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Amazing. I’m going to synthesize a beautiful, comprehensive engineering and vision report for BLAH – geared toward open source contributors and early adopters. I’ll include clear technical sections, conceptual visuals, architectural diagrams, and thoughtful critique with improvement ideas. This will balance deep protocol insight with approachable storytelling for devs and hackers who want to build or contribute.

I’ll get started and let you know when the report is ready for your review.

BLAH: Barely Logical Agent Host – Engineering Vision Report

BLAH (Barely Logical Agent Host) is an open-source platform that aspires to unify the fragmented world of AI agent tools. It provides a protocol-agnostic infrastructure for AI tool interoperability – essentially a “universal adapter” that lets AI agents use any tool through a standard interface. This report explores BLAH’s vision, architecture, current status, and future roadmap, aiming to inform and inspire open-source contributors and early adopters.

The Vision: Unifying a Fragmented AI Tool Ecosystem

Modern AI agents can use tools (APIs, functions, databases, etc.) to extend their capabilities. However, today’s AI tooling landscape is highly fragmented (BLAH - Barely Logical Agent Host). Competing protocols and interfaces exist – for example, Anthropic’s Model Context Protocol (MCP) vs. OpenAI’s experimental Simple Language and Object Protocol (SLOP) – each with different schemas, transports, and execution rules (BLAH - Barely Logical Agent Host). Tools built for one ecosystem often can’t be used in another without custom integration. This fragmentation creates silos, increases developer overhead, and slows innovation (BLAH - Barely Logical Agent Host).

BLAH’s core vision is to eliminate these silos by providing a unified abstraction layer for AI tools (BLAH - Barely Logical Agent Host). In the future BLAH envisions:

• “Write once, run anywhere” for AI tools: a developer can create a tool or API integration one time, and any AI agent (regardless of protocol or platform) can use it through BLAH’s adapters.
• A decentralized open tool ecosystem: tools are shared via a community-driven registry rather than locked into a single vendor’s platform. Anyone can publish or discover tools easily.
• Visual composition of complex behaviors: even non-experts can chain tools into workflows (flows) visually, allowing sophisticated agent behaviors without writing glue code.
• Standard infrastructure for agent-tool interaction: much like TCP/IP standardized network communication, BLAH aims to standardize how AI agents talk to tools, decoupling high-level logic from low-level protocols (BLAH - Barely Logical Agent Host).

In short, BLAH matters because it tackles the “AI tools fragmentation problem” head-on (BLAH - Barely Logical Agent Host). By bridging protocols and centralizing tool discovery, it can accelerate the development of agent capabilities and prevent the community from getting stuck in isolated tool fiefdoms. As AI agents become more central to applications, an open tooling layer ensures flexibility, innovation, and freedom from vendor lock-in.

Why BLAH? Key Highlights:

• Interoperability: BLAH acts as a universal protocol bridge so AI agents can use tools across MCP, SLOP, and other interfaces seamlessly (BLAH - Barely Logical Agent Host).
• Decentralized Registry: It provides a community-driven tool registry with versioning and dependency resolution to share and discover AI tools (BLAH - Barely Logical Agent Host) (BLAH - Barely Logical Agent Host).
• Composable Workflows: A visual flow engine allows chaining tools with conditional logic and compiling these flows into new reusable tools (BLAH - Barely Logical Agent Host) (BLAH - Barely Logical Agent Host).
• Flexible Execution: Tools can run locally or on cloud functions with support for STDIO, HTTP (REST/SSE), and gRPC transports under a unified API (BLAH - Barely Logical Agent Host).
• Security & Transparency: Optional signing of tools, sandboxed execution, and community oversight aim for a trustworthy ecosystem (GitHub - thomasdavis/blah) (blah-mcp | Glama).

This vision sets the stage for BLAH’s architecture and design decisions, which we’ll explore next.

Architecture Overview of the BLAH Ecosystem

BLAH’s architecture is designed to realize its interoperability vision through several key components working in tandem. At a high level, the BLAH ecosystem consists of: (1) a Core CLI & MCP Server, (2) a Decentralized Tool Registry, (3) Protocol Bridges for MCP/SLOP, (4) a Flow Composition Engine, and various execution backends (local and hosted). Let’s break down each piece.

