBUTING.md for the dev-loop, test matrix, and ADR workflow.

License

MIT — see LICENSE.