Agentlings

Lightweight single-process agent framework exposing both A2A and MCP on a single HTTP port.

Each agentling is a small, focused AI agent whose identity is defined by a YAML config file — name, description, system prompt, tools, and skills. The framework handles protocol compliance, conversation journaling, and context management. The LLM is the agent; the framework records and replays.

Quick start

pip install -e ".[dev]"

# Create your agent definition
cp agent.example.yaml agent.yaml

# Run with mock LLM (no API key needed)
AGENT_CONFIG=./agent.yaml AGENT_LLM_BACKEND=mock AGENT_API_KEY=dev agentling

# Run with Anthropic
AGENT_CONFIG=./agent.yaml ANTHROPIC_API_KEY=sk-ant-... AGENT_API_KEY=your-key agentling

# See available tools
agentling --list-tools

The agent serves:

GET /.well-known/agent-card.json — A2A Agent Card (public, no auth)
POST /a2a — A2A JSON-RPC endpoint
POST /mcp — MCP Streamable HTTP endpoint

Running as a daemon

systemd (Linux)

Create /etc/systemd/system/agentling.service:

[Unit]
Description=Agentling
After=network.target

[Service]
Type=simple
User=agentling
WorkingDirectory=/opt/agentling
EnvironmentFile=/opt/agentling/.env
ExecStart=/opt/agentling/venv/bin/agentling
Restart=on-failure
RestartSec=5

[Install]
WantedBy=multi-user.target

# Set up
sudo useradd -r -s /bin/false agentling
sudo mkdir -p /opt/agentling
sudo python3 -m venv /opt/agentling/venv
sudo /opt/agentling/venv/bin/pip install agentlings

# Copy your config
sudo cp agent.yaml /opt/agentling/agent.yaml
sudo cp .env /opt/agentling/.env    # ANTHROPIC_API_KEY, AGENT_API_KEY, AGENT_CONFIG=./agent.yaml

# Start
sudo systemctl daemon-reload
sudo systemctl enable --now agentling
sudo journalctl -u agentling -f

launchd (macOS)

Create ~/Library/LaunchAgents/com.donkeywork.agentling.plist:

<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
<plist version="1.0">
<dict>
    <key>Label</key>
    <string>com.donkeywork.agentling</string>
    <key>ProgramArguments</key>
    <array>
        <string>/path/to/venv/bin/agentling</string>
    </array>
    <key>WorkingDirectory</key>
    <string>/path/to/agentling</string>
    <key>EnvironmentVariables</key>
    <dict>
        <key>AGENT_CONFIG</key>
        <string>./agent.yaml</string>
        <key>ANTHROPIC_API_KEY</key>
        <string>sk-ant-...</string>
        <key>AGENT_API_KEY</key>
        <string>your-key</string>
    </dict>
    <key>KeepAlive</key>
    <true/>
    <key>StandardErrorPath</key>
    <string>/tmp/agentling.err</string>
</dict>
</plist>

launchctl load ~/Library/LaunchAgents/com.donkeywork.agentling.plist
tail -f /tmp/agentling.err

Agent definition

Agent identity lives in a YAML file (agent.yaml):

name: k3s-agentling
description: A k3s cluster management agent

tools:
  - bash
  - filesystem

skills:
  - id: k8s-ops
    name: Kubernetes Operations
    description: Manage cluster resources, diagnose issues, apply manifests
    tags: [kubernetes, k3s, devops]
  - id: file-management
    name: File Management
    description: Read, write, and search configuration files
    tags: [files, yaml]

system_prompt: |
  You are a DevOps engineer managing a k3s Kubernetes cluster.

  All configuration changes go through /mnt/lab/k3s as the source of truth.
  Never use kubectl patch/edit/set directly — write manifests and apply them.

  Before any destructive operation, describe the impact and ask for confirmation.

Point to it with AGENT_CONFIG=./agent.yaml.

Available tools

Group	Tools	Description
`bash`	`bash`	Shell command execution with timeout
`filesystem`	`read_file`, `write_file`, `edit_file`, `list_directory`, `search_files`	File operations with offset/limit, find-and-replace, glob search
`memory`	`memory_edit`	Read and write the agent's persistent long-term memory

Tools are off by default. Run agentling --list-tools for details.

