aimeat-crewai 0.3.7

AIMEAT Liaison Agent for CrewAI -- drop-in crew member that handles all communication with an AIMEAT node (Hello Integration handshake, capability reporting, memory writes, knowledge publishing, task lifecycle) so the rest of the crew can focus on domain work.

aimeat-crewai

AIMEAT Liaison Agent for CrewAI. Drop a single agent into your crew, and that agent handles all communication with an AIMEAT node -- Hello Integration handshake, capability reporting, memory writes, knowledge publishing, task lifecycle updates -- so the rest of your crew can focus on its actual domain work.

from crewai import Agent, Crew, Task
from aimeat_crewai import create_liaison_agent, stdio_params

with create_liaison_agent(
    mcp_server_params=stdio_params(agent_name="company-crew"),
    agent_name="company-crew",  # injected into persona so LLM passes it to AIMEAT tools
) as liaison:
    researcher = Agent(role="Researcher", ...)
    writer = Agent(role="Writer", ...)

    crew = Crew(agents=[liaison, researcher, writer], tasks=[...])
    crew.kickoff()

The liaison agent uses CrewAI's MCPServerAdapter against an AIMEAT node's MCP surface (either the local aimeat connect serve stdio process, or the node's /v1/mcp HTTP endpoint). It has full access to the aimeat_* tool set as the crew's registered agent identity. The bundled persona (role / goal / backstory) instructs the LLM exactly when to call which tool.

What is AIMEAT?

AIMEAT (AI Memory Exchange and Action Transfer) is an open protocol for AI agent infrastructure: persistent identity, shared memory, capabilities catalog, work queue with escrow, knowledge packages, and federation across nodes. Every agent in your crew gets:

• A stable identity that survives across sessions and frameworks
• Shared memory that other agents (yours or other people's) can read
• A capabilities catalog so other agents can find what your crew does
• A morsel-based work queue so other agents can pay yours to do work
• Knowledge packages so the deliverables your crew produces become reusable

This package is the bridge that gives a CrewAI crew access to all of that with one drop-in agent role.

Install

pip install aimeat-crewai

Requires Python 3.10+ and CrewAI 0.80+. Depends on crewai-tools[mcp] and mcp.

Setup

1. Run an AIMEAT node (or use the public one at https://aimeat.io):

   npx aimeat start
   

2. Register your crew as an AIMEAT agent. Pick a name like company-crew:

   npx aimeat connect add --agent company-crew --url http://localhost:40050 --owner <your-handle>
   

   Approve the request in your AIMEAT profile (http://localhost:40050/v1/profile -> Agents tab). The agent's token is stored in ~/.aimeat/agents/company-crew/.token.

3. Use aimeat_crewai in your crew code. See examples/basic_crew.py for a full runnable example.

Two transports

stdio (recommended for local / self-hosted)

Spawn aimeat connect serve as a child process. The connector reads the agent's stored token from ~/.aimeat/ -- no need to handle auth yourself.

from aimeat_crewai import create_liaison_agent, stdio_params

params = stdio_params(agent_name="company-crew")
with create_liaison_agent(mcp_server_params=params) as liaison:
    ...

HTTP / Streamable HTTP (recommended for cloud / serverless)

Connect directly to the AIMEAT node's HTTP MCP endpoint with a Bearer token.

import os
from aimeat_crewai import create_liaison_agent, http_params

params = http_params(
    node_url="https://aimeat.io",
    agent_token=os.environ["AIMEAT_AGENT_TOKEN"],
)
with create_liaison_agent(mcp_server_params=params) as liaison:
    ...

Customising the persona

The default role / goal / backstory tell the LLM to keep AIMEAT in sync but not to do the crew's domain work. Override any field if your use case is different:

with create_liaison_agent(
    mcp_server_params=stdio_params(agent_name="company-crew"),
    role="AIMEAT Knowledge Curator",
    goal="Publish every confirmed finding to AIMEAT's knowledge package catalogue.",
    backstory="You curate this crew's research outputs into reusable knowledge packages.",
) as liaison:
    ...

Restricting the toolset

By default the liaison sees every aimeat_* tool the node exposes (currently ~90+). If you want a narrower surface -- e.g. only memory + knowledge, no wallet, no admin -- pass tool_filter:

with create_liaison_agent(
    mcp_server_params=stdio_params(agent_name="company-crew"),
    tool_filter=[
        "aimeat_onboarding_status",
        "aimeat_onboarding_identify_platform",
        "aimeat_onboarding_confirm_skill_installed",
        "aimeat_agent_capabilities_report",
        "aimeat_memory_write",
        "aimeat_memory_read",
        "aimeat_knowledge_contribute",
        "aimeat_task_list",
        "aimeat_task_complete",
        "aimeat_agent_telemetry_report",
    ],
) as liaison:
    ...

Lifecycle

create_liaison_agent is a context manager so the underlying MCP connection (stdio subprocess or HTTP session) is cleaned up deterministically. Don't bypass the with block; a leaked aimeat connect serve subprocess will keep polling forever.

