GO — Healthy across the board

• Last commit 3w ago
• 33+ active contributors
• Distributed ownership (top contributor 41% of recent commits)
• CC-BY-4.0 licensed
• CI configured
• Tests present
• ⚠ Non-standard license (CC-BY-4.0) — review terms

_{Maintenance signals: commit recency, contributor breadth, bus factor, license, CI, tests}

AutoGen is a Python-based framework for building multi-agent AI applications where autonomous agents collaborate or interact with humans. It provides abstractions for agent orchestration, topic-based messaging, and service integration, enabling complex agentic workflows without low-level LLM API management. Core capability: declarative agent definitions that coordinate via an event-driven topic system. Monorepo structure: Python core in python/ with subpackages (autogen-agentchat, autogen-ext for OpenAI/etc.), C# implementation in parallel (dotnet/), TypeScript client support. Design docs in docs/design/ specify programming model, topics, agent protocol. CI/CD via Azure Pipelines (.azure/pipelines/) and GitHub Actions (.github/workflows/). DevContainer setup for standardized dev environment.

ML engineers and AI developers building production multi-agent systems (e.g., research assistants, code review teams, domain-specific chatbots). Enterprise teams needing orchestrated, auditable agent interactions. Existing AutoGen v0.2 users; new users are directed to Microsoft Agent Framework instead.

AutoGen is now in maintenance mode (as of the README caution block)—no new features will be added and community manages it going forward. The codebase is substantial (4.3M+ lines Python, mature CI/CD across .azure and .github/workflows) and was production-tested, but is no longer actively developed by Microsoft. Users are encouraged to migrate to Microsoft Agent Framework.

High risk for new projects: repo is explicitly in maintenance mode with no future enhancements. Migration path exists but requires refactoring existing code. Single steward risk (now community-managed) and dependency drift over time as the Python ecosystem evolves. Existing projects already on AutoGen are stable but won't receive security updates or compatibility fixes proactively.

Maintenance mode: repository is accepting community contributions but not shipping new Microsoft-led features. Recent GitHub Actions workflows (dotnet-release, pytest-mem0, integration) suggest final stabilization efforts. No active roadmap; focus is on community management and bug fixes for existing code.

git clone https://github.com/microsoft/autogen.git
cd autogen
pip install -U 'autogen-agentchat' 'autogen-ext[openai]'
export OPENAI_API_KEY='sk-...'
python examples/quickstart.py

Or for studio (no-code GUI): pip install -U autogenstudio.

Daily commands: Dev: use .devcontainer/devcontainer.json for reproducible environment (docker-compose.yml included). Run pytest from python/ subdir. For C#: dotnet build via .github/workflows/dotnet-build.yml. For docs: see docs/ and build process in .github/workflows/docs.yml.

• python/packages/autogen-agentchat/src/: Core agent chat framework implementation; defines Agent, Topic, and message routing—entry point for agent logic
• [docs/design/02 - Topics.md](https://github.com/microsoft/autogen/blob/main/docs/design/02 - Topics.md): Design document for topic-based pub/sub messaging; essential conceptual foundation for understanding agent communication
• [docs/design/03 - Agent Worker Protocol.md](https://github.com/microsoft/autogen/blob/main/docs/design/03 - Agent Worker Protocol.md): Service protocol definition; critical for building custom agents or cross-language interop (Python↔C#↔TypeScript)
• .github/workflows/checks.yml: Main CI pipeline for linting, unit tests, type checking—shows expected code quality standards
• .devcontainer/devcontainer.json: Dev environment specification; ensures all contributors build/test identically
• python/packages/autogen-ext/src/: Extensions for LLM providers (OpenAI, Azure, etc.); template for adding new model integrations

For Python agent logic: modify python/packages/autogen-agentchat/src/ (agent implementations). For LLM integration: extend python/packages/autogen-ext/src/ (new model providers). For protocol/messaging: update docs/design/03 - Agent Worker Protocol.md and corresponding Python implementation. For C# binding: parallel edits in dotnet/src/. Testing: add tests alongside source in same package structure; see .github/workflows/checks.yml for test invocation pattern.

