agent-o-rama
End-to-end platform for building and perfecting LLM agents on the JVM
Stars: 161
Agent-O-Rama is a powerful open-source tool designed for automating repetitive tasks in the field of software development. It provides a user-friendly interface to create and manage automated agents that can perform various tasks such as code deployment, testing, and monitoring. With Agent-O-Rama, developers can save time and effort by automating routine processes and focusing on more critical aspects of their projects. The tool is highly customizable and extensible, allowing users to tailor it to their specific needs and integrate it with other tools and services. Agent-O-Rama is suitable for both individual developers and teams working on projects of any size, providing a scalable solution for improving productivity and efficiency in software development.
README:
Agent-o-rama is an end-to-end LLM agent platform for building, tracing, testing, and monitoring agents with integrated storage and one-click deployment. Agent-o-rama provides two first-class APIs, one for Java and one for Clojure, with feature parity between them.
Building LLM-based applications requires being rigorous about testing and monitoring. Inspired by LangGraph and LangSmith, Agent-o-rama provides similar capabilities to support the end-to-end workflow of building LLM applications: datasets and experiments for evaluation, and detailed tracing, online evaluation, and time-series telemetry (e.g. model latency, token usage, database latency) for observability. All of this is exposed in a comprehensive web UI.
Agents are defined as simple graphs of Java or Clojure functions that execute in parallel, with built-in high-performance storage for any data model and integrated deployment. Agents have full support for streaming, and they're easy to scale by just adding more nodes.
- Overview
- Detailed comparisons against other agent tools
- Downloads
- Learning Agent-o-rama
- Tour of Agent-o-rama
LLMs are powerful but inherently unpredictable, so building applications with LLMs that are helpful and performant with minimal hallucination requires extensive testing and monitoring. Agent-o-rama addresses this by making evaluation and observability a first-class part of the development process, not an afterthought.
Agent-o-rama is deployed onto your own infrastructure on a Rama cluster. Rama is free to use for clusters up to two nodes and can scale to thousands with a commercial license. Every part of Agent-o-rama is built-in and requires no other dependency besides Rama, including high-performance, durable, and replicated storage of any data model that can be used as part of agents. Agent-o-rama also integrates seamlessly with any other tool, such as databases, vector stores, external APIs, or anything else. Unlike hosted observability tools, all data and traces stay within your infrastructure.
Agent-o-rama integrates with Langchain4j to capture detailed traces of model calls and embedding-store operations, and to stream model interactions to clients in real time. Integration is fully optional – if you prefer to use other APIs for model access, Agent-o-rama supports that as well.
Rama can be downloaded here, and instructions for setting up a cluster are here. A cluster can be as small as one node or as big as thousands of nodes. There's also one-click deploys for AWS and for Azure. Instructions for developing with and deploying Agent-o-rama are below.
Development of Agent-o-rama applications is done with "in-process cluster" (IPC), which simulates Rama clusters in a single process. IPC is great for unit testing or experimentation at a REPL. See below for examples in both Java and Clojure of agents utilizing an LLM run with IPC.
- Agent-o-rama vs. LangGraph / LangSmith
- Agent-o-rama vs. LangChain4j
- Agent-o-rama vs. Spring AI
- Agent-o-rama vs. Koog
- Agent-o-rama vs. Embabel
- Agent-o-rama vs. LangGraph4j
Download Agent-o-rama releases here. A release is used to run the Agent-o-rama frontend. See this section for instructions on deploying. For building agent modules, add these repositories to the Maven dependencies for your project:
<repositories>
<repository>
<id>nexus-releases</id>
<url>https://nexus.redplanetlabs.com/repository/maven-public-releases</url>
</repository>
<repository>
<id>clojars</id>
<url>https://repo.clojars.org/</url>
</repository>
</repositories>
The Maven target for Agent-o-rama is:
<dependency>
<groupId>com.rpl</groupId>
<artifactId>agent-o-rama</artifactId>
<version>0.7.0</version>
</dependency>
- Quickstart
- Full documentation
- Documentation chatbot
- Javadoc
- Clojuredoc
- Mailing list
- Discord server
- #rama channel on Clojurians
Below is a quick tour of all aspects of Agent-o-rama, starting with defining agents through running experiments and analyzing telemetry.
Agents are defined in "modules" which also contain storage definitions, agent objects (such as LLM or database clients), custom evaluators, and custom actions. A module can have any number of agents in it, and a module is launched on a cluster with one-line commands with the Rama CLI. For example, here's how to define a module BasicAgentModule with one agent that does a single LLM call and run it in the "in-process cluster" (IPC) development environment in both Java and Clojure:
public class BasicAgentModule extends AgentModule {
@Override
protected void defineAgents(AgentTopology topology) {
topology.declareAgentObject("openai-api-key", System.getenv("OPENAI_API_KEY"));
topology.declareAgentObjectBuilder(
"openai-model",
setup -> {
String apiKey = setup.getAgentObject("openai-api-key");
return OpenAiStreamingChatModel.builder()
.apiKey(apiKey)
.modelName("gpt-4o-mini")
.build();
});
topology.newAgent("basic-agent")
.node("chat",
null,
(AgentNode node, String prompt) -> {
ChatModel model = node.getAgentObject("openai-model");
node.result(model.chat(prompt));
});
}
}
try (InProcessCluster ipc = InProcessCluster.create();
AutoCloseable ui = UI.start(ipc)) {
BasicAgentModule module = new BasicAgentModule();
ipc.launchModule(module, new LaunchConfig(1, 1));
String moduleName = module.getModuleName();
AgentManager manager = AgentManager.create(ipc, moduleName);
AgentClient agent = manager.getAgentClient("basic-agent");
String result = agent.invoke("What are use cases for AI agents?");
System.out.println("Result: " + result);
}(aor/defagentmodule BasicAgentModule
[topology]
(aor/declare-agent-object topology "openai-api-key" (System/getenv "OPENAI_API_KEY"))
(aor/declare-agent-object-builder
topology
"openai-model"
(fn [setup]
(-> (OpenAiStreamingChatModel/builder)
(.apiKey (aor/get-agent-object setup "openai-api-key"))
(.modelName "gpt-4o-mini")
.build)))
(-> topology
(aor/new-agent "basic-agent")
(aor/node
"start"
nil
(fn [agent-node prompt]
(let [openai (aor/get-agent-object agent-node "openai-model")]
(aor/result! agent-node (lc4j/basic-chat openai prompt))
)))))
(with-open [ipc (rtest/create-ipc)
ui (aor/start-ui ipc)]
(rtest/launch-module! ipc BasicAgentModule {:tasks 4 :threads 2})
(let [module-name (rama/get-module-name BasicAgentModule)
agent-manager (aor/agent-manager ipc module-name)
agent (aor/agent-client agent-manager "basic-agent")]
(println "Result:" (aor/agent-invoke agent "What are use cases for AI agents?"))