(image) Figure: BLAH’s architecture as a protocol-agnostic hub. AI agent clients using MCP (e.g. Claude) or HTTP (SLOP) connect to BLAH’s server, which bridges requests to tools defined in manifests. Tools can be local functions/commands, hosted cloud functions (e.g. on ValTown), or even other API endpoints. BLAH also interfaces with a decentralized registry for tool discovery and versioning.

1. Core CLI and MCP Server

At the heart of BLAH is a command-line interface (CLI) tool (@blahai/cli) that developers use to manage configurations, run servers, and compose workflows. The CLI encapsulates an MCP server implementation that can expose your tools to any MCP-compatible AI client. Key aspects of the core stack include:

• MCP Server: BLAH fully implements the Model Context Protocol spec, using JSON-RPC messaging over flexible transports (stdio or network) (BLAH - Barely Logical Agent Host) (@blahai/cli - npm). It can launch a local server that listens for tool requests from AI models. For example, running blah mcp start will start an MCP server on stdio (for tools integration with local AI clients) (BLAH - Barely Logical Agent Host). BLAH’s MCP support is tested with known clients like Anthropic’s Claude (desktop and CLI), Cursor, Cline, Windsurf, etc., ensuring compatibility out-of-the-box (@blahai/cli - npm).
• SLOP Server (HTTP API): BLAH also speaks the Simple Language and Object Protocol by hosting an HTTP server. Running blah slop start spins up a RESTful API where each tool becomes an endpoint (@blahai/cli - npm) (@blahai/cli - npm). For example, it auto-generates routes like GET /tools (list tools) and POST /tools/:toolName (invoke a tool) (@blahai/cli - npm). Under the hood, BLAH converts between SLOP’s JSON request format and MCP’s format, so the same tool logic can serve both worlds transparently (@blahai/cli - npm).
• Transport Bridges: The core server is transport-agnostic. BLAH currently supports STDIO (for local processes or pipe integration) and Server-Sent Events (SSE) (for browser or web clients) as communication channels, with an eye towards gRPC in the future (BLAH - Barely Logical Agent Host). In practice, this means you can run BLAH in different environments (locally or hosted) and still communicate with it in real-time. (Today, local SSE streaming works; hosted SSE support is an acknowledged gap – more on that in Challenges).
• Configuration & CLI Features: The CLI manages a JSON configuration file conventionally named blah.json, which defines your tools and settings (similar to a package manifest). It provides commands like blah init to scaffold a new config, blah validate to check your manifest against the schema (@blahai/cli - npm) (@blahai/cli - npm), and blah mcp simulate to run a local simulation of an AI agent using your tools (@blahai/cli - npm). There’s even a blah flows command to launch a visual flow editor UI in your browser for designing tool workflows (@blahai/cli - npm). The CLI is thus both the runtime and the developer toolkit for BLAH.

2. Decentralized Tool Registry

A cornerstone of BLAH’s ecosystem is its decentralized tool registry (BLAH - Barely Logical Agent Host). Think of it as the “npm for AI agent tools” (GitHub - thomasdavis/blah). The registry’s purpose is to allow anyone to publish and share tool manifests, with features like semantic versioning, dependency/“extends” support, and multi-provider distribution.

• Global Tool Repository: The BLAH registry will aggregate tools from many providers (potentially GitHub, npm, personal servers, etc.) rather than a single centralized server (BLAH - Barely Logical Agent Host). This avoids vendor lock-in and single points of failure (BLAH - Barely Logical Agent Host). Each tool (or set of tools in a manifest) can be versioned (e.g. 1.0.0, 1.1.0) and include metadata like descriptions, authors, and tags for discovery.
• Composition via Extends: The registry and manifest format support a powerful concept of inheritance/composition. One manifest can extend another, effectively importing its tools into your namespace (GitHub - thomasdavis/blah). For example, you might create a custom manifest that extends a popular “toolbelt” manifest, instantly bringing in all its tools, then add your own on top. This encourages re-use and modularity – you can build on each other’s work rather than reinventing wheels. Figure 2 illustrates how manifests can extend one another across versions.

(image) Figure: Decentralized registry and manifest composition. Manifest A extends Manifest B and C (importing their tools); Manifest C in turn extends Manifest D. This hierarchy allows tools to be shared and composed flexibly across the community.