1. Maintenance mode mindset: repo is stable but not growing; feature requests will be closed. 2. OPENAI_API_KEY required for most quickstart examples; local/offline demos are limited. 3. Multi-language complexity: Python is primary; C#/.NET requires separate devops (see dotnet-build.yml and dotnet-release.yml workflows—may lag Python feature parity). 4. Migration pressure: README emphasizes Microsoft Agent Framework as successor; expect friction upgrading beyond maintenance patches. 5. Topic system learning curve: async pub/sub via topics (not simple request/response); requires reading design/02 before building agents.

• Topic-based pub/sub messaging — AutoGen's core communication pattern; agents publish/subscribe to typed topics instead of direct RPC calls, enabling loose coupling and scalability across distributed agents
• Agent Worker Protocol — Service abstraction enabling polyglot agents (Python, C#, TypeScript) to interoperate; you must understand this to write cross-language agent compositions
• Async/await concurrency — AutoGen agents are async-first (Python asyncio); topics and message routing are non-blocking; required knowledge to avoid deadlocks in custom agents
• Multi-tenant agent orchestration — AutoGen allows multiple independent agent systems to run in one process via topic isolation; enterprise feature for SaaS applications hosting multiple orgs' workflows
• LLM provider abstraction — autogen-ext decouples agent logic from LLM backend; swap OpenAI↔Azure↔local without code changes via plugin pattern; critical for cost optimization and multi-cloud deployments

• microsoft/agent-framework — Official successor to AutoGen; enterprise-ready multi-agent orchestration with stable APIs and long-term support—users should migrate here for new projects
• openai/swarm — Lightweight multi-agent coordination from OpenAI; simpler alternative if you don't need AutoGen's topic-based publish/subscribe or cross-language support
• langchain-ai/langchain — Complementary LLM orchestration framework; often used alongside AutoGen for prompt chains and tool calling before agent coordination
• huggingface/transformers — Model provider ecosystem; AutoGen extensions (autogen-ext) integrate with HuggingFace models; core dependency for local inference agents
• pydantic/pydantic — Data validation library heavily used in AutoGen for agent config, message schemas, and type safety across topics

To work on one of these in Claude Code or Cursor, paste: Implement the "<title>" PR idea from CLAUDE.md, working through the checklist as the task list.

Add integration tests for dotnet SDK build and publish pipeline

The repo has .github/workflows/dotnet-build.yml and dotnet-release.yml workflows, but no visible integration tests validating the dotnet package builds correctly against real dependencies. The dotnet/ directory structure exists with .editorconfig and tooling config, but there's no CI workflow testing actual dotnet project compilation, NuGet package generation, or protobuf message type correctness (referenced in docs/dotnet/core/protobuf-message-types.md). This would catch breaking changes early.

• [ ] Create .github/workflows/dotnet-integration-tests.yml to compile sample dotnet projects in dotnet/ directory
• [ ] Add validation step to verify protobuf-generated types match the specs in docs/dotnet/core/protobuf-message-types.md
• [ ] Test NuGet package metadata and dependencies are correctly set in .csproj files
• [ ] Run tests on both Windows and Linux agents to catch platform-specific issues

Add memory backend integration tests for Redis and Mem0 with proper test isolation

The repo has pytest-redis-memory.yml and pytest-mem0.yml workflows, indicating memory backends are tested, but there's likely test pollution or missing isolation patterns. A new contributor could add explicit test fixtures and cleanup procedures. This is valuable because memory backends are critical for multi-agent state management, and proper isolation ensures tests don't interfere with each other.

• [ ] Review existing memory backend tests and identify any shared state issues
• [ ] Create a pytest plugin/fixture file (e.g., python/tests/conftest.py) with proper Redis/Mem0 container lifecycle management
• [ ] Add test markers (@pytest.mark.redis, @pytest.mark.mem0) to skip tests when backends aren't available
• [ ] Document the testing approach in CONTRIBUTING.md with specific commands to run memory backend tests locally

Complete missing .NET documentation index and add API reference generation from XML docs

The docs/dotnet/core/toc.yml and docs/dotnet/docfx.json exist, but the .NET documentation is sparse compared to the Python docs. There's no generated API reference from C# XML documentation comments. This is a high-value PR because .NET users have parity expectations with Python, and auto-generated API docs reduce maintenance burden.