))These examples also launch the Agent-o-rama UI locally at http://localhost:1974.
See this page for all the details of coding agents, including having multiple nodes, getting human input as part of execution, and aggregation. For lots of examples of agents in either Java or Clojure, see the examples directory in the repository.
Modules are launched, updated, and scaled on a real cluster with the Rama CLI. Here's an example of launching:
rama deploy --action launch \
--jar my-application-1.0.0.jar \
--module com.mycompany.BasicAgentModule \
--tasks 32 \
--threads 8 \
--workers 4 \
--replicationFactor 2
The launch parameters are detailed more in this section of the Rama docs.
Updating a module to change agent definitions, add/remove storage definitions, or any other change looks like:
rama deploy \
--action update \
--jar my-application-1.0.1.jar \
--module com.mycompany.BasicAgentModule
Finally, scaling a module to add or remove resources looks like:
rama scaleExecutors \
--module com.mycompany.BasicAgentModule \
--threads 16 \
--workers 8
A trace of every agent invoke is viewable in the Agent-o-rama web UI. Every aspect of execution is captured, including node emits, node timings, model calls, token counts, database read/write latencies, tool calls, subagent invokes, and much more. On the right is aggregated stats across all nodes.
From the trace UI you can also fork any previous agent invoke, changing the arguments for any number of nodes and rerunning from there. This is useful to quickly see how small tweaks change the results to inform further iteration on the agent.
Agent-o-rama has a first-class API for dynamically requesting human input in the middle of agent execution. Pending human input is viewable in traces and can be provided there or via the API.
Human input is requested in a node function with the blocking call agentNode.getHumanInput(prompt) in Java and (aor/get-human-input agent-node prompt) in Clojure. Since nodes run on virtual threads, this is efficient. This returns the string the human provided that can be utilized in the rest of the agent execution.
The client API can stream results from individual nodes. Nested model calls are automatically streamed back for the node, and node functions can also use the Agent-o-rama API to explicitly stream chunks as well. Here's what it looks like in Java and Clojure to register a callback to stream a node:
client.stream(invoke, "someNode", (List<String> allChunks, List<String> newChunks, boolean isReset, boolean isComplete) -> {
System.out.println("Received new chunks: " + newChunks);
};(aor/agent-stream client invoke "some-node"
(fn [all-chunks new-chunks reset? complete?]
(println "Received new chunks:" new-chunks)))See this page for all the info on streaming.
Datasets of examples can be created and managed via the UI or API. Examples can be added manually, imported in bulk via JSONL files, or added automatically from production runs with actions. See all the info about datasets on this page.
Datasets can then be used to run experiments to track agent performance, do regression testing, or test new agents. Experiments can be run on entire agents or on individual nodes of an agent. A single target can be tested at a time, or comparative experiments can be done to evaluate multiple different targets (e.g. the same agent parameterized to use different models).
Experiment results look like:
Experiments use "evaluators" to score performance. Evaluators are functions that return scores, and they can use models or databases during their execution. Agent-o-rama has built-in evaluators like the "LLM as judge" evaluator which uses an LLM with a prompt to produce a score.
See all the info about experiments on this page.
Actions can be set up via the UI to run automatically on the results of production runs. Actions can do online evaluation, add to datasets, trigger webhooks, or run any custom function. Actions receive as input the run input/output, run statistics (e.g. latency, token counts), and any errors during the run. Actions can set a sampling rate or filter for runs matching particular parameters.
Online evaluation gets added as feedback on the run that is viewable in traces, and time-series charts are automatically created that are viewable in the analytics section. Here's an example of setting up an action to do online evaluation:
Here's an example of creating an action to add slow runs to a dataset:
See this page for the details on creating actions.
A comprehensive human feedback system integrates both structured and unstructured human evaluation directly into your agent development workflow.
Feedback queues organize evaluation work across your team. Automatically route agent runs for review, configure which feedback to collect, and give reviewers a streamlined interface that displays inputs and outputs alongside the feedback form, advancing to the next item after submission.
Feedback can also be recorded directly on traces, making it easy to record insights during debugging or analysis.
Human feedback flows into the telemetry system where you can visualize trends over time and track whether agent improvements align with human evaluations.
Agent-o-rama automatically tracks time-series telemetry for all aspects of agent execution.
You can also attach metadata to any agent invoke, and all time-series telemetry can be split by the values for each metadata key. So if one of your metadata keys is the choice of model to use, you can see how invokes, token counts, latencies, and everything else vary by choice of model.
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