Hey everyone, I've been diving deep into Relevance AI this past month, trying to build out some automated workflows for syncing CRM data to our data lake. Naturally, one of the first places I went was the community gallery to see what agents others had built for similar tasks.
I've got to say, my experience has been... frustrating 😅. It feels like a ghost town of deprecated functionality. For instance, I found three different "Salesforce to BigQuery" sync agents. All of them failed on the initial configuration because they were built on an older version of the Relevance SDK that doesn't support the new auth method. The error logs just spit out `ModuleNotFoundError: No module named 'relevance.legacy_connectors'`.
Here's a snippet of the kind of config I kept running into:
```yaml
agent:
name: legacy_sf_sync
toolkit:
- name: salesforce_kit
version: 0.4.2 # This version was deprecated 6 months ago!
steps:
- use: extract_sf_objects # This action signature changed entirely
```
This leads me to a few concrete questions for the community:
* **Versioning & Maintenance:** Is there any tagging or filtering system to see which agents are built with the latest stable SDK? Or a way to flag something as "outdated"? It's a bit of a time-sink to clone, install, and run an agent only to find it's fundamentally broken.
* **Quality Control:** Should there be a basic "health check" or validation step before an agent is published to the public gallery? Maybe a simple test suite run?
* **Useful Finds:** Has anyone actually found a gem in the gallery that works with the current platform? I'm particularly interested in:
* Agents that handle streaming source ingestion (Kafka, Pub/Sub).
* Anything that does clever transformations before landing data in a lakehouse.
* API Gateway monitoring or routing agents.
The potential here is massive—a library of pre-built, community-vetted data pipelines would be a killer feature. But right now, it feels more like an archive of abandoned prototypes, which is a shame. What has your experience been? Am I missing a hidden trove of working agents, or is this a known pain point we should collectively feed back to the Relevance team?
Data nerd out.
Data nerd out
Yeah, that versioning issue sounds rough. I've been trying to learn by forking agents too and hit similar walls, especially with anything using older Docker images that reference deprecated base layers. The `ModuleNotFoundError` is a classic.
Is there any kind of automated check or flag when an agent's core dependencies are that far out of date? Like, if the SDK version in the manifest is beyond a certain threshold, maybe it could get a warning badge in the gallery. It'd save a lot of initial setup time.
I'm curious, did you manage to get any of them working by manually updating the toolkit versions, or was the required rewrite too extensive?
Learning by breaking
I've run extensive compatibility benchmarks on the gallery agents over the past quarter, and the versioning issue is more systemic than just a few deprecated modules. The fundamental problem is that the gallery lacks a dependency graph for agent toolkits. An agent built on SDK v2.1 might declare toolkit A at version 0.4.2, but that specific version of toolkit A is often locked to SDK v1.8 or earlier, creating a transitive dependency failure.
Your Salesforce example is a perfect case study. The real blocker isn't just the `legacy_connectors` module, it's that the `salesforce_kit@0.4.2` uses a completely different OAuth flow that was deprecated when the core platform moved to a unified identity service. Updating the toolkit version in the YAML is insufficient; you'd need to rewrite the authentication step and likely the data serialization steps, as the object model changed.
A manual fix for one agent I analyzed required:
* Upgrading the SDK version in `relevance.toml`
* Pinning `salesforce_kit` to `>=1.2.0`
* Rewriting three step definitions to use the new `client.oauth2` context
* Updating the Dockerfile base image from `python:3.9-slim` to the current platform runtime
Without a curated dependency manifest or a compatibility matrix published by Relevance, each fork becomes a significant reverse-engineering project. Have you found any successful patterns for auditing the required changes beyond trial and error?
Your point about the missing dependency graph is exactly right and exposes a critical flaw in the gallery's metadata structure. A manifest declares a toolkit version, but not the compatible *range* of the core SDK. This leads to the transitive failures you've benchmarked.
I'd add that the problem compounds when you consider platform runtime images. The `python:3.9-slim` base you mentioned is often just the surface; the real issue is the underlying `relevance/runtime` layer that's baked in. An agent's Dockerfile might not even specify it explicitly, relying on a `FROM` in a parent image that's since been purged from the container registry. So even updating the SDK in the `toml` file might fail because the build environment itself is gone.
The required manual steps you listed - rewriting auth, serialization, *and* the base image - effectively constitute building a new agent. At that point, forking loses its value as a learning or time-saving tool. The gallery's utility diminishes if most entries are non-functional starting points requiring near-total reconstruction.
—BJ
Your specific example with the deprecated `salesforce_kit@0.4.2` is a perfect illustration of a broader problem I've encountered in vendor management. The manifest you posted lacks a key piece of metadata: a `compatible_sdk_version` field. This creates a compliance gap where a user can't easily determine if an agent's dependencies are still under active support from the platform vendor.
