updated more go streaming examples

[nihalbaig0]

• added streaming example of autogen ,crewai

Summary by CodeRabbit

• New Features
  • Added new Go example scripts demonstrating how to perform streaming conversations with agent clients, including handling different types of streaming messages.
• Refactor
  • Updated existing Go example scripts to be executable by uncommenting code and simplifying client initialization.

[coderabbitai]

Walkthrough

Four Go test scripts were updated or introduced in the test_scripts/go/ directory. Two scripts were converted from commented-out examples to runnable Go programs, with changes to the client initialization method. Two new scripts were added, each demonstrating streaming interactions with an agent client, including context management and streaming data handling.

Changes

Cohort / File(s)	Change Summary
Uncommented and Updated Existing Streaming Examples test_scripts/go/client_test_2.go, test_scripts/go/client_test_ag2_stream.go	Converted from fully commented-out examples to runnable Go programs; updated client initialization to use client.New with simplified parameters; replaced agent ID with a placeholder.
New Streaming Example Scripts test_scripts/go/client_test_autogen_stream.go, test_scripts/go/client_test_llamaindex_stream.go	Added new Go scripts demonstrating streaming conversations with an agent client, including context setup, streaming invocation, data reception, and resource cleanup.

Sequence Diagram(s)

sequenceDiagram
    participant User as User (main)
    participant Client as RunAgent Client
    participant Agent as Agent Service

    User->>Client: Initialize client with agent ID and entrypoint
    User->>Client: Start streaming session with query
    Client->>Agent: Send streaming request
    Agent-->>Client: Stream data chunks (status, message, summary, etc.)
    Client-->>User: Yield stream data to user loop
    User->>Client: Close stream and client on completion or error

Loading

Estimated code review effort

🎯 2 (Simple) | ⏱️ ~8 minutes

Poem

🐇
Four scripts awakened, now ready to stream,
Agents and clients in a Go-powered dream.
From commented silence to code that can run,
Streaming responses, the testing’s begun!
With context and queries, the rabbits rejoice—
More examples for all, in the language of choice!

Note

⚡️ Unit Test Generation is now available in beta!

Learn more here, or try it out under "Finishing Touches" below.

---

📜 Recent review details

Configuration used: CodeRabbit UI
Review profile: CHILL
Plan: Pro

📥 Commits

Reviewing files that changed from the base of the PR and between 19090c5 and cfa13ec.

📒 Files selected for processing (4)

• test_scripts/go/client_test_2.go (1 hunks)
• test_scripts/go/client_test_ag2_stream.go (1 hunks)
• test_scripts/go/client_test_autogen_stream.go (1 hunks)
• test_scripts/go/client_test_llamaindex_stream.go (1 hunks)

🔇 Additional comments (13)

test_scripts/go/client_test_2.go (4)

16-24: LGTM: Proper client initialization and resource management.

The client initialization follows good Go practices with proper error handling and deferred cleanup. The use of a placeholder agent ID is appropriate for test examples.

---

26-28: LGTM: Appropriate context management.

The 2-minute timeout is reasonable for streaming operations, and the deferred cancel ensures proper context cleanup.

---

31-38: LGTM: Well-structured streaming initialization.

The streaming setup properly handles errors and includes deferred stream cleanup. The input parameters are well-structured for demonstration purposes.

---

41-54: LGTM: Robust stream reading loop.

The streaming loop properly handles all expected conditions:

• Error handling with logging and graceful exit
• Stream completion detection
• Data processing with clear output

This follows excellent Go patterns for stream processing.

test_scripts/go/client_test_llamaindex_stream.go (3)

13-21: LGTM: Consistent client initialization pattern.

The client setup follows the same pattern as other examples with proper error handling and resource cleanup. The "math_stream" entrypoint tag clearly indicates the purpose.

---

27-33: LGTM: Appropriate parameter structure for math queries.

The streaming request uses a clear "math_query" parameter that's semantically appropriate for the llamaindex math streaming example.

---

36-49: LGTM: Consistent stream processing implementation.

The stream reading loop maintains the same robust pattern as other examples, ensuring consistent behavior across all streaming test scripts.

test_scripts/go/client_test_ag2_stream.go (3)

16-24: LGTM: Proper AG2 client setup.

The client initialization follows the established pattern with "ag2_stream" entrypoint tag that clearly identifies the AG2 streaming functionality.

---

31-38: LGTM: Appropriate AG2-specific parameters.