• Discovery & Metadata: Tools in the registry are organized with semantic metadata to aid discovery (blah-mcp | Glama). BLAH will support tagging (e.g. labeling a tool as #websearch or #datascience), and possibly usage analytics to highlight popular tools (blah-mcp | Glama). In the future, there may be social features like ratings or personalized recommendations (“people who use X also liked Y”) (blah-mcp | Glama) (blah-mcp | Glama).
• Registry Backend: Currently, BLAH is using a temporary backend on ValTown (a serverless platform) as a proof-of-concept registry store (blah-mcp | Glama). The project’s vision is to migrate to a more robust solution (e.g. a dedicated PostgreSQL-backed service) once the schema stabilizes. This interim approach lets development move fast, but a full registry service (with a web UI for browsing/publishing tools) is on the roadmap.

3. Manifest Schema and Tool Definition

At the core of BLAH’s functionality is the manifest file (commonly blah.json). This JSON schema defines what tools are available and how they are implemented. BLAH’s manifest is designed to be protocol-agnostic, meaning you describe each tool’s inputs/outputs and implementation once, and BLAH handles exposing it via MCP, SLOP, etc.

A simple example tool entry might look like:

{
  "name": "hello_name",
  "description": "Says hello to the name",
  "inputSchema": {
    "type": "object",
    "properties": {
      "name": { "type": "string", "description": "Name to say hello to" }
    },
    "required": ["name"]
  },
  "command": "echo Hello, ${name}!"
}

This defines a tool called hello_name that expects a JSON object with a name field, and its implementation is given by a shell command (just echoing a greeting). BLAH can take this manifest and register hello_name as an MCP method and a SLOP endpoint automatically, without the developer worrying about those specifics.

Some key features of the manifest and tool definitions:

• Standard Schema: Tools are described in JSON with an input schema (using JSON Schema vocabulary) and optional output schema and description. This ensures that AI agents know what inputs a tool expects and can validate arguments. (In practice, MCP clients like Claude will read these schemas to decide how to call the tool).
• Multiple Implementation Types: A tool can be implemented in various ways:
  • A local shell or Node.js command (as in the example above). This could call scripts, invoke other packages ("command": "npx -y @modelcontextprotocol/server-brave-search" is a real example for a web search tool (@blahai/cli - npm)), or run any executable logic on the host machine. BLAH will execute the command and capture its output.
  • A function defined in code. In the future, BLAH aims to support embedding code (e.g. a JavaScript function) directly in the manifest or linking to a local file, so you can write custom logic in-line.
  • A remote endpoint or cloud function. BLAH can fetch tools from a URL – for instance, your manifest could include a pointer to another host or use the extends feature to include tools from an online source. BLAH’s CLI supports pointing --config at a URL, meaning the manifest itself can live remotely.
  • ValTown-hosted tools: As an example of remote execution, if no local command is provided for a tool, BLAH can default to looking up a ValTown function by the tool’s name (@blahai/cli - npm). ValTown is currently used to host some example tools and manifests; BLAH constructs a URL (using a VALTOWN_USERNAME setting) to call the cloud function and get a result (@blahai/cli - npm) (@blahai/cli - npm). This provides a fallback so that even if you haven’t defined a tool locally, you might get one from a community cloud space if available.
• Hierarchical Config: BLAH supports configuration inheritance and overrides. For instance, you might have environment variables or default settings in a base manifest and override them in an extending manifest (BLAH - Barely Logical Agent Host). This allows layering of configs from local and hosted sources, and merging them deeply.
• Agents.json compatibility: The design is influenced by emerging standards like agents.json (a proposed format for tool manifests) (blah-mcp | Glama). BLAH’s long-term goal is to be compatible with many formats – MCP, SLOP, OpenAI function specs, etc. – by providing converters or adapters (blah-mcp | Glama). So, a manifest could potentially import an OpenAI Plugin’s spec or a LangChain tool definition in the future.

In summary, the manifest is the single source of truth for what tools an AI agent can use via BLAH. This one file (which can be composed from many files via extends) captures a snapshot of capabilities that can be seamlessly exposed to various AI frameworks.

4. Flow Composition and Tool Chaining

A standout feature of BLAH is its support for visual workflow composition. Not only can you host individual tools, you can also chain them into flows (Directed Acyclic Graphs of tool invocations with conditional logic) and treat those flows as new higher-level tools. This is critical for orchestrating multi-step agent behaviors.