Docker

docker build -t agentling:latest .
docker run -e AGENT_API_KEY=your-key -e AGENT_LLM_BACKEND=mock -p 8420:8420 agentling

Environment variables

Secrets and runtime settings stay in env vars (or .env file):

Variable	Default	Description
`AGENT_CONFIG`	—	Path to agent YAML definition
`ANTHROPIC_API_KEY`	—	Anthropic API key (required for real LLM)
`AGENT_API_KEY`	—	API key for authenticating clients
`AGENT_MODEL`	`claude-sonnet-4-6`	Anthropic model ID
`AGENT_MAX_TOKENS`	`4096`	Max tokens per LLM response
`AGENT_HOST`	`0.0.0.0`	Bind address
`AGENT_PORT`	`8420`	Bind port
`AGENT_DATA_DIR`	`./data`	JSONL journal storage directory
`AGENT_LOG_LEVEL`	`INFO`	Log level
`AGENT_LLM_BACKEND`	`anthropic`	`anthropic` or `mock`
`AGENT_EXTERNAL_URL`	—	Public URL for Agent Card (needed in Docker/k8s)
`AGENT_OTEL_ENDPOINT`	—	OpenTelemetry collector endpoint
`AGENT_OTEL_PROTOCOL`	`http`	Collector protocol (`http` or `grpc`)
`AGENT_OTEL_INSECURE`	`true`	Disable TLS for collector connection
`AGENT_OTEL_HEADERS`	—	Comma-separated `key=value` pairs for collector auth

Memory

Agentlings can maintain persistent long-term memory — a curated set of key-value facts that survive across conversations. Memory transforms an agent from a tool that forgets into one that learns.

How it works

Memory is a JSON file (data/memory/memory.json) containing entries like:

{
  "entries": [
    {
      "key": "cluster-node-count",
      "value": "4 nodes: node1 (control), node2-4 (workers)",
      "recorded": "2026-04-01T10:00:00Z"
    }
  ]
}

The memory block is injected into the system prompt on every LLM call, between the agent's identity and the conversation history. The agent sees its accumulated knowledge as working context, not as a separate tool call.

The memory tool

When the memory tool group is enabled, the agent gets a memory_edit tool with three operations:

Operation	Description
`set`	Upsert an entry by key. Updates the timestamp.
`remove`	Delete an entry by key.
`list`	Return all current entries.

The agent decides what to remember based on its system prompt. A DevOps agent might store cluster topology and known issues. A support agent might store escalation paths and recurring problems.

CLI

# Show current memory
agentling memory show

Configuration

memory:
  token_budget: 2000        # max tokens for the memory block in the system prompt
  # injection_prompt: null   # override the memory/data-dir-awareness template

Sleep cycle

The sleep cycle is a nightly process that journals the day's activity, consolidates new knowledge into memory, prunes stale entries, and cleans up old files. It maps to biological sleep phases.

graph LR
    L[Light Sleep<br/>Gate check] --> D[Deep Sleep<br/>Replay & journal]
    D --> R[REM<br/>Integrate & prune]
    R --> H[Housekeeping<br/>Retention cleanup]

Phase 1: Light sleep — gate check

Quick check: were there any conversations today? If not, skip everything. No LLM calls, no cost.

Phase 2: Deep sleep — replay and file

For each conversation from today, the sleep cycle reads the JSONL journal from the last compaction marker and submits all summaries as a single batch request to the Anthropic Message Batches API. Batch processing runs at 50% cost and processes in parallel.

Each summary call receives the agent's system prompt (so the agent's persona shapes what it considers important), current memory, and the conversation content. The LLM returns a structured ConversationSummary with a narrative and memory candidates.

Results are written to data/journals/YYYY-MM-DD.md.

Phase 3: REM — integrate and prune

A single LLM call receives current memory, today's journal, and all extracted memory candidates. It integrates new facts, deduplicates, reviews existing entries for staleness, and returns a ConsolidatedMemory — the complete updated memory store. Written atomically to memory.json.

Phase 4: Housekeeping — retention cleanup

Deletes conversation JSONL files older than conversation_retention_days and journal files older than journal_retention_days.