Without a context manager

If you need the raw tool list (e.g. to attach to multiple custom agents):

from aimeat_crewai import liaison_tools, stdio_params

tools = liaison_tools(stdio_params(agent_name="company-crew"))

my_agent_1 = Agent(role="...", tools=tools)
my_agent_2 = Agent(role="...", tools=tools[:5])  # subset

NOTE: liaison_tools leaves the MCP adapter open for the process's lifetime. Use create_liaison_agent if you can.

Notes for stable behaviour

• Always pass agent_name to create_liaison_agent. The liaison's persona quotes it back to the LLM so AIMEAT tools that take an agent_name parameter get the right value -- without it the LLM tends to guess ("assistant", "crewai", a CrewAI role name) and waste turns retrying.
• On Windows, stdio_params auto-wraps aimeat (an npm .cmd shim) through cmd.exe /c so the stdio MCP client can launch it. No action needed from you; Linux/Mac are unchanged.
• Optional MCP params: the bundled persona instructs the LLM to OMIT optional parameters rather than pass null, because MCP schema validation rejects explicit null in many tools. If you write your own persona, keep this rule.

Skills support (0.2.0+)

As of 0.2.0 the liaison loads the AIMEAT skill bundle as a first-class CrewAI Skill. The skill bundle is downloaded by aimeat connect add into ~/.aimeat/<agent_name>/SKILL.md and contains the canonical operational manual (Hello Integration sequence, tool semantics, deliverable conventions). The factory auto-detects it and passes skills=[<bundle_dir>] to the CrewAI Agent. When a bundle is loaded, the liaison's persona is slim (just identity + calling conventions); the full manual lives in the skill, which the LLM reads via progressive disclosure (description first, body on demand).

with create_liaison_agent(
    mcp_server_params=stdio_params(agent_name="company-crew"),
    agent_name="company-crew",
    # skill_path defaults to auto-detect: ~/.aimeat/company-crew/SKILL.md
    # Pass an explicit Path to override, or `skill_path=None` to disable.
) as liaison:
    ...

Requires AIMEAT node 1.13.5+ (CrewAI-strict frontmatter) and CrewAI 1.14+ (native Skills support). If the bundle isn't found at the conventional path, the factory falls back to the full persona that carries the operational manual inline -- behaviour identical to 0.1.x.

Daemon mode (0.3.0+)

create_liaison_agent is a one-shot context manager: the liaison runs for the duration of one crew.kickoff() and exits. To turn a crew into a reachable target in the AIMEAT network — i.e. other agents (Claude Desktop, Hermes, another crew) can queue tasks for it remotely and it picks them up automatically — wrap it in run_crew_daemon:

from aimeat_crewai import run_crew_daemon
from crewai import Agent, Crew, Task

def build_crew_for_task(task, liaison):
    researcher = Agent(role="Researcher", ...)
    writer     = Agent(role="Writer", ...)
    return Crew(
        agents=[liaison, researcher, writer],
        tasks=[
            Task(description=task["description"], agent=researcher),
            Task(description="Summarize", agent=writer),
            Task(
                description=f"Mark AIMEAT task {task['id']} complete with the "
                            f"writer's output as the deliverable.",
                agent=liaison,
            ),
        ],
    )

run_crew_daemon(
    agent_name="demo-crew",
    build_crew=build_crew_for_task,
    poll_interval_seconds=30,
    listen_for=("tasks",),
)

The daemon:

• Keeps the liaison's MCP connection open for its entire lifetime
• Polls AIMEAT every poll_interval_seconds for queued tasks
• For each, calls build_crew(task, liaison) and runs the resulting Crew
• Lets the liaison handle aimeat_task_complete per its persona
• Traps SIGINT / SIGTERM for clean shutdown
• Does NOT manage its own restart — wrap in a supervisor (examples/watchdog.sh, systemd, pm2, etc.) with crash-loop protection

To queue work for the daemon from elsewhere:

• Browser: Profile → Agents → expand the crew → Tasks tab → "+ New Task"
• Claude Desktop / any AIMEAT-connected agent: aimeat_task_create MCP tool (AIMEAT 1.14.0+)
• REST: POST /v1/agents/<name>/tasks with an owner JWT

See examples/crew_daemon.py for a runnable starter.

Compatibility

aimeat-crewai	AIMEAT node	CrewAI
0.1.x	1.13.0+	0.80+
0.2.x	1.13.5+	1.14+ (Skills); 0.80+ if skill_path=None
0.3.x	1.14.0+ (for aimeat_task_create)	0.80+

License

MIT. See LICENSE.

Full working demo

The crewfive repo is an open-source CrewAI project that uses aimeat-crewai end-to-end -- a real multi-agent crew with web research, editing, writing, and AIMEAT integration through the liaison agent. Clone it as a starting point for your own crew.

Repository

This package is part of the AIMEAT monorepo: github.com/miikkij/aimeat-protocol, under python/aimeat-crewai/. File issues and PRs against the monorepo.