BLAH includes a React Flow-based visual editor that lets you design these flows graphically (BLAH - Barely Logical Agent Host). In the flow editor, you can draw nodes (each node represents a tool or a logical operation) and connect them with edges that may have conditions. For example, you might create a flow where tool A’s output is checked, and then it either runs tool B or tool C next depending on some condition (e.g. “if result contains X, do B, else do C”).

(image) Figure: Example of a simple tool flow (DAG) in BLAH. In this conceptual flow, the agent starts by running Tool A, then branches: if condition X is true, it executes Tool B next, otherwise it executes Tool C. Finally it proceeds to the end. BLAH’s flow engine supports such conditional branching and sequential tool execution.

Under the hood, these flows are represented in JSON (likely using a subset of the agnt.gg flow schema, according to the notes (blah-mcp | Glama)). When you save a flow, BLAH can compile it into a standalone tool definition. Essentially, the flow becomes a new tool in your manifest, one that when invoked will orchestrate the sequence of internal tool calls as defined by the DAG. This compile step could generate code or a structured plan that the BLAH runtime executes.

Key points about the flow engine:

• Conditional Logic & Branching: Flows can encode if/else decisions, loops or iterations (planned), and parallel branches (since it’s a DAG) (BLAH - Barely Logical Agent Host) (BLAH - Barely Logical Agent Host). This means your AI agent can handle complex tasks by invoking one “flow tool” that encapsulates multiple steps.
• Parameter Binding: The flow system manages passing outputs from one tool into inputs of the next. You can map fields (e.g., take the "summary" output from Tool A and feed it as "text" input to Tool B) through the flow editor. This eliminates the need for glue code in most cases.
• Reusable Subflows: Because flows are first-class tools, you can compose flows within flows. A complex workflow might be broken into subflows, each tested independently, then wired together – enabling hierarchical compositions.
• Visual Debugging: In the future, the flow editor can serve as a debugger – visualizing which nodes executed and what data passed through, making it easier to troubleshoot multi-step reasoning in agents. This is an area of active development (to improve overall developer experience).

By integrating a flow-based approach, BLAH caters to both developers who want fine-grained control and more visual/low-code users who want to assemble AI behaviors without writing code. It’s a compelling answer to the “limited composability” problem in current tools ecosystems (BLAH - Barely Logical Agent Host).

5. Execution Backends: Local vs Hosted Tools

Flexibility in execution is a major design consideration for BLAH. Contributors wanted to ensure tools can run wherever it’s most convenient – be it on the local machine, or on cloud infrastructure – without changing how the agent calls them. BLAH achieves this with a pluggable execution layer (BLAH - Barely Logical Agent Host):

• Local Execution: If your manifest defines a tool with a local command or function, the MCP server will execute it on the local host. For example, a tool might run a Python script on your machine or call a local database. Local execution is great for custom logic, or when running BLAH on a server you control. It’s also the default for quick experimentation.
• Hosted Execution (ValTown or Custom Cloud): BLAH supports remote tool execution by fetching results from URLs. The primary implementation here is integration with ValTown, which allows any user to deploy JavaScript functions at a unique URL. If configured with a VALTOWN_USERNAME, BLAH will assume tools without a local command are meant to be ValTown functions and construct the URL accordingly (@blahai/cli - npm) (@blahai/cli - npm). It then performs an HTTP request to execute the tool and returns the response as if it ran locally. This effectively offloads compute to the cloud and allows sharing tools without requiring users to install anything locally. In the future, more hosting options are planned – e.g., Cloudflare Workers or Vercel functions – so that developers can choose their preferred serverless platform to host tool logic.
• HTTP/URI Execution: Beyond ValTown, any tool can also be defined with a direct URI endpoint. For instance, you could point a tool to an existing REST API (with a fixed URL pattern) and BLAH will invoke that when the tool is called. Authentication or API keys can be handled via environment variables in the manifest’s config. This essentially turns any web API into a “tool” that an AI agent can call via BLAH.
• Combined and Fallback: BLAH’s execution strategy is smart – it can combine multiple sources. If a tool is defined both locally and hosted, local might take precedence but the hosted could serve as backup (or vice versa, depending on config). There’s a notion of a fallback mechanism so that if a tool isn’t implemented locally, BLAH will attempt to retrieve it from the network (registry or ValTown) before giving up (@blahai/cli - npm). This ensures resilience and a smooth developer experience – you might run blah mcp start and immediately have a bunch of tools available because you extended someone’s remote manifest, for example.