Configuration

sleep:
  schedule: "0 2 * * *"           # cron expression (default: 2am daily)
  journal_retention_days: 30       # keep journals for 30 days
  conversation_retention_days: 14  # keep JSONL conversations for 14 days
  memory_max_entries: 50           # hard cap after consolidation
  # model: null                    # override model for sleep calls
  # summary_prompt: null           # override per-conversation summary prompt
  # consolidation_prompt: null     # override REM consolidation prompt

CLI

# Trigger sleep cycle manually
agentling sleep --date 2026-04-01

Data directory layout

data/
  abc123.jsonl           # conversation journals (flat, as before)
  def456.jsonl
  memory/
    memory.json          # persistent memory store
  journals/
    2026-04-01.md        # daily sleep journal
    2026-04-02.md

The agent is told about this directory structure and can use its filesystem tools to search past journals and conversation logs for context beyond what fits in memory.

OpenTelemetry

The sleep cycle and memory tool emit spans and metrics to an OpenTelemetry collector when telemetry is enabled.

telemetry:
  enabled: true
  endpoint: "http://otel-collector:4318"
  protocol: "http"                        # "http" or "grpc"
  service_name: "agentling"
  insecure: true
  headers:                                # optional auth headers
    Authorization: "Bearer your-token"

Or via env vars: AGENT_OTEL_ENDPOINT=http://collector:4318 AGENT_OTEL_HEADERS="Authorization=Bearer tok".

When telemetry is disabled (the default) or the OpenTelemetry packages are not installed, all instrumentation is a no-op.

Architecture

graph TB
    A2A[A2A Client] -->|POST /a2a| A2ASDK[a2a-sdk Server]
    MCP[MCP Client] -->|POST /mcp| MCPSDK[mcp SDK Server]
    A2ASDK --> Executor[AgentlingExecutor]
    Executor --> Loop[MessageLoop]
    MCPSDK --> Loop
    Loop --> Store[JSONL Store]
    Loop --> LLM[LLM Client]
    Loop --> Tools[Tool Registry]

Both protocols feed into a single MessageLoop.process_message() entrance. Conversations are persisted as append-only JSONL journals with compaction markers as replay cursors.

Testing

# Unit tests (no network, no LLM)
pytest tests/unit/ -v

# Integration tests (starts real server with mock LLM)
pytest tests/integration/ -v

# All tests
pytest tests/ -v

Integration tests use native SDK clients — a2a-sdk ClientFactory for A2A and mcp ClientSession for MCP — talking to a real server over HTTP. All LLM responses are mocked.

Built with

a2a-sdk — A2A protocol server + client
mcp — MCP protocol server + client
anthropic — LLM backend
starlette + uvicorn — HTTP server

Name		Name	Last commit message	Last commit date
Latest commit History 40 Commits
.github/workflows		.github/workflows
src/agentlings		src/agentlings
tests		tests
.env.example		.env.example
.gitignore		.gitignore
CLAUDE.md		CLAUDE.md
Dockerfile		Dockerfile
README.md		README.md
agent.example.yaml		agent.example.yaml
docker-compose.test.yml		docker-compose.test.yml
logo.png		logo.png
pyproject.toml		pyproject.toml
sleep.png		sleep.png

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Agentlings

Quick start

Running as a daemon

systemd (Linux)

launchd (macOS)

Agent definition

Available tools

Docker

Environment variables

Memory

How it works

The memory tool

CLI

Configuration

Sleep cycle

Phase 1: Light sleep — gate check

Phase 2: Deep sleep — replay and file

Phase 3: REM — integrate and prune

Phase 4: Housekeeping — retention cleanup

Configuration

CLI

Data directory layout

OpenTelemetry

Architecture

Testing

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Agentlings

Quick start

Running as a daemon

systemd (Linux)

launchd (macOS)

Agent definition

Available tools

Docker

Environment variables

Memory

How it works

The memory tool

CLI

Configuration

Sleep cycle

Phase 1: Light sleep — gate check

Phase 2: Deep sleep — replay and file

Phase 3: REM — integrate and prune

Phase 4: Housekeeping — retention cleanup

Configuration

CLI

Data directory layout

OpenTelemetry

Architecture

Testing

Built with

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