The streaming request includes AG2-relevant parameters:

• "message": Clear input text for conversation
• "max_turns": Reasonable limit (4) for controlling conversation length

These parameters are well-suited for AG2 streaming interactions.

---

41-54: LGTM: Consistent stream handling.

The stream processing maintains the same robust error handling and completion detection pattern used across all examples, ensuring predictable behavior.

test_scripts/go/client_test_autogen_stream.go (3)

17-25: LGTM: Proper AutoGen client initialization.

The client setup uses "autogen_token_stream" entrypoint tag, which clearly indicates token-based streaming functionality for AutoGen.

---

32-38: LGTM: Appropriate task-based parameter structure.

The "task" parameter provides a clear, educational query about AutoGen itself, which is perfect for demonstrating the streaming functionality.

---

54-76: LGTM: Sophisticated and robust message type handling.

This implementation demonstrates excellent structured data processing:

• Safe type assertion with ok check before accessing map fields
• Comprehensive handling of different message types (status, message, summary, error, complete)
• Meaningful output formatting for each message type
• Fallback handling for unrecognized types and non-map data
• Proper extraction of nested fields like turn, sender, content

This provides a great example of how to handle complex streaming responses while maintaining robustness.

✨ Finishing Touches

• 📝 Generate Docstrings

🧪 Generate unit tests

• Create PR with unit tests
• Post copyable unit tests in a comment
• Commit unit tests in branch nihalbaig/auto_port

---

🪧 Tips

Chat

There are 3 ways to chat with CodeRabbit:

• Review comments: Directly reply to a review comment made by CodeRabbit. Example:
  • I pushed a fix in commit <commit_id>, please review it.
  • Explain this complex logic.
  • Open a follow-up GitHub issue for this discussion.
• Files and specific lines of code (under the "Files changed" tab): Tag @coderabbitai in a new review comment at the desired location with your query. Examples:
  • @coderabbitai explain this code block.
  • @coderabbitai modularize this function.
• PR comments: Tag @coderabbitai in a new PR comment to ask questions about the PR branch. For the best results, please provide a very specific query, as very limited context is provided in this mode. Examples:
  • @coderabbitai gather interesting stats about this repository and render them as a table. Additionally, render a pie chart showing the language distribution in the codebase.
  • @coderabbitai read src/utils.ts and explain its main purpose.
  • @coderabbitai read the files in the src/scheduler package and generate a class diagram using mermaid and a README in the markdown format.
  • @coderabbitai help me debug CodeRabbit configuration file.

Support

Need help? Create a ticket on our support page for assistance with any issues or questions.

Note: Be mindful of the bot's finite context window. It's strongly recommended to break down tasks such as reading entire modules into smaller chunks. For a focused discussion, use review comments to chat about specific files and their changes, instead of using the PR comments.

CodeRabbit Commands (Invoked using PR comments)

• @coderabbitai pause to pause the reviews on a PR.
• @coderabbitai resume to resume the paused reviews.
• @coderabbitai review to trigger an incremental review. This is useful when automatic reviews are disabled for the repository.
• @coderabbitai full review to do a full review from scratch and review all the files again.
• @coderabbitai summary to regenerate the summary of the PR.
• @coderabbitai generate docstrings to generate docstrings for this PR.
• @coderabbitai generate sequence diagram to generate a sequence diagram of the changes in this PR.
• @coderabbitai generate unit tests to generate unit tests for this PR.
• @coderabbitai resolve resolve all the CodeRabbit review comments.
• @coderabbitai configuration to show the current CodeRabbit configuration for the repository.
• @coderabbitai help to get help.

Other keywords and placeholders

• Add @coderabbitai ignore anywhere in the PR description to prevent this PR from being reviewed.
• Add @coderabbitai summary to generate the high-level summary at a specific location in the PR description.
• Add @coderabbitai anywhere in the PR title to generate the title automatically.

CodeRabbit Configuration File (.coderabbit.yaml)

• You can programmatically configure CodeRabbit by adding a .coderabbit.yaml file to the root of your repository.
• Please see the configuration documentation for more information.
• If your editor has YAML language server enabled, you can add the path at the top of this file to enable auto-completion and validation: # yaml-language-server: $schema=https://coderabbit.ai/integrations/schema.v2.json

Documentation and Community

• Visit our Documentation for detailed information on how to use CodeRabbit.
• Join our Discord Community to get help, request features, and share feedback.
• Follow us on X/Twitter for updates and announcements.