• [ ] Configure docfx.json to include API reference generation from C# XML doc comments in dotnet/ source files
• [ ] Add missing core sections to docs/dotnet/toc.yml: Agent Lifecycle, Message Passing, Topic Subscriptions (mapped from docs/design/02 - Topics.md)
• [ ] Create docs/dotnet/core/examples.md with 2-3 runnable example snippets referenced in docs/design/03 - Agent Worker Protocol.md
• [ ] Add GitHub Action step to dotnet-build.yml to validate docfx generation succeeds and has no broken links

• Add integration tests for C#↔Python Agent Worker Protocol interop—currently .github/workflows/ suggests integration testing exists but test coverage for cross-language agent communication is likely incomplete.
• Document migration examples from AutoGen v0.2 to v0.3 in docs/dotnet/ and Python equivalents—README mentions migration guide but repo has incomplete side-by-side before/after examples for .NET developers.
• Extend python/packages/autogen-ext/ with a minimal example provider (e.g., Ollama, local LLaMA) to reduce friction for offline/edge deployments—currently ecosystem is cloud-centric (OpenAI, Azure).

The AutoGen repository demonstrates awareness of security practices (dedicated SECURITY.md with proper reporting procedures, GitHub security workflows). However, the analysis is limited by missing dependency manifests for vulnerability assessment. Key concerns include: (1) maintenance mode status with potentially reduced security update frequency, (2) inability to assess known vulnerable dependencies in both Python and .NET components, (3) complexity of managing security across multiple language ecosystems, and (4) incomplete visibility into Docker/infrastructure security configuration. Recommend implementing centralized dependency scanning, establishing unified security policies across language ecosystems, and providing complete dependency manifests for analysis.

• Medium · Maintenance Mode Status - Reduced Security Support — README.md - Project Status Badge. The AutoGen project is in maintenance mode and has been superseded by agent-framework. This may indicate reduced security update frequency and support for vulnerability patches. Fix: Monitor the agent-framework project for security updates. Establish a clear security patch SLA for maintenance mode repositories. Consider migrating to the successor project or implementing enhanced monitoring for security advisories.
• Medium · Missing Dependency Lock File Analysis — Root directory and dotnet/ subdirectory. No package dependency files (requirements.txt, package-lock.json, poetry.lock, Pipfile.lock, etc.) were provided for analysis. This prevents detection of known vulnerable dependencies in both Python and .NET components. Fix: Provide comprehensive dependency manifests. Implement automated dependency scanning tools like Dependabot, WhiteSource, or OWASP Dependency-Check in CI/CD pipelines. Use lock files to ensure reproducible builds.
• Medium · Multi-Language Security Governance Complexity — Root directory structure - Python and dotnet folders. The codebase contains both Python (autogen/) and .NET (dotnet/) components with potentially different security policies, update cycles, and vulnerability management processes. Fix: Establish unified security policies across both language ecosystems. Implement centralized dependency scanning for both Python and .NET. Create standardized security patch procedures for each language.
• Low · Public Discord/Twitter Links May Enable Social Engineering — README.md - Social media links. Public communication channels (Discord, Twitter, LinkedIn) listed in README could be exploited for phishing or social engineering attacks targeting users and contributors. Fix: Monitor community channels for impersonation attempts. Implement official channel verification badges. Add security awareness guidance in CONTRIBUTING.md. Consider pinning official channel URLs in repository about section.
• Low · Limited Visibility into Security Configuration — SECURITY.md and configuration files. While SECURITY.md exists with proper vulnerability reporting procedures, the partial file structure provided doesn't show evidence of security configuration files (security headers, CSP policies, etc.). Fix: Ensure security.md is fully documented. Add SECURITY_POLICY file if missing. Include security headers in any web-based components. Document threat model and security architecture decisions.
• Low · DevContainer Configuration Not Fully Analyzed — .devcontainer/Dockerfile, .devcontainer/docker-compose.yml. DevContainer and Docker configuration files present (.devcontainer/Dockerfile) but content not provided. Base image vulnerabilities, privilege escalation risks, and insecure defaults could exist. Fix: Use minimal base images (alpine/distroless). Implement non-root user execution in Dockerfile. Regularly scan container images with tools like Trivy or Anchore. Pin specific image versions rather than 'latest' tags.