The upshot is that BLAH abstracts where a tool runs. To an AI client, it doesn’t matter – it sends a JSON request to BLAH, and BLAH handles routing that to either a local process, a serverless function call, or an HTTP request. This abstraction is key to its role as a “universal adapter” in AI tooling.

Current Status: What Works and What’s Missing

BLAH is an ambitious project and still very early in its development. As of now (early 2025), it’s in an “EXTREME POC (Proof-of-Concept) MODE” (BLAH - Barely Logical Agent Host). Many core pieces are functional, but the edges are rough and some intended features are not built yet. Let’s evaluate the current features and notable engineering decisions:

✅ What’s Working Well (POC Features):

• MCP Server (Local): The basic MCP server functionality is solid. You can define tools in blah.json, start the server, and have an AI like Claude use your tools via standard MCP calls. The use of JSON-RPC and adherence to the MCP spec means BLAH behaves like any other MCP server from the client’s perspective (@blahai/cli - npm). Community feedback indicates that hooking up BLAH to various MCP clients generally “just works,” which validates the core interoperability approach.
• SLOP Adapter (HTTP API): Similarly, the SLOP/REST interface is functional in local mode. This effectively turns your toolset into a local web service. It’s useful for testing (you can cURL your tools) and integration with non-MCP systems. BLAH automatically translates tool definitions into clean REST endpoints, which is a nice touch for developers who want to poke at their tool with standard HTTP.
• Basic Tool Execution Modes: Local tool execution (running commands) and the ValTown integration for remote execution are both implemented. For example, you can include a tool that calls out to a ValTown function and it will retrieve it properly (@blahai/cli - npm). The fallback logic for missing commands is in place (@blahai/cli - npm). This means even at POC stage, BLAH can demonstrate the “write once, deploy anywhere” concept to some extent.
• Visual Flow Editor: The React Flow-based editor is up and running (accessible via blah flows). You can create a flow graphically and save it. It compiles into the manifest (though this feature may be basic right now) and the DAG execution engine will run it. The choice to leverage an existing library (React Flow) was smart – it saved a lot of overhead in building a custom graph UI from scratch. Developers can already toy with chaining tools using this interface, which is a big validation of the composability goal.
• Config Inheritance: The manifest system supports merging configurations (local and extended manifests) and environment variable management. This is visible via the blah init creating a base config, and one can include environment keys like API tokens in the manifest’s env section (@blahai/cli - npm) (@blahai/cli - npm). Having this baked in early is great for real-world usage, since any non-trivial tool will need secrets or config values.
• Open Source & Community Driven: Perhaps the most important feature: BLAH is fully open-source (MIT licensed (GitHub - thomasdavis/blah)) and aiming to build a community. Already, the project encourages contributions and ideas from others, which helps identify pain points early. The design is being discussed in the open, with developers like Lisa Watts and Wombat credited for their contributions to BLAH’s ideas (GitHub - thomasdavis/blah). This openness is crucial given BLAH’s goal to become a common infrastructure for all – it can’t succeed as a single-company product with closed governance.

❌ What Needs Improvement (Known Gaps):