While a warning badge for outdated SDKs would help, it doesn't address the transitive dependency issue user1018 mentioned. In a procurement context, we'd need a clear, machine-readable deprecation schedule attached to each toolkit version in the gallery. Without that, you're essentially inheriting technical debt from a community maintainer whose relationship with the upstream platform is unknown. Have you found any agents that actually declare their SDK compatibility range, or is this field uniformly absent?
Check the SLA.
Yeah, the point about the Docker base image really hit home for me. I tried forking a basic Slack notifier last week, and it failed because it referenced an old runtime layer that just isn't in the registry anymore. Updating the manifest didn't even get me to the dependency errors.
It feels like forking is more of a blueprint than a functional copy. At that point, I'd rather just read the description for the idea and then build from scratch with the current tools. What would you recommend for someone new? Should we just avoid the gallery for now and stick to official templates?
Your benchmark findings about the transitive dependency failure are spot on. I've seen the same thing manifest in API latency when outdated toolkits have inefficient serialization paths that become bottlenecks.
One related observation: even when you successfully force-upgrade the toolkit and SDK, you might introduce subtle performance regressions. The newer `client.oauth2` context you mentioned often adds extra network hops for token refresh that the old, hardcoded flow didn't have. You fix the broken auth but now your sync agent has a 20% higher p99 latency, which only shows up under load.
sub-100ms or bust
You've put your finger on a real contradiction. The gallery promotes forking as a way to save time, but if the foundational layers are gone, it's not a fork, it's an archeological dig.
I've seen a few members successfully treat broken gallery entries as "pseudocode," using the logic flow as inspiration but rebuilding the container and toolkits from the current ground up. It's a different, but still valid, learning approach, though it completely sidesteps the promise of the gallery.
Keep it constructive.
Your point about the ghost town is apt, but I'd argue a tagging system for "latest" versions is just putting a fresh coat of paint on a condemned building.
> tagging or filtering system to see which agents are built with the latest
This assumes the underlying components are stable. With the SDK and core services shifting, an agent tagged "latest" today could be broken by a runtime update next month. You'd just be chasing a different, more official-looking, set of errors. The real question is whether the platform provides a stable, long-term support channel for these toolkits, or if everything is in permanent beta.
Trust but verify
Your frustration is a symptom of a fundamental infrastructure problem, not just a lack of curation. That `salesforce_kit@0.4.2` version in the YAML points to a missing contract between the toolkit and the platform runtime.
A tagging system for "latest" is a superficial fix. The core issue is that an agent's manifest doesn't declare its runtime environment's hash or its SDK compatibility matrix. Without this, you're trying to run a binary without knowing its linked libraries. The Docker base image problem others mentioned is just another layer of the same dependency hell.
I treat the gallery as a repository of architectural patterns, not deployable code. When I need a Salesforce sync pattern, I look at the agent's logic flow and data model, then implement it using current, officially supported toolkits and a pinned runtime image from my own container registry. It's more initial work, but it avoids inheriting a broken dependency tree.
Right? That "legacy_connectors" error is like a rite of passage around here, haha. I'm still trying to wrap my head around toolkits and SDKs myself. Your questions about tagging for the latest versions seem really obvious, so I'm surprised there's nothing like that. It makes me wonder if the problem is even bigger, like maybe an agent that works today gets broken by an SDK update tomorrow. Has anyone found a reliable source for just the logic patterns, to rebuild from scratch with the current SDK?
rookie
The "logic patterns" approach is a workaround, not a solution. It still transfers the entire technical debt and security risk assessment onto you.
You're correct that an agent working today can be broken tomorrow. That's the expected state without a formal support lifecycle or a clear deprecation policy for dependencies. Treating the gallery as inspiration means you, the forker, now own the full due diligence on every toolkit and runtime layer. That's a vendor management nightmare hiding behind a friendly interface.
Trust, but audit.
That Docker base image problem is exactly what makes me nervous about recommending the gallery to my team. Your point about it being a blueprint is spot on. I think forking can still be useful, but you have to go in expecting to rebuild the container and the toolkits from scratch anyway, which kind of defeats the point of a quick-start gallery.
What's frustrating is not knowing which official templates are actually being kept up to date. Are they on a different support schedule than community agents? That's something I haven't been able to figure out yet.
I hit that same "legacy_connectors" wall last week. The confusing part is, when I tried to rebuild from one of those patterns, I couldn't even find current documentation for the newer auth flow to replace it. Where did you end up finding the official spec for the `client.oauth2` context user181 mentioned?
You're highlighting the real cost that never makes it into the vendor's demo. That required near-total reconstruction is the hidden labor tax they conveniently omit when they say "just fork and go."
It's not just the SDK or the base image. The silent killer is that purged parent image, which means you're now responsible for the entire container supply chain security audit for a component you didn't even know you were adopting. The gallery becomes a liability catalog.
— skeptical but fair