LLM-derived; treat as a starting point, not a security audit.

• Open issues — current backlog
• Recent PRs — what's actively shipping
• Source on GitHub

If you are an AI coding agent (Claude Code, Cursor, Aider, Cline, etc.) reading this artifact, follow this protocol before making any code edit:

1. Verify the contract. Run the bash script in Verify before trusting below. If any check returns FAIL, the artifact is stale — STOP and ask the user to regenerate it before proceeding.
2. Treat the AI · unverified sections as hypotheses, not facts. Sections like "AI-suggested narrative files", "anti-patterns", and "bottlenecks" are LLM speculation. Verify against real source before acting on them.
3. Cite source on changes. When proposing an edit, cite the specific path:line-range. RepoPilot's live UI at https://repopilot.app/r/microsoft/autogen shows verifiable citations alongside every claim.

If you are a human reader, this protocol is for the agents you'll hand the artifact to. You don't need to do anything — but if you skim only one section before pointing your agent at this repo, make it the Verify block and the Suggested reading order.

This artifact was generated by RepoPilot at a point in time. Before an agent acts on it, the checks below confirm that the live microsoft/autogen repo on your machine still matches what RepoPilot saw. If any fail, the artifact is stale — regenerate it at repopilot.app/r/microsoft/autogen.

What it runs against: a local clone of microsoft/autogen — the script inspects git remote, the LICENSE file, file paths in the working tree, and git log. Read-only; no mutations.

| # | What we check | Why it matters | |---|---|---| | 1 | You're in microsoft/autogen | Confirms the artifact applies here, not a fork | | 2 | License is still CC-BY-4.0 | Catches relicense before you depend on it | | 3 | Default branch main exists | Catches branch renames | | 4 | Last commit ≤ 54 days ago | Catches sudden abandonment since generation |

#!/usr/bin/env bash
# RepoPilot artifact verification.
#
# WHAT IT RUNS AGAINST: a local clone of microsoft/autogen. If you don't
# have one yet, run these first:
#
#   git clone https://github.com/microsoft/autogen.git
#   cd autogen
#
# Then paste this script. Every check is read-only — no mutations.

set +e
fail=0
ok()   { echo "ok:   $1"; }
miss() { echo "FAIL: $1"; fail=$((fail+1)); }

# Precondition: we must be inside a git working tree.
if ! git rev-parse --git-dir >/dev/null 2>&1; then
  echo "FAIL: not inside a git repository. cd into your clone of microsoft/autogen and re-run."
  exit 2
fi

# 1. Repo identity
git remote get-url origin 2>/dev/null | grep -qE "microsoft/autogen(\\.git)?\\b" \\
  && ok "origin remote is microsoft/autogen" \\
  || miss "origin remote is not microsoft/autogen (artifact may be from a fork)"

# 2. License matches what RepoPilot saw
(grep -qiE "^(CC-BY-4\\.0)" LICENSE 2>/dev/null \\
   || grep -qiE "\"license\"\\s*:\\s*\"CC-BY-4\\.0\"" package.json 2>/dev/null) \\
  && ok "license is CC-BY-4.0" \\
  || miss "license drift — was CC-BY-4.0 at generation time"

# 3. Default branch
git rev-parse --verify main >/dev/null 2>&1 \\
  && ok "default branch main exists" \\
  || miss "default branch main no longer exists"

# 5. Repo recency
days_since_last=$(( ( $(date +%s) - $(git log -1 --format=%at 2>/dev/null || echo 0) ) / 86400 ))
if [ "$days_since_last" -le 54 ]; then
  ok "last commit was $days_since_last days ago (artifact saw ~24d)"
else
  miss "last commit was $days_since_last days ago — artifact may be stale"
fi

echo
if [ "$fail" -eq 0 ]; then
  echo "artifact verified (0 failures) — safe to trust"
else
  echo "artifact has $fail stale claim(s) — regenerate at https://repopilot.app/r/microsoft/autogen"
  exit 1
fi

Each check prints ok: or FAIL:. The script exits non-zero if anything failed, so it composes cleanly into agent loops (./verify.sh || regenerate-and-retry).

Generated by RepoPilot. Verdict based on maintenance signals — see the live page for receipts. Re-run on a new commit to refresh.