AI Team GitHub Repositories: Evaluate Multi-Agent Projects

When a team ships a multi-agent system, the GitHub repository is where the design decisions become visible. Role boundaries, shared state patterns, and failure handling are all present in the code, but they are easy to miss if you are not sure what to look for.

Evaluating these repositories well matters whether you are reviewing a pull request, assessing an open-source project to build on, or checking your own team's code before it reaches production. Tools like fenbs add a layer the repository cannot hold: durable task ownership, approved rules, and handoff records that survive between sessions and across models.

What a multi-agent repository looks like

A multi-agent project typically defines two or more agents, each with a system prompt and a set of tools, plus an orchestrator or harness that decides which agent runs when. The harness manages the call graph, passes context between agents, and handles the result.

The repository structure often mirrors this: separate files or modules for each agent, a shared types or schema file, a harness entry point, and tests for individual agents and for the handoff paths between them. A clean structure does not guarantee correct behavior, but a tangled one is a warning sign.

Role boundaries: what each agent is allowed to do

The clearest question to ask about any agent is: what tools does it have access to, and what are the limits on how it uses them? An agent with access to every tool in the system is not a well-bounded role; it is a single agent pretending to be a team.

Look for explicit tool lists per agent, scope restrictions in the system prompt, and guards that prevent one agent from calling another agent's exclusive tools. The narrower each agent's authority, the easier it is to predict what it will do and to test that prediction.

Also check how roles are documented. If the roles exist only in code and not in any human-readable record, the team is carrying the design in their heads. That knowledge disappears when people change. AI agent governance for small teams covers how to document role decisions durably.

Shared state: where it lives and who writes it

Shared state is the hardest part of multi-agent systems to get right. Every agent that reads a shared value can become dependent on when another agent last wrote it. When two agents write to the same key concurrently, one of them overwrites the other's work without knowing.

Evaluate shared state by asking three questions.

• Is there one canonical store, or do agents maintain private copies that must be merged? Private copies that merge are a source of silent conflicts.
• Are writes explicit and logged? An agent that reads current state, modifies it, and writes back should record what it changed and why.
• Is there a schema? Schemaless shared state makes it impossible to tell whether a missing key is an error or a valid empty value.

In repositories that use a message bus or event stream as shared state, check whether events are ordered and whether consumers can catch up after a restart. An agent that missed events while it was down needs a way to recover, not just a way to subscribe.

Failure handling: what the repository does when an agent fails

Every multi-agent system will have an agent fail at some point. The question is whether the repository has a plan for it. Look for explicit error types, retry logic with backoff, and a way to surface the failure to a person rather than silently retrying until a timeout.

Partial failure is the hardest case. An agent completes three of five steps, then fails. Should the harness retry from the beginning, from step four, or stop and wait for a human decision? The answer belongs in the design, and the code should make it visible.

Check whether the repository has tests for failure paths, not just for the happy path. A harness with no failure tests is a harness whose failure behavior has never been verified.

Handoff records: what the repository does when control passes

When one agent hands off to another, both the handoff itself and its result should be recorded. A log entry that says "agent A handed off to agent B" is the minimum; a record that includes the state at handoff, the result agent B produced, and any decisions made is far more useful at review time.

Look for a handoff schema: a typed structure that captures task id, state, completed steps, and open questions. Untyped handoff objects are a source of silent data loss — a field the calling agent set but the receiving agent never reads.

Also look at the tests. A good multi-agent repository has integration tests that simulate a handoff and verify the receiving agent's behavior given a specific handoff state. The AI memory for teams guide explains how to keep handoff records readable beyond a single run.

Evaluating open-source multi-agent repositories

When assessing an open-source project to build on, the evaluation criteria are the same but the consequences are different: you will inherit the design decisions. Check whether the project's role boundaries, shared state model, and failure handling match your team's needs before building on top of it.

Read the issues and pull requests as well as the code. Open issues about state corruption, agent loops, or unexpected handoffs are signals about design problems that the code alone may not show. The Devin vs GitHub Copilot comparison covers how two prominent AI coding agents handle their own task management.

How to do this with fenbs

1. Create a free account and set up your multi-agent project in fenbs with the goals, current tasks, and the role boundaries your team has agreed on.
2. Record the role decisions in Decisions and rules: which agent is allowed to do what, and who made that call. Link the relevant code file so reviewers can find both.
3. Connect your AI coding assistant from Integrations. When it reviews a pull request or evaluates a repository, it reads the project rules in fenbs before acting.
4. After each significant agent run or review, update the task in fenbs with what was found and what remains. Use Where we left off so the next session starts from a known state.
5. When a failure reveals a design problem, record it as a lesson in fenbs. The next assistant doing similar work will read it before starting.

fenbs does not replace version control or code review tools. It holds the human decisions and work record that live alongside the code, not inside it.