agenteng
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@agentengShow me upcoming Agent Engineering events in London"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
AgentEng · Agent Engineering HQ
Find the Agent Engineering Conference and Agent Engineering HQ events in London and San Francisco, explore the speakers, and participate from your coding agent or terminal.
AgentEng provides an offline-capable Python CLI, a single-tool MCP server and an A2A 1.0 agent over one public catalogue. Default requests use zero server-side model calls. Optional model synthesis and bounded RLM are included, disabled by default.
Source · Issues · Documentation · Getting started · Coding-agent integrations · Participation · Contribute · Security
What it does
Discover public London and San Francisco events, speakers, agendas and recordings.
Browse 456 tool/model/infrastructure listings across twelve disciplines, with search, filters, pagination and source links.
Read published ticket prices and follow the official registration link.
Search attributed public sources, select sessions by topic and export a UTC calendar.
Use the same typed requests from the CLI, MCP, A2A or HTTP.
See source links, snapshot freshness, date precision and timezone-aware event state.
Prepare reusable local talk, workshop and event-idea drafts, with preview and Markdown export.
Run an optional, credentialed private intake pilot for one organizer, disabled by default.
The catalogue is a snapshot. It does not verify live ticket availability or registration approval. Unknown historical dates and session times remain unknown. Consult the linked organizer/registration page for current details. See data and attribution.
Related MCP server: docfy-mcp
Getting started
Python 3.12+ and uv are required. Once the repository is published, clone it:
git clone https://github.com/SuperagenticAI/agenteng.git
cd agentengFrom the source checkout:
uv sync --frozen
uv run --frozen agenteng events --upcoming
uv run --frozen agenteng discover
uv run --frozen agenteng disciplines
uv run --frozen agenteng tools --discipline memory
uv run --frozen agenteng tool langgraph
uv run --frozen agenteng tickets agenteng-london-2026
uv run --frozen agenteng agenda agenteng-london-2026 --topic memory
uv run --frozen agenteng ask 'When is the next London conference?'
uv run --frozen agenteng participate
uv run --frozen agenteng plan agenteng-london-2026 --interest evaluation --format ics --output agenda.icsFor an executable available outside the checkout, use uv tool install . or uv tool install '.[mcp]' for local MCP. With pip, use a virtual environment and python -m pip install . (or '.[mcp]'). The published installer requires Python 3.12+, curl, venv and pip on a POSIX system.
This is an initial 0.1.0 alpha. Public package/installer distribution and hosted endpoints have not yet been published. Source installs work today; registry and website installation instructions will be announced after verified publication.
The CLI uses its bundled catalogue offline. Put global options before the command:
agenteng --json events --city London
agenteng --remote https://YOUR_HOST events --upcoming
agenteng --catalogue ./catalogue.json speakers --city 'San Francisco'
agenteng query '{"operation":"events","upcoming":true}'Run agenteng --help or agenteng COMMAND --help for command options.
Draft an idea without sending it:
agenteng engage --city London --output draft.json
agenteng proposal preview draft.json
agenteng proposal export draft.json --format markdown --output draft.mdTool directory
The directory covers all 461 entries in the imported SuperRadar snapshot, normalized into 456 listings after duplicate merges and separating Graphiti from Zep. Browse alphabetically across the website's twelve disciplines; there is no fixed category quota. Listings provide attributed names and links, not popularity rankings or integrations. Source Hold/deprecated entries remain accessible with --status all and are omitted from default browse results.
agenteng tools --discipline inference --kind runtime
agenteng tools --search 'Gemini CLI'
agenteng --json tools --limit 100 --offset 100 --status all
agenteng tool letta-memoryRepeat filters with the returned next_offset to retrieve every matching listing. All directory operations work offline with zero model calls. See directory usage, provenance and contribution policy.
Connect an agent
Local MCP exposes exactly one typed agenteng tool:
uv run --frozen --extra mcp agenteng mcpagenteng connect codex, agenteng connect claude-code and agenteng connect cursor print local setup instructions. Add --transport http --url https://YOUR_HOST for a running remote service. They do not modify editor settings. See integration examples.
After a tool install, a stdio client can use:
{"mcpServers":{"agenteng":{"command":"agenteng","args":["mcp"]}}}Run the combined HTTP, MCP and A2A service:
uv run --frozen --extra server agenteng serve --host 127.0.0.1 --port 8000Interface | Route |
MCP Streamable HTTP |
|
A2A 1.0 discovery |
|
A2A JSON-RPC |
|
Typed HTTP query |
|
Catalogue / health |
|
Crawlable discovery |
|
Full tool directory feed |
|
Crawler / agent guides |
|
MCP call example:
{"name":"agenteng","arguments":{"request":{"operation":"tickets","event_id":"agenteng-london-2026"}}}Public operations are disciplines, tools, tool, discover, events, event, agenda, speakers, tickets, recordings, search, plan, ask and participate. Draft operations are proposal_draft, proposal_preview and proposal_export. Begin with discover for featured London/San Francisco events and interfaces, or events for published IDs. The same request can be an A2A JSON data part or an HTTP body; plain A2A text uses question routing or public-source search. Responses include supporting sources and snapshot metadata. A2A returns immediate messages and advertises no streaming or push notifications.
The intended official host is a2a.agentengineering.world, once published. To self-host, set AGENTENG_PUBLIC_URL to your HTTPS origin and configure allowed origins. See deployment, architecture and the HTTP MCP config.
Optional model engines
lookup and auto remain model-free. standard performs one provider request. rlm offers a persistent Monty sandbox with one model-visible run_code tool and scoped evidence reads. The root can make one child OR leaf delegation total, at maximum depth 1. Children cannot delegate. All calls share model-call, token-reservation and deadline limits.
