DSPy-Go

What does this agent do, and when should you use it?

DSPy-Go is a native Go implementation of DSPy for teams building modular LLM applications and agents. It defines input and output contracts with Signatures and supplies composable Predict, ChainOfThought, ReAct, RLM, Refine, and Parallel modules. At execution time, a module resolves its model from SetLLM, request-scoped core.WithRuntime configuration, or the package default set by core.SetDefaultLLM. The pkg/llms compatibility layer connects to Anthropic, OpenAI, Google Gemini, Ollama, llama.cpp, and OpenAI-compatible services, while llm-go owns generation, streaming, structured output, and tool calls. The repository also documents A2A multi-agent composition, ACE self-improving agents, MCP integration, intelligent tool selection, and optimizers including GEPA, MIPRO, and SIMBA. It is delivered primarily as a Go library with a buildable dspy-cli, leaving deployment, credentials, and OAuth lifecycle management to the host application.

A program defines named inputs, outputs, descriptions, and instructions with core.NewSignature, then passes that contract to modules such as NewChainOfThought, Predict, ReAct, RLM, Refine, or Parallel. Calling Process(context.Context, map[string]interface{}) resolves the configured LLM, sends the task to that provider, and returns a field-keyed result; the introductory example reads result["answer"]. ReAct combines reasoning with tool use, while the tool system supports Bayesian selection, chaining, composition, and MCP integration; A2A provides hierarchical multi-agent composition. For machine-readable results, WithStructuredOutput() enables JSON output, while interceptors.ApplyXMLInterceptors configures the XML alternative. Optimization can run through GEPA, MIPRO, SIMBA, BootstrapFewShot, or COPRO, and the CLI can enumerate optimizers, try one against a dataset, and inspect RLM JSONL session logs.

1. A Go backend team adding contract-based question answering, classification, or generation can combine a Signature with Predict or ChainOfThought.
2. Developers whose models must invoke external capabilities can build ReAct flows with tool chaining, composite tools, and the smart tool registry.
3. Teams exploring large contexts can use RLM with full, checkpointed, or adaptive replay policies and inspect recorded sessions through the CLI.
4. Prompt-optimization researchers can try MIPRO on a dataset such as GSM8K and evaluate GEPA, SIMBA, BootstrapFewShot, or COPRO.
5. Go developers building research or collaboration systems can compose agents hierarchically with A2A or experiment with ACE self-improving agents.
6. Products that need backend flexibility can configure Anthropic, OpenAI, Gemini, Ollama, llama.cpp, or another OpenAI-compatible endpoint.

How do you install or deploy this agent?

Install the package in an existing Go module:

go get github.com/XiaoConstantine/dspy-go

The supplied material does not state a minimum Go version. To build the CLI from the repository, run:

cd cmd/dspy-cli && go build -o dspy-cli

Configure credentials for the chosen provider; the documented CLI example uses:

export GEMINI_API_KEY="your-api-key"

Ollama and llama.cpp must expose an OpenAI-compatible endpoint. OpenAI Codex subscription generation additionally requires the host application to own OAuth login, token refresh, and credential storage.

How do you use this agent?

For an initial CLI run, use:

./dspy-cli list
./dspy-cli try mipro --dataset gsm8k
./dspy-cli view session.jsonl --stats

In Go, create a provider with llms.NewGeminiLLM("", core.ModelGoogleGeminiPro) and register it using core.SetDefaultLLM(llm). Define question and answer fields with core.NewSignature, construct modules.NewChainOfThought(signature), then call cot.Process(context.Background(), map[string]interface{}{"question": "What is the capital of France?"}) and read result["answer"]. Production callers should handle errors from both model construction and Process. Use SetLLM for a module-specific override or core.WithRuntime for a request-scoped model.

What are this agent's strengths and limitations?

How does this agent compare with similar options?

Key facts side by side with the most closely related agents.

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Recent releases

Latest entries from the project's GitHub Releases. Notes are quoted from upstream as written, not rewritten or tested by FollowAgents.

1. v0.89.1 2026-10-17
   Release notes excerpt

   Changes _From v0.89.0 to v0.89.1_ - b0c3273 build(deps): bump thulite theme packages and Hugo for docs (#385) - 0599943 build(deps): bump Go dependencies and fix x/net vulnerabilities (#378) - 686b915 build(deps): bump docs npm dependencies and fix audit alerts (#379) - 7242bb1 fix: use modernc.org/sqlite for CGO-free SQLite stores (#367) - b96618b build(deps): consolidate open Go dependency updates - ee9c335 fix(deps): patch docs brace-expansion vulnerabilities - be36e3a build(deps): bump google.golang.org/genai in / (#368) …

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2. v0.89.0 2026-09-23
   Release notes excerpt

   Changes _From v0.88.1 to - d68228d fix(cli): sync llm-go dependency - 95f3845 fix(rlm): hide failed batch responses - d58dca9 fix(rlm): preserve subquery token totals - cad2f00 fix(rlm): preserve provider token totals - 171f594 fix(rlm): preserve failed query accounting - d89a0fc fix(rlm): propagate query accounting failures - 84bfeb1 fix(rlm): make async cleanup deterministic - e0e69e0 fix(rlm): enforce budgets between subcalls - 0717a26 fix(core): normalize incomplete token totals - 29b544d fix(rlm): enforce tree-wide token budgets - 92a4530 fix(config): wrap metrics with tracing …

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3. v0.88.1 2026-09-04
   Release notes excerpt

   Changes _From v0.88.0 to - 57bf1f1 bump llm-go to v0.3.1 - e4f58b8 configure grouped Go dependency updates - 67c2a8a fix Apache Thrift vulnerability - 10394ca fix vulnerable dependencies - b5ba0d3 bump llm-go dep - ee8649e fix cli dep - 3f0d2a0 bump llm-go - 732ea61 refactor: remove direct Anthropic SDK dependency

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