• Hosted/Remote Features: Running BLAH purely in the cloud or as a service is still experimental. For instance, hosted SSE support is missing – currently SSE streaming is only proven for local use. If you deploy BLAH on a platform like ValTown (or eventually Vercel/Cloudflare), getting real-time streaming responses to a client might not work due to limitations in those environments. This affects use cases where an AI (like Claude in the browser) connects to a remote BLAH server for streaming outputs. Solving this likely requires some architectural adjustments or using a different transport (web sockets or long polling).
• Registry UI & Management: The decentralized registry exists in concept but not yet in polished form. There’s no nice web interface to browse available tools or publish your own from a dashboard (as of now, publishing might be a manual or CLI-driven process). The backing store is still ValTown which was a stopgap. This means features like search, tagging, ratings, etc., are not user-facing yet. Improving the registry (both backend and frontend) is a top priority to unlock the community network effects.
• Error Handling & Debugging: As a POC, BLAH’s error handling is rudimentary. The author half-jokingly noted a plan to “just gather logs from everybody’s clients to figure out all the f***ing errors” (GitHub - thomasdavis/blah). While not literally doing that, it highlights that robust logging, clear error messages, and debugging tools are currently lacking. For example, if a tool command crashes or returns malformed JSON, how does BLAH convey that to the user or developer? Right now, it might be cryptic or silent. Improving this will greatly enhance developer experience.
• Stability and Performance: BLAH is still evolving its API and schema, so expect breaking changes. The README explicitly warns that nothing is stable yet (GitHub - thomasdavis/blah). Early adopters have to tolerate this churn. Performance hasn’t been a focus yet either – e.g., how BLAH scales with many simultaneous tool calls or very large manifests is untested (“security - not tested, quality - not tested” per an automated report (blah-mcp | Glama)). There’s also technical debt in the code given the fast prototyping (the repo shows raw notes and some colorful language, which is normal in early-stage projects). Refactoring and optimization will eventually be needed.
• SLOP/Protocol Edge Cases: While basic SLOP is in place, advanced features like sub-tools (tools that have names like parentTool_subTool) are only partially handled (@blahai/cli - npm). Also, supporting additional protocols (like OpenAI’s function calling or LangChain’s API) will require more adapters. Ensuring BLAH can gracefully convert between different JSON schema dialects or call conventions is an ongoing challenge.
• UI/UX Polish: The developer-facing UX (CLI commands, documentation, playground) is in progress. The CLI has many commands, but new users might find it confusing without better docs or an interactive tutorial. The Playground (an interactive testing environment for manifests) exists in basic form (GitHub - thomasdavis/blah) but could be more powerful or user-friendly. Likewise, the documentation site is not fully fleshed out; contributors might have to read source code or raw notes to understand some parts. As the project matures, refining the UX will be key to onboarding more users.

In summary, BLAH works today as a proof-of-concept – you can use it and see the promise of unified tool interoperability – but it’s not production-ready for mission-critical use. The core ideas are validated (MCP↔SLOP bridging, manifest-driven tools, etc.), while the surrounding ecosystem (registry, UI, stability) needs significant development. The good news is that the team and community are aware of these gaps and actively working to close them.

Roadmap: What’s Next for BLAH

The roadmap for BLAH is packed with exciting enhancements, as it transitions from POC to a robust platform. Here are some of the planned and proposed features on the horizon:

• Full Registry & Web Explorer: The decentralized registry will be fully implemented with a database-backed service (likely PostgreSQL plus a Node/TypeScript backend) and a user-friendly web app. This will allow developers to publish tools with a simple CLI command (blah publish maybe) and have them listed on a website where others can search, filter by tags, read documentation, and quickly import those tools into their own manifest. A web-based tool explorer is explicitly on the roadmap (blah-mcp | Glama), as is a “documentation site” for browsing schemas. This will truly make BLAH a hub for the community.
• Cloud Hosting & SSE Support: Making BLAH trivially deployable to cloud platforms is a priority. The team is exploring hosting the MCP server on serverless platforms like Cloudflare Workers or Vercel Functions (blah-mcp | Glama) (GitHub - thomasdavis/blah). The goal is for a user to be able to one-click deploy their BLAH configuration online (for example, via a Vercel deploy button or a CLI command) and get a public URL for their agent’s tool API. Solving SSE in those environments (or providing a fallback to websockets) will be part of this. This will unlock use cases like plugging BLAH into chat interfaces or other systems as a remote service.
• LangChain and LLM Integration: BLAH plans to add bridges to popular AI tool frameworks such as LangChain and LlamaIndex (BLAH - Barely Logical Agent Host). This could mean two things: (1) Allowing LangChain agents to use BLAH as a tool source (so LangChain’s tools can come from the BLAH registry), and (2) allowing LangChain-defined tools/chains to be imported into BLAH’s manifest. Since LangChain has its own ecosystem of tools and chains, a converter would let BLAH tap into that rich library. Likewise, OpenAI function spec compatibility would let BLAH serve as a host for ChatGPT plugins defined in an OpenAI format. Essentially, BLAH can become a universal adapter not just for protocols, but for agent frameworks.
• Improved Tool Composability: The initial flows feature is just the beginning. Future enhancements may include trigger-based flows (tools that run automatically on certain events or schedules), support for loops and more complex logic in flows, and perhaps a library of common flow patterns (for example, a “retrieval-augmented generation” flow template that others can reuse). The concept of “portals” has been floated (in notes) which could imply linking flows between different agents or contexts. All of this would advance BLAH’s ability to coordinate multi-step, multi-agent processes, moving closer to the vision of easy orchestration of agent behaviors.
• Developer Experience Improvements: A smoother dev experience is a constant goal:
  • A Playground 2.0 where you can interactively run an agent conversation with your tools loaded, see step-by-step what tool it picks, inspect the inputs/outputs, etc. This might be a web app that complements the CLI.
  • Better debugging: for example, a mode where BLAH logs every request and response in a nice formatted way, or even a UI timeline of tool calls.
  • A tool creation wizard has been mentioned (blah-mcp | Glama) – guiding users to define a new tool through a form or CLI prompts, which then generates the JSON schema automatically (especially helpful for those not familiar with JSON Schema details).
  • Validation and Testing: adding more thorough blah validate rules (e.g. ensuring no duplicate tool names, checking that extended manifests don’t conflict, etc.), and possibly a blah test to run sample inputs through tools to verify they behave as expected.
• Recommendation & Analytics: Once the registry is populated, BLAH could implement a system to recommend tools or flows. The roadmap mentions popularity-based and user-based recommendations (blah-mcp | Glama) (blah-mcp | Glama). Imagine logging into the BLAH registry site and it suggests “You’ve used several web search tools, you might like this new Wikipedia tool.” This involves gathering anonymized usage metrics and perhaps allowing users to opt into sharing data about what tools they use. It’s a delicate balance with privacy, but it could greatly help surface useful tools in a vast registry.
• Security and Sandbox: As usage grows, security becomes paramount. The team plans to support signing of tool manifests and verification, so you can trust that a tool hasn’t been tampered with (GitHub - thomasdavis/blah) (blah-mcp | Glama). They also want to sandbox execution (e.g. running tools in a restricted environment via technologies like Deno, Docker, or VM isolation) (blah-mcp | Glama). This would allow running untrusted community-contributed tools more safely, which is crucial in a decentralized system. Additionally, a governance model for the registry might be developed (to handle abuse, compliance with local laws, etc.) (blah-mcp | Glama).
• New Protocols and Extensibility: BLAH will continue to track emerging standards. If a new AI assistant protocol comes out (for instance, something from OpenAI or a W3C standard), BLAH could support it via plugins or converters. The architecture is meant to be extensible – one could imagine a plugin system where you drop in a module to teach BLAH how to speak Protocol X, similar to how one can add a new language to a compiler. This future-proofs the platform in the fast-moving AI landscape.

Overall, the roadmap is about turning BLAH from a great idea in prototype form into a production-grade, versatile platform. Contributors are actively coding many of these features, and there’s a clear invitation for the community to join in shaping the future.

Getting Started with BLAH and How to Contribute

Excited about BLAH’s vision? Here’s how you can get your hands dirty and be a part of its journey:

Setting up BLAH: You can get started with BLAH in a few minutes:

1. Install the CLI: BLAH is distributed as an npm package. If you have Node.js (18+ recommended) and pnpm:

   npm install -g @blahai/cli

   Alternatively, clone the GitHub repo and run pnpm install && pnpm run build to build from source (GitHub - thomasdavis/blah).
2. Initialize a project: In any directory, run:

   blah init

   This will create a blah.json in the directory with a basic template. You can edit this file to define your own tools. For example, add a tool that calls an API or a local script. (Refer to the documentation for the manifest schema or look at examples in the repo.)
3. Run the MCP server: Once you have some tools defined, start BLAH:

   blah mcp start

   By default this connects to your blah.json and waits for an AI client to connect via stdio. If you have Anthropic’s Claude or another MCP client, you can now point it to BLAH. (For Claude Desktop, you’d select “Add Custom Tool” and provide the connection details, which could be a command that launches BLAH.)
   • You can also run blah mcp start --sse --port 4200 to start a local SSE server on port 4200 (BLAH - Barely Logical Agent Host). This is useful if you want to connect a web-based client or test via a browser.
   • To test SLOP/HTTP, run blah slop start --port 5000 and open http://localhost:5000/tools in a browser – you should see your tool list in JSON form.
4. Use the visual editor: If you want to play with flows, run:

   blah flows

   and open the indicated localhost port (defaults to 3333) in your browser. You’ll see the visual flow editor where you can drag nodes and connect them. Save the flow to update your blah.json.