Install the RLM extra and configure an operator environment only when you intend to test it:
uv sync --frozen --extra rlm
# Configure the enable flag, operator credential, provider key and model in your environment.
uv run --frozen --extra rlm agenteng ask 'Compare memory sessions' --engine rlm --event agenteng-london-2026Use .env.example as a variable reference; it is not loaded automatically. AGENTENG_ENABLE_STANDARD=1 and AGENTENG_ENABLE_RLM=1 enable the respective paths. Both require AGENTENG_OPERATOR_TOKEN, AGENTENG_MODEL_API_KEY and an operator-selected AGENTENG_MODEL; remote callers must supply the operator bearer credential. An OpenAI-compatible provider can be selected with AGENTENG_MODEL_BASE_URL and must support the required tool/JSON features.
Defaults cap requests at 6 model calls, 1,200 output tokens per call, 12,000 conservatively reserved tokens and 45 seconds. One recursion can involve several model calls. These are per-request limits, not a daily spending cap. Model code cannot access host files, network or environment callbacks. Citation checks enforce source membership, not the truth of generated prose. Live-provider answer quality and billing are not yet validated; tests use scripted providers. The public container omits the RLM runtime and keeps both engines disabled.
Participate and contribute
Have an idea for a talk, workshop or future event in London or San Francisco? See Community participation and draft/intake usage. Private proposals go only to Agent Engineering HQ; public event discussions are opt-in. An idea or submission does not guarantee review, acceptance, a response or an event. London 2026 has an invited programme and no public CFP.
agenteng participate (or a typed {"operation":"participate"} agent request) returns the public organizer contact, a proposal checklist and the current capability limits. It sends no message and creates no submission receipt.
Offline drafting is available now. Optional private intake requires persistent storage, an operator-approved privacy notice and separate participant credentials. proposal_prepare previews the exact draft; proposal_submit requires explicit confirmation and returns a receipt after storage. Authors can inspect status or withdraw. The organizer reviews through local private-store administration. The pilot does not provide public signup, notifications or automatic publication. An enabled intake advertises write behavior through MCP; the default service stays read-only. The supplied Cloud Run deployment keeps intake disabled.
Code, documentation and source-backed data improvements are welcome. Start with CONTRIBUTING.md, follow the Code of Conduct, and use private security reporting for vulnerabilities.
uv sync --frozen --all-extras
uv run --frozen pytest -q
uv run --frozen ruff check src tests scripts
uv run --frozen python scripts/check-public-release.pyThe website is the event-data source. Updating its public catalogue requires a website checkout with TypeScript installed:
node scripts/export-website.mjs /path/to/agent-engineering-summitExports record their source revision/content hash and publication time. Changed event content requires a refreshed build; no background synchronization is claimed. Version tags trigger verified PyPI publishing and a GitHub release once the repository publishing secret is configured. See release verification and publisher setup and CHANGELOG.md.
License
Source code and original documentation are licensed under Apache-2.0. Third-party event/speaker material and linked recordings retain their own rights; see NOTICE and DATA.md.
Available Tools
1 toolagentengCRead-onlyIdempotent
Discover AgentEng conferences in London and San Francisco, browse agent-engineering tools across twelve disciplines, query public facts and prepare proposals for Agent Engineering HQ.
| Name | Required | Description | Default |
|---|---|---|---|
| request | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
| data | No | |
| stale | Yes | |
| usage | No | |
| answer | Yes | |
| engine | No | |
| status | No | |
| sources | No | |
| artifact | No | |
| evaluated_at | Yes | |
| published_at | Yes | |
| catalogue_version | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, and the description adds only scope: London/SF conferences, twelve disciplines, public facts, and proposals. It does not disclose auth, rate limits, pagination, or clarify the write-like `proposal_submit` and `proposal_withdraw` operations present in the schema.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence with no filler and is reasonably front-loaded around discovery. But it is under-structured for a 21-operation tool and provides no operation-level routing or structure to help an agent parse it.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the massive nested input schema and 0% parameter description coverage, the description should at least explain operation-based dispatch and map common tasks to operation values. It provides only a broad domain list, leaving the agent to reverse-engineer the schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the nested `request` object is large and undocumented. The description mentions no parameter names or values, not even the central `operation` field, so it does not compensate for the schema gap.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states four high-level activities (discover conferences, browse tools, query public facts, prepare proposals), which is more than a tautology. However, it never mentions the `request.operation` dispatcher or that 21 operations exist, so an agent cannot tell how to invoke specific capabilities from the description alone.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No when-to-use guidance, no conditions, and no mapping from task to operation value are provided. The agent gets only broad topical hints and must infer everything from the 21-value operation enum in the schema.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
1 tool update
v0.1.0- First observed
agenteng
TDQS
Scored across 1 tool
With only one tool, there is no risk of selecting the wrong tool from a set. However, the tool's description bundles multiple distinct functions (discovering conferences, browsing tools, querying facts, preparing proposals), making its scope broad and less clearly bounded.
A single tool name cannot demonstrate a consistent pattern across a set. The name 'agenteng' matches the server but is a bare noun rather than a predictable verb_noun action, which is readable but not ideal for an MCP tool.
The server's apparent purpose spans multiple operations across conferences, tools, facts, and proposals. A single catch-all tool is too few to expose those operations clearly, forcing agents to rely on one broad interface.
The domain implies distinct lifecycle actions such as discovering, browsing, querying, and preparing proposals. The surface provides no separate operations for these tasks, leaving severe gaps for agents attempting specific workflows.
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