Learning by examples: Check out the Tool Playground (coming soon on the website) where you can find example blah.json manifests and test them live. For instance, an example manifest might show how to integrate a Web Search tool and a Summarizer tool together. The playground will let you simulate an agent’s behavior using those tools, helping you understand how BLAH mediates the calls. Additionally, the GitHub repository contains a packages/cli/examples folder (if available) or you can find community-contributed manifests in the registry once it’s populated.

Contributing: BLAH is open-source and needs your help! Whether you’re an experienced systems engineer or just a developer passionate about AI, there are many ways to contribute:

• Build new Features: Pick an item from the roadmap that excites you – be it the registry web UI, a new protocol adapter, or improving the flow compiler – and start hacking. There is an open issue tracker on GitHub where core maintainers (like @thomasdavis) tag help-wanted items. For example, implementing the LangChain adapter or adding support for scheduled tools (cron) could be great projects to tackle.
• Improve Documentation: Because the project is moving fast, docs can lag behind. You can contribute by writing clear documentation, tutorials, or example manifests. If you go through setup and find something confusing, consider opening a PR to the docs (/apps/docs in the monorepo) to clarify it for the next person.
• Create and Share Tools: One of the easiest ways to contribute is by adding your own tools to the ecosystem. Write a manifest with something useful (perhaps a connector to a public API, or a utility like a text formatter) and share it. Right now, sharing might mean posting the JSON on GitHub Gist or the Discord, but soon the registry will allow publishing officially. Early contributors who add quality tools will help bootstrap the ecosystem and inspire others.
• Report Issues and Feedback: Try using BLAH with different clients and scenarios. If you find a bug (such as a tool not working through SLOP that works via MCP, or a crash when combining certain flows), report it on GitHub. Also share any ideas or pain points – the maintainers are very receptive to feedback. For instance, if the error messages were unclear in a certain case, opening an issue can lead to a quick improvement.
• Join the Community: Engage with the BLAH community via their Discord or discussion forums (check the GitHub README for invite links). Sometimes just discussing design ideas or brainstorming can be a contribution. The project is in a stage where fresh ideas can significantly influence its direction. (Fun fact: the name “BLAH” itself hints the project doesn’t take itself too seriously – all respectful, creative input is welcome!)

By contributing, you’re not only improving BLAH, but also shaping the future of open AI tooling infrastructure. There’s a sense among the community that this is something important – “the infrastructure layer for tool interoperability” (BLAH - Barely Logical Agent Host) – and that by working on BLAH, you’re pushing the whole AI ecosystem toward greater openness and capability.

Conclusion: Empowering the Next Generation of AI Tools

BLAH is more than just a funky acronym – it’s a bold attempt to bring order to the chaos of AI agent tools. By providing a common host for tools, bridging multiple protocols, and enabling creative composition, BLAH lowers the barrier for developers and tinkerers to supercharge AI agents with new abilities. It stands at the intersection of many trends – the rise of agentic AI, the proliferation of APIs, and the open-source movement in AI – and offers a path that leverages all of them.

In this early stage, BLAH’s promise can already be glimpsed: you can imagine a future where an AI assistant easily taps into a vast library of community-contributed tools – no matter who developed them or for what platform – through the unifying layer of BLAH. Need to do X? There’s a tool for that in the registry. Need to combine Y and Z? Just drag-and-drop a flow. It’s a compelling vision of AI interoperability that puts power in the hands of developers and users, rather than siloed corporations.

As we’ve discussed, there’s plenty of work ahead to fulfill this vision. But the foundation is set, and it’s solid. The project’s authenticity (you can see the real engineering thought process in those raw notes and commits!) and technical insight shine through. This isn’t vaporware or marketing fluff – it’s a labor of love by engineers who needed something like BLAH and decided to build it.

Come be a part of it. Whether you want to adopt BLAH in your own AI projects or contribute to its development, now is the perfect time. As the tagline on the website proclaims: “Unifying the AI Tool Ecosystem” (BLAH - Barely Logical Agent Host) – that’s the magic of BLAH, and with the community’s help, it won’t be “barely logical” for long, it will be brilliantly logical. Let’s build the future of AI tooling, together!